Simulation study demonstrates superior frequency regulation performance for lithium iron phosphate batteries in high-renewable grids, indicating high applicability for coordinated storage.
Electrochemical energy storage is considered a key solution for addressing frequency regulation in power systems with high proportions of renewable energy. However, the varying costs of different energy storage types complicate the effective evaluation of electrochemical energy storage’s role in frequency regulation, hindering its widespread adoption. Addressing this challenge, this paper proposes a comprehensive system frequency regulation control strategy from the perspective of source-load interaction. The frequency deviation signal is used to categorize regions based on deviation severity, enabling coordinated operation of frequency regulation capacity across zones. Building upon this control strategy, the paper analyzes the performance of electrochemical energy storage by factoring in electricity benefits, compensation, environmental benefits, maintenance costs, penalties, and more, creating a comprehensive economic benefit and cost model for frequency regulation. Additionally, the paper establishes performance, technical, and economic indicators for various operational conditions of electrochemical energy storage, integrating subjective and objective weighting methods to develop a comprehensive evaluation and scoring system for its applicability. Finally, the proposed strategy’s effectiveness is validated through simulations, with results indicating that lithium iron phosphate batteries achieve the highest comprehensive evaluation score among electrochemical energy storage options. • A system frequency regulation control strategy considering the interaction between source and load is proposed. • Establish scoring system for electrochemical energy storage considering cost, benefits, frequency regulation effects. • The scoring system assesses comp perf of various electrochemical energy storage types and visually shows pros/cons via data.
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Guo et al. (2025) studied this question.
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