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Simultaneous community energy supply-demand optimization by microgrid operation scheduling optimization and occupant-oriented flexible energy-use regulation

Author

Listed:
  • Zhang, Chengyu
  • Rezgui, Yacine
  • Luo, Zhiwen
  • Jiang, Ben
  • Zhao, Tianyi

Abstract

Community microgrids represent a pivotal solution for addressing energy conservation and reducing carbon emissions. However, few studies focused on the methods of concurrently optimizing both the supply and demand sides within campus settings, a unique form of community. Moreover, few studies on energy demand optimization rarely delved into human-centered approaches and employed multi-strategy parallel flexible regulations, while few studies on energy supply optimization discussed the comprehensive environment-energy-society-investment benefits as optimization objectives with suitable algorithms for calculation. Consequently, this study introduced a novel approach to energy system supply-demand optimization by simultaneous flexible energy-use regulation and microgrid operation scheduling, to explore human-centered multi-strategy parallel flexible energy-use regulations, and comprehensive evaluation parameters for energy supply-demand optimization. (1) Regarding flexible energy-use regulation, this study proposed several adjustments including adjusting the start-up temperature of air conditioners (ACs), organized charging of electric vehicles (EVs), light or plug management, and course optimization. These parallel adjustments can reduce or shift 12.12% of energy demand during peak hours and 8.06% during sharp hours, yielding 4.449%–8.820% of the annual average benefits in different cases. (2) Regarding microgrid optimization scheduling, this study introduced novel objectives aimed at maximizing investment-environment-energy-society benefits and solving algorithms, the jellyfish search algorithm (JSA), for calculation. Results showed 10.085%–11.728% of the annual average benefits in different cases. Ultimately, the optimization integration of flexible energy-use regulations and microgrid operation scheduling yields 15.655%–18.016% of the annual average benefits in different cases.

Suggested Citation

  • Zhang, Chengyu & Rezgui, Yacine & Luo, Zhiwen & Jiang, Ben & Zhao, Tianyi, 2024. "Simultaneous community energy supply-demand optimization by microgrid operation scheduling optimization and occupant-oriented flexible energy-use regulation," Applied Energy, Elsevier, vol. 373(C).
    • Handle: RePEc:eee:appene:v:373:y:2024:i:c:s0306261924013059
    DOI: 10.1016/j.apenergy.2024.123922
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    References listed on IDEAS

    as
    1. Guo, Tianyu & Guo, Qi & Huang, Libin & Guo, Haiping & Lu, Yuanhong & Tu, Liang, 2023. "Microgrid source-network-load-storage master-slave game optimization method considering the energy storage overcharge/overdischarge risk," Energy, Elsevier, vol. 282(C).
    2. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Zaman, Forhad & Guerrero, Josep M., 2019. "Energy scheduling of community microgrid with battery cost using particle swarm optimisation," Applied Energy, Elsevier, vol. 254(C).
    3. Wu, Yan & Aziz, Syed Mahfuzul & Haque, Mohammed H., 2023. "Techno-economic modelling for energy cost minimisation of a university campus to support electric vehicle charging with photovoltaic capacity optimisation," Renewable Energy, Elsevier, vol. 219(P1).
    4. Zhang, Chengyu & Ma, Liangdong & Han, Xing & Zhao, Tianyi, 2024. "Reconstituted data-driven air conditioning energy consumption prediction system employing occupant-orientated probability model as input and swarm intelligence optimization algorithms," Energy, Elsevier, vol. 288(C).
    5. Tayyab, Qudratullah & Qani, Nazir Ahmad & Elkholy, M.H. & Ahmed, Shoaib & Yona, Atsushi & Senjyu, Tomonobu, 2024. "Techno-economic configuration of an optimized resident microgrid: A case study for Afghanistan," Renewable Energy, Elsevier, vol. 224(C).
    6. Li, Ling-Ling & Ji, Bing-Xiang & Liu, Guan-Chen & Yuan, Jian-Ping & Tseng, Shuan-Wei & Lim, Ming K. & Tseng, Ming-Lang, 2024. "Grid-connected multi-microgrid system operational scheduling optimization: A hierarchical improved marine predators algorithm," Energy, Elsevier, vol. 294(C).
    7. Dong, Haiyan & Fu, Yanbo & Jia, Qingquan & Zhang, Tie & Meng, Dequn, 2023. "Low carbon optimization of integrated energy microgrid based on life cycle analysis method and multi time scale energy storage," Renewable Energy, Elsevier, vol. 206(C), pages 60-71.
    8. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).
    9. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    10. Yan, Laiqing & Deng, Xiwei & Li, Ji, 2024. "Integrated energy hub optimization in microgrids: Uncertainty-aware modeling and efficient operation," Energy, Elsevier, vol. 291(C).
    11. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    12. Homan, Bart & ten Kortenaar, Marnix V. & Hurink, Johann L. & Smit, Gerard J.M., 2019. "A realistic model for battery state of charge prediction in energy management simulation tools," Energy, Elsevier, vol. 171(C), pages 205-217.
    13. Pang, Kexin & Zhou, Jian & Tsianikas, Stamatis & Coit, David W. & Ma, Yizhong, 2024. "Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    14. Fotouhi, Abbas & Auger, Daniel J. & Propp, Karsten & Longo, Stefano & Wild, Mark, 2016. "A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1008-1021.
    15. Garmabdari, R. & Moghimi, M. & Yang, F. & Lu, J., 2020. "Multi-objective optimisation and planning of grid-connected cogeneration systems in presence of grid power fluctuations and energy storage dynamics," Energy, Elsevier, vol. 212(C).
    16. Lei, Lei & Shao, Suola & Liang, Lixia, 2024. "An evolutionary deep learning model based on EWKM, random forest algorithm, SSA and BiLSTM for building energy consumption prediction," Energy, Elsevier, vol. 288(C).
    17. Mota, Bruno & Faria, Pedro & Vale, Zita, 2024. "Energy cost optimization through load shifting in a photovoltaic energy-sharing household community," Renewable Energy, Elsevier, vol. 221(C).
    18. Husham Muayad Nayyef & Ahmad Asrul Ibrahim & Muhammad Ammirrul Atiqi Mohd Zainuri & Mohd Asyraf Zulkifley & Hussain Shareef, 2023. "A Novel Hybrid Algorithm Based on Jellyfish Search and Particle Swarm Optimization," Mathematics, MDPI, vol. 11(14), pages 1-29, July.
    19. Zhang, Guodao & Ge, Yisu & Pan, Xiaotian & Zheng, Yun & Yang, Yanhong, 2023. "Hybrid robust-stochastic multi-objective optimization of combined cooling, heating, hydrogen and power-based microgrids," Energy, Elsevier, vol. 274(C).
    20. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
    21. Barua, Sujoy & Merabet, Adel & Al-Durra, Ahmed & El-Fouly, Tarek & El-Saadany, Ehab F., 2024. "Lévy arithmetic optimization for energy Management of Solar Wind Microgrid with multiple diesel generators for off-grid communities," Applied Energy, Elsevier, vol. 371(C).
    22. Lei, Lei & Wu, Bing & Fang, Xin & Chen, Li & Wu, Hao & Liu, Wei, 2023. "A dynamic anomaly detection method of building energy consumption based on data mining technology," Energy, Elsevier, vol. 263(PA).
    23. Yang, Wenqiang & Zhu, Xinxin & Xiao, Qinge & Yang, Zhile, 2023. "Enhanced multi-objective marine predator algorithm for dynamic economic-grid fluctuation dispatch with plug-in electric vehicles," Energy, Elsevier, vol. 282(C).
    24. Zhang, Chengyu & Ma, Liangdong & Luo, Zhiwen & Han, Xing & Zhao, Tianyi, 2024. "Forecasting building plug load electricity consumption employing occupant-building interaction input features and bidirectional LSTM with improved swarm intelligent algorithms," Energy, Elsevier, vol. 288(C).
    25. Liu, Bingchun & Song, Jiangji & Wang, Qingshan & Xu, Yan & Liu, Yifan, 2023. "Charging station forecasting and scenario analysis in China," Transport Policy, Elsevier, vol. 139(C), pages 87-98.
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