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Energy Storage – Trading Tool or Revenue Asset?

In the era of growing importance of renewable sources and dynamic changes in the energy market, energy storage systems are becoming an increasingly important element of power infrastructure. For investors in the renewable energy sector, a question arises — should energy storage be treated as a trading tool or as a predictable revenue asset like PV or Wind? przychodowe takie samo jak PV czy Wiatr?

For investors expecting stable revenues: VESS implements repeatable strategies, with charging/discharging settings and participation in the balancing power market. Such strategies are characterized by “capturing” 75-85% of the potential each day.

What are the revenue streams from energy storage?

Energy storage can generate revenue from various sources, but two business models are key in the operational approach: energy arbitrage and regulatory system services.

  • Energy Arbitrage 

Energy storage earns money through arbitrage, which means buying energy during low-price hours and selling during high-price hours. This is a classic operational model where what matters is the price difference (the “spread”), the efficiency of the charge/discharge cycle, and the number of possible cycles per day. Arbitrage can be implemented both on the day-ahead market (RDN) and the intraday market, and thanks to increasing price volatility – the profit potential is growing. This model works well as a primary revenue source for storage, but it’s worth remembering that it comes with the risk of forecast errors, which may cause us to “miss” the most expensive and cheapest hours.

  • System Services

Energy storage can provide system services for the transmission system operator, such as mFRR, aFRR, or FCR, ensuring fast power delivery or frequency regulation. For this, it receives compensation both for readiness to act and for actual activation. This revenue model is characterized by greater stability, as prices for power reservation do not take negative values, and importantly, there is interdependence between power regulation service prices and activation compensation.

Do these models exclude each other?

Not at all — they should actually work together. In each balancing period (currently 15 minutes), we can provide both the work plan resulting from transactions concluded on the exchange, as well as reservations and activation of balancing services. It’s worth remembering that activation of reserved power within system services cannot be fully controlled, and their activation depends on the system operator, who makes decisions based on the current situation. This factor is random from the energy storage perspective, so trading actions on the exchange will often be necessary to compensate for activations.

How to determine the minimum earnings of storage?

The simplest way is to calculate a conservative arbitrage strategy based on:

  • Fixed charging and discharging settings – meaning the storage always charges during specific low-price hours (e.g., night) and discharges during high-price hours (e.g., afternoon peak).
  • With appropriate assumptions about the work cycle and storage efficiency, one can calculate the minimum level of annual revenue, which forms the basis for assessing investment profitability.

This model doesn’t require advanced forecasting – knowledge of the hourly price distribution of the day-ahead market and basic technical data of the storage are sufficient. Why is this so important? Because by determining this level, we can see that the expected value from the basic and fully naive model is not equal to 0. What does this mean for the storage owner? That we have something like guaranteed profit, which works similarly to financial bonds, so any more advanced actions don’t just have to earn, but have to earn more than the baseline scenario.

If trading, then risk?

This is an inseparable pair, and additionally, they follow the principle of correlation preservation, i.e., the more we want to earn, the more risk we must accept. As I mentioned earlier, the initial value of risk, when certain parameters are fixed, is close to 0, but in this case, the revenue is 30-40% lower than with a full optimization strategy.

Between the baseline level and the ideal one, there are infinitely many scenarios in between, and sometimes by increasing this minimal risk, we can significantly increase the expected value.

Which strategy to choose?

VESS, a company specializing in managing BESS (Battery Energy Storage System) assets, applies an approach tailored to the investor’s profile and risk tolerance.

  • For investors expecting stable revenues: VESS implements repeatable strategies, with charging/discharging settings and participation in the balancing power market. Such strategies are characterized by “capturing” 75-85% of the potential each day.
  • For investors seeking profit maximization: dynamic strategies are used, utilizing short-term price forecasts and real-time asset portfolio management, which achieve daily results in the range of 90% of potential, however, there are days when this share of potential drops to 70% due to an aggressive strategy.

Summary

Energy storage today is not just a technical buffer — it’s a financial asset that can work in both a safe and speculative model. The key is to consciously select a strategy that matches the investor’s goals and market conditions. But it’s worth remembering that we’re not asking if energy storage will earn money — but how much it will earn.

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Maciej Konieczka

CEO of VESS, Expert in Energy Analytics and Storage An experienced expert in the energy sector, specializing in the use of data analytics, artificial intelligence, and machine learning (ML) to optimize energy management. He is the founder and CEO of VESS (Virtual Energy Storage Systems) – an innovative technology company that develops advanced algorithms for managing energy storage systems, maximizing their profitability and operational efficiency. Before founding VESS, he served as the Director of Data and Analytics at Veolia Energy Contracting Poland, where he was responsible for data management, analytics, and modeling optimization strategies for energy markets and energy storage systems. Previously, he also worked at PGE S.A. Capital Group, PKN Orlen, and TRMEW Obrót (now Respect Energy), where he developed data-driven strategies and implemented Data Science solutions in energy trading, capacity market analyses, and technical forecasting for power plants. He is a visiting lecturer at Kozminski University and a PhD candidate at Warsaw University of Technology, where he conducts research on advanced data analytics and process modeling in the energy sector. He holds a DAMA CDMP certification in data management.