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Profitability of batteries in day-ahead and intraday electricity markets: Assessment of operation strategies with endogenous prices

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  • Veenstra, Arjen T.
  • Mulder, Machiel

Abstract

Batteries can support future electricity systems by assisting price arbitrage in electricity markets, enhancing self-consumption, and providing ancillary services. Determining the profitability of price arbitrage , i.e. charge at low prices and discharge at high prices., requires evaluating the strategy that is used to benefit from future price fluctuations, and considering its impact on prices. This study examines battery profitability based on price arbitrage in day-ahead and intraday auction markets, where both temporal price differences within markets and price differences between markets are exploited. This is done with an electricity-market model, calibrated on historical Dutch bid-curve data, which estimates the impact of battery bids on market equilibria. We compare profits under perfect foresight with a strategy where the storage operator has perfect foresight of prices but ignores the own effect on prices, and two simpler historical-data-based strategies. It appears that the latter type of simpler strategies can result in profitable operations in case of small battery capacity, and that most profits can be made in the intraday market. However, when battery capacity increases, profitability declines, especially in the intraday market, due to lower market volumes. Increased battery capacity also calls for more advanced operation strategies, as historical price patterns may no longer predict future prices accurately. Even in a year with high price volatility as in 2023, with perfect foresight of prices and 60% lower battery costs, no more than 15% of Dutch households with solar panels can profitably invest in home batteries.

Suggested Citation

  • Veenstra, Arjen T. & Mulder, Machiel, 2025. "Profitability of batteries in day-ahead and intraday electricity markets: Assessment of operation strategies with endogenous prices," Energy Economics, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:eneeco:v:148:y:2025:i:c:s0140988325004359
    DOI: 10.1016/j.eneco.2025.108608
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    References listed on IDEAS

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    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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