基于Benders分解和纳什议价的分布式热电联合优化调度

doi:10.19595/j.cnki.1000-6753.tces.221155

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Abstract

Combined heat and power dispatch considering pipeline energy storage of district heating networks can improve the flexibility of electric power systems (EPSs) and promote a high proportion of renewable energy consumption. EPSs and district heating systems (DHSs) are operated by different entities. However, combined heat and power dispatch may deviate from the optimal scheduling strategy of DHSs. In other words, combined heat and power dispatch can harm individual interests when maximizing overall utility. Recently, some methods for combined heat and power optimal scheduling have been proposed, but most do not consider both privacy protection and incentive compatibility. To address these issues, the paper proposes a distributed optimal scheduling method for integrated electricity and heat systems (IEHSs) based on Benders decomposition and Nash bargaining.
First, DHSs and EPSs calculate optimal heat flow and power dispatch to obtain the respective total operating cost. Next, Benders decomposition is employed to calculate the optimal energy flow of IEHSs, and the total operating cost of IEHSs is derived accordingly. In addition, this paper strictly proves that combined heat and power optimal scheduling reduces the total operating cost of EPSs while increasing the total operating cost of DHSs. Finally, the cooperative surplus is redistributed through Nash bargaining to encourage DHSs to participate in cooperation, which decreases the total cost of EPSs and DHSs. The proposed method can achieve incentive compatibility and promote renewable energy accommodation while protecting information privacy.
The simulation results of the small-scale system show that combined heat and power dispatch considering pipeline energy storage reduces wind curtailment by 223.15 MW and increases the heat output of combined heat and power (CHP) units by 26.43 WM. The centralized scheduling of IEHSs requires disclosure of all information privacy, such as topology structure, operating status, and network parameters. In contrast, EPNs and DHSs only need to communicate with 115 feasibility or optimality cut plants in a distributed way based on Benders decomposition. The total cost of EPNs in the combined heat and power optimal scheduling is decreased by \$10 809 compared to the separate dispatch, but the total cost of DHSs is raised by \$787. As a result, DHSs have no incentive to cooperate with EPNs. To promote cooperation, EPNs and DHSs redistribute cooperative surplus through Nash bargaining. The overall cost of EPNs is lowered by \$5 011 compared to separate dispatch, and the total cost of DHNs is decreased by \$5 011 compared to separate dispatch. According to the simulation results for the large-scale system, EPNs and DHSs only need to interact with 2 765 feasibility or optimality cut plants instead of disclosing 10 440 constraints. The overall cost of EPNs and DHNs are both decreased by \$41 865 by sharing the cooperative surplus through Nash bargaining.
The following conclusions can be drawn from the simulation analysis: (1) Combined heat and power dispatch considering pipeline energy storage can improve the flexibility of EPSs and reduce wind curtailment. (2) Compared with centralized scheduling, the distributed optimal dispatch for IEHSs based on Benders decomposition can fully protect the information privacy between EPSs and DHSs. (3) The proposed IEHS decentralized dispatch method based on Nash bargaining can decrease the total cost of EPSs and DHSs, respectively.

Key words： Pipeline energy storage combined heat and power optimal scheduling Bender decomposition Nash bargaining incentive compatibility

Received: 18 June 2022

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TM732

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	Zhu Haohao
	Zhu Jizhong
	Li Shenglin
	Chen Ziyu
	Dong Hanjiang

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Zhu Haohao,Zhu Jizhong,Li Shenglin等. Distributed Combined Heat and Power Optimal Scheduling Based on Benders Decomposition and Nash Bargaining[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5808-5820.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.221155 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I21/5808

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