Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages

被引:111
作者
He, Yi [1 ]
Guo, Su [2 ]
Zhou, Jianxu [1 ]
Ye, Jilei [3 ]
Huang, Jing [2 ]
Zheng, Kun [2 ]
Du, Xinru [2 ]
机构
[1] Hohai Univ, Coll Water Conservancy & Hydropower Engn, Nanjing 210024, Peoples R China
[2] Hohai Univ, Coll Energy & Elect Engn, Nanjing 211000, Peoples R China
[3] Nanjing Technol Univ, Sch Energy Sci & Engn, Nanjing 211816, Peoples R China
关键词
Renewable energy; Hybrid energy storage; Operation threshold optimization; Planning-operation co-optimization; Multi-objective evolutionary algorithm; Data-driven model; PUMPED HYDRO STORAGE; POWER-GENERATION; PV PLANT; SOLAR; ALGORITHM; FEASIBILITY; BATTERY; DESIGN;
D O I
10.1016/j.renene.2021.11.116
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimization. The hybrid system utilizes the cost-effectiveness of thermal energy storage and flexibility of battery to jointly tackle the intermittency of renewable energy. A novel coordinated operation strategy based on the operation threshold of power block is proposed, and the planning-operation co-optimization model considers the minimization of net present cost and loss of power supply probability to determine the optimal operation threshold and sizing decision variables. The co-optimization problem is solved by a proposed multi-objective evolutionary algorithm with decision-making (MOEA-DM), which introduces the preference information of decision-maker to guide the evolution towards preferred region. Furthermore, the uncertainties and losses of wind power are captured by a data-driven forecast model. Finally, the results of case study show that: (1) the data-driven model performs higher accuracy in wind power forecast compared to commonly-used physical models; (2) The proposed MOEA-DM has better convergence, diversity and robustness performance in decision-maker's preferred region compared to widely-used Non-dominated Sorting Genetic Algorithm-II (NSGA-II); (3) Hybrid battery-thermal energy storage system achieves better economy and reliability through the optimal coordinated operation strategy compared to either single energy storage under different test conditions. (c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页码:776 / 790
页数:15
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