Optimal bidding strategy for price maker battery energy storage systems in energy and regulation reserves markets

Abstract

This study presents a novel methodology to address bi-level optimization challenges, specifically targeting Battery Energy Storage Systems (BESSs) in competitive energy and regulation reserve markets. The approach Incorporates non-convex constraints inherent in real-world market models by sequentially solving each problem level, avoiding simplifications into single-level equivalents. Heuristic optimization, specifically the Particle Swarm Optimization (PSO) algorithm, is implemented to solve the complex solution space. The methodology incorporates a degradation cost model and semi-elastic demand behavior to emulate real market conditions. A performance analysis identifies optimal PSO parameters, demonstrating the methodology’s scalability for real-size transmission networks. Extensive simulations underscore the transformative role of BESSs as price-maker assets, revealing the critical impact of non-convex constraints, quantity-price bidding, and load modeling on strategy and profitability, achieving up to a 10% increase in profit compared to models that overlook these aspects.