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| Bi-Level Collaborative Configuration Optimization of Integrated Community Energy System Considering Economy and Reliability |
| Bian Xiaoyan1, Shi Yueqi1, Pei Chuanxun2, Cui Yong3, Lin Shunfu1 |
1. Shanghai University of Electric Power Shanghai 200090 China;
2. State Grid Zhejiang Province Ningbo Power Supply Company Ningbo 315000 China;
3. State Grid Shanghai Municipal Electric Power Company Shanghai 200122 China |
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Abstract In order to consider the economy and reliability of integrated community energy system (ICES) in planning, in traditional planning approaches, the goal of minimizing the ICES total cost under constraints of load supply reliability was achieved. But the relationship between economy and reliability of ICES was ignored in traditional approaches, which had defects that the selectivity and practicability of configuration schemes were limited, and led to problems such as excessive investment or insufficient reliability. In this paper, a bi-level multi-objective ICES optimal configuration model considering economical and reliable optimization objectives was proposed. Planning and optimal operation of ICES were combined in the model, and time sequence characteristic of equipment states transition was considered. The model was divided into two levels: in the upper level, the minimum comprehensive cost and expected energy not serve (EENS) were obtained, and the NSGA-II algorithm was applied to obtain the Pareto optimal solutions of the configuration scheme; in the lower level, the configuration scheme obtained by the upper level was converted into linear constraints, then the loss of load capacity and operation cost were minimized to achieve the optimal operation of the system. Sequential Monte Carlo simulation method was applied to quantify the reliability, then the operation cost and quantified value of reliability were fed back to the upper level. To verify the proposed ICES planning approach, the configuration optimization of a microgrid-based ICES was investigated, and the relationship between the economics and reliability of different configuration schemes was analyzed. Finally, the selectivity of multi-objective optimization schemes was realized by giving the Pareto optimal solutions.
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Received: 15 September 2020
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2021, Vol. 36 
