[{"data":1,"prerenderedAt":550},["ShallowReactive",2],{"search-api":3},[4,11,22,34,44,52,61,70,78,85,92,99,108,117,125,137,145,156,168,177,187,198,224,233,244,254,261,270,281,288,295,302,309,319,329,344,353,363,371,380,387,394,400,406,411,416,421,426,431,436,440,444,448,452,457,462,466,470,474,478,483,489,495,500,504,508,513,518,524,532,539],{"id":5,"path":6,"dir":7,"title":8,"description":7,"keywords":9,"body":10},"content:0.index.md","/","","Re-Twin Energy Documentation",[],"    ",{"id":12,"path":13,"dir":7,"title":14,"description":15,"keywords":16,"body":21},"content:1.products.md","/products","Products","The Re-Twin platform is built around three modules. Each answers a different type of question, but they rely on the same models and data sources, so the results fit together seamlessly:",[17,18,19,20],"Market Data (Free Access)","Investment Analysis","Virtual Trading","How the modules connect","  Products  The Re-Twin platform is built around three modules. Each answers a different type of question, but they rely on the same models and data sources, so the results fit together seamlessly:    Market Data (Free)  → What information is available in the markets, and how can I explore it right away?   Investment Analysis (Backtest and Future)  → What revenues can my project achieve, both historically and in future scenarios, and how does that affect its bankability?   Virtual Trading  → How would a trading strategy perform in practice, and how does it compare to forecasts or other traders?   Market Data (Free Access)  This module provides open access to essential information on European power markets, serving as the cornerstone for the entire platform. The datasets powering Market Data are seamlessly integrated into both Investment Analysis and Virtual Trading, ensuring consistency across all modules.  With Market Data, you can:   Explore historical time series, including:\n    Capacity markets   Energy markets   Generation   Load  Effortlessly export data via interactive dashboards or CSV downloads   Who it's for:  Analysts, traders, developers, students — anyone seeking to understand market trends and dynamics.  The data is updated daily, and we are continuously expanding coverage to include more countries and data sources, making it easier for everyone to access and analyze critical market information.   Tip: Market Data is free for all users.   Investment Analysis  Investment Analysis empowers developers, investors, and lenders to assess the long-term financial performance of storage and renewable energy projects. By combining backtesting with advanced market modeling, this module delivers   bankable revenue projections  that support informed decision-making.  Supported Asset Classes:  We currently support investment analysis for the following asset classes:    Standalone BESS  (Battery Energy Storage Systems)   PV + Storage   Wind + Storage  Additional asset classes, such as   C&I + Storage  and   Hybrid Systems , are planned for future releases.  Key Outputs:    Revenue Breakdown : Insights by market (Day-Ahead, Intraday, Ancillary Services)   Financial Metrics : Comprehensive analysis including   Revenue, NPV, IRR, and Payback Periods   Long-Term Forecast Pathways : Multiple long-term cases (e.g., national trends, net-zero pathways)   Scenario Modeling : Evaluate market participation strategies and risk estimations   Comparative Analysis : Standalone vs. co-located projects (e.g., Solar + Storage)   Export Options : Generate detailed reports in PDF or Excel formats   Scenario Comparisons : Compare metrics like IRR and NPV across different scenarios   Who it's for:  Developers, investors, and financial institutions seeking reliable and actionable investment insights.  For a deeper dive into the revenue estimation methodology, visit the   Methodology Page .   Virtual Trading  Virtual Trading allows you to assess how an asset or strategy would perform under real market conditions through live simulations. By leveraging actual market forecasts, it generates bids and matches them virtually to simulate trades, creating a   \"shadow portfolio\"  for benchmarking, strategy testing, and performance comparison — all without financial risk.  Supported Asset Classes:    Standalone BESS  (Battery Energy Storage Systems)  Additional asset classes, such as   PV + Storage  and   Wind + Storage , are planned for future releases.  Key Features:    Bid Schedules : Monitor bid submissions and their outcomes (accepted or rejected).   Trading Strategies : Experiment with various strategies, such as day-ahead-only, fully merchant, allowed cycles per day, or custom approaches.   Bidding Approaches : Choose from conservative, balanced, or high-risk bidding approach.   Revenue Insights : Analyze revenues generated from simulated trading runs.   Bid Insights : Use heatmaps to identify markets requiring improved forecasting or bidding strategies.   Dynamic Asset Updates : Update asset parameters, such as degradation, to simulate real-world behavior accurately.   Performance Benchmarks : Compare your strategy against market performance and third-party traders.   Who it's for:  Asset owners, traders, and portfolio managers looking to measure and optimize trading performance.  Simulations are updated daily (with hourly updates coming soon), calculating revenues based on bids derived from real market forecasts and simulating asset behavior accordingly.   How the modules connect  Each module plays a different role but they are designed to work together:    Market Data  is the free foundation — it provides the information base.   Investment Analysis  uses that information to build long-term, bankable project results.   Virtual Trading  applies the same market logic to short-term, strategy-driven simulations.  Together, they give you a complete picture: from   market exploration , to   long-term planning , to   day-to-day trading performance .",{"id":23,"path":24,"dir":7,"title":25,"description":26,"keywords":27,"body":33},"content:2.concepts-background.md","/concepts-background","Concepts & Background","Welcome! This section covers the basics you'll need to understand energy storage optimization in power markets. Whether you're new to the scene or a seasoned pro looking for a refresher, these concepts will help you get a handle on your energy storage projects.",[28,29,30,31,32],"What's Inside","Power Markets","Storage Systems","Revenue Stacking","Getting Started","  Concepts & Background  Welcome! This section covers the basics you'll need to understand energy storage optimization in power markets. Whether you're new to the scene or a seasoned pro looking for a refresher, these concepts will help you get a handle on your energy storage projects.  What's Inside     Power Markets : A look at how electricity is bought, sold, and priced.    Storage Systems : The tech behind battery energy storage and what it can do.    Revenue Stacking :. How to make the most money by tapping into different markets.   Power Markets  Think of power markets as the stock exchange for electricity. They're where electricity is traded between generators and consumers, all while keeping the grid running smoothly.   The Main Parts:     Day-ahead markets : Planning and scheduling electricity for the next day.    Intraday markets : Making short-term adjustments closer to delivery time.   Balancing markets : Keeping the grid stable in real-time.    Ancillary services : Support functions like frequency, voltage, and reserves.   How it works in Europe: \nEuropean power markets are connected through major exchanges like   EPEX SPOT  and   Nord Pool . This setup allows for cross-border trading and helps find the best prices.    Dive into Power Markets 101   Storage Systems  Battery Energy Storage Systems (BESS) are the key to playing in multiple power markets at once, giving you the flexibility you need.   Key Specs to Know:    Power Rating (MW) : How fast you can charge or discharge.   Energy Capacity (MWh) : How much energy the battery can hold.   Duration : How long it can run at full power (e.g., 1-hour, 2-hour, 4-hour).   Round-trip Efficiency : Energy you get out vs. what you put in, usually 85-95% for lithium-ion.   Things to Keep in Mind:   Batteries degrade over time, which impacts your bottom line.  Warranties usually last 10-20 years.  How you use the battery (cycling) affects its lifespan.    Check out Storage Systems 101   Revenue Stacking  Revenue stacking is all about getting the most value from your storage asset. You do this by participating in several markets at the same time, balancing the risks and rewards.   Where the Money Comes From:    Energy Arbitrage : The classic \"buy low, sell high.\"   Ancillary Services : Getting paid for helping with grid stability (like frequency regulation).   Capacity Markets : Payments for being available to support the grid when needed.   Grid Services : Helping with local network issues like congestion.   The Tricky Parts:   Juggling the battery's state of charge for different jobs.  Balancing guaranteed income with more variable revenue.  Factoring in battery degradation for long-term plans.  Dealing with complex market rules and technical limits.    Explore Revenue Stacking Strategies   Getting Started  Ready to go deeper? Start with   Power Markets 101  to get the fundamentals down. Then, see how   Storage Systems  fit in, and finally, learn how to maximize your returns with   Revenue Stacking .",{"id":35,"path":36,"dir":37,"title":38,"description":39,"keywords":40,"body":43},"content:2.concepts-background:1.power-market-101.md","/concepts-background/power-market-101","concepts-background","Power Markets 101","Think of power markets as the stock exchange for electricity. They're the platforms where electricity is traded between generators, suppliers, and consumers. The main goal is to keep the grid running smoothly by ensuring supply always meets demand in the most cost-effective and reliable way. These markets are key to balancing the grid, making the best use of energy resources, and encouraging competition.",[41,42],"How It Works in Europe","The Main Market Segments","  Power Markets 101  Think of power markets as the stock exchange for electricity. They're the platforms where electricity is traded between generators, suppliers, and consumers. The main goal is to keep the grid running smoothly by ensuring supply always meets demand in the most cost-effective and reliable way. These markets are key to balancing the grid, making the best use of energy resources, and encouraging competition.   How It Works in Europe  The European power market isn't just one big entity; it's a network of connected national markets that allow for cross-border trading. This setup, driven by the EU's goal for a single, competitive energy market, helps find the best prices across the continent. Major exchanges like   EPEX SPOT  and   Nord Pool  are central to this system. The key players are the grid operators (TSOs), energy producers, suppliers, and regulators who set the rules.   The Main Market Segments  The power market is divided into several key segments, each with a distinct role:     Day-Ahead Market : This is the primary market where most electricity is traded for delivery on the following day. Prices are determined by supply and demand forecasts, making it crucial for advance planning.    Intraday Market : This market allows for real-time adjustments. When unforeseen events occur, like a sudden change in wind power or a power plant outage, participants can adjust their energy positions closer to the delivery time.    Ancillary Services Market : These are essential support services that ensure grid stability. They include frequency control, voltage support, and maintaining standby reserves to keep the grid reliable.    Forward and Futures Markets : For long-term planning and risk management. These markets allow participants to lock in prices for electricity to be delivered weeks, months, or even years ahead, hedging against price volatility.   Capacity Markets : These markets ensure there is enough generation capacity to meet peak demand. Participants are compensated for having capacity available, providing an extra layer of security to prevent blackouts.  These interconnected markets work together to ensure a reliable power supply, foster competition, and support the integration of renewable energy sources into the grid.",{"id":45,"path":46,"dir":47,"title":48,"description":49,"keywords":50,"body":51},"content:2.concepts-background:1.power-market-101:1.day-ahead.md","/concepts-background/power-market-101/day-ahead","power-market-101","Day-ahead Market","On the day-ahead market, participants can sell and buy electricity in a pan-European auction for the 24h of the next day in (hourly - currently being transitioned to quarter hourly) blocks. The day-ahead market is cleared at 12:00 o'clock noon each day of the year. At this time, the intersection of demand and quantity offered determines the electricity price and volume for each hour. This price is then either paid or received by all market participants who were successful in the auction.",[],"  Day-ahead Market  On the day-ahead market, participants can sell and buy electricity in a pan-European auction for the 24h of the next day in (hourly - currently being transitioned to quarter hourly) blocks. The day-ahead market is cleared at 12:00 o'clock noon each day of the year. At this time, the intersection of demand and quantity offered determines the electricity price and volume for each hour. This price is then either paid or received by all market participants who were successful in the auction.  Because the day-ahead market is organized shortly before delivery and has a single clearing price (per hour), it best reflects the value of electricity during different hours. The clearing price of the day-ahead market is therefore often referred to as \"the electricity price.\" The price is determined per bidding zone, which in Europe mostly corresponds to the borders of a country.",{"id":53,"path":54,"dir":47,"title":55,"description":56,"keywords":57,"body":60},"content:2.concepts-background:1.power-market-101:2.intraday.md","/concepts-background/power-market-101/intraday","Intraday Market","The intraday market opens after the day-ahead market has been cleared, providing a platform for continuous power trading closer to the time of delivery. It allows market participants to fine-tune their energy positions based on more accurate, short-term information.",[58,59],"Key Functions","Trading and Pricing Mechanism","  Intraday Market  The intraday market opens after the day-ahead market has been cleared, providing a platform for continuous power trading closer to the time of delivery. It allows market participants to fine-tune their energy positions based on more accurate, short-term information.  Key Functions  The primary role of the intraday market is to enable buyers and sellers to react to unforeseen events and updated forecasts. Participants can adjust their commitments in response to:   Changes in demand forecasts.  Fluctuations in generation from intermittent renewable sources like wind and solar.  Unexpected power plant outages or other grid disturbances.  This flexibility is crucial for maintaining a stable balance between electricity supply and demand.  Trading and Pricing Mechanism  Trading on the intraday market is continuous and can be done for intervals as short as 15 minutes. A deal is finalized the moment a buyer's bid matches a seller's offer, and trading for a specific delivery period continues until a \"gate closure\" time, typically just 5 minutes before physical delivery.",{"id":62,"path":63,"dir":47,"title":64,"description":65,"keywords":66,"body":69},"content:2.concepts-background:1.power-market-101:3.ancillary-services.md","/concepts-background/power-market-101/ancillary-services","Ancillary Services","Ancillary services are essential components of power systems that ensure the stability, reliability, and quality of electricity supply. These services support the transmission of electricity from generators to consumers while maintaining the balance between supply and demand. In European markets, ancillary services play a critical role in maintaining grid stability, especially as the integration of renewable energy sources increases.",[67,68],"Key Markets for Ancillary Services","Regelleistung: A Platform for Ancillary Services","  Ancillary Services  Ancillary services are essential components of power systems that ensure the stability, reliability, and quality of electricity supply. These services support the transmission of electricity from generators to consumers while maintaining the balance between supply and demand. In European markets, ancillary services play a critical role in maintaining grid stability, especially as the integration of renewable energy sources increases.  Key Markets for Ancillary Services  European electricity markets are structured to include various ancillary services, which are typically procured through competitive markets or bilateral agreements. These services include frequency containment reserves (FCR), frequency restoration reserves (FRR), and replacement reserves (RR). Each of these services addresses specific needs, such as frequency regulation, voltage control, and black start capabilities.  For instance, FCR ensures that the grid frequency remains stable by automatically adjusting power output within seconds. FRR, on the other hand, restores the frequency to its nominal value within minutes, while RR provides additional reserves to handle prolonged imbalances. These markets are coordinated at both national and European levels, with organizations like ENTSO-E (European Network of Transmission System Operators for Electricity) playing a key role in harmonizing practices across countries.  Regelleistung: A Platform for Ancillary Services  Regelleistung is a platform used in several European countries, including Germany, to procure ancillary services. It facilitates the transparent and efficient procurement of reserves, allowing market participants to offer their services and compete for contracts. The platform supports the integration of renewable energy by enabling flexible and decentralized resources, such as battery storage and demand-side management, to participate in ancillary service markets.  In conclusion, ancillary services are vital for the smooth operation of European electricity markets. They ensure that the grid remains stable and reliable, even as the energy landscape evolves with the increasing adoption of renewable energy sources.",{"id":71,"path":72,"dir":73,"title":74,"description":75,"keywords":76,"body":77},"content:2.concepts-background:1.power-market-101:3.ancillary-services:1.fcr.md","/concepts-background/power-market-101/ancillary-services/fcr","ancillary-services","Frequency Containment Reserve (FCR)","The Frequency Containment Reserve (FCR) is designed to manage minor imbalances in the system. Services are automatically offered by FCR providers as required.",[],"  Frequency Containment Reserve (FCR)  The Frequency Containment Reserve (FCR) is designed to manage minor imbalances in the system. Services are automatically offered by FCR providers as required.      FCR      Capacity Market  Gate-Opening-Time: 11 o'clock, d-7, Gate-Closure-Time: 8 o'clock, d-1, Product length: 4 hours, 6 windows per day    Energy Market  -    Core Share  Core shares and cross border capacities are determined for the individual Load-Frequency Control blocks    Minimum offer capacity  1 MW    Maximum offer capacity  25 MW    Pooling  Possible inside balancing area    Indivisible offer  Indivisible offer possible    Compensation  Capacity price    Activation  Automatic activation    Activation period  Full activation within 30 seconds    Period to be covered per incident  15 minutes    Backup  Backup not possible    Symmetrical/Asymmetrical  Symmetrical    Deadband  ∆f = ±10 mHz: from 49.99 Hz to 50.01 Hz",{"id":79,"path":80,"dir":73,"title":81,"description":82,"keywords":83,"body":84},"content:2.concepts-background:1.power-market-101:3.ancillary-services:2.afrr.md","/concepts-background/power-market-101/ancillary-services/afrr","aFRR - Capacity and Energy Market","The Automatic Frequency Restoration Reserve (aFRR) is utilized to reduce the reliance on Frequency Containment Reserves.",[],"  aFRR - Capacity and Energy Market  The Automatic Frequency Restoration Reserve (aFRR) is utilized to reduce the reliance on Frequency Containment Reserves.      aFRR      Capacity Market  Gate-Opening-Time: 10 o'clock, d-7; Gate-Closure-Time: 9 o'clock, d-1; Product length: 4 hours    Energy Market  Gate-opening-Time: approx. 12 o'clock, d-1; Gate-Closure-Time: t-25; Product length: 15 minutes    Core Share  Countries provide the core share and cross-border capacities, no core share within Germany    Minimum offer capacity  1 MW    Maximum offer capacity  TBD    Pooling  Possible inside control area    Indivisible offer  Indivisible offer not possible    Compensation  Capacity price or energy price    Activation  Automatic activation through TSO    Activation period  Full activation within 5 minutes    Period to be covered per incident  5 minutes    Backup  TBD    Symmetrical/Asymmetrical  Asymmetrical    Deadband  TBD",{"id":86,"path":87,"dir":73,"title":88,"description":89,"keywords":90,"body":91},"content:2.concepts-background:1.power-market-101:3.ancillary-services:3.mfrr.md","/concepts-background/power-market-101/ancillary-services/mfrr","mFRR - Capacity and Energy Market","The manual Frequency Restoration Reserve covers bigger system imbalances. It is requested manually by the TSO and should decrease the need for automatic Frequency Restoration Reserve.",[],"  mFRR - Capacity and Energy Market  The manual Frequency Restoration Reserve covers bigger system imbalances. It is requested manually by the TSO and should decrease the need for automatic Frequency Restoration Reserve.      mFRR      Capacity Market  Gate-Opening-Time: 10 o'clock, d-7; Gate-Closure-Time: 10 o'clock, d-1; Product length: 4 hours    Energy Market  Gate-opening-Time: approx. 12 o'clock, d-1; Gate-Closure-Time: t-25; Product length: 15 minutes    Core Share  Countries provide the core share and cross-border capacities, no core share within Germany    Minimum offer capacity  1 MW    Maximum offer capacity  TBD    Pooling  Possible inside control area    Indivisible offer  Indivisible offer possible    Compensation  Capacity price or energy price    Activation  Automatic activation through TSO    Activation period  Full activation within 15 minutes    Period to be covered per incident  TBD    Backup  Backup possible    Symmetrical/Asymmetrical  Asymmetrical    Deadband  TBD",{"id":93,"path":94,"dir":47,"title":95,"description":96,"keywords":97,"body":98},"content:2.concepts-background:1.power-market-101:4.forward-futures.md","/concepts-background/power-market-101/forward-futures","Forward and Futures Market","On the forward and futures market, electricity is traded between four years and one month before delivery.",[],"  Forward and Futures Market  On the forward and futures market, electricity is traded between four years and one month before delivery.  Forwards and futures are financial products, which are settled against spot market prices of future delivery periods. While futures are standardized contracts on power exchanges, forwards are traded bilaterally (over the counter) and are not standardized. The forward and futures market is important for many large producers, large consumers, suppliers, Balance Responsible Parties (BRPs) or traders who trade power bilaterally to hedge electricity prices for a certain period of time. Hedging of prices gives these parties financial certainty that they will be able to purchase or sell a certain volume for a pre-agreed price.",{"id":100,"path":101,"dir":37,"title":102,"description":103,"keywords":104,"body":107},"content:2.concepts-background:2.storage-101.md","/concepts-background/storage-101","Storage 101: Short Intro to BESS","So, you've heard that Battery Energy Storage Systems (BESS) are a game-changer for navigating modern power markets. You know the key specs like power (MW) and energy (MWh). Now, let's pop the hood and see what really makes these systems tick. A BESS isn't just a giant battery; it's a sophisticated, integrated system of hardware and software working in concert.",[105,106],"The Core Components of a BESS","Understanding Performance and Lifespan","  Storage 101: Short Intro to BESS  So, you've heard that Battery Energy Storage Systems (BESS) are a game-changer for navigating modern power markets. You know the key specs like power (MW) and energy (MWh). Now, let's pop the hood and see what really makes these systems tick. A BESS isn't just a giant battery; it's a sophisticated, integrated system of hardware and software working in concert.   The Core Components of a BESS  Think of a BESS as having four critical parts: the heart, the brain, the muscle, and the strategist.    The Battery System (The Heart):  This is where the energy is actually stored. It's not one monolithic block but a hierarchy of components.\n    Cells:  The fundamental building block, much like a single AA battery.   Modules:  A group of cells wired together and enclosed in a frame.   Packs/Racks:  Multiple modules are assembled into racks.   Container:  Finally, multiple racks are installed inside a container, complete with all the necessary safety and control systems.\nThe dominant chemistry today is Lithium-ion, with common variants like Nickel Manganese Cobalt (NMC) and Lithium Iron Phosphate (LFP) each offering different trade-offs in energy density, cost, and safety.  Battery Chemistry Comparison: The Strategic Choice  When selecting a BESS, the battery chemistry is a fundamental decision that impacts performance, economics, and safety. Here's how the leading chemistries compare:      Characteristic   LFP (Lithium Iron Phosphate)   NMC (Nickel Manganese Cobalt)   Impact on BESS Applications     Safety  Excellent thermal stability, minimal fire risk  Good with proper BMS, higher thermal runaway risk  LFP preferred for large-scale installations    Cycle Life  3,000-6,000+ cycles at 80% DoD  1,000-3,000 cycles at 80% DoD  LFP offers 2-3x longer lifespan    Energy Density  90-160 Wh/kg  150-250 Wh/kg  NMC requires less space/weight    Cost (2025)  $80-100/kWh  $120-150/kWh  LFP ~30% cheaper upfront    Temperature Range  -20°C to 60°C  0°C to 45°C  LFP better for extreme climates    Degradation Rate  0.5-1% per 1000 cycles  1-2% per 1000 cycles  LFP maintains capacity longer    Round-Trip Efficiency  92-95%  90-93%  LFP slightly more efficient    Maintenance Requirements  Lower (simpler thermal management)  Higher (advanced cooling needed)  LFP reduces operational complexity   Key Takeaway:  LFP dominates grid-scale BESS (70%+ market share) due to superior safety, longevity, and cost-effectiveness. NMC is preferred only where space constraints are critical or extreme power density is required.    The Battery Management System (BMS) (The Brain):  If the batteries are the heart, the BMS is the brain that keeps it beating safely and efficiently. Each battery rack has a BMS that constantly monitors critical parameters like voltage, current, and temperature for every single cell. Its primary jobs are:    Protection:  Preventing over-charging, over-discharging, and overheating, which can damage the battery and create safety hazards.   Balancing:  Ensuring all cells in a module charge and discharge evenly. Without balancing, some cells would wear out much faster than others, crippling the entire system's performance.   Reporting:  Calculating and reporting the   State of Charge (SoC) —the battery's current energy level, like a fuel gauge—and the   State of Health (SoH) , which is a measure of its degradation over time.   The Power Conversion System (PCS) (The Muscle):  Batteries store and release energy as Direct Current (DC). The grid, however, operates on Alternating Current (AC). The PCS, essentially a large, bi-directional inverter, is the muscle that handles this crucial conversion.   When   charging , it converts AC from the grid into DC to be stored in the battery.  When   discharging , it converts DC from the battery into grid-compliant AC to be sold to the market.\nThe   Power Rating (MW)  of a BESS is determined by the capacity of its PCS. A 100 MW PCS can push or pull 100 MW of power, regardless of how much energy (MWh) is stored behind it.   The Energy Management System (EMS) (The Strategist):  The EMS is the high-level controller that tells the whole system what to do and when. It's the software that executes your trading strategy. The EMS communicates with the market operator, trading platform, and the BESS components. It takes in market prices, grid signals, and the battery's current status (from the BMS) and makes the economic decision:   \"Price is low, charge the battery now.\"  \"Price is high, discharge and sell power to the grid.\"  \"Hold the current charge and wait for a better opportunity.\"\nIt sends precise commands to the PCS, telling it exactly how much power to charge or discharge to maximize revenue.   Understanding Performance and Lifespan  The intro mentioned a few key concepts; let's explore them further.    C-Rate and Duration:  These are two sides of the same coin. The C-rate measures how fast a battery is charged or discharged relative to its capacity. A 100 MWh battery discharging at 100 MW is operating at a 1C rate, meaning it would be depleted in one hour (a 1-hour duration system). If it discharged at 50 MW, that's a 0.5C rate, and it would last for two hours. High C-rates (fast charging/discharging) put more stress on the battery and can accelerate degradation.   Depth of Discharge (DoD):  This refers to how much of the battery's total capacity is used in a cycle. To extend a battery's life, operators rarely cycle it from 100% down to 0%. A more common strategy is to operate within a narrower SoC window, for example, from 90% down to 10% (an 80% DoD). This is less stressful on the battery chemistry and significantly increases its cycle life.   Degradation:  This is the unavoidable, gradual loss of energy capacity over time. It's caused by two things:    Calendar Aging:  The battery loses a small amount of capacity just by existing, even if it's not used.   Cycle Aging:  The physical and chemical stress of charging and discharging causes wear and tear.\nThe rate of degradation is heavily influenced by how the battery is operated. High temperatures, high C-rates, and deep discharge cycles all accelerate it. This is why a thermal management system (for cooling and heating) is another non-negotiable component of any BESS.  Ultimately, a BESS is a complex asset where performance, longevity, and profitability are all intertwined. Understanding these technical details is the first step toward operating it effectively and maximizing its value in the energy markets. ",{"id":109,"path":110,"dir":37,"title":111,"description":112,"keywords":113,"body":116},"content:2.concepts-background:3.revenue-stacking.md","/concepts-background/revenue-stacking","What is Revenue Stacking?","Revenue stacking is the key strategy for turning a significant capital investment into a profitable asset. At its core, revenue stacking means participating in multiple markets and providing various services—often simultaneously—to create several streams of income from a single battery system. It’s about not putting all your eggs in one basket and instead, intelligently layering different revenue opportunities to maximize returns and mitigate risks.",[114,115],"Deconstructing the Revenue Stack","The Power of Co-location: A Natural Synergy","  What is Revenue Stacking?  Revenue stacking is the key strategy for turning a significant capital investment into a profitable asset.   At its core, revenue stacking means participating in multiple markets and providing various services—often simultaneously—to create several streams of income from a single battery system.  It’s about not putting all your eggs in one basket and instead, intelligently layering different revenue opportunities to maximize returns and mitigate risks.  Think of a BESS not just as a simple battery, but as a versatile grid tool. Its ability to charge and discharge energy in milliseconds makes it uniquely capable of performing a wide range of tasks that are highly valuable to grid operators, utilities, and energy consumers. The art of revenue stacking lies in identifying the most lucrative combination of these services based on market conditions, regulatory frameworks, and the specific technical capabilities of the battery.   Deconstructing the Revenue Stack  The potential income streams for a BESS can be broadly categorized. A successful strategy involves stacking services from two or more of these categories.  1. Energy Arbitrage (Time-Shifting)  This is the most straightforward revenue stream. It involves charging the battery when electricity prices are low and discharging it when prices are high.    How it works:  In markets with significant renewable energy penetration (like solar), electricity prices can drop dramatically during midday when the sun is shining brightest. A BESS can buy and store this cheap energy. Later, during the evening peak demand when solar generation ceases and prices spike, the BESS sells the stored energy back to the grid for a profit.   Key Factor:  Success depends on a significant price spread (volatility) between off-peak and peak hours.  2. Ancillary Services  These are services essential for maintaining the stability and reliability of the electrical grid. BESS are exceptionally good at providing them due to their near-instantaneous response time.    Frequency Regulation:  The grid must maintain a stable frequency (e.g., 50 Hz or 60 Hz). Deviations can damage equipment and cause outages. A BESS can continuously make micro-adjustments, either injecting or absorbing power to instantly correct frequency fluctuations. This is often a high-value, premium service.   Operating Reserves:  Grid operators need to have backup power ready in case a large power plant or transmission line suddenly fails. A BESS can act as a \"spinning reserve,\" standing by, ready to inject its full power capacity within seconds or minutes of a grid event.  3. Capacity Markets  In some regions, grid operators run capacity markets to ensure there will be enough total generation available to meet the highest predicted demand of the year (e.g., on the hottest summer afternoon).    How it works:  BESS owners can bid their assets into these markets. If their bid is accepted, they receive a steady, predictable payment simply for being available and guaranteeing their capacity during critical periods. This provides a reliable baseline revenue, even if the battery is never actually dispatched.  4. Network Services  These services address local grid constraints rather than system-wide issues.    Congestion Management:  In certain parts of the grid, transmission lines can become overloaded or \"congested.\" A BESS placed strategically can absorb excess power before the congestion point and discharge it later, effectively relieving the strain on the network and deferring the need for expensive infrastructure upgrades.   Voltage Support:  BESS can help maintain stable voltage levels on the local distribution network, improving power quality for all connected customers.   Traders often integrate these strategies to create a comprehensive revenue stacking approach, enabling participation in multiple markets simultaneously to optimize returns and reduce risks.   The Power of Co-location: A Natural Synergy  Revenue stacking becomes even more powerful when a BESS is co-located with other assets, creating a symbiotic relationship that enhances the value of both.    Solar + Storage (PV+BESS):  This is the most common pairing. A solar farm's output is intermittent—it only produces power when the sun shines. By adding a battery, the project owner can:    Store excess solar energy  generated midday and sell it during the more valuable evening peak (energy arbitrage).   \"Firm\" the solar output,  creating a dispatchable resource that can provide power on demand, even when the sun isn't shining.   Provide ancillary services  using the battery, adding another revenue layer on top of energy sales.   Wind + Storage (Wind+BESS):  Similar to solar, wind power is variable. Wind farms often produce the most power at night when demand and prices are low. Co-locating a BESS allows the asset owner to:    Capture low-cost wind energy  generated overnight and shift it to higher-priced daytime hours.   Avoid curtailment  by storing energy that would otherwise be wasted when the grid is congested or demand is too low.   Create a firm, dispatchable product  by smoothing out the fluctuating wind output, making the asset more valuable to the grid.   Commercial & Industrial (C&I) + BESS:  For large businesses, a co-located BESS can be a financial game-changer. It allows them to:    Reduce Peak Demand Charges:  Many commercial electricity bills include hefty charges based on the highest peak power usage in a month. A BESS can discharge during these peaks to lower the facility's demand from the grid, directly cutting costs.   Increase Self-Consumption:  If the C&I facility has its own solar panels, the battery can store excess solar generation for later use, reducing the amount of energy the business needs to buy from the utility.   Participate in Grid Services:  When not being used for bill management, the C&I-sited battery can still bid into ancillary service or capacity markets, generating external revenue for the business.  In conclusion, revenue stacking is not just an option; it's the fundamental business model for modern BESS deployments. By moving beyond a single application and embracing a multifaceted strategy that includes energy arbitrage, ancillary services, and capacity payments—often enhanced through co-location—asset owners can unlock the full economic potential of energy storage and accelerate the transition to a more flexible and resilient energy grid.",{"id":118,"path":119,"dir":7,"title":120,"description":121,"keywords":122,"body":124},"content:3.methodology.md","/methodology","Re-Twin Methodology","At Re-Twin Energy, we publish our full methodology because transparency is essential for trust. This section explains how we forecast prices, model storage assets, and simulate trading strategies.",[123],"Methodology breakdown","  Re-Twin Methodology  At   Re-Twin Energy , we publish our full methodology because transparency is essential for trust. This section explains how we forecast prices, model storage assets, and simulate trading strategies.  \n      \n          \n          Figure: The Re-Twin Methodology \n      On a high-level, the   Re-Twin’s modeling framework  links the    Fundamentals Model  with the   Battery Dispatch Model  to deliver realistic market-wise revenue estimates for energy storage systems.  As shown in above figure on the left, the    Fundamentals Model  combines key market drivers. It integrates    demand forecasts  (retail, EVs, industrial),   generation forecasts  (renewables, conventional power, storage, and green hydrogen), and   price forecasts  (oil, gas, CO₂). It also accounts for   grid aspects  such as interconnections and constraints. Together, these inputs produce   pricing outputs  for the day-ahead, intraday, and ancillary service markets.  On the   right , the   Battery Dispatch Model  applies these prices to the operational reality of a battery. It considers the   technical setup  (efficiency, degradation, battery limits),   constraints  (substation capacity, ramping rules, and time-based limits), and   trading configurations . These factors create a   digital twin  of the battery, ensuring that simulated results reflect practical operation.  Finally, the   Re-Twin Optimization Model  merges both sides. It uses pricing from the fundamentals model and applies asset-specific characteristics from the dispatch model to simulate real trading behavior. The outcome is   robust revenue estimates  across markets, giving developers and investors a transparent view of how a battery can generate value under different market conditions.   Methodology breakdown  Our approach is structured into five layers which are described in the next sections:     Fundamentals Model  → long-term market forecasts (demand, renewables, commodities, capacity expansion, price formation)    Storage Dispatch Modeling  → optimal operation of batteries under market and asset constraints with the ability to select trader-like strategies    Co-location & Trading Layer  → hybrid assets, shared grid limits and shared optimization   Calibration & Backtesting  → benchmarking against real-world data to ensure reliability   Data Sources  → Transparent and auditable datasets that underpin all our models, ensuring consistency and reliability.",{"id":126,"path":127,"dir":128,"title":129,"description":130,"keywords":131,"body":136},"content:3.methodology:1.fundamentals-model.md","/methodology/fundamentals-model","methodology","Fundamentals Model","Our long-term revenue forecasts for energy storage are based on fundamentals model scenarios, providing a comprehensive outlook on demand, renewables, commodities, capacity expansion, and price formation for the coming decades.",[132,133,134,135],"What is a Fundamental Model?","How is it Generated? Inputs and Outputs","TYNDP and Role of Forecasting Pathways","Re-Twin Modeled Pathways","  Fundamentals Model  Our long-term revenue forecasts for energy storage are based on fundamentals model scenarios, providing a comprehensive outlook on demand, renewables, commodities, capacity expansion, and price formation for the coming decades.   What is a Fundamental Model?  At its core, a fundamental model is a detailed simulation of the electricity market. Unlike purely statistical models that rely on historical price trends, a fundamental model simulates the physical and economic dispatch of power plants to meet electricity demand. The key significant factors—from fuel costs and carbon prices to transmission grid constraints and renewable energy availability are taken into consideration. By modeling the supply and demand balance on an hourly basis, prices, asset utilization, and investment signals are drawn from the ground up.  \n      \n          \n          Fig 2. Fundamentals Model \n       How is it Generated? Inputs and Outputs  Generating a fundamental model forecast involves gathering extensive data and running powerful simulations.   Key Inputs:     Demand Forecasts:  Projections of electricity consumption, factoring in economic growth, energy efficiency, and the electrification of transport and heat.   Commodity Prices:  Long-term forecasts for natural gas, coal, and carbon (EUA) prices.   Technology Assumptions:  Costs, efficiencies, and lifetimes for all generation technologies, including solar, wind, batteries, and thermal plants.   Renewable Profiles:  Hourly generation profiles for wind and solar assets based on historical weather data.   Policy and Regulations:  National and international energy policies, renewable energy targets, and capacity market regulations.   Grid Topology:  Representation of the transmission network, including transfer capacities between market zones.   Key Outputs:    Wholesale Price Forecasts:  Hourly, monthly, and annual electricity price projections.   Generation Mix:  The projected dispatch from each technology type to meet demand.   Capacity Expansion:  Endogenous results on which new technologies are built, where, and when.   TYNDP and Role of Forecasting Pathways  Because the future is uncertain, long-term forecasts are rarely presented as a single pathway. Instead,   scenario frameworks  are used to explore a range of possible futures.  For modeling European electricity markets, the   European Network of Transmission System Operators (ENTSO-E)  develops several trajectories within its    Ten-Year Network Development Plan (TYNDP) . The TYNDP outlines the pan-European vision for the electricity grid, and its pathways (i.e. scenarios) provide different views of how Europe’s power system could evolve:    Global Ambition:  A pathway where the EU successfully achieves its   net-zero targets , supported by global cooperation, rapid electrification, and strong technological innovation.   National Trends:  A slower transition pathway where each country pursues its own policies, reflecting fragmented ambition levels and a slower rollout of new technologies.   Distributed Energy:  A more   bottom-up scenario  driven by households, communities, and prosumers with widespread adoption of rooftop solar, local storage, and energy efficiency.  These pathways are not predictions but   narratives of possible futures . They allow modelers to test sensitivities and better understand the range of risks facing the energy system. The grid expansion projects and scenarios defined in the TYNDP directly impact fundamental models by setting the future transmission capacities between countries, which is a key driver of price convergence and the business case for new generation assets.   Re-Twin Modeled Pathways  Re-Twin develops the fundamental model forecasts in collaboration with partners, such as from   FFE  and   MAON , which allows capturing different pathways and approaches to the evolution of power markets. The forecasts are updated regularly (typically every 3 to 6 months) to ensure they reflect the latest market developments.  To capture a range of possible futures, we model several pathways with every partner model. These are often aligned with established frameworks like those developed for the   TYNDP Scenarios , which provide consistent storylines for policy ambition and technological development across Europe.  Details around the latest available long term forecast pathways are provided on the   Forecast Scenarios  page.",{"id":138,"path":139,"dir":140,"title":141,"description":142,"keywords":143,"body":144},"content:3.methodology:1.fundamentals-model:1.Inputs.md","/methodology/fundamentals-model/inputs","fundamentals-model","Inputs to the Fundamentals Model","The fundamental power market forecast model aims to capture the drivers of electricity prices and demand in a structured, transparent way. At its core, the model combines multiple input streams – each representing a critical part of the power system – to build a coherent view of how markets may evolve. Getting these inputs right is essential for producing reliable forecasts.",[],"  Inputs to the Fundamentals Model  The   fundamental power market forecast model  aims to capture the drivers of electricity prices and demand in a structured, transparent way. At its core, the model combines multiple input streams – each representing a critical part of the power system – to build a coherent view of how markets may evolve. Getting these inputs right is essential for producing reliable forecasts.  The first building block is the    Demand Forecast . Demand is not a single number but a dynamic hourly profile shaped by households, industry, and the uptake of new technologies such as electric vehicles and heat pumps. Structural changes in how societies consume energy, combined with efficiency gains, play a defining role in the demand outlook.  On the supply side, the    Generation Forecast  is equally important. This includes both   renewables  (wind, solar, hydro) and    Conventional Generation  (gas, coal, nuclear), along with the growing role of   storage  and emerging vectors such as   green hydrogen . Capturing the availability and cost structure of these resources is central to the model.  Markets are also increasingly interconnected. Market Coupling & Interconnection allow electricity to flow across borders, smoothing supply and demand imbalances. At the same time, Other Inputs such as weather patterns, grid expansion projects, and hydrogen pipeline development shape how flexible or constrained the system may be in the future.  Finally, Price Formation factors like oil, gas, and CO₂ markets feed directly into marginal cost calculations. Because these global commodities often set the price in European markets, they form an indispensable part of the fundamentals framework.  Each of these inputs is explored in detail in the following pages, ensuring transparency and clarity in how the model is built.",{"id":146,"path":147,"dir":148,"title":149,"description":150,"keywords":151,"body":155},"content:3.methodology:1.fundamentals-model:1.inputs:1.demand_forecasts.md","/methodology/fundamentals-model/inputs/demand_forecasts","inputs","Demand Forecasts","Demand forecasts are an essential component of any fundamental power market model, which includes forward-looking projections of electricity consumption. Rather than being a single annual figure, these forecasts are detailed hourly demand profiles that capture the rhythm of a country’s energy usage over years and decades.",[152,153,154],"Key Drivers of Electricity Demand","Hourly Demand Shape","Example demand profile","  Demand Forecasts  Demand forecasts are an essential component of any   fundamental power market model , which includes forward-looking projections of electricity consumption. Rather than being a single annual figure, these forecasts are detailed   hourly demand profiles  that capture the rhythm of a country’s energy usage over years and decades.   Key Drivers of Electricity Demand  The trajectory of future demand is shaped by several major factors:    Macroeconomic activity : Economic growth, industrial output, and service-sector expansion all affect baseline consumption levels.   Electrification of transport and heating : The shift to   electric vehicles (EVs)  and   heat pumps  is expected to drive significant new load, particularly overnight (EV charging) and during winter months (heating).   Energy efficiency improvements : More efficient appliances, buildings, and industrial processes act as a counterbalance, slowing overall demand growth.   Policy and societal choices : National climate targets, building codes, and consumer adoption patterns add uncertainty to long-term outlooks.   Hourly Demand Shape  For power market modeling, it is essential to build a   granular hourly load curve . This is done by:    Analyzing historical demand patterns  (weekdays vs weekends, seasonal variations).   Adjusting for structural shifts :\n   EV charging increases   overnight demand , flattening the curve.  Heat pumps add   seasonal peaks  in winter.   Aggregating to gross demand at the transmission level , before subtracting embedded generation (e.g., rooftop PV). This ensures solar and distributed resources are accounted for properly on the supply side.   Example demand profile  \n      \n          \n          Figure: Germany Daily Median Demand Profiles (2025–2050) \n      This figure shows the   median daily demand shape  for Germany across years 2025 to 2050 (excluding embedded generation). Demand rises sharply in the morning, peaks late morning to midday, and gradually declines into the evening. Over time, the entire curve shifts upward, reflecting   structural demand growth . The spread between the earliest (2025) and latest (2050) curves highlights the   long-term electrification effect .   \n      \n          \n          Figure: Germany Spot Demand Forecast (2025–2050) \n      This long-term view shows Germany’s   spot electricity demand forecast  in terawatt-hours (TWh). Seasonal cycles are clearly visible, with higher winter loads. The steady upward slope reflects   increasing electrification  and   economic activity , with demand nearly doubling between 2025 and 2050. Short-term volatility is preserved in the forecast, emphasizing the importance of flexible supply and storage solutions.  The charts above serve only as   illustrative examples . Details around the latest available long term forecast pathways are provided on the   Forecast Scenarios  page.",{"id":157,"path":158,"dir":148,"title":159,"description":160,"keywords":161,"body":167},"content:3.methodology:1.fundamentals-model:1.inputs:2.renewable-energy-integration.md","/methodology/fundamentals-model/inputs/renewable-energy-integration","Renewable Energy Integration","A reliable power market model requires a robust view of future renewable generation. Renewables are now the backbone of Europe’s energy transition, and their expansion sets the tone for capacity mixes, price formation, and system flexibility needs. Long-term outlooks, such as those provided by ENTSO-E’s Ten-Year Network Development Plan (TYNDP), explore different pathways that reflect varying levels of policy ambition, technology adoption, and system integration.",[162,163,164,165,166],"Solar Photovoltaics (PV)","Wind Offshore","Wind Onshore","Biomass","Modeling Considerations","  Renewable Energy Integration  A reliable power market model requires a robust view of   future renewable generation . Renewables are now the backbone of Europe’s energy transition, and their expansion sets the tone for capacity mixes, price formation, and system flexibility needs. Long-term outlooks, such as those provided by ENTSO-E’s   Ten-Year Network Development Plan (TYNDP) , explore different pathways that reflect varying levels of policy ambition, technology adoption, and system integration.  The figures below illustrate   example trajectories  for key renewable technologies in a trend-style scenario. These are not forecasts in themselves, but serve as visual references to show the scale of growth and structural shifts expected across technologies. The detailed assumptions and scenario-specific pathways are presented separately.   Solar Photovoltaics (PV)  PV is expected to deliver the largest single source of new generation over the coming decades.  \n      \n          \n          Example: PV Generation Trend Scenario (2025–2050) \n       Wind Offshore  Offshore wind will play a central role in Europe’s decarbonization.  \n      \n          \n          Example: Offshore Wind Generation Trend Scenario (2025–2050) \n       Wind Onshore  Onshore wind remains an important contributor despite land-use and permitting challenges.  \n      \n          \n          Example: Onshore Wind Generation Trend Scenario (2025–2050) \n       Biomass  Biomass is expected to decline in importance over time due to sustainability constraints.  \n      \n          \n          Example: Biomass Generation Trend Scenario (2025–2050) \n       Modeling Considerations  When using renewable inputs in a fundamental power market model, it is critical to go beyond installed capacity numbers. The model must reflect:    Load factors and generation profiles  at an hourly level   Integration challenges  such as curtailment and transmission bottlenecks   Flexibility requirements  from storage, hydrogen, and demand-side response   Scenario differentiation , as explored in ENTSO-E’s TYNDP (e.g.   Global Ambition ,   National Trends ,   Distributed Energy )  The charts above serve only as   illustrative examples . Details around the latest available long term forecast pathways are provided on the   Forecast Scenarios  page.",{"id":169,"path":170,"dir":148,"title":171,"description":172,"keywords":173,"body":176},"content:3.methodology:1.fundamentals-model:1.inputs:3.conventional-generation.md","/methodology/fundamentals-model/inputs/conventional-generation","Conventional Generation","Conventional generation continues to play a critical role in Europe’s electricity system, even as renewables expand. In a fundamental power market model, these assets provide stability, flexibility, and price-setting functions. The forecasts must carefully capture how conventional generation evolves, accounting for retirements and new investments. While policy ambitions point towards a steady decline, the trajectory varies significantly depending on scenario assumptions.",[174,175,166],"Thermal Plant Retirement Schedules","New Capacity Investments","  Conventional Generation  Conventional generation continues to play a critical role in Europe’s electricity system, even as renewables expand. In a   fundamental power market model , these assets provide stability, flexibility, and price-setting functions. The forecasts must carefully capture how conventional generation evolves, accounting for   retirements and new investments . While policy ambitions point towards a steady decline, the trajectory varies significantly depending on scenario assumptions.   Thermal Plant Retirement Schedules  Many coal and older gas-fired power plants are scheduled to retire before 2040. National policies and EU climate legislation drive accelerated phase-outs, particularly for lignite and hard coal. Retirement schedules are therefore a key modeling input. The model reflects published phase-out commitments but also applies assumptions for early closures where plants become uneconomic due to carbon costs.  \n      \n          \n          Example: Coal Plant Retirement Trajectory (2025–2050) \n       New Capacity Investments  Despite retirements, new investments in   flexible gas capacity  continue, particularly in open-cycle gas turbines (OCGTs) and reciprocating engines. These plants provide peaking support during periods of low renewable output. Beyond 2030, some gas units are assumed to be   hydrogen-ready  or equipped with   carbon capture and storage (CCS) . Nuclear also plays a role in certain scenarios, with lifetime extensions and new builds reflected in country-specific plans.   Modeling Considerations  Conventional generation forecasts balance three key themes:    Decarbonization policies , which drive retirements and fuel-switching.   Security of supply , requiring flexible backup to variable renewables.   Market dynamics , shaped by commodity prices and carbon costs.  The charts above serve only as   illustrative examples . Details around the latest available long term forecast pathways are provided on the   Forecast Scenarios  page.",{"id":178,"path":179,"dir":128,"title":180,"description":181,"keywords":182,"body":186},"content:3.methodology:2.storage-dispatch-modeling.md","/methodology/storage-dispatch-modeling","Storage Dispatch Modeling","The storage dispatch model optimizes battery operations under market and asset constraints. It determines how battery systems operate to maximize revenue while adhering to technical and market limitations. This capability is central to our investment analysis and trading simulations.",[183,184,185],"Key Inputs to the Dispatch Model","Revenue Estimation","Why Structure by Market Schedule?","  Storage Dispatch Modeling  The storage dispatch model optimizes battery operations under market and asset constraints. It determines how battery systems operate to maximize revenue while adhering to technical and market limitations. This capability is central to our investment analysis and trading simulations.  Battery Energy Storage Systems (BESS) are unique in their ability to act as both electricity consumers and producers, dynamically adjusting operations in response to market signals. Our   storage dispatch model  aligns with the   power market schedule , treating the battery as a digital twin of a real trading asset. This is not a theoretical optimization in isolation but a practical representation of how a BESS participates, wins, or loses bids in each market.  \n      \n          \n          Figure: Battery Dispatch Model – Inputs and Optimization Flow \n      Key Inputs to the Dispatch Model  The dispatch model integrates several critical input categories:    Pricing:  Day-ahead, intraday, and ancillary market price forecasts form the economic foundation of trading decisions.    Technical Setup:  Efficiency losses, degradation considerations, and state-of-charge (SoC) limits ensure realistic dispatch outcomes.   Constraints:  Substation capacities, ramping requirements, and time-based operational restrictions are explicitly modeled.    Trading Configuration:  Bid structures and participation rules define how the BESS competes in each market.  These inputs feed into the   Re-Twin Optimization Model , which simulates sequential bidding decisions. The model mirrors real-world trading by submitting bids into each market, with some winning, others being rejected, and subsequent rounds adapting to prior results. This approach captures the inherent uncertainty and dynamics of market-based storage revenues.   Revenue Estimation  The model estimates revenue from three primary sources, simulating how a BESS monetizes its capabilities across different markets.  1. Revenue from Ancillary Services (Capacity)  This income is earned by committing capacity to the grid operator, regardless of whether it is dispatched. It is a stable revenue stream based on the battery’s power rating and substation capacity. For each service (e.g., FCR, aFRR, mFRR), the revenue is calculated as:   Revenue = Power Committed (MW) * Capacity Price (€/MW)  The model assumes no energy is dispatched (lost or gained) when calculating this baseline capacity revenue.  2. Revenue/Cost from Wholesale Markets (Arbitrage)  This represents the core profit from buying low and selling high in the spot markets. The model optimizes bidding in both the Day-Ahead (DA) and Intraday Continuous (IDC) markets. For each time interval, the net revenue is:   Net Revenue = (Energy Sold * Market Price) - (Energy Bought * Market Price)  This captures the fundamental value of energy arbitrage.  3. Revenue/Cost from Ancillary Services (Energy)  This is the income earned or cost incurred when reserved ancillary capacity is activated to deliver or absorb energy. The model pursues these opportunities based on the results from the day-ahead market.    Positive Activation (e.g., aFRR positive):  Energy delivered to the grid generates revenue.   Negative Activation (e.g., aFRR negative):  Energy absorbed from the grid is typically a revenue stream (being paid to take energy) but is treated as a negative cost.  To account for the higher uncertainty in aFRR energy dispatch compared to the day-ahead market, the model applies a strategic bidding approach:    Positive (aFRR_POS) Energy Bids:  Priced at a premium: 50% above day-ahead prices.   Negative (aFRR_NEG) Energy Bids:  Priced at a discount: 50% below day-ahead prices.  With these price levels established, the model then determines the optimal volume to bid based on potential revenues and the battery's state of charge. This sequential process allows the model to pursue additional opportunities in intraday, aFRR energy, and imbalance markets after day-ahead positions are secured.  4. Intraday Temporal Arbitrage  The continuous, pay-as-bid nature of the intraday market creates opportunities for   temporal arbitrage . This is a purely financial strategy where a trader leverages short-term price volatility by executing multiple buy and sell orders for the same delivery period. The objective is to secure a profit from price differentials while ensuring a net-zero physical position by the time of delivery.  Consider the following illustrative trade sequence for a 1 MWh delivery block between 11:00 and 12:00:    07:15:  A contract is executed to   buy  1 MWh at €40/MWh.   09:15:  A subsequent contract is executed to   sell  0.7 MWh at €50/MWh.   10:34:  A final contract is executed to   sell  the remaining 0.3 MWh at €70/MWh.  The financial outcome of this sequence is a profit of €16, calculated as   (€50 * 0.7) + (€70 * 0.3) - (€40 * 1) . The trader's physical position for the delivery hour is zero, isolating the activity to a financial gain derived solely from market price movements.  Re-Twin's Intraday Temporal Arbitrage Estimation  Re-Twin employs a quantitative model to estimate the potential financial value of intraday temporal arbitrage. The methodology is grounded in the analysis of historical price volatility and incorporates conservative assumptions to produce a realistic valuation.    Data Source:  The model utilizes historical Volume-Weighted Average Price (VWAP) data at a 5-minute resolution.   Volatility Analysis:  For each hourly delivery product, we analyze the 8-hour period immediately preceding delivery. Within this window, we identify the maximum and minimum 5-minute VWAP.   Gross Spread Calculation:  The potential arbitrage spread is defined as the difference between the observed maximum and minimum VWAP within the analysis window.   Conservative Adjustments:  To reflect real-world trading limitations and risk, we apply two key derating factors:\n    Capture Rate:  We assume that only   10%  of the gross VWAP spread can be practically captured due to market friction, timing, and transaction costs.   Traded Volume:  We model a traded volume equivalent to   10%  of the asset's rated capacity (in MWh), representing a prudent risk management approach.  Valuation Formula  The estimated arbitrage value for a given hour is derived from the following formula:   Arbitrage Value (€) = (Asset Capacity [MWh] * 10%) * (Max VWAP - Min VWAP [€/MWh]) * 10%\n  Where:    Max VWAP:  The maximum 5-minute VWAP observed in the 8-hour window prior to delivery.   Min VWAP:  The minimum 5-minute VWAP observed in the 8-hour window prior to delivery.   Why Structure by Market Schedule?  Our dispatch framework offers an   investor-grade methodology  for evaluating BESS revenues under real market rules. Unlike perfect hindsight models, our approach incorporates conservative assumptions and reflects the operational challenges of bidding. The model can be customized with scenario-specific parameters based on user inputs, such as:   Alternative bidding strategies  Adjusted SoC or efficiency assumptions  Region-specific constraints and rules  Ultimately, the dispatch model bridges the gap between   forecasted market opportunities  and   practical asset operations , providing a realistic view of how a BESS generates revenue across Europe’s power markets.",{"id":188,"path":189,"dir":190,"title":191,"description":192,"keywords":193,"body":197},"content:3.methodology:2.storage-dispatch-modeling:1.technical-setup.md","/methodology/storage-dispatch-modeling/technical-setup","storage-dispatch-modeling","Technical Setup","Technical setup define the operational boundaries of a Battery Energy Storage System (BESS). They ensure that the dispatch and optimization models reflect realistic physical limitations rather than unconstrained, theoretical performance. Capturing these correctly is essential for producing credible forecasts of revenues and asset behavior.",[194,195,196],"Power Limits","Energy Constraints","Degradation","  Technical Setup  Technical setup define the operational boundaries of a Battery Energy Storage System (BESS). They ensure that the dispatch and optimization models reflect realistic physical limitations rather than unconstrained, theoretical performance. Capturing these correctly is essential for producing credible forecasts of revenues and asset behavior.  \n      \n          \n          Figure: Interface for configuring BESS technical parameters \n       Power Limits  The   power rating  of a battery determines its maximum charging and discharging capability at any moment, typically expressed in MW. This rating is governed by inverter capacity, thermal design, and grid connection limits. In practice, this ensures that the BESS cannot instantly charge or discharge beyond its designed maximum power, even if energy is available.   Energy Constraints  Energy constraints govern the usable energy storage within the system:    Total capacity (MWh):  Defines the energy reservoir available for shifting load or trading in markets.   State-of-Charge thresholds:  Minimum and maximum limits (e.g., 10–90%) are applied to avoid full depletion or overcharging, which would accelerate degradation and void warranties.   Initial State-of-Charge (SoC):  Sets the starting balance for dispatch simulations, often chosen to allow flexibility for both charging and discharging.   Round-trip efficiency:  Real-world systems incur losses when charging and discharging. Typical lithium-ion batteries achieve ~85–92% efficiency overall, ensuring that modeled revenues account for these unavoidable losses.   Cycle limits:  To safeguard asset life, daily cycling is capped at a realistic value (e.g., between 1–2 cycles per day depending on design and strategy).   Degradation  Battery degradation represents the gradual decline in usable capacity over time. It depends strongly on the   number of cycles  performed and the   depth of discharge (DoD)  applied in each cycle. Shallow cycling reduces wear, while frequent deep discharges accelerate it.  \n      \n          \n          Figure: Example degradation relationship between cycle depth and capacity fade \n      Typical patterns observed:   Shallow cycling (e.g., 20% DoD) extends lifetime, often allowing thousands of cycles before significant capacity fade.  Moderate cycling (e.g., 50% DoD) leads to noticeable degradation after several thousand cycles.  Deep cycling (e.g., 80% DoD) can result in much faster capacity loss, reducing lifetime to a fraction of shallow-cycle operation.  The Re-Twin dispatch model incorporates degradation to balance   short-term revenue maximization  against   long-term asset value preservation . This ensures that financial projections are grounded in the physical aging of the battery, not just theoretical market opportunities.",{"id":199,"path":200,"dir":190,"title":201,"description":202,"keywords":203,"body":223},"content:3.methodology:2.storage-dispatch-modeling:2.bess-degradation-modelling.md","/methodology/storage-dispatch-modeling/bess-degradation-modelling","BESS Degradation Model","This document explains how battery degradation is calculated in the BESS sizing and simulation code.",[204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222],"Scope of the Degradation Model","State Variables","Degradation Flow Overview","Calendar Aging Model","Cycle Aging Model","Total Degradation Update","Model Assumptions and Simplifications","Literature and Reference Basis","Summary","Asset Definition","Degradation Model Configuration","Step 1: Cycles per Year","Step 2: Equivalent Full Cycles per Year (Model Logic)","Step 3: Annual Cycle-Related Degradation","Step 4: Annual Calendar Degradation","Step 5: Total Annual SoH Degradation","Step 6: SoH and Energy Capacity Over Time","Step 7: Power Degradation (Model Logic)","Interpretation","  BESS Degradation Model  This document explains how battery degradation is calculated in the BESS sizing and simulation code.   Scope of the Degradation Model  The degradation model estimates the   loss of usable energy capacity over time . Power capability degradation is explicitly modeled and represented as a reduction in available energy capacity (MWh).  Two degradation mechanisms are modeled:   Calendar aging  Cycle aging  Both mechanisms are assumed to be   additive and independent .   State Variables  At any simulation step t , the battery state is defined as:  Nominal (nameplate) energy capacity:         E  0   [  MWh  ]  E_0 \\quad [\\text{MWh}]      E         0  ​      [   MWh  ]  Remaining usable energy capacity:         E  t   [  MWh  ]  E_t \\quad [\\text{MWh}]      E         t  ​      [   MWh  ]  State of Health (SoH):         SoH  t  =    E  t   E  0  \\text{SoH}_t = \\frac{E_t}{E_0}       SoH         t  ​      =               E         0  ​            E         t  ​     ​      Battery degradation reduces Et monotonically over time.   Degradation Flow Overview  At each simulation step, degradation is computed using the following sequence:   Elapsed calendar time is evaluated  Energy throughput from charging and discharging is accumulated  Energy throughput is converted to equivalent full cycles  Calendar aging increment is calculated  Cycle aging increment is calculated  Remaining usable capacity is updated   Calendar Aging Model  Calendar aging represents capacity loss that occurs independently of cycling, driven by time-dependent electrochemical processes.  A   linear annual degradation model  is applied:        Δ   E  calendar  (  t  )  =   E  0  ⋅   r  cal  ⋅  Δ  t  \\Delta E_{\\text{calendar}}(t) = E_0 \\cdot r_{\\text{cal}} \\cdot \\Delta t     Δ   E           calendar  ​     (  t  )   =      E         0  ​      ⋅      r           cal  ​      ⋅     Δ  t  Where:   r_cal is the calendar degradation rate {fraction per year}  (delta_t) is the elapsed time in years for the simulation step  Typical parameter range:         r  cal  ∈  [  0.005  ,      0.015  ]  r_{\\text{cal}} \\in [0.005,\\; 0.015]      r           cal  ​      ∈     [  0.005  ,    0.015  ]  Calendar degradation accumulates continuously over the full simulation horizon.   Cycle Aging Model  Cycle aging represents degradation caused by energy throughput during charging and discharging.  Energy Throughput  For each time step, total energy throughput is computed as:         E  throughput  (  t  )  =   E  charge  (  t  )  +   E  discharge  (  t  )  E_{\\text{throughput}}(t) = E_{\\text{charge}}(t) + E_{\\text{discharge}}(t)      E           throughput  ​     (  t  )   =      E           charge  ​     (  t  )   +      E           discharge  ​     (  t  )  Cumulative throughput over time:         E  throughput,cum  =   ∑  t   E  throughput  (  t  )  E_{\\text{throughput,cum}} = \\sum_t E_{\\text{throughput}}(t)      E           throughput,cum  ​      =            t     ∑  ​       E           throughput  ​     (  t  )   Equivalent Full Cycles (EFC)  Rather than counting discrete cycles, degradation is based on   equivalent full cycles (EFC) :        EFC  (  t  )  =    E  throughput,cum   2  ⋅   E  0  \\text{EFC}(t) = \\frac{E_{\\text{throughput,cum}}}{2 \\cdot E_0}      EFC  (  t  )   =              2   ⋅    E         0  ​            E           throughput,cum  ​     ​      This formulation correctly accounts for partial cycling and varying depth of discharge.  Incremental EFC for a time step:        Δ  EFC  (  t  )  =     E  throughput  (  t  )   2  ⋅   E  0  \\Delta \\text{EFC}(t) = \\frac{E_{\\text{throughput}}(t)}{2 \\cdot E_0}     Δ   EFC  (  t  )   =              2   ⋅    E         0  ​            E           throughput  ​     (  t  )  ​       Cycle Degradation Calculation  Cycle-related capacity loss is calculated as:        Δ   E  cycle  (  t  )  =   E  0  ⋅   r  cycle  ⋅  Δ  EFC  (  t  )  \\Delta E_{\\text{cycle}}(t) = E_0 \\cdot r_{\\text{cycle}} \\cdot \\Delta \\text{EFC}(t)     Δ   E           cycle  ​     (  t  )   =      E         0  ​      ⋅      r           cycle  ​      ⋅     Δ   EFC  (  t  )  Where:   r_cycle is degradation per equivalent full cycle {fraction per year}  Typical parameter range:         r  cycle  ∈  [  0.0002  ,      0.0005  ]  r_{\\text{cycle}} \\in [0.0002,\\; 0.0005]      r           cycle  ​      ∈     [  0.0002  ,    0.0005  ]  These values correspond to approximately 2,000–5,000 full cycles to 80% SoH, consistent with grid-scale lithium-ion warranties.   Total Degradation Update  Total degradation per time step is computed as the sum of calendar and cycle contributions:        Δ   E  t  =  Δ   E  calendar  (  t  )  +  Δ   E  cycle  (  t  )  \\Delta E_t = \\Delta E_{\\text{calendar}}(t) + \\Delta E_{\\text{cycle}}(t)     Δ   E         t  ​      =     Δ   E           calendar  ​     (  t  )   +     Δ   E           cycle  ​     (  t  )  Remaining usable energy capacity is updated as:         E   t  +  1  =  max  ⁡   (   E  t  −  Δ   E  t  ,       E  min  )  E_{t+1} = \\max \\left( E_t - \\Delta E_t,\\; E_{\\text{min}} \\right)      E          t  +  1  ​      =     max    (   E         t  ​      −   Δ   E         t  ​     ,     E           min  ​     )  Where E_min is a lower bound on usable capacity to prevent non-physical negative values.   Model Assumptions and Simplifications  The following modeling assumptions are intentionally applied:   Battery temperature is not explicitly modeled  C-rate–dependent degradation effects are not differentiated  Depth-of-discharge dependence is approximated through EFCs  Power capability degradation is not modeled separately  Degradation is path-independent and memoryless  These assumptions are appropriate for   system-level planning, sizing, and revenue studies , but not for electrochemical design or warranty validation.   Literature and Reference Basis  The degradation structure and parameter ranges are aligned with established literature and standards, including:   Schmalstieg et al.,   A holistic aging model for Li(NiMnCo)O₂ based 18650 lithium-ion batteries , Journal of Power Sources, 2014  Keil & Jossen,   Calendar aging of lithium-ion batteries , Journal of The Electrochemical Society, 2017  EPRI,   Battery Energy Storage Overview , 2020  NREL,   Battery Lifetime Analysis and Degradation Modeling , 2019  IEC 62933-2-1,   Electrical Energy Storage Systems – Unit Parameters and Testing  Where explicit numerical values are not defined, mid-range industry values commonly used in grid-scale feasibility studies are applied.   Summary  The degradation model combines calendar aging and cycle aging using a transparent, throughput-based formulation.  It balances physical realism with computational simplicity, making it suitable for energy system modeling where traceability and clarity are more important than electrochemical detail.  Example: BESS Degradation   \n      \n          \n          Fig 1. Degradation Graph \n      Asset Definition  Battery Energy Storage System (BESS):   Power rating:   10 MW  Energy capacity:   20 MWh  Nominal duration:   2 hours  Cycling intensity:   1.5 cycles per day  Simulation resolution:   annual  Degradation model:   linear EFC-based (conservative)  Initial state at beginning of life (BOL):   Nominal energy capacity:         E  0  =  20       M  W  h  E_0 = 20 \\; \\mathrm{MWh}      E         0  ​      =     20    MWh   Nominal power capacity:         P  0  =  10       M  W  P_0 = 10 \\; \\mathrm{MW}      P         0  ​      =     10    MW   Initial State of Health:          S  o  H  0  =  1.0  \\mathrm{SoH}_0 = 1.0       SoH         0  ​      =     1.0   Degradation Model Configuration  The project uses the following   default degradation parameters :  Cycle degradation   Linear EFC model enabled.  Fade per equivalent full cycle:         k   E  F  C  =   0.20  6000  =  3.333  ×   10   −  5  k_{\\mathrm{EFC}} = \\frac{0.20}{6000} = 3.333 \\times 10^{-5}      k           EFC  ​      =              6000        0.20  ​       =     3.333   ×     1   0          −  5    Default depth-of-discharge factor :          D  o  D  model  =  0.50  \\mathrm{DoD}_{\\text{model}} = 0.50       DoD           model  ​      =     0.50   Important:  The model does   not  assume 80% DoD by default.  Unless a degradation curve is provided,   50% DoD is default.  Depending on the markets selected or trading strategy selected, the model uses the following   assumed Depth of Discharge (DoD)  (as a percentage):   If the asset participates in   FCR + aFRR + mFRR + Day-ahead + Intraday , we assume   50% DoD .  If the asset participates in   FCR + aFRR + mFRR + Day-ahead , but   not  Intraday, we assume   40% DoD .  If the asset participates in   Day-ahead only  (no Intraday and no balancing services like FCR/aFRR/mFRR), we assume   60% DoD .  If the asset participates in   Day-ahead + Intraday only  (no balancing services like FCR/aFRR/mFRR), we assume   60% DoD .  In all other cases, we assume   60% DoD .  We assume a DoD for a quick analysis although the actual DoD could vary year over year.  Calendar degradation   Annual calendar fade:         r   c  a  l  =  0.007   (  0.7  %  /  year  )     r_{\\mathrm{cal}} = 0.007 \\quad (0.7\\%/\\text{year})       r           cal  ​      =     0.007   (  0.7%/   year  )     End-of-life   Minimum State of Health:          S  o  H  EOL  =  0.60  \\mathrm{SoH}_{\\text{EOL}} = 0.60       SoH           EOL  ​      =     0.60   Step 1: Cycles per Year  From the operational assumption:        cycles per day  =  1.5     \\text{cycles per day} = 1.5       cycles per day   =     1.5         cycles per year  =  1.5  ⋅  365  =  547.5  \\text{cycles per year} = 1.5 \\cdot 365 = 547.5      cycles per year   =     1.5   ⋅     365   =     547.5  This value is passed directly into the degradation model.   Step 2: Equivalent Full Cycles per Year (Model Logic)  The model computes   effective EFCs  as:          E  F  C  year  =  cycles per year  ⋅    D  o  D  model  \\mathrm{EFC}_{\\text{year}}\n=\n\\text{cycles per year} \\cdot \\mathrm{DoD}_{\\text{model}}       EFC           year  ​      =      cycles per year   ⋅       DoD           model  ​             E  F  C  year  =  547.5  ⋅  0.50  =  273.75  \\mathrm{EFC}_{\\text{year}}=547.5 \\cdot 0.50=273.75       EFC           year  ​      =     547.5   ⋅     0.50   =     273.75   Step 3: Annual Cycle-Related Degradation  Cycle degradation is computed as a   fractional SoH loss :        Δ    S  o  H  cycle  =   k   E  F  C  ⋅    E  F  C  year  \\Delta \\mathrm{SoH}_{\\text{cycle}}=k_{\\mathrm{EFC}} \\cdot \\mathrm{EFC}_{\\text{year}}     Δ    SoH           cycle  ​      =      k           EFC  ​      ⋅       EFC           year  ​           Δ    S  o  H  cycle  =  3.333  ×   10   −  5  ⋅  273.75  =  0.00913  \\Delta \\mathrm{SoH}_{\\text{cycle}}=3.333 \\times 10^{-5} \\cdot 273.75=0.00913     Δ    SoH           cycle  ​      =     3.333   ×     1   0          −  5   ⋅     273.75   =     0.00913  That is:             0.913  %   SoH loss per year from cycling  \\boxed{0.913\\% \\text{ SoH loss per year from cycling}}              0.913%    SoH loss per year from cycling     ​      Step 4: Annual Calendar Degradation  Calendar degradation is applied linearly:        Δ    S  o  H  calendar  =  0.007  \\Delta \\mathrm{SoH}_{\\text{calendar}} = 0.007     Δ    SoH           calendar  ​      =     0.007             0.7  %   SoH loss per year from calendar aging  \\boxed{0.7\\% \\text{ SoH loss per year from calendar aging}}              0.7%    SoH loss per year from calendar aging     ​      Step 5: Total Annual SoH Degradation        Δ    S  o  H  year  =  0.00913  +  0.007  =  0.01613  \\Delta \\mathrm{SoH}_{\\text{year}}=0.00913 + 0.007=0.01613     Δ    SoH           year  ​      =     0.00913   +     0.007   =     0.01613             1.61  %   total SoH loss per year  \\boxed{1.61\\% \\text{ total SoH loss per year}}              1.61%    total SoH loss per year     ​      Step 6: SoH and Energy Capacity Over Time  State of Health after ( n ) years:          S  o  H  n  =  1.0  −  n  ⋅  0.01613  \\mathrm{SoH}_n = 1.0 - n \\cdot 0.01613       SoH         n  ​      =     1.0   −     n   ⋅     0.01613  Example: Year 8 (≈ 2031 in chart)          S  o  H  8  =  1.0  −  8  ⋅  0.01613  =  0.871  \\mathrm{SoH}_8 = 1.0 - 8 \\cdot 0.01613 = 0.871       SoH         8  ​      =     1.0   −     8   ⋅     0.01613   =     0.871  Remaining energy capacity:         E  8  =   E  0  ⋅    S  o  H  8  E_8 = E_0 \\cdot \\mathrm{SoH}_8      E         8  ​      =      E         0  ​      ⋅       SoH         8  ​            E  8  =  20  ⋅  0.871  =  17.42       M  W  h  E_8 = 20 \\cdot 0.871 = 17.42 \\; \\mathrm{MWh}      E         8  ​      =     20   ⋅     0.871   =     17.42    MWh  This aligns with the   gradual stair-step decline  in the energy curve.   Step 7: Power Degradation (Model Logic)  Power does   not  degrade one-to-one with energy.  The model applies a slower power fade using:         P  t  =   P  0  ⋅   (  1  −   γ   p  o  w  e  r  ⋅  (  1  −    S  o  H  t  )  )  P_t = P_0 \\cdot \\left( 1 - \\gamma_{\\mathrm{power}} \\cdot (1 - \\mathrm{SoH}_t) \\right)      P         t  ​      =      P         0  ​      ⋅      (  1   −    γ           power  ​      ⋅   (  1   −     SoH         t  ​     )  )  Where:         γ   p  o  w  e  r  =  0.20  \\gamma_{\\mathrm{power}} = 0.20      γ           power  ​      =     0.20  At ~80% SoH:   Energy ≈ 16–17 MWh  Power ≈ 9.5–9.6 MW  This matches the tooltip values in the degradation chart.    Interpretation  For a   10 MW / 20 MWh BESS  operating at   1.5 cycles per day , the model produces:   Effective EFCs:   ~274 per year  Annual SoH degradation:   ~1.6%  Gradual energy decline with slower power derating  End-of-life triggered once SoH reaches   60%  All values shown in the degradation chart are a direct consequence of these calculations.",{"id":225,"path":226,"dir":190,"title":227,"description":228,"keywords":229,"body":232},"content:3.methodology:2.storage-dispatch-modeling:3.bess-capex-calculation.md","/methodology/storage-dispatch-modeling/bess-capex-calculation","BESS Capex Calculation","This document describes how the total upfront investment cost (CAPEX) of a\nBattery Energy Storage System (BESS) is calculated for financial modeling.",[230,231],"Cost Structure","Capex Calculation","  BESS Capex Calculation  This document describes how the total upfront investment cost (CAPEX) of a\nBattery Energy Storage System (BESS) is calculated for financial modeling.  The calculation produces a single Year-0 CAPEX value used across financial\nanalysis.   Cost Structure  All cost inputs are defined on a per-kWh basis and assumed to scale linearly\nwith installed energy capacity.  Core System Costs  Core system costs represent the technical building blocks of the BESS that enable energy storage and grid interaction. These include the battery pack, power conversion system (PCS), energy management and control systems (EMS), HVAC and safety systems, and overall system integration.  They are aggregated into a single base cost per kWh:         C   b  a  s  e  =   C   b  a  t  t  e  r  y  +   C   P  C  S  +   C   E  M  S  +   C   H  V  A  C  +   C   i  n  t  e  g  r  a  t  i  o  n  C_{base}=C_{battery}+ C_{PCS}+ C_{EMS}+ C_{HVAC}+ C_{integration}      C          ba  se  ​      =      C          ba  tt  ery  ​      +      C          PCS  ​      +      C          EMS  ​      +      C          H  V  A  C  ​      +      C          in  t  e  g  r  a  t  i  o  n  ​      Additional Project Costs  In addition to core system components, the model accounts for costs required to deploy the system as an operational, grid-connected project. These cover installation works, grid connection infrastructure, project development activities, and soft costs including contingency allowances.         C   o  t  h  e  r  =   C   i  n  s  t  a  l  l  a  t  i  o  n  +   C   g  r  i  d  +   C   d  e  v  e  l  o  p  m  e  n  t  +   C   c  o  n  t  i  n  g  e  n  c  y  C_{other}=C_{installation}+ C_{grid}+ C_{development}+ C_{contingency}      C          o  t  h  er  ​      =      C          in  s  t  a  ll  a  t  i  o  n  ​      +      C          g  r  i  d  ​      +      C          d  e  v  e  l  o  p  m  e  n  t  ​      +      C          co  n  t  in  g  e  n  cy  ​      Capex Calculation  The   base investment cost  is calculated by combining core system costs and additional project costs, then scaling by the total installed energy capacity:        C  A  P  E   X   b  a  s  e  =   (   C   b  a  s  e  +   C   o  t  h  e  r  )  ⋅   E   k  W  h  CAPEX_{base}=\\left( C_{base} + C_{other} \\right)\\cdot E_{kWh}     C  A  PE   X          ba  se  ​      =      (   C          ba  se  ​      +    C          o  t  h  er  ​     )   ⋅      E          kWh  ​      Escalation  To account for expected cost uplift between the reference cost year and project execution, a linear escalation factor is applied:        C  A  P  E   X   e  s  c  =  C  A  P  E   X   b  a  s  e  ⋅   r   e  s  c  CAPEX_{esc}=CAPEX_{base} \\cdot r_{esc}     C  A  PE   X          esc  ​      =     C  A  PE   X          ba  se  ​      ⋅      r          esc  ​     where         r   e  s  c  r_{esc}      r          esc  ​     is cost escalation rate   Total Capex        C  A  P  E   X   t  o  t  a  l  =  C  A  P  E   X   b  a  s  e  +  C  A  P  E   X   e  s  c  CAPEX_{total}=CAPEX_{base}+ CAPEX_{esc}     C  A  PE   X          t  o  t  a  l  ​      =     C  A  PE   X          ba  se  ​      +     C  A  PE   X          esc  ​     The final CAPEX value is rounded to whole currency units for reporting.",{"id":234,"path":235,"dir":190,"title":236,"description":7,"keywords":237,"body":243},"content:3.methodology:2.storage-dispatch-modeling:4.degradation-by-years.md","/methodology/storage-dispatch-modeling/degradation-by-years","Degradation Based on Years",[238,239,240,241,242],"Asset Degradation — Degradation by Years","Worked Example","How the Platform Uses This Data","Visualising the Profile","DoD Based vs. Degradation by Years","  Degradation Based on Years  Asset Degradation — Degradation by Years   Degradation by Years  lets you define exactly how the asset's capacity, power, and efficiency declines on a year-by-year basis — exactly as a manufacturer warranty sheet specifies it.   Input Data  Your table needs one row per operational year (starting at year 0) with these five columns:     Column  What it represents     Operational Year  0, 1, 2 … n — year 0 is the as-new state    Effective Full Cycles (Cumulative)  Total equivalent full cycles accumulated up to that year    Asset Capacity Over Years (MWh)  Usable energy capacity at that year    Asset Power Rating Over Years (MW)  Rated power at that year    Round Trip Efficiency (%)  AC-AC round trip efficiency at that year   The downloaded sample template pre-fills asset capacity, power rating, and round trip efficiency with the starting values from your technical form.   All rows must be filled — interpolate or extrapolate from your warranty data if a year is missing.   Effective Full Cycles must be monotonically increasing (each year ≥ the previous year).  Once filled, click the   Eye  icon on the upload field to validate your data before proceeding. After upload, click   Visualise  to view the full degradation profile as a chart.  \n      \n          \n          Figure: Input Field for Degradatiom Based on Year \n       Worked Example  The table below is from a manufacturer warranty sheet for a LFP BESS, rated at 21.05 MWh / 10 MW initially, with 700 nominal equivalent full cycles per year:     Year  EFC Total  Asset Capacity Over Years (MWh)  Asset Power Rating Over Years (MW)  Round Trip Efficiency (%)    0  0  21.05  10.00  90.25   1  700  20.32  9.95  89.80   2  1400  19.55  9.88  89.32   3  2100  18.79  9.80  88.79   4  2800  18.12  9.71  88.21   5  3500  17.51  9.63  87.68   6  4200  16.92  9.55  87.10   7  4900  16.37  9.46  86.55   8  5600  15.85  9.38  85.97   9  6300  15.35  9.29  85.42   10  7000  14.86  9.20  84.80  How the Platform Uses This Data  Cycles Per Day  The platform derives the average daily cycle rate from the incremental change in cumulative EFC across all years:        Cycles per day  =     ∑   i  =  1  n  (   C  i  −   C   i  −  1  )   n  ×  365  \\text{Cycles per day} = \\frac{\\sum_{i=1}^{n}(C_i - C_{i-1})}{n \\times 365}      Cycles per day   =               n  ×  365          ∑          i  =  1      n  ​     (   C         i  ​     −   C          i  −  1  ​     )  ​       Using the example above  — each year adds exactly 700 EFC (700 − 0, 1400 − 700, … ) across all 10 years:        Cycles per day  =    700  ×  10   10  ×  365  =   7000  3650  ≈  1.92   cycles/day  \\text{Cycles per day} = \\frac{700 \\times 10}{10 \\times 365} = \\frac{7000}{3650} \\approx 1.92 \\text{ cycles/day}      Cycles per day   =               10  ×  365         700  ×  10  ​       =               3650         7000  ​       ≈     1.92    cycles/day  This derived value   overrides  the max cycles per day entered in the technical form — the uploaded file is treated as the source of truth.   SoH at End of Life  State of Health at end of life is calculated from the final year's capacity relative to year 0:         SoH  EoL  =    C  n   C  0  ×  100  \\text{SoH}_{\\text{EoL}} = \\frac{C_n}{C_0} \\times 100       SoH           EoL  ​      =                C         0  ​             C         n  ​     ​       ×     100   Using the example:         SoH  EoL  =   14.86  21.05  ×  100  ≈  70.6  \\text{SoH}_{\\text{EoL}} = \\frac{14.86}{21.05} \\times 100 \\approx 70.6%       SoH           EoL  ​      =               21.05         14.86  ​       ×     100   ≈     70.6  This value is automatically populated in the SoH field — no manual entry needed.   Visualising the Profile  Click   Visualise  to open the   Degradation Over Time  chart — a stepped graph plotted across calendar years for the full project lifetime.    Left Y-axis  — Energy capacity (MWh)   Right Y-axis  — Power rating (MW)   X-axis  — Calendar year from project start to end  Both Energy and Power are plotted as step lines, each holding flat within a year and dropping at the year boundary.   DoD Based vs. Degradation by Years      DoD Based  Degradation by Years     Best for  Cycle-count warranty curves  Year-by-year capacity warranty sheets    Degradation model  Capacity fade vs. DoD cycles  Step function across operational years    Cycles/day  Entered manually  Derived from cumulative EFC column    SoH at EoL  Derived from cycle limits  Derived from final vs. initial capacity",{"id":245,"path":246,"dir":190,"title":247,"description":248,"keywords":249,"body":253},"content:3.methodology:2.storage-dispatch-modeling:5.degradation-by-dod.md","/methodology/storage-dispatch-modeling/degradation-by-dod","Depth of Discharge Based Degradation","Upload battery degradation data based on Depth of Discharge (DoD), showing how capacity loss increases with cycle count for different DoD levels.",[250,251,252,212],"File Requirements","Understanding the Data","Step-by-Step Instructions","  Depth of Discharge Based Degradation  Upload battery degradation data based on Depth of Discharge (DoD), showing how capacity loss increases with cycle count for different DoD levels.   File Requirements     Property  Value     File Format  Excel (  .xlsx ) or CSV    Required Columns  cycle_count, depth_of_discharge_20_percent, depth_of_discharge_50_percent, depth_of_discharge_80_percent    Units  Cycle count as integer, capacity loss as percentage (%)    Excel Sheet Name  DEGRADATION-TABLE   Understanding the Data  This dataset represents how a battery degrades over time depending on how deeply it is discharged during each cycle.  Each row corresponds to a specific cycle count, and each column represents the cumulative capacity loss at a given Depth of Discharge (DoD).   Step-by-Step Instructions   LFP battery with manufacturer cycle-life data at different DoD levels:    Fill the first row (0 cycles) with 0% loss for all DoD levels, then add a row for each cycle milestone in your manufacturer data.    Example: at 100 cycles — 0.16% (20% DoD), 0.40% (50% DoD), 0.63% (80% DoD).    Continue adding milestone rows until you have covered the full expected cycle life of your battery.    Example: at 1,000 cycles — 1.61% (20% DoD), 4.04% (50% DoD), 6.46% (80% DoD).    Place your completed data in the DEGRADATION-TABLE sheet using the required column headers.    Column names must exactly match those shown in the example CSV below.    Save the file and upload it    Check that capacity loss values increase monotonically down each column, and that 80% DoD ≥ 50% DoD ≥ 20% DoD at every row.   Summary  You are uploading a degradation profile that defines how battery capacity reduces over time at different Depth of Discharge levels. This allows the system to accurately model battery performance and lifetime under varying usage conditions.",{"id":255,"path":256,"dir":190,"title":257,"description":258,"keywords":259,"body":260},"content:3.methodology:2.storage-dispatch-modeling:6.p50-generation-profile.md","/methodology/storage-dispatch-modeling/p50-generation-profile","P50 Generation Profile (P50)","Upload hourly P50 generation data normalized to installed capacity (values between 0 and 1) for PV or Wind resources.",[250,252,212],"  P50 Generation Profile (P50)  Upload hourly P50 generation data normalized to installed capacity (values between 0 and 1) for   PV or Wind  resources.   File Requirements     Property  What it represents     File Format  Excel (.xlsx) or CSV    Required Columns  'ts', 'pv_generation' or 'wind_generation'    Units  Fraction of installed capacity (0 to ~1)    Granularity  Hourly (8,760 rows for a standard year)    Timestamp Format  'YYYY-MM-DD HH:MM:SS+HH:MM' (e.g. '2018-01-01 00:00:00+01:00')    Excel Sheet Name  'P50-DATA'   Step-by-Step Instructions   A 5 MW plant with hourly generation data from a resource assessment:    Obtain your hourly generation profile for Year 1 from a solar or wind resource assessment tool or your energy consultant.    The profile must already include all losses — inverter losses, shading, soiling, and efficiency losses. It should show the net power output (in MW) your plant delivers each hour of the year.    Normalize the values: divide each hourly generation figure (in MW) by your plant's total installed capacity (in MW).    Example: if your 5 MW plant generates 3.5 MW at noon, enter 3.5 ÷ 5 = 0.70. A value of 1.0 means full capacity; 0 means no generation. When uploading as XLSX (Excel), both '.' and ',' are accepted as decimal separators.    Arrange your data in a spreadsheet with two columns: 'ts' (timestamp) and 'pv_generation'/'wind_generation' (your normalized value). Name the sheet 'P50-DATA'.    Timestamps must include the timezone offset — for example: 2018-01-01 10:00:00+01:00 (CET) or 2018-07-01 10:00:00+02:00 (CEST).    Save the file as CSV and upload it.    Check that your file has exactly 8,760 rows — one per hour of the year. All values must be between 0 and 1.   Summary  You are uploading a normalized hourly generation profile that represents how your plant performs over a full year. The platform will use this normalized data to scale generation based on different plant capacities and scenarios.",{"id":262,"path":263,"dir":190,"title":264,"description":265,"keywords":266,"body":269},"content:3.methodology:2.storage-dispatch-modeling:10.trading-model-configuration.md","/methodology/storage-dispatch-modeling/trading-model-configuration","Trading Model Configuration","Choose between a preset strategy for a quick, optimized setup or a custom approach for full control.\nPresets offer a standard configuration, while customization lets you fine-tune risk appetite\nand market focus—whether prioritizing stability vs. revenue or selecting between\nancillary services and spot markets for trading execution.",[267,268],"Preset cases","Custom","  Trading Model Configuration  Choose between a preset strategy for a quick, optimized setup or a custom approach for full control.\nPresets offer a standard configuration, while customization lets you fine-tune risk appetite\nand market focus—whether prioritizing stability vs. revenue or selecting between\nancillary services and spot markets for trading execution.  Preset cases    Day Ahead  \nSolely day-ahead market with low-risk strategy - no exposure to real-time market fluctuations​   Revenue Floor  \nGuaranteed revenues through ancillary services - minimizing exposure to market volatility​   Base Case  \nCross-market trading with moderate-risk - optimizing returns while maintaining revenue stability​   Spot Markets only  \nCross-market trading with moderate-risk - only in the spot markets namely day ahead, intraday continuous and intraday temporal arbitrage.   Fully Merchant  \nCross-market trading with high-risk, high-reward approach - prioritizing higher-price opportunities for max. revenue potential​   Custom  Leverage risk appetite and market focus to assess their impact on revenues.  \nSelect specific markets for active participation in trading.  \nGain fine-grained control over analysis parameters for precise insights.  The different BESS revenue scenarios with their respective strategies and risk levels is summarized below:     Scenario  Market Participation  Strategy  Risk Level    Day Ahead Only  Day-Ahead Only  Low-Risk Strategy  Low Risk   Floor (Revenue Floor)  Max 10% Day-Ahead  No Intraday  No aFRR Energy  Pure Ancillary Market Strategy  Stable Revenue Floor  Low-Medium Risk   Base Case (All Markets, Imperfect)  Day-Ahead  Intraday (Hourly, Limited)  No aFRR Energy  No Temporal Arbitrage  Ancillary Market Focused  Medium Trading Exposure  Medium Risk   Spot Markets Only  Day-Ahead  Intraday Continuous  Intraday Temporal Arbitrage  Moderate-Risk Strategy  Spot Market Focused  Medium Risk   Merchant Case (All Markets, Perfect)  Day-Ahead  Intraday (Hourly & Temporal Arbitrage)  aFRR Energy Revenues  Capture Market Spreads  Active Trading Strategy  High Risk   Custom  User-Selected Markets  User-Defined Strategy  User-Defined",{"id":271,"path":272,"dir":273,"title":274,"description":275,"keywords":276,"body":280},"content:3.methodology:2.storage-dispatch-modeling:10.trading-model-configuration:1.day-ahead-only.md","/methodology/storage-dispatch-modeling/trading-model-configuration/day-ahead-only","trading-model-configuration","Day-Ahead Only","The Day-Ahead Only Trading Model represents the most conservative approach to BESS operation, focusing exclusively on the day-ahead market with no exposure to real-time market fluctuations.",[277,278,279],"Strategy Overview","Key Features","Risk Assessment","  Day-Ahead Only  The Day-Ahead Only Trading Model represents the most conservative approach to BESS operation, focusing exclusively on the day-ahead market with no exposure to real-time market fluctuations.  Strategy Overview    Market Participation :\n   Day-Ahead market only  No participation in other markets  Key Features   Simplest trading strategy with lowest operational complexity  Trading decisions made 24+ hours in advance  No exposure to real-time price volatility  Minimal operational changes once day-ahead schedule is established  Predictable revenue stream based on day-ahead price forecasts  Limited upside potential but also limited downside risk  Risk Assessment    Risk Level : Low Risk   Strategy Type : Low-Risk Strategy focused on simplicity and predictability   Objective : Minimize operational complexity while securing basic revenue streams  The day-ahead market is a forward market where participants can bid and secure energy prices one day before the actual delivery. This market is highly liquid, providing reliable revenue estimates that serve as a benchmark for comparison with other trading strategies.  In a day-ahead market, participants submit their bids based on their forecasted demand and supply. The market operator then matches these bids to determine the market-clearing price for each hour of the following day. This process helps in mitigating risks associated with real-time market fluctuations, as   prices are locked in advance .  The Day Ahead Only trading model configuration for Battery Energy Storage Systems (BESS) or a combination of BESS and other assets such as photovoltaic (PV) systems, wind turbines, etc., leverages the day-ahead market to optimize asset charging and discharging. This model operates exclusively within the day-ahead market, buying power when forecasted prices are low to store energy and selling it when forecasted prices are high. This pure arbitrage strategy provides a clear indication of the business case solely within the day-ahead market.  Key aspects of this trading model include:    Optimization : The model optimizes the charging and discharging cycles of the assets based on forecasted prices, ensuring maximum profitability.   Arbitrage : By capitalizing on price differences between low and high forecasted prices, the model ensures efficient energy trading.   Business Case Clarity : This strategy offers a straightforward evaluation of the financial viability of participating in the day-ahead market, without the complexities of real-time market dynamics.  This approach is particularly beneficial for participants looking to maximize returns from their energy assets while minimizing exposure to the uncertainties of real-time market fluctuations.",{"id":282,"path":283,"dir":273,"title":284,"description":285,"keywords":286,"body":287},"content:3.methodology:2.storage-dispatch-modeling:10.trading-model-configuration:2.revenue-floor.md","/methodology/storage-dispatch-modeling/trading-model-configuration/revenue-floor","Revenue Floor","The Revenue Floor Trading Model aims to ensure stable returns by prioritizing participation in ancillary capacity markets. This strategy provides predictable revenue streams and reduces the risk associated with real-time price fluctuations.",[277,278,279],"  Revenue Floor  The Revenue Floor Trading Model aims to ensure stable returns by prioritizing participation in ancillary capacity markets. This strategy provides predictable revenue streams and reduces the risk associated with real-time price fluctuations.  Strategy Overview    Market Participation :\n   Max 10% Day-Ahead market participation  No Intraday market participation  No aFRR Energy market participation  Focus on FCR and aFRR capacity auctions  Key Features   Participation in FCR and aFRR capacity auctions to secure fixed capacity payments  State of charge constraints (35–60%) to limit exposure to spot market volatility  Pure ancillary market strategy focusing on guaranteed ancillary service revenues  Allocation of up to 10% of BESS power capacity to Day Ahead markets for incremental gains  Used to establish a foundation for structuring tolling agreements and other revenue-secure mechanisms  Risk Assessment    Risk Level : Low-Medium Risk   Revenue Profile : Stable Revenue Floor with predictable returns",{"id":289,"path":290,"dir":273,"title":291,"description":292,"keywords":293,"body":294},"content:3.methodology:2.storage-dispatch-modeling:10.trading-model-configuration:3.base-case.md","/methodology/storage-dispatch-modeling/trading-model-configuration/base-case","Base Case","The Base Case Trading Model involves cross-market trading with a moderate-risk approach, optimizing returns while maintaining revenue stability.",[277,278,279],"  Base Case  The Base Case Trading Model involves cross-market trading with a moderate-risk approach, optimizing returns while maintaining revenue stability.  Strategy Overview    Market Participation :\n   Day-Ahead market participation  Intraday market participation (Hourly, Limited)  No aFRR Energy participation  No Temporal Arbitrage  Key Features   Leverages the flexibility and responsiveness of BESS to participate in multiple markets  Balances risk and return through diversified market engagement  Engages in day-ahead and limited intraday markets with hourly trading windows  Avoids more volatile opportunities like aFRR energy and temporal arbitrage  Prioritizes consistent revenue streams over high-risk, high-reward opportunities  Risk Assessment    Risk Level : Medium Risk   Strategy Type : Ancillary Market Focused with Medium Trading Exposure   Objective : Optimize returns without compromising stability",{"id":296,"path":297,"dir":273,"title":298,"description":299,"keywords":300,"body":301},"content:3.methodology:2.storage-dispatch-modeling:10.trading-model-configuration:4.fully-merchant.md","/methodology/storage-dispatch-modeling/trading-model-configuration/fully-merchant","Fully Merchant","The Fully Merchant Trading Model (All Markets, Perfect) represents a cross-market trading approach with high-risk, high-reward potential, prioritizing higher-price opportunities for maximum revenue potential.",[277,278,279],"  Fully Merchant  The Fully Merchant Trading Model (All Markets, Perfect) represents a cross-market trading approach with high-risk, high-reward potential, prioritizing higher-price opportunities for maximum revenue potential.  Strategy Overview    Market Participation :\n   Day-Ahead market participation  Intraday market participation (Hourly & Temporal Arbitrage)  aFRR Energy Revenues included  Key Features   Takes full advantage of all available market opportunities  Implements active trading across multiple timeframes and market segments  Leverages temporal arbitrage opportunities to maximize profit potential  Incorporates aFRR energy revenues as an additional value stream  Assumes perfect or near-perfect market prediction capabilities  Prioritizes maximum revenue potential over stability  Risk Assessment    Risk Level : High Risk   Strategy Type : Active Trading Strategy focused on Capturing Market Spreads   Objective : Maximize returns by taking advantage of price volatility and market inefficiencies",{"id":303,"path":304,"dir":273,"title":268,"description":305,"keywords":306,"body":308},"content:3.methodology:2.storage-dispatch-modeling:10.trading-model-configuration:5.custom.md","/methodology/storage-dispatch-modeling/trading-model-configuration/custom","The platform allows your to configure a custom strategy, fine-tuning it based on:",[307],"Custom Trading Strategy","  Custom  The platform allows your to configure a   custom strategy , fine-tuning it based on:    Risk Appetite : Prioritizing stability vs. maximizing revenue.   Market Focus : Emphasizing ancillary services vs. spot market trading.   Market particitpation  and percentage  Custom Trading Strategy  Creating a custom trading strategy allows you to tailor your approach to meet specific goals and market conditions e.g. what happens if the FCR market is sturated, impact of high-risk bidding on the revenues etc.",{"id":310,"path":311,"dir":128,"title":312,"description":313,"keywords":314,"body":318},"content:3.methodology:3.co-location-modeling.md","/methodology/co-location-modeling","Co-location & Hybrid Assets","This section provides an in-depth overview of co-location and hybrid asset cases. Currently, our platform supports co-located configurations such as PV+Storage, Wind+Storage, and C&I+Storage (coming soon), as well as hybrid cases (coming soon). Below, we outline our modeling approach for these assets and their market trading mechanisms.",[315,316,317],"Optimization Approach","Behind-the-Meter vs Front-of-the-Meter","Indivdual cases","  Co-location & Hybrid Assets  This section provides an in-depth overview of co-location and hybrid asset cases. Currently, our platform supports co-located configurations such as PV+Storage, Wind+Storage, and C&I+Storage (coming soon), as well as hybrid cases (coming soon). Below, we outline our modeling approach for these assets and their market trading mechanisms.  Optimization Approach  Co-location addresses the complexities of hybrid renewable energy projects where multiple assets share grid connections and must coordinate operations for optimal performance. For example, consider a PV farm with a capacity of 6 MWp AC co-located with a BESS (12 MWh/6 MW), sharing a common substation with a maximum capacity of 5 MW. In this scenario, the PV farm and BESS share the same grid connection and can trade with the grid at a maximum capacity of 5 MW. The goal is to maximize the combined revenue potential of these assets rather than optimizing them individually.  Behind-the-Meter vs Front-of-the-Meter  Co-located assets can be configured in two primary ways: behind-the-meter (BTM) and front-of-the-meter (FTM). These configurations differ in metering setups, grid interactions, power flows, revenue models, regulatory classifications, and typical use cases. The table below compares these two approaches:     Grid Integration Type  Front-of-the-Meter (FTM)  Behind-the-Meter (BTM)    Metering  ✅ Separate meters for Wind and BESS  ✅ Single meter behind which Wind + BESS operate   Grid import possible?  ✅ Yes – BESS can charge from the grid  ❌ No – only export is allowed, cannot import from the grid   Power flow  Two-way for BESS (import/export), Wind exports  One-way export only   Revenue model  Arbitrage, ancillary services, and generation revenue  Only generation revenue from Wind+BESS exports   Regulatory classification  Treated as flexible assets or dispatchable units  Treated purely as a generation unit   Typical Use Case  Market participation, flexibility services  Utility-scale BESS farms that only inject power  Indivdual cases  Further details on individual cases can be found below:    PV+Storage  Wind+Storage  C&I+Storage (coming soon)  Hybrid Cases (coming soon)",{"id":320,"path":321,"dir":322,"title":323,"description":324,"keywords":325,"body":328},"content:3.methodology:3.co-location-modeling:0.pv-storage.md","/methodology/co-location-modeling/pv-storage","co-location-modeling","PV + Storage Dispatching","The dispatching strategy for co-located PV and storage systems in Re-Twin is based on market schedules, similar to the approach for a standalone Battery Energy Storage System (BESS). The key difference is the presence of an on-site generation source, which introduces the decision to sell, store, or curtail (not send to the grid) the generated power.",[326,184,327],"Dispatching Scenarios","Related links","  PV + Storage Dispatching  The dispatching strategy for co-located PV and storage systems in Re-Twin is based on market schedules, similar to the approach for a standalone Battery Energy Storage System (BESS). The key difference is the presence of an on-site generation source, which introduces the decision to sell, store, or curtail (not send to the grid) the generated power.    Important:  The PV and storage components are dispatched together as a single, unified portfolio to maximize overall profitability. The model does not simulate an internal \"buy/sell\" transaction between the two assets. Instead, it makes decisions based on the opportunity cost presented by external market prices. For the PV-generated power, the model chooses the most profitable action: sell it to the market, charge the battery for later dispatch, or curtail it during negative price periods.  Below, we explain how different co-location configurations are dispatched.  Dispatching Scenarios  PV Only  In a PV-only configuration, the model's objective is to maximize profitability by deciding whether to sell the generated power or curtail it. Curtailment is the default action during negative price periods, as we assume no EEG subsidy is available under these conditions. The dispatch model chooses between the Day-Ahead and Intraday Continuous spot markets based on the price forecast for each.   Note: Intraday Auctions are not currently modeled.  Behind the Meter (BTM)  In a Behind the Meter setup, the storage is located \"behind\" the grid connection point and can only be charged by the co-located PV system. It cannot import power from the grid.  The dispatch logic is an extension of the PV-only case. The model optimizes the use of PV generation by choosing one of three actions:    Sell  the power directly to the market.   Store  the power in the battery for later dispatch.   Curtail  the PV generation.  This decision is based on an imperfect forecast of the Day-Ahead and Intraday market prices. Since the storage is charged by a controlled generation source, it is   eligible to participate in the positive aFRR market . The optimization model factors in this potential revenue stream when making dispatch decisions.  Front of the Meter (FTM)  In a Front of the Meter configuration, the PV system and the storage are modeled as two independent but co-located assets, each with its own connection to the grid.  The primary distinction from the BTM case is the storage's ability to charge from   both  the co-located PV system and the grid itself. This flexibility unlocks significant advantages:    Enhanced Arbitrage:  The storage can buy cheap power from the grid during periods of low market prices or when PV generation is unavailable (e.g., at night).   Full Ancillary Service Participation:  The ability to import from the grid allows the storage to participate in the full spectrum of ancillary service markets, including both FCR and positive and negative aFRR.  This dual-charging capability enables more complex and potentially more lucrative revenue stacking, as the optimization model can leverage a wider range of market opportunities for both the PV and the storage unit.  Revenue Estimation  The model estimates revenue from three primary sources:  1. Revenue from Ancillary Services (Capacity)  This is the income earned for making capacity available to the grid operator, regardless of whether it is activated. For each service (FCR, aFRR, mFRR), the revenue is calculated as:   Revenue = Power Committed (MW) * Capacity Price (€/MW)  2. Revenue/Cost from Ancillary Services (Energy)  This is the income earned or cost incurred when the reserved capacity is activated to deliver or absorb energy.    Positive Activation (e.g., aFRR positive):  Energy delivered is multiplied by the energy price, generating revenue.   Negative Activation (e.g., aFRR negative):  Energy absorbed is multiplied by its price. This is typically a revenue stream (being paid to take energy) but is treated as a negative cost.  3. Revenue/Cost from Wholesale Markets (Arbitrage)  This represents the core profit from buying low and selling high in the spot markets. For both the Day-Ahead (DA) and Intraday Continuous (IDC) markets, the net revenue is calculated for each time interval:   Net Revenue = (Energy Sold - Energy Bought) * Market Price  Related links    PV P50 Profile Generation   PV BESS Revenue Split Estimation   EEG Subsidy Calculation",{"id":330,"path":331,"dir":322,"title":332,"description":7,"keywords":333,"body":343},"content:3.methodology:3.co-location-modeling:1.pv-generation-modeling-approach.md","/methodology/co-location-modeling/pv-generation-modeling-approach","P50 Profile Modeling Approach",[334,335,336,337,338,339,340,341,342],"Overview","What is a P50 PV Generation Profile?","Our Ensemble Approach","Data Sources","Modeling Process","Using the Generation Profiles","Performance Indicators","Advantages of Our Approach","References and Resources","  P50 Profile Modeling Approach  Overview  This document explains how our system creates P50 (50th percentile) solar generation profiles for any site in Europe using an ensemble approach that combines multiple trusted data sources.  What is a P50 PV Generation Profile?  A P50 generation profile represents the expected energy output where there is a 50% probability that actual generation will exceed or fall below this estimate. It serves as the median expected generation under typical meteorological conditions, making it the standard reference for energy yield assessments and financial models.  Our Ensemble Approach  We use a multi-source ensemble methodology that:   Collects data from two leading solar resource databases  Models PV generation for each dataset separately  Combines the results using a median approach to reduce bias  Normalizes the output as capacity factors (0-1 range)  Data Sources  PVGIS (European Commission)  The Photovoltaic Geographical Information System provides high-resolution solar radiation data specific to European conditions.   PVGIS API Documentation  NSRDB (NREL)  The National Solar Radiation Database offers comprehensive solar resource data with global coverage.   NSRDB API Documentation  Modeling Process  1. Location-Specific Parameters  For any given site coordinates, we:   Identify the nearest country in our database  Apply appropriate tilt and azimuth angles for fixed-mount systems  Set expected irradiance ranges for validation  2. Data Validation and Processing  For each data source, we:   Validate irradiance components (GHI, DNI, DHI) against expected ranges  Apply physical corrections to ensure consistency  Adjust data for nighttime, clear-sky limits, and overcast conditions  3. PV System Modeling  We use industry-standard methods to model:   Plane-of-array irradiance using the Pérez model  Cell temperature effects on panel efficiency  Inverter performance characteristics  DC to AC conversion with appropriate losses  The model assumes:   Fixed-tilt mounting with country-specific angles  DC/AC ratio of 1.2-1.3 (slightly oversized DC capacity)  Modern module and inverter technology  4. Ensemble Creation  Rather than relying on a single source, we:   Calculate generation profiles from each data source  Take the median value at each timestamp  This approach reduces biases in individual datasets  5. Standardization and Time Handling  The output is standardized to:   A reference year (2024)  UTC timezone  15-minute resolution  Normalized capacity factors (0-1 range)  Using the Generation Profiles  The generation profiles can be:   Scaled to any system capacity  Mapped to any date range while maintaining time-of-day and seasonal patterns  Used for Re-Twin PV modeling setup e.g. PV+BESS setup  Performance Indicators  The system calculates expected capacity factors for each location, providing a quick reference for annual energy yield as a percentage of nameplate capacity.  Advantages of Our Approach    Robust : Less sensitive to biases in any single dataset   Geographically optimized : Uses parameters specific to each European country   Physically sound : Ensures adherence to solar physics principles   Flexible : Normalized output can be scaled to any system size   High resolution : Captures intra-hour variability important for grid studies  References and Resources    PVGIS API (European Commission Joint Research Centre)   NSRDB API (National Renewable Energy Laboratory)   PVlib Python Documentation   Renewables.ninja API",{"id":345,"path":346,"dir":322,"title":347,"description":348,"keywords":349,"body":352},"content:3.methodology:3.co-location-modeling:8.pv-bess-revenue-split.md","/methodology/co-location-modeling/pv-bess-revenue-split","PV - BESS Revenue Split Estimation","In this article, we present the estimation of revenue split between photovoltaic (PV) systems and battery energy storage systems (BESS).",[350,351],"Key Assumptions","Revenue Calculation Methodology","  PV - BESS Revenue Split Estimation  In this article, we present the estimation of revenue split between photovoltaic (PV) systems and battery energy storage systems (BESS).  Key Assumptions    Ancillary Revenues : All ancillary revenues, such as those from frequency containment reserves (FCR) and automatic frequency restoration reserves (aFRR), are attributed entirely to the BESS. This is because these services are typically provided by the storage system.   Energy Transactions :   The BESS purchases power from the PV system at zero cost, reflecting the internal transfer of energy within the hybrid system.  Revenue Calculation Methodology  The revenue split is determined using the following steps:    BESS Revenue from Energy Markets :   For each market (e.g., day-ahead energy and intraday continuous hourly energy), the revenue is calculated based on the volume of energy traded to or from the BESS.  Only the energy directly associated with the BESS is considered, ensuring accurate attribution.   Ancillary Service Revenues :   Revenues from ancillary services, such as capacity and energy payments for aFRR, mFRR, and FCR, are summed and fully allocated to the BESS.   PV Revenue :   The total system revenue is calculated, and the BESS revenue is subtracted to determine the PV revenue.  This is done to ensure only the power sold to the market has a revenue associated with it.",{"id":354,"path":355,"dir":322,"title":356,"description":357,"keywords":358,"body":362},"content:3.methodology:3.co-location-modeling:9.eeg-subsidy-calculation.md","/methodology/co-location-modeling/eeg-subsidy-calculation","EEG Subsidy Calculation Explained","Germany's Renewable Energy Sources Act (EEG) provides financial support for renewable energy projects. This guide breaks down how we calculate the EEG subsidy for different solar and battery storage setups within our model, reflecting the logic embedded in our calculation engine.",[359,360,361],"Core Subsidy Concepts","How Our Model Calculates the Subsidy","Eligibility and Asset-Specific Logic","  EEG Subsidy Calculation Explained   Germany's Renewable Energy Sources Act (EEG)  provides financial support for renewable energy projects. This guide breaks down how we calculate the EEG subsidy for different solar and battery storage setups within our model, reflecting the logic embedded in our calculation engine.   Core Subsidy Concepts  The EEG offers a couple of ways for renewable energy producers to get paid. The method used generally depends on the size of the system.  1. Market Premium Model (For systems > 100 kW)  This is the default for larger installations. Instead of a simple fixed payment, you get two streams of income:    Market Price : Whatever you earn by selling your electricity on the open market (e.g., the day-ahead spot market).   Market Premium : An additional top-up from the government. It's calculated as the difference between a pre-defined   EEG Reference Value  and the   monthly average market value for solar power .    Total Compensation = Market Price + Market Premium  Essentially, the Market Premium ensures your total earnings reach the target EEG Reference Value. If the market price is high, the premium is low, and vice-versa. The reference value itself is set either through competitive auctions (for systems > 1 MW) or by law.  2. Fixed Feed-in Tariff (For systems ≤ 100 kW)  This is a simpler model for smaller systems. You feed your power into the grid and receive a straightforward, fixed rate for every kilowatt-hour (kWh) you deliver.   How Our Model Calculates the Subsidy  Our model focuses on the   Market Premium Model , as it applies to the commercial-scale assets we typically analyze. The calculation is performed for each 15-minute interval, taking into account the asset type and how it's connected to the grid.  A key piece of data we use is the   monthly weighted average solar price . This value is based on data from   Netztransparenz  for the backtest (historical) estimates and for the long term forecast pathways is estimated based on the day ahead and solar generation prices. It represents the average market value of solar energy for a given month.  The fundamental subsidy formula applied in our model is:   Subsidy = Eligible Energy * max(0, EEG Reference Price - Monthly Weighted Solar Price)\n   Crucially, the subsidy is only calculated for intervals where the day-ahead market price is positive.  If the market price is zero or negative, we assume that no EEG subsidy is paid for that period.  Eligibility and Asset-Specific Logic  Not all energy is eligible for the subsidy. The model carefully distinguishes between different scenarios based on asset type and configuration.     Setup Type  Subsidy Eligibility  How It's Calculated in Our Model     Standalone BESS  ❌   Not Eligible  The model skips any EEG calculation for battery-only assets. The EEG is designed to support renewable   generation .    PV-Only  or   PV + BESS (Behind-the-Meter)  ✅   Eligible  For these setups, any power exported to the grid is considered eligible. The model tracks all energy sold to wholesale markets and applies the subsidy formula to that volume.    PV + BESS (Front-of-the-Meter)  ✅   Eligible, but with a catch  This is the most complex case. Only the energy that   originates from the PV panels  is eligible for the subsidy, even if it's first stored in the battery and sold to the grid later. The model calculates the total available PV generation (minus any curtailment and accounting for efficiency losses) in each interval. This amount becomes the maximum volume eligible for a subsidy in that period. If the asset sells more power than what the PV generated (i.e., by discharging previously stored grid power), that extra portion does not receive a subsidy.  By implementing this detailed, case-by-case logic, our model provides a precise and realistic calculation of EEG subsidy revenues, reflecting the specific operational strategy and physical constraints of each asset.",{"id":364,"path":365,"dir":7,"title":366,"description":367,"keywords":368,"body":370},"content:4.resources.md","/resources","Resources","Supporting material including glossary, FAQs, case studies, and release notes.",[369],"System Requirements","  Resources  Supporting material including glossary, FAQs, case studies, and release notes.    System Requirements  Minimum hardware and software specifications for optimal platform performance.",{"id":372,"path":373,"dir":374,"title":369,"description":7,"keywords":375,"body":379},"content:4.resources:1.system-requirements.md","/resources/system-requirements","resources",[376,377,378],"Recommended system requirements for Re-Twin Energy Web App","Hardware Requirements:","Browser Requirements:","  System Requirements   Recommended system requirements for Re-Twin Energy Web App  To ensure optimal performance and usability of the   Re-Twin Energy Web App , please make sure your system meets the following requirements:   Hardware Requirements:   A   desktop or laptop computer  (mobile devices such as smartphones and tablets are not supported).  At least 4  GB of RAM  (16GB recommended for larger datasets).  A   stable internet connection  (broadband recommended).   Browser Requirements:   Our preferred browsers are the latest versions of   Google Chrome  and   Safari .  We also support Mozilla Firefox and Microsoft Edge, but these are not our preferred options, and we cannot guarantee that our applications will run as smoothly as they do in the recommended browsers.   JavaScript and cookies must be enabled  for full functionality.  Internet Explorer is not supported (legacy browsers are not compatible).  For any questions or technical support, feel free to reach out to our team (  info@re-twin.energy ).",{"id":381,"path":382,"dir":7,"title":383,"description":384,"keywords":385,"body":386},"content:6.advanced-methodology.md","/advanced-methodology","Advanced Methodology","Detailed advanced methodology documentation",[],"   This content requires authentication",{"id":388,"path":389,"dir":390,"title":391,"description":7,"keywords":392,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways.md","/advanced-methodology/long-term-forecast-pathways","advanced-methodology","Long Term Forecast Pathways",[],"    This section requires a subscription.",{"id":395,"path":396,"dir":397,"title":398,"description":7,"keywords":399,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:1.Germany-jan-2026.md","/advanced-methodology/long-term-forecast-pathways/germany-jan-2026","long-term-forecast-pathways","Germany (Jan 2026)",[],{"id":401,"path":402,"dir":403,"title":404,"description":7,"keywords":405,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:1.Germany-jan-2026:1.ffe-trends.md","/advanced-methodology/long-term-forecast-pathways/germany-jan-2026/ffe-trends","germany-jan-2026","Ffe Trends",[],{"id":407,"path":408,"dir":403,"title":409,"description":7,"keywords":410,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:1.Germany-jan-2026:2.ffe-target.md","/advanced-methodology/long-term-forecast-pathways/germany-jan-2026/ffe-target","Ffe Target",[],{"id":412,"path":413,"dir":403,"title":414,"description":7,"keywords":415,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:1.Germany-jan-2026:3.maon-national-trends.md","/advanced-methodology/long-term-forecast-pathways/germany-jan-2026/maon-national-trends","Maon National Trends",[],{"id":417,"path":418,"dir":403,"title":419,"description":7,"keywords":420,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:1.Germany-jan-2026:4.maon-low-demand.md","/advanced-methodology/long-term-forecast-pathways/germany-jan-2026/maon-low-demand","Maon Low Demand",[],{"id":422,"path":423,"dir":403,"title":424,"description":7,"keywords":425,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:1.Germany-jan-2026:5.maon-net-zero.md","/advanced-methodology/long-term-forecast-pathways/germany-jan-2026/maon-net-zero","Maon Net Zero",[],{"id":427,"path":428,"dir":397,"title":429,"description":7,"keywords":430,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:2.Austria-jan-2026.md","/advanced-methodology/long-term-forecast-pathways/austria-jan-2026","Austria (Jan 2026)",[],{"id":432,"path":433,"dir":434,"title":404,"description":7,"keywords":435,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:2.Austria-jan-2026:1.ffe-trends.md","/advanced-methodology/long-term-forecast-pathways/austria-jan-2026/ffe-trends","austria-jan-2026",[],{"id":437,"path":438,"dir":434,"title":409,"description":7,"keywords":439,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:2.Austria-jan-2026:2.ffe-target.md","/advanced-methodology/long-term-forecast-pathways/austria-jan-2026/ffe-target",[],{"id":441,"path":442,"dir":434,"title":414,"description":7,"keywords":443,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:2.Austria-jan-2026:3.maon-national-trends.md","/advanced-methodology/long-term-forecast-pathways/austria-jan-2026/maon-national-trends",[],{"id":445,"path":446,"dir":434,"title":419,"description":7,"keywords":447,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:2.Austria-jan-2026:4.maon-low-demand.md","/advanced-methodology/long-term-forecast-pathways/austria-jan-2026/maon-low-demand",[],{"id":449,"path":450,"dir":434,"title":424,"description":7,"keywords":451,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:2.Austria-jan-2026:5.maon-net-zero.md","/advanced-methodology/long-term-forecast-pathways/austria-jan-2026/maon-net-zero",[],{"id":453,"path":454,"dir":397,"title":455,"description":7,"keywords":456,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:3.Spain-jan-2026.md","/advanced-methodology/long-term-forecast-pathways/spain-jan-2026","Spain (Jan 2026)",[],{"id":458,"path":459,"dir":460,"title":404,"description":7,"keywords":461,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:3.Spain-jan-2026:1.ffe-trends.md","/advanced-methodology/long-term-forecast-pathways/spain-jan-2026/ffe-trends","spain-jan-2026",[],{"id":463,"path":464,"dir":460,"title":409,"description":7,"keywords":465,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:3.Spain-jan-2026:2.ffe-target.md","/advanced-methodology/long-term-forecast-pathways/spain-jan-2026/ffe-target",[],{"id":467,"path":468,"dir":460,"title":414,"description":7,"keywords":469,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:3.Spain-jan-2026:3.maon-national-trends.md","/advanced-methodology/long-term-forecast-pathways/spain-jan-2026/maon-national-trends",[],{"id":471,"path":472,"dir":460,"title":419,"description":7,"keywords":473,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:3.Spain-jan-2026:4.maon-low-demand.md","/advanced-methodology/long-term-forecast-pathways/spain-jan-2026/maon-low-demand",[],{"id":475,"path":476,"dir":460,"title":424,"description":7,"keywords":477,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:3.Spain-jan-2026:5.maon-net-zero.md","/advanced-methodology/long-term-forecast-pathways/spain-jan-2026/maon-net-zero",[],{"id":479,"path":480,"dir":397,"title":481,"description":7,"keywords":482,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived.md","/advanced-methodology/long-term-forecast-pathways/archived","Archived",[],{"id":484,"path":485,"dir":486,"title":487,"description":7,"keywords":488,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived:1.July-2025.md","/advanced-methodology/long-term-forecast-pathways/archived/july-2025","archived","July 2025",[],{"id":490,"path":491,"dir":492,"title":493,"description":7,"keywords":494,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived:1.July-2025:1.Germany.md","/advanced-methodology/long-term-forecast-pathways/archived/july-2025/germany","july-2025","Germany",[],{"id":496,"path":497,"dir":498,"title":404,"description":7,"keywords":499,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived:1.July-2025:1.Germany:1.ffe-trends.md","/advanced-methodology/long-term-forecast-pathways/archived/july-2025/germany/ffe-trends","germany",[],{"id":501,"path":502,"dir":498,"title":409,"description":7,"keywords":503,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived:1.July-2025:1.Germany:2.ffe-target.md","/advanced-methodology/long-term-forecast-pathways/archived/july-2025/germany/ffe-target",[],{"id":505,"path":506,"dir":498,"title":414,"description":7,"keywords":507,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived:1.July-2025:1.Germany:3.maon-national-trends.md","/advanced-methodology/long-term-forecast-pathways/archived/july-2025/germany/maon-national-trends",[],{"id":509,"path":510,"dir":498,"title":511,"description":7,"keywords":512,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived:1.July-2025:1.Germany:4.maon-base-case.md","/advanced-methodology/long-term-forecast-pathways/archived/july-2025/germany/maon-base-case","Maon Base Case",[],{"id":514,"path":515,"dir":498,"title":516,"description":7,"keywords":517,"body":393},"content:6.advanced-methodology:1.Long-Term-forecast-pathways:4.Archived:1.July-2025:1.Germany:5.maon-double-battery.md","/advanced-methodology/long-term-forecast-pathways/archived/july-2025/germany/maon-double-battery","Maon Double Battery",[],{"id":519,"path":520,"dir":7,"title":521,"description":522,"keywords":523,"body":386},"content:7.tutorials.md","/tutorials","Tutorials","Advanced tutorials and guides",[],{"id":525,"path":526,"dir":7,"title":527,"description":7,"keywords":528,"body":531},"content:8.index-methodology.md","/index-methodology","Index Methodology",[529,530],"Revenue Optimization for Battery Energy Storage Systems (BESS)","Market-Specific Constraints and Strategies","  Index Methodology  Revenue Optimization for Battery Energy Storage Systems (BESS)  Our methodology provides a comprehensive approach to estimating revenue opportunities for Battery Energy Storage Systems (BESS). It evaluates how revenue potentials evolve over time and considers the impact of participating in multiple power markets simultaneously. The model is designed to optimize market participation strategies over a defined timeframe, offering actionable insights for investors and operators.  The calculations are based on imperfect hindsight, where randomness is introduced to the perfect hindsight prices by adding a standard deviation of 10%, reflecting actual market conditions during the analyzed period. To further counter the advantages of perfect hindsight, a conservative approach is taken for each market where the BESS participates. Up to 80% of its power rating is allocated to ancillary markets, while the remaining capacity is used for trading in energy markets.  While this analysis offers a preview of our model's capabilities as a demo case, our investment analysis tool supports more detailed modeling, including different bidding strategies and selecting market-participation.  Due to constraints in publicly available data, certain assumptions and simplifications are applied.     Key Assumptions and Parameters  The following parameters are integral to our methodology:    Battery size:  10 MW/10 MWh (1-hr), 10 MW/20 MWh (2-hr) and 10 MW/40 MWh (4-hr).   Geography:  Germany, Austria, Belgium (additional regions will be included in future versions).   Roundtrip efficiency:  90.25% (95% efficiency for both charging and discharging).   Daily cycle limit:  2 cycles/day.   State of charge (SoC):  Managed as a rolling variable based on prior day activities, without reset.   Min/Max SoC limits:  10% (minimum) and 90% (maximum).   Asset degradation:  Not included in this demonstration (available in the investment analysis tool).   Market behavior assumption:  BESS is treated as a price-taker, submitting bids based on internal costs, constraints, and strategies with the market prices not being influenced.   Market Approach and Assumptions  In our approach, the revenues from the ancillary markets are combined with trading revenues from the wholesale market. For trading on the wholesale market, it is assumed that trading first takes place on the day-ahead market and then, taking into account the results from the day-ahead market, the remaining capacity is offered on the aFRR Energy markets. The revenues from intraday markets (auction and continuous) will be added in the future versions.  Our model integrates revenues from ancillary services and wholesale markets, combining:    Capacity Markets : Up to 80% of the battery's capacity is allocated to these markets (FCR, aFRR Positive and aFRR Negative Capacity) to guarantee certain revenues from the ancillary markets. Participation in mFRR Capacity markets (Positive and Negative) is assumed to be not available.   Energy Markets : Remaining capacity is allocated to trading in the day-ahead (DA) and aFRR Energy Positive and Negative markets. The decision on participation in these markets is through optimization.   Market-Specific Constraints and Strategies  The calculations are based on imperfect hindsight, where randomness is introduced to the perfect hindsight prices by adding a standard deviation of 10%, reflecting actual market conditions during the analyzed period. To further counter the advantages of perfect hindsight, a conservative approach is taken for each market where the BESS participates. Up to 80% of its power rating is allocated to ancillary markets, while the remaining capacity is used for trading in energy markets.    Day-Ahead and Intraday Trading   The power limit (Pmax) and state-of-charge limits (10-90%) of the battery must not be exceeded.   Frequency Containment Reserve (FCR)   Maximum marketable capacity is reduced by 20% to align with pre-qualification (PQ) standards. We assume that the marketable power is reserved for 30 mins delivery. It is assumed that no power is lost or gained when participating in FCR.   Automatic Frequency Restoration Reserve Capacity (aFRR Capacity)   Maximum marketable capacity is reduced by 20% according to the pre-qualification (PQ) conditions. We assume that the marketable power is reserved for 1h delivery. It is assumed that no power is lost or gained when participating in aFRR capacity markets and therefore the bids are assumed to be highest to avoid dispatching.   Automatic Frequency Restoration Reserve Energy (aFRR Energy)   Given aFRR Energy dispatch has higher uncertainity than Day Ahead, Positive (aFRR_POS Energy) bid prices are set at 50% above day-ahead prices, while negative (aFRR_NEG) bid prices are set 50% below day-ahead prices. Since day ahead results are available, the bid prices for this market can be based on the day ahead prices. Given fixed price, our model decides the volume to be bid depending on the revenues from the market.   Optimizing Bids Across Multiple Power Markets: A Baseline Strategy  The baseline strategy applied here aims to optimize bids across various power markets, including ancillary services (FCR, aFRR, mFRR) and energy markets (day-ahead and aFRR energy) with the goal of maximizing revenues. We leverage a sequential approach to generate, submit, and evaluate bids, ensuring that decisions are progressively informed by prior results. The following steps outline the core methodology:    Preliminary Optimization for Ancillary Capacity and Day-Ahead Markets  \nA preliminary optimization is performed for ancillary services and day-ahead market, taking a conservative approach of winning bids (upto 80% of the power capacity of the BESS) in these markets, making sure minimum revenues are achieved.   Sequential Market-Specific Bid Generation and Evaluation \nBased on the preliminary optimization, market-specific bids are generated and evaluated within its unique context (e.g. FCR is pay as cleared market while aFRR capacity is pay as bid market). If bids are won then the conditions necessary for that market are maintained in the other markets going forward. The ancillary capacity markets are prioritized, followed by the day-ahead and other energy markets.    Key Strategy Components:    Sequential Approach:  The optimization process follows a step-by-step approach, starting with ancillary services and progressing through the day-ahead and intraday markets.   Bid Feedback Loop:  The results from each market optimization are used as feedback, informing subsequent optimization runs. This iterative approach is used to refine decisions and maintain alignment with changing market conditions.   Conservative Design:  The strategy takes a conservative approach by fixing ancillary market results before revisiting energy market bids. The approach accounts for market-specific constraints and priorities.",{"id":533,"path":534,"dir":7,"title":535,"description":536,"keywords":537,"body":538},"content:9.disclaimer.md","/disclaimer","Disclaimer","The information and results provided in this analysis are for informational purposes only and are based on historical data, assumptions, and modeling to the best of our knowledge and capabilities. While every effort has been made to ensure the accuracy and reliability of the information presented, we cannot guarantee its correctness, completeness, or applicability to specific circumstances.",[],"  Disclaimer  The information and results provided in this analysis are for informational purposes only and are based on historical data, assumptions, and modeling to the best of our knowledge and capabilities. While every effort has been made to ensure the accuracy and reliability of the information presented, we cannot guarantee its correctness, completeness, or applicability to specific circumstances.  This analysis is not intended as a forecast or a guarantee of future performance. Users are encouraged to verify the information and assumptions independently.  Re-Twin Energy assumes no liability for any losses or damages resulting from the use of this information. All data and results are subject to change as models, assumptions, and market conditions evolve.  If you have any questions or would like further clarifications, please feel free to contact us.  Re-Twin Energy does not take any responsibility for the completeness, accuracy, and actuality of the information provided. This article is for information purposes only and does not replace individual legal advice.",{"id":540,"path":541,"dir":7,"title":542,"description":543,"keywords":544,"body":549},"content:10.changelogs.md","/changelogs","Changelogs","All notable changes to this project are documented here.",[545,546,547,548],"February 2026 Updates","December 2025 Updates","October 2025 Updates","September 2025 Updates","  Changelogs  All notable changes to this project are documented here.   February 2026 Updates   The long-term power market forecasts have been updated with clearer scenario definitions and improved assumptions on how markets and technologies develop under MAON and FFE for    Austria .  The long-term power market forecasts have been updated with clearer scenario definitions and improved assumptions on how markets and technologies develop under MAON and FFE for    Germany .  A new   BESS Degradation Model  has been added. It explains how battery degradation is handled, the main assumptions used, and how battery performance changes over time.  A   BESS CAPEX calculation  has been added. It explains the cost components, how costs scale with system size, and how the total upfront investment cost is calculated.   December 2025 Updates   Updated graphs and charts to ensure long-term forecast consistency for   MAON  and   FFE , improving comparability across scenarios.   October 2025 Updates   Published the    PV–BESS Revenue Split Estimation  article.\n   Documents assumptions and methodology used to allocate revenues between PV and BESS assets.  Improves transparency and interpretability of hybrid system revenue attribution.   September 2025 Updates   Added    Long-Term Forecast documentation  for:\n     FFE Target    FFE Trends    MAON Natural Trends    MAON Base Case    MAON Double Battery  Updated and expanded    intraday power market  documentation for clearer modelling assumptions.  Introduced    Battery Chemistry Comparison  documentation to support strategic BESS design decisions. ",1776150154878]