Research on Two-stage Optimization Approach of Community Integrated Energy System Considering Load Supply Reliability

doi:10.12096/j.2096-4528.pgt.21110

Abstract

Abstract:

The community integrated energy system (CIES) can significantly improve energy utilization and promote the consumption of renewable energy through multi-energy coupling complementary and collaborative optimization scheduling. It has become a new energy utilization realization approach for users to meet multi-energy supply and demand. Taking a community in Xiong’an New District, Hebei province as the research object, this paper designed a two-stage optimization approach for the CIES that takes into account the reliability of load supply. The first stage is based on the non-dominated sorting genetic algorithm II(NSGA-II) with elite preservation strategy to optimize the equipment type and capacity of the community energy station. It is a multi-objective planning optimization problem, and its purpose is to achieve the coordinated optimization of economic costs and environmental costs. The second stage is an operation optimization problem. As for the multiple Pareto frontier solutions obtained in the previous planning stage, the mixed integer linear programming (MILP) was used to separately optimize the operation cost and load supply reliability indicators of each planning scheme, and the result is used as an important reference for determining the best planning scheme. Case studies show that the designed planning approach can effectively reduce the system operating cost and guarantee the reliability of load supply, and it is more practical for instructing the CIES planning.

Key words: community integrated energy system, planning optimization, load supply reliability, non-dominated sorting genetic algorithm II (NSGA-II), mixed integer linear programming

CLC Number:

TK 01

Meng KANG, Yiqing ZHONG, Xin SHI, Gangcheng WEN, Fang FANG. Research on Two-stage Optimization Approach of Community Integrated Energy System Considering Load Supply Reliability[J]. Power Generation Technology, 2023, 44(1): 25-35.

Figures/Tables 14

Fig. 1 Structure diagram of community energy supply in the heating period

Fig. 2 System optimization flow chart in the community heating period

Fig. 3 Flow chart of model solving algorithm

Tab. 1 Candidate devices in different scenarios

场景	CHP	PV	WT	EB	GSHP	GB	ES	HS
1	√	√	√	√	√	√	√	√
2	√	—	—	—	—	√	—	√
3	√	√	—	√	—	—	√	—

Fig. 4 Iterative convergence process of NSGA-II algorithm

Fig. 5 Pareto frontier solution of the first-stage planning model

Tab. 2 Capacity allocation of different planning schemes in the first stage

方案	容量/kW								年经济成本/万元	年环境成本/万元
方案	CHP	PV	WT	EB	GSHP	GB	ES	HS	年经济成本/万元	年环境成本/万元
1	0	1 000	0	0	1 100	3 300	900	0	1 321	152
2	1 500	1 000	400	0	3 300	0	2 200	1 000	2 614	115
3	3 600	1 000	400	0	3 500	0	2 800	4 000	3 985	108

Tab. 3 Typical daily operating costs of different planning

方案	电交易成本	系统运维成本	污染物排放成本	总运行成本
1	11.11	0.09	0.37	11.65
2	8.31	0.12	0.26	8.69
3	8.68	0.15	0.21	9.04

Fig. 6 Typical daily load deviation rate of different planning schemes

Tab. 4 Average load deviation rate of different planning schemes

方案	平均负荷偏差率/%
1	1.98
2	1.24
3	2.31

Tab. 5 Capacity configuration of the best planning scheme in different scenarios

场景	容量/kW								年经济成本/万元	年环境成本/万元
场景	CHP	PV	WT	EB	GSHP	GB	ES	HS	年经济成本/万元	年环境成本/万元
1	1 500	1 000	400	0	3 300	0	2 200	1 000	2 614	115
2	5 400	0	0	0	0	2 300	0	5 000	4 140	148
3	8 800	1 000	0	5 400	0	0	2 800	0	3 824	144

Tab. 6 Typical daily operating costs of the best planning

场景	电交易成本	系统运维成本	污染物排放成本	总运行成本
1	8.31	0.12	0.26	8.69
2	9.62	0.22	0.87	10.71
3	9.56	0.19	0.63	10.38

Fig. 7 Typical daily load deviation rate of the best planning scheme in different scenarios

Tab. 7 Average load deviation rate of the best planning scheme in different scenarios

场景	平均负荷偏差率/%
1	1.24
2	1.65
3	1.67

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