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Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimization Model for Stand-Alone Microgrid Operation

Author

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  • Fei Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
    Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana 61801, IL, USA)

  • Lidong Zhou

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Hui Ren

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)

  • Xiaoli Liu

    (Shuozhou Power Company of State Grid Shanxi Electric Power Company, Shuozhou 036000, China)

Abstract

The optimal dispatching model for a stand-alone microgrid (MG) is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditions as storms or abnormally low or high temperatures. A new two-time scale multi-objective optimization model, including day-ahead cursory scheduling and real-time scheduling for finer adjustments, is proposed to optimize the operational cost, load shedding compensation and environmental benefit of stand-alone MG through controllable load (CL) and multi-distributed generations (DGs). The main novelty of the proposed model is that the synergetic response of CL and energy storage system (ESS) in real-time scheduling offset the operation uncertainty quickly. And the improved dispatch strategy for combined cooling-heating-power (CCHP) enhanced the system economy while the comfort is guaranteed. An improved algorithm, Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy (SIP-CO-PSO-ERS) algorithm with strong searching capability and fast convergence speed, was presented to deal with the problem brought by the increased errors between actual renewable generation and load and prior predictions. Four typical scenarios are designed according to the combinations of day types (work day or weekend) and weather categories (sunny or rainy) to verify the performance of the presented dispatch strategy. The simulation results show that the proposed two-time scale model and SIP-CO-PSO-ERS algorithm exhibit better performance in adaptability, convergence speed and search ability than conventional methods for the stand-alone MG’s operation.

Suggested Citation

  • Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1936-:d:120111
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    Cited by:

    1. Lizhi Zhang & Fan Li & Bo Sun & Chenghui Zhang, 2019. "Integrated Optimization Design of Combined Cooling, Heating, and Power System Coupled with Solar and Biomass Energy," Energies, MDPI, vol. 12(4), pages 1-21, February.
    2. Yongli Wang & Yujing Huang & Yudong Wang & Haiyang Yu & Ruiwen Li & Shanshan Song, 2018. "Energy Management for Smart Multi-Energy Complementary Micro-Grid in the Presence of Demand Response," Energies, MDPI, vol. 11(4), pages 1-19, April.
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Muhammad Yousif & Qian Ai & Yang Gao & Waqas Ahmad Wattoo & Ziqing Jiang & Ran Hao, 2018. "Application of Particle Swarm Optimization to a Scheduling Strategy for Microgrids Coupled with Natural Gas Networks," Energies, MDPI, vol. 11(12), pages 1-16, December.
    5. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    6. Jiyuan Kuang & Chenghui Zhang & Fan Li & Bo Sun, 2018. "Dynamic Optimization of Combined Cooling, Heating, and Power Systems with Energy Storage Units," Energies, MDPI, vol. 11(9), pages 1-16, August.
    7. Sylwia Sysko-Romańczuk & Grzegorz Kluj & Liliana Hawrysz & Łukasz Rokicki & Sylwester Robak, 2021. "Scalable Microgrid Process Model: The Results of an Off-Grid Household Experiment," Energies, MDPI, vol. 14(21), pages 1-26, November.
    8. Jinming Jiang & Xindong Wei & Weijun Gao & Soichiro Kuroki & Zhonghui Liu, 2018. "Reliability and Maintenance Prioritization Analysis of Combined Cooling, Heating and Power Systems," Energies, MDPI, vol. 11(6), pages 1-24, June.

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