Optimal Energy Management Strategy of Battery Energy Storage for Primary Frequency Control

In order to maximize the economic benefit within battery life span, it's necessary to weigh the operating costs and profits for battery energy storage systems (BESSs) under primary frequency control (PFC) market mechanism. We reveal that the sequential decision of energy management is essentially a controlled Markov process. Therefore, we describe the dynamic transfer of PFC demand sequence using Continuous-time Markov chain, and characterize the dynamic degradation of battery capacity based on lifecycle throughput, then build an expected economic benefit-maximizing Markov decision process model. Approximating expectation forward dynamic programming algorithm is applied to solve the curses of dimensionality. Our results suggest that BESSs can achieve much larger economic benefit using dynamic threshold structure optimal energy management strategy.