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Optimal scheduling of multiple multi-energy supply microgrids considering future prediction impacts based on model predictive control

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  • Li, Bei
  • Roche, Robin

Abstract

Renewable energy based multi-energy supply microgrids not only can cover different types of demands (such as, electricity/heat/gas), but also can interconnect with different utility grid networks (electricity/heat/gas). When there are large numbers of grid-connected microgrids, how to operate these multiple microgrids in real-time is a problem. In this paper, day-ahead stochastic optimization scheduling and real-time sliding window model predictive control are used to control the operation of microgrids. In order to consider the influence of future prediction on the current optimal decision results, different prediction methods are adopted to predict the load demands and renewable energy output. For example, online learning Markov chain prediction, and support vector machine are used to predict the future values. As for comparison, robust prediction and bilevel optimization are adopted to describe the future prediction uncertainty. The real-time operation of microgrids aims to follow the day-ahead exchanged energy with utility grids, which can minimize the impact of the microgrid on the utility grids. The supply network is an IEEE30 + gas20+heat14 network. The results show that: 1) when the sliding window number is smaller, the total operation cost is larger, but the calculation time is smaller, the trade-off between sliding window numbers and calculation time should be considered; 2) the accuracy of the prediction impacts the 2-norm error of the operation cost, when we decrease by “1” unit of 2-norm prediction error of the whole system, the 2-norm operation cost will decrease by “0.15” unit; 3) from the view of the post-event analysis (total operation cost), for the Markov chain prediction method, the relative error is about 0.32%, is better than the support vector machine method; 4) in the robust cases, the larger the conservative value, the higher the stored hydrogen energy. At last, the results of real-time sliding window model predictive control problem are influenced by the future prediction methods and the window numbers.

Suggested Citation

  • Li, Bei & Roche, Robin, 2020. "Optimal scheduling of multiple multi-energy supply microgrids considering future prediction impacts based on model predictive control," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220302875
    DOI: 10.1016/j.energy.2020.117180
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    as
    1. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    2. Petrollese, Mario & Valverde, Luis & Cocco, Daniele & Cau, Giorgio & Guerra, José, 2016. "Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid," Applied Energy, Elsevier, vol. 166(C), pages 96-106.
    3. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    4. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    5. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    6. Elsied, Moataz & Oukaour, Amrane & Youssef, Tarek & Gualous, Hamid & Mohammed, Osama, 2016. "An advanced real time energy management system for microgrids," Energy, Elsevier, vol. 114(C), pages 742-752.
    7. Meng, Lexuan & Sanseverino, Eleonora Riva & Luna, Adriana & Dragicevic, Tomislav & Vasquez, Juan C. & Guerrero, Josep M., 2016. "Microgrid supervisory controllers and energy management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1263-1273.
    8. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2017. "Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation," Applied Energy, Elsevier, vol. 205(C), pages 1244-1259.
    9. Shams, Mohammad H. & Shahabi, Majid & Khodayar, Mohammad E., 2018. "Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response," Energy, Elsevier, vol. 155(C), pages 326-338.
    10. Shams, Mohammad H. & Shahabi, Majid & Kia, Mohsen & Heidari, Alireza & Lotfi, Mohamed & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Optimal operation of electrical and thermal resources in microgrids with energy hubs considering uncertainties," Energy, Elsevier, vol. 187(C).
    11. Gorissen, Bram L. & Yanıkoğlu, İhsan & den Hertog, Dick, 2015. "A practical guide to robust optimization," Omega, Elsevier, vol. 53(C), pages 124-137.
    12. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2018. "Optimal sizing of distributed generation in gas/electricity/heat supply networks," Energy, Elsevier, vol. 151(C), pages 675-688.
    13. Xie, Shanshan & He, Hongwen & Peng, Jiankun, 2017. "An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 196(C), pages 279-288.
    14. Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    15. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2019. "A copula-based method to consider uncertainties for multi-objective energy management of microgrid in presence of demand response," Energy, Elsevier, vol. 175(C), pages 879-890.
    16. DE WOLF, Daniel & SMEERS, Yves, 2000. "The gas transmission problem solved by an extension of the simplex algorithm," LIDAM Reprints CORE 1489, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Daniel De Wolf & Yves Smeers, 2000. "The Gas Transmission Problem Solved by an Extension of the Simplex Algorithm," Management Science, INFORMS, vol. 46(11), pages 1454-1465, November.
    Full references (including those not matched with items on IDEAS)

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    7. Maryam Khanbaghi & Aleksandar Zecevic, 2020. "Jump Linear Quadratic Control for Microgrids with Commercial Loads," Energies, MDPI, vol. 13(19), pages 1-21, September.
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    11. Zhou, Yuekuan, 2023. "Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation," Renewable Energy, Elsevier, vol. 202(C), pages 1324-1341.
    12. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    13. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
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