Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications. (October 2020) - Hannan, M.A.; Faisal, M.; Jern Ker, Pin; Begum, R.A.; Dong, Z.Y.; Zhang, C.
Abstract: Carbon emission from the burning of fossil fuel has resulted in global warming. Climate change and global warming are among the most complex issues requiring immediate solutions. Microgrid (MG) based on renewable energy sources (RESs) can be used to reduce the carbon intensity of electricity and achieve the global decarbonization goal by 2050. Optimizing the size of the energy storage system (ESS) can ensure the sustainable, resilient, and economic operation of the MG. Thus, key features of the optimal ESS, including methods and algorithms of ESS sizing, power quality, reliability, connection mode, and public policy enforcement for low-carbon emission, must be identified. Existing literature mostly focuses on the cost-effective optimal sizing method based on capacity minimization, which overlooks other issues. This work reviews the features of optimal ESS sizing methods and algorithms, their characteristics, and the scenarios between ESS and decarbonization in MG applications to address their shortcomings. ESS characteristics on storage type, energy density, efficiency, advantages, and issues are analyzed. This review highlights details of ESS sizing to optimize storage capacity, reduce consumption, minimize storage cost, determine the optimal placement and mitigate carbon emission for decarbonization. The analyses on the understanding of decarbonization in relation to the use of ESS in MG scenarios are explained rigorously. Existing research gaps, issues, and challenges of ESS sizing for next-generation MG development are also highlighted. This review will strengthen the efforts of researchers and industrialists to develop an optimally sized ESS for future MGs that can contribute toward achieving the decarbonization goal. Highlights: Optimal ESS sizing can ensure the sustainable and economic operation of MG. Methods and algorithms of ESS sizing must be identified for reliability and low-carbon emission policy. Optimal ESS sizing increases storage capacity, reduce consumption and power cost. Existing ESS sizing technologies are performing, however, not reliable and optimal enough yet. Research gaps, and challenges on ESS sizing are highlighted for future MG integration.