IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v213y2023icp205-217.html
   My bibliography  Save this article

Renewable energy effects on energy management based on demand response in microgrids environment

Author

Listed:
  • Yan, Zhongzhen
  • Zhu, Xinyuan
  • Chang, Yiming
  • Wang, Xianglong
  • Ye, Zhiwei
  • Xu, Zhigang
  • Fars, Ashk

Abstract

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications.

Suggested Citation

  • Yan, Zhongzhen & Zhu, Xinyuan & Chang, Yiming & Wang, Xianglong & Ye, Zhiwei & Xu, Zhigang & Fars, Ashk, 2023. "Renewable energy effects on energy management based on demand response in microgrids environment," Renewable Energy, Elsevier, vol. 213(C), pages 205-217.
    • Handle: RePEc:eee:renene:v:213:y:2023:i:c:p:205-217
    DOI: 10.1016/j.renene.2023.05.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123006742
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.05.051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    2. Huang, Shoujun & Abedinia, Oveis, 2021. "Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market," Energy, Elsevier, vol. 225(C).
    3. Wang, Yuwei & Tang, Liu & Yang, Yuanjuan & Sun, Wei & Zhao, Huiru, 2020. "A stochastic-robust coordinated optimization model for CCHP micro-grid considering multi-energy operation and power trading with electricity markets under uncertainties," Energy, Elsevier, vol. 198(C).
    4. Bhatti, Bilal Ahmad & Broadwater, Robert, 2020. "Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs," Energy, Elsevier, vol. 202(C).
    5. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N. & Burmester, Daniel, 2021. "Strategic design optimisation of multi-energy-storage-technology micro-grids considering a two-stage game-theoretic market for demand response aggregation," Applied Energy, Elsevier, vol. 287(C).
    6. Roy, Kallol & Mandal, Kamal Krishna & Mandal, Atis Chandra, 2019. "Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system," Energy, Elsevier, vol. 167(C), pages 402-416.
    7. Wu, Gang & Xiang, Yue & Liu, Junyong & Shen, Xiaodong & Cheng, Shikun & Hong, Bowen & Jawad, Shafqat, 2020. "Distributed energy-reserve Co-Optimization of electricity and natural gas systems with multi-type reserve resources," Energy, Elsevier, vol. 207(C).
    8. Wang, Han & Riaz, Shariq & Mancarella, Pierluigi, 2020. "Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization," Applied Energy, Elsevier, vol. 259(C).
    9. Zhou, Kaile & Wei, Shuyu & Yang, Shanlin, 2019. "Time-of-use pricing model based on power supply chain for user-side microgrid," Applied Energy, Elsevier, vol. 248(C), pages 35-43.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aslani, Mehrdad & Mashayekhi, Mehdi & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2022. "Robust optimal operation of energy hub incorporating integrated thermal and electrical demand response programs under various electric vehicle charging modes," Applied Energy, Elsevier, vol. 321(C).
    2. Yuanyuan, Zhang & Huiru, Zhao & Bingkang, Li, 2023. "Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties," Applied Energy, Elsevier, vol. 343(C).
    3. 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).
    4. Yang, Xiaohui & Wang, Xiaopeng & Leng, Zhengyang & Deng, Yeheng & Deng, Fuwei & Zhang, Zhonglian & Yang, Li & Liu, Xiaoping, 2023. "An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG," Renewable Energy, Elsevier, vol. 211(C), pages 307-325.
    5. Xian Huang & Wentong Ji & Xiaorong Ye & Zhangjie Feng, 2023. "Configuration Planning of Expressway Self-Consistent Energy System Based on Multi-Objective Chance-Constrained Programming," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    6. 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).
    7. Barik, Soumyabrata & Das, Debapriya, 2020. "A novel Q−PQV bus pair method of biomass DGs placement in distribution networks to maintain the voltage of remotely located buses," Energy, Elsevier, vol. 194(C).
    8. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    9. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    10. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    11. Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.
    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. Zhao, Huiru & Li, Bingkang & Lu, Hao & Wang, Xuejie & Li, Hongze & Guo, Sen & Xue, Wanlei & Wang, Yuwei, 2022. "Economy-environment-energy performance evaluation of CCHP microgrid system: A hybrid multi-criteria decision-making method," Energy, Elsevier, vol. 240(C).
    14. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.
    15. Jiang, Aihua & Yuan, Huihong & Li, Delong, 2021. "Energy management for a community-level integrated energy system with photovoltaic prosumers based on bargaining theory," Energy, Elsevier, vol. 225(C).
    16. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    17. Marek Stawowy & Adam Rosiński & Jacek Paś & Stanisław Duer & Marta Harničárová & Krzysztof Perlicki, 2023. "The Reliability and Exploitation Analysis Method of the ICT System Power Supply with the Use of Modelling Based on Rough Sets," Energies, MDPI, vol. 16(12), pages 1-18, June.
    18. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    19. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).
    20. Silvestri, Luca & De Santis, Michele, 2024. "Renewable-based load shifting system for demand response to enhance energy-economic-environmental performance of industrial enterprises," Applied Energy, Elsevier, vol. 358(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:213:y:2023:i:c:p:205-217. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.