Integrated Energy System Optimization Considering EnergyCommunities with Prosumers

doi:10.11930/j.issn.1004-9649.202209073

Abstract

Abstract:

The rapid development of distributed renewable energy gradually transforms energy users to prosumers. Energy communities coordinate the local energy distribution and help users to trade energy, which provides a new direction for energy systems to improve its efficiency and reduce energy cost. Most existing researches either focus on integrated energy systems (IES) design but without considering energy trading among users, or focus on energy trading but without consideration of meeting the users’ multi-energy demand. Therefore, this study proposes an optimization approach for IES considering energy communities with prosumers. Taking the integrated energy service providers and uses as different stakeholders, an energy community trading model is established based on the bid-response mechanism, and the optimal design and operation schemes of IES are studied with the aim to maximize economic benefits. The case study results indicate that users can efficiently shift the peak demand through energy trading, and reduce the energy cost and gain profits by trading within the energy community. Meanwhile, the prosumers within energy communities can reduce their dependence on the upper-level energy system through energy trading and coordination, subsequently mitigating the impact of local renewable energy generation on the upper-level energy system.

Key words: integrated energy systems, energy community, prosumer, energy trading, modelling and optimization

Jiguang KANG, Jiehua JU, Yanmin ZHAO, Yutong ZHAO, Xuetao BAI, Yingru ZHAO, Rui JING. Integrated Energy System Optimization Considering EnergyCommunities with Prosumers[J]. Electric Power, 2023, 56(11): 206-216.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202209073

https://www.electricpower.com.cn/EN/Y2023/V56/I11/206

Figures/Tables 12

Fig.1 Schematic diagram of IES considering energy communities with prosumers

Fig.2 The energy trading procedure within energy communities

Fig.3 Technology options for integrated energy service providers

Table 1 Environmental parameters

碳排放因子/(kg·(kW·h)^–1)
天然气		电网电能
0.18		0.94

Table 2 Major technical parameters

热电联产机组		燃气锅炉	热泵
发电效率/%	热电比	热效率/%	性能系数
42	0.9	90	4.5

电制冷机组	吸收式制冷机组	电池储能系统
性能系数	性能系数	充电效率	放电效率
3.0	0.9	0.98	0.98

Table 3 Equipment investment and operation & maintenance cost

设备	投资成本/(元·kW^–1)	年维护成本/（元·kW^–1）
热电联产机组	4900	0.03250
燃气锅炉	800	0.00195
热泵	1200	0.00800
电制冷机组	1000	0.00200
吸收式制冷机组	1500	0.00130
光伏系统	4000	0.01200
电池储能系统	1800	0.00230

Table 4 Time of use tariff for purchasing and selling electricity

时段	购电价格/(元·(kW·h)^–1)	售电价格/(元·(kW·h)^–1)
00:00—06:00	0.39	0.35
06:00—08:00	0.75	0.52
08:00—11:00	0.98	0.52
11:00—15:00	1.16	0.62
15:00—18:00	0.98	0.52
18:00—22:00	1.16	0.62
22:00—24:00	0.39	0.35

Fig.4 The scenario tree with 16 energy demand scenarios

Fig.5 Electricity demand, PV output and state of charge before & after energy trading in a typical scenario and corresponding hourly electricity selling price, electricity purchasing price & intra-community electricity trading price

Fig.6 Optimal installed capacities of IES in different modes

Fig.7 The electricity, heating and cooling balance under different scenarios

Fig.8 The annual total cost and annual total carbon emissions of users and integrated energy service providers under different modes

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