4.7 Article

The multi-stage framework for optimal sizing and operation of hybrid electrical-thermal energy storage system

期刊

ENERGY
卷 245, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123248

关键词

Hybrid energy storage system; Sizing and operation co-optimization; Levelized cost of storage; Particle swarm optimization; Mixed-integer linear programming; Demand response

资金

  1. National Key Research and Development Program of China [2018YFE0128500]
  2. Hong Kong, Macao and Taiwan Science and technology coop-eration program of Jiangsu Province of China [BZ2021057]
  3. Fundamental Research Funds for the Central Universities of China [B210202069]

向作者/读者索取更多资源

This paper proposes a hybrid electrical thermal energy storage system to mitigate the intermittency of renewable energy. The optimal sizing and operation of the system are achieved through a multi-stage framework that considers the minimization of net load and levelized cost of storage. The study shows that the hybrid system is more reliable and cost-effective compared to single thermal energy storage or single battery systems. The multi-stage framework outperforms rule-based operation strategies, and demand response can reduce the investment cost of the system effectively.
In order to mitigate the intermittency of renewable energy, the paper proposes a hybrid electrical thermal energy storage system, which complementarily utilizes the cost-effective two-tank direct molten salt thermal energy storage system and the flexible lead-acid battery. A multi-stage framework is further proposed for optimal sizing and operation of the hybrid energy storage system. First, the renewable energy capacity is optimized considering the minimization of total net load. Then, the typical net load profiles are selected by K-means clustering algorithm. Finally, a bi-level optimization model considers the minimization of levelized cost of storage (LCOS) and power deviation to optimize the sizing and operation strategy of the hybrid energy storage system, and the optimization problem is solved by meta-heuristic algorithm and mixed-integer linear programming. The case studies show that: (1) the hybrid energy storage system is more reliable than single thermal energy storage and more cost-effective than single battery; (2) the multi-stage framework outperforms the commonly-used rule-based operation strategy; (3) demand response strategy can effectively reduce the investment cost of the proposed system. (c) 2022 Elsevier Ltd. All rights reserved.

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