Adaptive optimal operation of grid-connected battery systems under varying electricity market volatility

• Optimization adjusts cycle timing, duration, and frequency to price signals. • Strategy balances short-term revenue with long-term battery health. • Strategy incorporates battery model capturing condition-dependent performance. • Boosts profit and durability, lifespan in stable and revenue in volatile markets. Battery energy storage systems are increasingly important for market-driven applications such as price arbitrage, yet their long-term profitability and durability depend strongly on how operations are scheduled. Conventional static or rule-based strategies often fall short of fully capturing opportunities in volatile electricity markets. This creates a need for adaptive approaches that respond optimally to electricity price variability while balancing short-term profitability with long-term battery health. This study proposes a novel condition-responsive optimization strategy to evaluate how different levels of electricity market volatility affect optimal operational decisions, technical performance, and economic outcomes over the entire battery lifetime. The strategy dynamically adjusts daily charge–discharge frequency, duration, and timing in response to both market conditions and the internal state of the battery. The optimization objective is to identify an optimal trade-off between operational revenues and degradation-related costs, incorporating an advanced battery model that captures condition-dependent performance and degradation. To test adaptability, the strategy is applied to two contrasting market scenarios representing low and high price variability. Results show that under the high-volatility scenario, the strategy increases annual profit by 80 % (€43,500 vs. €24,500 per installed MWh), raises net present value by 50 % (€415,500 vs. €279,000), and shortens payback time by 35 % (3.7 vs. 5.6 years) compared to the low-volatility case, while still maintaining nearly a decade of operation. These findings demonstrate that adaptive, market-aware scheduling significantly enhances profitability, favoring longevity-oriented operation in stable markets and revenue-driven cycling under volatile conditions. The study provides practical insights into how dynamic, condition-responsive strategies perform under diverse market conditions, offering guidance for the development of more profitable, resilient, and sustainable energy storage systems.