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Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm

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  • Jiming Chen

    (College of New Energy, China University of Petroleum (East China), Qingdao 266580, China)

  • Ke Ning

    (College of New Energy, China University of Petroleum (East China), Qingdao 266580, China)

  • Xingzhi Xin

    (Electric Power Branch Company of Shengli Oilfield, SINOPEC, Dongying 257001, China)

  • Fuhao Shi

    (Public Service Center of Shengli Oilfield, SINOPEC, Dongying 257001, China)

  • Qing Zhang

    (Qingdao Ruinengda Electrical Technology Limited Company, Qingdao 266580, China)

  • Chaolin Li

    (Qingdao Ruinengda Electrical Technology Limited Company, Qingdao 266580, China)

Abstract

The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs is an ideal method to solve this problem. In this study, a day-ahead optimal scheduling model for IES that included an electrical system, a natural gas system, and an energy hub (EH), was established. The proposed EH contained detailed models of the fuel cell (FC) and power to gas (P2G) system. Considering that the optimal scheduling of an IES is a non-convex complex optimal problem, a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on multistage chaotic mapping was proposed to solve it. The objective was to minimize the operating cost of the IES. Three operation scenarios were designed to analyze the operation characteristics of the system under different coupling conditions. The simulation results showed that the PCAPSO algorithm improved the convergence rate and stability compared to the original PSO. An analysis of the results demonstrated the economics of an IES with the proposed EHs and the advantage of cooperation between the FC and P2G system.

Suggested Citation

  • Jiming Chen & Ke Ning & Xingzhi Xin & Fuhao Shi & Qing Zhang & Chaolin Li, 2022. "Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:690-:d:727468
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    References listed on IDEAS

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    1. Hoseinzadeh, Siamak & Ghasemi, Mohammad Hadi & Heyns, Stephan, 2020. "Application of hybrid systems in solution of low power generation at hot seasons for micro hydro systems," Renewable Energy, Elsevier, vol. 160(C), pages 323-332.
    2. Tomin, Nikita & Shakirov, Vladislav & Kozlov, Aleksander & Sidorov, Denis & Kurbatsky, Victor & Rehtanz, Christian & Lora, Electo E.S., 2022. "Design and optimal energy management of community microgrids with flexible renewable energy sources," Renewable Energy, Elsevier, vol. 183(C), pages 903-921.
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    Cited by:

    1. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
    2. Tingling Wang & Tianyu Huo & Huihang Li, 2024. "Bi-Layer Planning of Integrated Energy System by Incorporating Power-to-Gas and Ground Source Heat Pump for Curtailed Wind Power and Economic Cost Reduction," Energies, MDPI, vol. 17(6), pages 1-22, March.

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