IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3356-d1120440.html
   My bibliography  Save this article

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Author

Listed:
  • Jianying Li

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Minsheng Yang

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Yuexing Zhang

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianqi Li

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

  • Jianquan Lu

    (School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China
    Key Laboratory of Hunan Province for Control Technology of Distributed Electric Propulsion Air Vehicle, Changde 415000, China)

Abstract

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

Suggested Citation

  • Jianying Li & Minsheng Yang & Yuexing Zhang & Jianqi Li & Jianquan Lu, 2023. "Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-15, April.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3356-:d:1120440
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3356/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/8/3356/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ono, Hitoi & Ohtani, Yuichi & Matsuo, Minoru & Yamaguchi, Toru & Yokoyama, Ryohei, 2021. "Optimal operation of heat source and air conditioning system with thermal storage tank using nonlinear programming," Energy, Elsevier, vol. 222(C).
    2. Das, Ridoy & Wang, Yue & Putrus, Ghanim & Kotter, Richard & Marzband, Mousa & Herteleer, Bert & Warmerdam, Jos, 2020. "Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services," Applied Energy, Elsevier, vol. 257(C).
    3. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    4. Julian Garcia-Guarin & David Alvarez & Arturo Bretas & Sergio Rivera, 2020. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints," Energies, MDPI, vol. 13(17), pages 1-19, September.
    5. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    6. Karimi, Hamid & Jadid, Shahram, 2020. "Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework," Energy, Elsevier, vol. 195(C).
    7. Yang, Zaoli & Li, Qin & Yan, Yamin & Shang, Wen-Long & Ochieng, Washington, 2022. "Examining influence factors of Chinese electric vehicle market demand based on online reviews under moderating effect of subsidy policy," Applied Energy, Elsevier, vol. 326(C).
    8. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
    9. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources," Energy, Elsevier, vol. 239(PB).
    10. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    11. Gomes, I.L.R. & Melicio, R. & Mendes, V.M.F., 2021. "A novel microgrid support management system based on stochastic mixed-integer linear programming," Energy, Elsevier, vol. 223(C).
    12. Hou, Hui & Xue, Mengya & Xu, Yan & Xiao, Zhenfeng & Deng, Xiangtian & Xu, Tao & Liu, Peng & Cui, Rongjian, 2020. "Multi-objective economic dispatch of a microgrid considering electric vehicle and transferable load," Applied Energy, Elsevier, vol. 262(C).
    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. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
    2. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    3. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    4. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    5. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).
    6. Tan, Zhongfu & Wang, Guan & Ju, Liwei & Tan, Qingkun & Yang, Wenhai, 2017. "Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand r," Energy, Elsevier, vol. 124(C), pages 198-213.
    7. Kong, Xiangyu & Xiao, Jie & Wang, Chengshan & Cui, Kai & Jin, Qiang & Kong, Deqian, 2019. "Bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant," Applied Energy, Elsevier, vol. 249(C), pages 178-189.
    8. Kim, Seokwoo & Choi, Dong Gu, 2024. "A sample robust optimal bidding model for a virtual power plant," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1101-1113.
    9. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.
    10. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    11. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
    12. Zandrazavi, Seyed Farhad & Guzman, Cindy Paola & Pozos, Alejandra Tabares & Quiros-Tortos, Jairo & Franco, John Fredy, 2022. "Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles," Energy, Elsevier, vol. 241(C).
    13. Kalim Ullah & Quanyuan Jiang & Guangchao Geng & Rehan Ali Khan & Sheraz Aslam & Wahab Khan, 2022. "Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses," Energies, MDPI, vol. 15(9), pages 1-22, April.
    14. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    15. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    16. Yao Wang & Yan Lu & Liwei Ju & Ting Wang & Qingkun Tan & Jiawei Wang & Zhongfu Tan, 2019. "A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism," Energies, MDPI, vol. 12(3), pages 1-28, January.
    17. Li, Qiang & Wei, Fanchao & Zhou, Yongcheng & Li, Jiajia & Zhou, Guowen & Wang, Zhonghao & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2023. "A scheduling framework for VPP considering multiple uncertainties and flexible resources," Energy, Elsevier, vol. 282(C).
    18. Amit Kumer Podder & Sayemul Islam & Nallapaneni Manoj Kumar & Aneesh A. Chand & Pulivarthi Nageswara Rao & Kushal A. Prasad & T. Logeswaran & Kabir A. Mamun, 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants," Energies, MDPI, vol. 13(23), pages 1-46, November.
    19. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    20. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(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:gam:jeners:v:16:y:2023:i:8:p:3356-:d:1120440. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.