Modeling and Optimization Methods for Controlling and Sizing Grid-connected Energy Storage: A Review

Wu, Di; Ma, Xu

doi:10.1007/s40518-021-00181-9

Title: Modeling and Optimization Methods for Controlling and Sizing Grid-connected Energy Storage: A Review

Journal Article · Tue Jun 01 00:00:00 EDT 2021 · Current Sustainable/Renewable Energy Reports

DOI:https://doi.org/10.1007/s40518-021-00181-9· OSTI ID:1813587

^[1]; Ma, Xu ^[1]

BATTELLE (PACIFIC NW LAB)

Purpose of Review Energy storage is capable of providing a variety of services and solving a multitude of issues in today's rapidly evolving electric power grid. This paper reviews recent research on modeling and optimization methods for optimally controlling and sizing grid-connected battery energy storage systems (BESSs). Open issues and promising research directions are discussed. Recent Findings Recent studies on BESS dispatch, evaluation, and sizing focus on advanced modeling and optimization methods to maximize stacked value streams from multiple services. BESS models have been improved to better represent operational characteristics or capture degradation effects. Different solution methods and optimization techniques have been proposed to improve the benefits and cost-effectiveness of BESSs, using deterministic approaches prevalently but with impressive progress in capturing and addressing uncertainties. Summary Recent progress in BESS scheduling and sizing better supports planning and operational decision-making in different use cases, which is highly important to advance the deployment of BESSs. Additional research is required to properly model the trade-off between short-term benefits and service life with multiple degradation effects explicitly considered in the decision-making process. Advanced methods are to be developed for effectively determining optimal BESS sizes that maximize overall benefits within a varying lifetime considering diversified system, conditions as well as uncertainties at planning and operational stages.

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Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1813587

Report Number(s):: PNNL-SA-157337

Journal Information:: Current Sustainable/Renewable Energy Reports, Vol. 8, Issue 2

Country of Publication:: United States

Language:: English

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