IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v271y2020ics030626192030667x.html
   My bibliography  Save this article

Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response

Author

Listed:
  • Ju, Liwei
  • Wu, Jing
  • Lin, Hongyu
  • Tan, Qinliang
  • Li, Gen
  • Tan, Zhongfu
  • Li, Jiayu

Abstract

A new two-stage demand response is designed for the electricity retailers with energy storage system (ESS-ER) in the deregulated power market. The ESS-ER could response to the output of different power sources by adjusting the charging-discharging behavior according to the bidding power price. The paper models the two-stage demand response for electric power retailers and proposed a two-layer coordinated optimal model for the purchase and sale of the electric power retailers. In the upper layer model, the conditional value at risk method and robust stochastic theory are applied to describe the uncertainty influence of wind power and Photovoltaic (PV) power, and the minimum whole cost of power purchasing is taken as the objective. In the lower-layer, the power consumption behaviors of different customers are considered to get the maximum revenue of power selling by implementing differentiated demand response. Then, to solve the two-layer mathematical model, the lower-layer model is converted into the Karush-Kuhn-Tucker (KKT) optimality conditions. The results show that: (1) The two-stage demand response could smooth the curves of power purchasing and terminal users’ load, which could bring more flexible transaction space. (2) The proposed two-layer transaction model could balance the cost and risk of power purchasing, bringing more trading opportunities for wind power and PV, which can also reduce the energy consumption cost of the end-users. (3) By introducing the risk cost coefficient, confidence degree and robust coefficient, the decision-makers can adjust the power trading behaviors, and establish the optimal power trading scheme in line with their expected situation. (4) When higher energy storage capacity is set, the efficiency of demand response rises. When the capacity ratio of wind to energy storage is 4:1, the efficiency of demand response reaches the best. When larger energy storage capacity is set, the demand response turns to be more effective. However, when the capacity ratio of wind and PV to energy storage is 4:1, the effect of demand response reaches the best. Overall, the proposed model could provide an effective tool for power retailers in China's electric power market.

Suggested Citation

  • Ju, Liwei & Wu, Jing & Lin, Hongyu & Tan, Qinliang & Li, Gen & Tan, Zhongfu & Li, Jiayu, 2020. "Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response," Applied Energy, Elsevier, vol. 271(C).
  • Handle: RePEc:eee:appene:v:271:y:2020:i:c:s030626192030667x
    DOI: 10.1016/j.apenergy.2020.115155
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192030667X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2020.115155?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. Meng, Ming & Mander, Sarah & Zhao, Xiaoli & Niu, Dongxiao, 2016. "Have market-oriented reforms improved the electricity generation efficiency of China's thermal power industry? An empirical analysis," Energy, Elsevier, vol. 114(C), pages 734-741.
    2. She, Zhen-Yu & Meng, Gang & Xie, Bai-Chen & O'Neill, Eoghan, 2020. "The effectiveness of the unbundling reform in China’s power system from a dynamic efficiency perspective," Applied Energy, Elsevier, vol. 264(C).
    3. 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.
    4. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    5. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    6. Boroumand, Raphaël-Homayoun & Goutte, Stéphane & Guesmi, Khaled & Porcher, Thomas, 2019. "Potential benefits of optimal intra-day electricity hedging for the environment: The perspective of electricity retailers," Energy Policy, Elsevier, vol. 132(C), pages 1120-1129.
    7. Peng, Xu & Tao, Xiaoma, 2018. "Cooperative game of electricity retailers in China's spot electricity market," Energy, Elsevier, vol. 145(C), pages 152-170.
    8. Fotouhi Ghazvini, Mohammad Ali & Faria, Pedro & Ramos, Sergio & Morais, Hugo & Vale, Zita, 2015. "Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market," Energy, Elsevier, vol. 82(C), pages 786-799.
    9. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    10. Lynch, Muireann Á. & Nolan, Sheila & Devine, Mel T. & O’Malley, Mark, 2019. "The impacts of demand response participation in capacity markets," Applied Energy, Elsevier, vol. 250(C), pages 444-451.
    11. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
    12. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).
    13. Ihsan, Abbas & Jeppesen, Matthew & Brear, Michael J., 2019. "Impact of demand response on the optimal, techno-economic performance of a hybrid, renewable energy power plant," Applied Energy, Elsevier, vol. 238(C), pages 972-984.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong, Jun & Jiang, Yuzheng & Liu, Dongran & Dou, Xihao & Liu, Yao & Peng, Shicheng, 2022. "Promoting dynamic pricing implementation considering policy incentives and electricity retailers’ behaviors: An evolutionary game model based on prospect theory," Energy Policy, Elsevier, vol. 167(C).
    2. Ma, Mingtao & Huang, Huijun & Song, Xiaoling & Peña-Mora, Feniosky & Zhang, Zhe & Chen, Jie, 2022. "Optimal sizing and operations of shared energy storage systems in distribution networks: A bi-level programming approach," Applied Energy, Elsevier, vol. 307(C).
    3. Wanting Yu & Xin Zhang & Mingli Cui & Tiantian Feng, 2024. "Construction and Application of the Double Game Model for Direct Purchase of Electricity by Large Consumers under Consideration of Risk Factors," Energies, MDPI, vol. 17(8), pages 1-24, April.
    4. Mohammad Hossein Nejati Amiri & Mehdi Mehdinejad & Amin Mohammadpour Shotorbani & Heidarali Shayanfar, 2023. "Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model," Energies, MDPI, vol. 16(3), pages 1-21, January.
    5. Ju, Liwei & Lu, Xiaolong & Yang, Shenbo & Li, Gen & Fan, Wei & Pan, Yushu & Qiao, Huiting, 2022. "A multi-time scale dispatching optimal model for rural biomass waste energy conversion system-based micro-energy grid considering multi-energy demand response," Applied Energy, Elsevier, vol. 327(C).
    6. Artur Felipe da Silva Veloso & José Valdemir Reis Júnior & Ricardo de Andrade Lira Rabelo & Jocines Dela-flora Silveira, 2021. "HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service," Future Internet, MDPI, vol. 13(11), pages 1-45, October.
    7. Zare Oskouei, Morteza & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Shafiee, Mahmood & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "A hybrid robust-stochastic approach to evaluate the profit of a multi-energy retailer in tri-layer energy markets," Energy, Elsevier, vol. 214(C).
    8. Qiu, Dawei & Wang, Yi & Wang, Junkai & Jiang, Chuanwen & Strbac, Goran, 2023. "Personalized retail pricing design for smart metering consumers in electricity market," Applied Energy, Elsevier, vol. 348(C).
    9. Fan, Wei & Tan, Qingbo & Zhang, Amin & Ju, Liwei & Wang, Yuwei & Yin, Zhe & Li, Xudong, 2023. "A Bi-level optimization model of integrated energy system considering wind power uncertainty," Renewable Energy, Elsevier, vol. 202(C), pages 973-991.
    10. Tianlei Zang & Shijun Wang & Zian Wang & Chuangzhi Li & Yunfei Liu & Yujian Xiao & Buxiang Zhou, 2024. "Integrated Planning and Operation Dispatching of Source–Grid–Load–Storage in a New Power System: A Coupled Socio–Cyber–Physical Perspective," Energies, MDPI, vol. 17(12), pages 1-43, June.
    11. Ju, Liwei & Liu, Li & Han, Yingzhu & Yang, Shenbo & Li, Gen & Lu, Xiaolong & Liu, Yi & Qiao, Huiting, 2023. "Robust Multi-objective optimal dispatching model for a novel island micro energy grid incorporating biomass waste energy conversion system, desalination and power-to-hydrogen devices," Applied Energy, Elsevier, vol. 343(C).
    12. Hui Wang & Congcong Wang & Wenhui Zhao, 2022. "Decision on Mixed Trading between Medium- and Long-Term Markets and Spot Markets for Electricity Sales Companies under New Electricity Reform Policies," Energies, MDPI, vol. 15(24), pages 1-23, December.
    13. Sandra Giraldo & David la Rotta & César Nieto-Londoño & Rafael E. Vásquez & Ana Escudero-Atehortúa, 2021. "Digital Transformation of Energy Companies: A Colombian Case Study," Energies, MDPI, vol. 14(9), pages 1-14, April.
    14. Xia, Yuanxing & Xu, Qingshan & Chen, Lu & Du, Pengwei, 2022. "The flexible roles of distributed energy storages in peer-to-peer transactive energy market: A state-of-the-art review," Applied Energy, Elsevier, vol. 327(C).
    15. Humberto Verdejo Fredes & Benjamin Acosta & Mauricio Olivares & Fernando García-Muñoz & Francisco Tobar & Vannia Toro & Cesar Smith & Cristhian Becker, 2021. "Impact of Energy Price Stabilization Mechanism on Regulated Clients’ Tariffs: The Case of Chile," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    16. Tian, Xiaoge & Chen, Weiming & Hu, Jinglu, 2023. "Game-theoretic modeling of power supply chain coordination under demand variation in China: A case study of Guangdong Province," Energy, Elsevier, vol. 262(PA).
    17. József Magyari & Krisztina Hegedüs & Botond Sinóros-Szabó, 2022. "Integration Opportunities of Power-to-Gas and Internet-of-Things Technical Advancements: A Systematic Literature Review," Energies, MDPI, vol. 15(19), pages 1-19, September.

    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. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. 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.
    4. Chen, Xiaodong & Ge, Xinxin & Sun, Rongfu & Wang, Fei & Mi, Zengqiang, 2024. "A SVM based demand response capacity prediction model considering internal factors under composite program," Energy, Elsevier, vol. 300(C).
    5. Liu, Yang & Jiang, Zhigao & Guo, Bowei, 2022. "Assessing China’s provincial electricity spot market pilot operations: Lessons from Guangdong province," Energy Policy, Elsevier, vol. 164(C).
    6. Jun Dong & Yuanyuan Wang & Xihao Dou & Zhengpeng Chen & Yaoyu Zhang & Yao Liu, 2021. "Research on Decision Optimization Model of Microgrid Participating in Spot Market Transaction," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    7. Tong Xing & Hongyu Lin & Zhongfu Tan & Liwei Ju, 2019. "Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization," Energies, MDPI, vol. 12(23), pages 1-27, November.
    8. 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.
    9. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    10. Peng, Xu & Tao, Xiaoma, 2018. "Cooperative game of electricity retailers in China's spot electricity market," Energy, Elsevier, vol. 145(C), pages 152-170.
    11. Li, T. & Gao, C. & Pollitt, M. & Chen, T. & Ming H., 2022. "Measuring the effects of power system reform in Jiangsu province, China from the perspective of Social Cost Benefit Analysis," Cambridge Working Papers in Economics 2247, Faculty of Economics, University of Cambridge.
    12. Jing, Rui & Xie, Mei Na & Wang, Feng Xiang & Chen, Long Xiang, 2020. "Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management," Applied Energy, Elsevier, vol. 262(C).
    13. Fang, Fang & Yu, Songyuan & Liu, Mingxi, 2020. "An improved Shapley value-based profit allocation method for CHP-VPP," Energy, Elsevier, vol. 213(C).
    14. 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.
    15. Ju, Liwei & Yin, Zhe & Zhou, Qingqing & Li, Qiaochu & Wang, Peng & Tian, Wenxu & Li, Peng & Tan, Zhongfu, 2022. "Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas," Applied Energy, Elsevier, vol. 310(C).
    16. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.
    17. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
    18. Ju, Liwei & Zhang, Qi & Tan, Zhongfu & Wang, Wei & Xin, He & Zhang, Zehao, 2018. "Multi-agent-system-based coupling control optimization model for micro-grid group intelligent scheduling considering autonomy-cooperative operation strategy," Energy, Elsevier, vol. 157(C), pages 1035-1052.
    19. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    20. Liu, Y. & Jiang, Z. & Guo, B., 2021. "Assessing China's Provincial Electricity Spot Market Pilot Operations: Lessons from the Guangdong Province," Cambridge Working Papers in Economics 2165, Faculty of Economics, University of Cambridge.

    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:appene:v:271:y:2020:i:c:s030626192030667x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.