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

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  • 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
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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    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. 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.
    8. 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.
    9. Peng, Xu & Tao, Xiaoma, 2018. "Cooperative game of electricity retailers in China's spot electricity market," Energy, Elsevier, vol. 145(C), pages 152-170.
    10. 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).
    11. 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).
    12. 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.
    13. 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.
    14. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2016. "Prosumer bidding and scheduling in electricity markets," Energy, Elsevier, vol. 94(C), pages 828-843.
    Full references (including those not matched with items on IDEAS)

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