Optimizing Multi-Market Participation of Battery and Electrolyser Systems Based on Field Performance

Chunyang Zhao; Stoyan Trenchev; Shi You; Chresten Træholt

cs.LGAug 17, 2026

Optimizing Multi-Market Participation of Battery and Electrolyser Systems Based on Field Performance

Authors: Chunyang Zhao, Stoyan Trenchev, Shi You, Chresten Træholt

Organizations: Department of Wind and Energy Systems Technical University of Denmark Kongens Lyngby, Denmark

Abstract

The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability. With the expansion of electricity markets and ancillary service products, opportunities arise to stack revenues across multiple services. Long-term Power-to-X (PTX) electrolysers and short-term battery energy storage systems (BESS) are prevalent flexible resources, yet most studies neglect real hardware behavior, such as ramp limits, efficiency, and setpoint-tracking accuracy. This work presents experimental and modeling results for a 55 kW/79 kWh BESS and an electrolyser comprising three 2.4 kW units. Key characteristics are identified through measurements and embedded into a price-driven optimization framework for participation in the Danish electricity and ancillary service markets, utilizing real market data from 2022 to 2025. The optimized daily profits for multi-market participation are 1,749.27 DKK and 289.46 DKK for the BESS and electrolyser, respectively. With the demonstrated business cases for BESS and PTX systems, this work highlights the importance of incorporating experimental performance when evaluating participation across multiple markets and years.

Figures & tables

Parameter	BESS	Electrolyzer
Power	$55\text{\,}\mathrm{k}\mathrm{W}$ ( $30\text{\,}\mathrm{k}\mathrm{W}$ used)	3 $\times$ $2.4\text{\,}\mathrm{k}\mathrm{W}$
Energy	$79\text{\,}\mathrm{k}\mathrm{W}\mathrm{h}$ ( $52\text{\,}\mathrm{k}\mathrm{W}\mathrm{h}$ used)	–
Efficiency	90–98% RTE	62–70% HHV
Response time (up/down)	$2\text{\,}\mathrm{s}$ / $2\text{\,}\mathrm{s}$	$21\text{\,}\mathrm{s}$ / $1.6\text{\,}\mathrm{s}$
Preheat / standby power	–	$0.2\text{\,}\mathrm{k}\mathrm{W}$ per module
SOC / power limits	12–95% SOC	60–100% $P_{\text{nom}}$

TABLE I : Key parameters of the assets.

Fig. 2 : BESS capacity test results and SOH projection

Panel of Figure 4 — Fig. 3 : Tested electrolyzer response behavoir

Fig. 4 : Optimal BESS schedule in all available markets.

	Spot	FFR	FCRD-U	FCRD-D	FCRN	Totals
DKK	103.17	216.07	289.01	1061.76	79.26	1749.27
kWh In	37.51	–	0.00	30.92	8.17	76.60
kWh Out	31.60	10.80	17.10	0.00	8.17	67.67

TABLE II : Revenue and energy exchange in Fig. 4 .

Fig. 5 : Optimal electrolyzer schedule in all available markets.

Item/[DKK]	H 2	FFR	FCRD-U	FCRN(E)	FCRN(C)	Total
Revenue	243.86	82.86	89.96	3.35	76.05	496.08
Cost	204.95	0.00	0.00	1.67	0.00	206.62
Profit	38.91	82.86	89.96	1.67	76.05	289.46

TABLE III : Financial outcomes of in Fig. 5 .

Fig. 6 : BESS case study with 1 cycle per day limit.

Fig. 7 : Electrolyser case study with 6.61 EUR/kg hydrogen price.

Panel of Figure 11 — Fig. 8 : Multi-year performance of the BESS and electrolyzer under multi-market participation.

Power Systems

Power systems research studies the planning, operation, control, and protection of electrical grids and their integration with energy resources.

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Multi-market value-stacking: Battery control for combined imbalance participation and non-uniform FCR bidding

Celle Hendrickx, Fabio Pavirani, Chris Develder

Gent University - imec, IDLab · Ghent, Belgium

Power Systems

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The growing share of Renewable Energy Sources (RES) in modern power systems increases both grid imbalances and frequency deviations, reinforcing the need for ancillary services such as Frequency Containment Reserve (FCR) and passive balancing. Battery Energy Storage Systems (BESS) are well-suited for these services, but prior research typically relies on uniform FCR bids that remain constant throughout the control period. Such static bids fail to fully exploit BESS flexibility, as they do not balance the trade-off between reserving energy for FCR delivery and using it for imbalance arbitrage, limiting the achievable value in value-stacking settings. To address this limitation, we propose a two-stage control framework for the European context that introduces non-uniform FCR bids. In the first stage, we derive a time-varying bid sequence using data-driven Monte Carlo (MC) optimization. In the second stage, a Deep Reinforcement Learning (DRL) agent leverages the residual flexibility for real-time imbalance trading while proactively managing the State of Energy (SoE) to ensure compliance with FCR requirements. The framework is presented as a proof of concept, highlighting the potential benefits of time-varying bidding strategies. By incorporating daily cycle budgets and time-varying reserve commitments, our approach achieves a 7.56% profit increase compared to uniform baselines. These results show that non-uniform bidding can unlock additional value by more effectively aligning reserve obligations with rapidly changing imbalance opportunities.

Abstract

Power Systems

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