Paper Type

Complete

Abstract

Assessing the financial viability of Battery Energy Storage System is challenging due to the volatility of electricity markets. This research develops a stochastic simulation framework to analyze their profitability under volatile conditions. We combine long-term, high-resolution electricity price scenario generation with a battery operational model. A Rolling Horizon Optimization determines the operational dispatch, co-optimizing for energy arbitrage and Frequency Containment Reserve revenues. Simulations across an ensemble of scenarios produce statistical distributions of metrics, such as lifetime profit and State of Health, enabling a quantification of investment risk. We show that average daily price spread is a stronger predictor of arbitrage profitability than overall price levels. We demonstrate a trade-off between a system’s C-rate, its degradation trajectory and its revenue potential. We find that maximizing lifetime value is achieved by aligning the system’s technical parameters and the optimization strategy with the target revenue streams.

Paper Number

1325

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Aug 15th, 12:00 AM

Scenario-Based Profitability Analysis for Battery Energy Storage System Investments

Assessing the financial viability of Battery Energy Storage System is challenging due to the volatility of electricity markets. This research develops a stochastic simulation framework to analyze their profitability under volatile conditions. We combine long-term, high-resolution electricity price scenario generation with a battery operational model. A Rolling Horizon Optimization determines the operational dispatch, co-optimizing for energy arbitrage and Frequency Containment Reserve revenues. Simulations across an ensemble of scenarios produce statistical distributions of metrics, such as lifetime profit and State of Health, enabling a quantification of investment risk. We show that average daily price spread is a stronger predictor of arbitrage profitability than overall price levels. We demonstrate a trade-off between a system’s C-rate, its degradation trajectory and its revenue potential. We find that maximizing lifetime value is achieved by aligning the system’s technical parameters and the optimization strategy with the target revenue streams.

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