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Shamsini Ghiasvand, F., Afshar, K., Bigdeli, N.. (1403). Bi-level Programming of Retailer and Prosumers' Aggregator to Clear the Energy of the Day Ahead Using the Combined Method of Mixed Integer Linear Programming and Mayfly Optimization in Smart Grid. سامانه مدیریت نشریات علمی, 12(2), 163-174. doi: 10.22098/joape.2023.10455.1742
F. Shamsini Ghiasvand; K. Afshar; N. Bigdeli. "Bi-level Programming of Retailer and Prosumers' Aggregator to Clear the Energy of the Day Ahead Using the Combined Method of Mixed Integer Linear Programming and Mayfly Optimization in Smart Grid". سامانه مدیریت نشریات علمی, 12, 2, 1403, 163-174. doi: 10.22098/joape.2023.10455.1742
Shamsini Ghiasvand, F., Afshar, K., Bigdeli, N.. (1403). 'Bi-level Programming of Retailer and Prosumers' Aggregator to Clear the Energy of the Day Ahead Using the Combined Method of Mixed Integer Linear Programming and Mayfly Optimization in Smart Grid', سامانه مدیریت نشریات علمی, 12(2), pp. 163-174. doi: 10.22098/joape.2023.10455.1742
Shamsini Ghiasvand, F., Afshar, K., Bigdeli, N.. Bi-level Programming of Retailer and Prosumers' Aggregator to Clear the Energy of the Day Ahead Using the Combined Method of Mixed Integer Linear Programming and Mayfly Optimization in Smart Grid. سامانه مدیریت نشریات علمی, 1403; 12(2): 163-174. doi: 10.22098/joape.2023.10455.1742

Bi-level Programming of Retailer and Prosumers' Aggregator to Clear the Energy of the Day Ahead Using the Combined Method of Mixed Integer Linear Programming and Mayfly Optimization in Smart Grid

Journal of Operation and Automation in Power Engineering
مقاله 7، دوره 12، شماره 2، تیر 2024، صفحه 163-174    اصل مقاله (952.07 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10455.1742
نویسندگان
F. Shamsini Ghiasvand* ؛ K. Afshar؛ N. Bigdeli
Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran
چکیده
In the restructured electricity industry, the electricity retailer, as a profit-oriented company, buys electricity from wholesale electricity markets and sells it to end customers. On the other hand, with the move of the electricity networks towards smart grids, small customers who, in addition to receiving energy from the distribution network, can generate power on a small scale, have emerged as prosumers in the electricity market environment. Therefore, the prosumers' aggregator is defined to maximize the profit of a set of prosumers in this environment. In this paper, the energy exchange between the retailer and the aggregator has been modeled as a bi-level game. At a higher level, the retailer, as a leader to maximize its profit or minimize its expenses, offers a price to buy or sell energy to the prosumers' aggregator. The aggregator also decides on the amount of exchange energy to buy or sell, to minimize the energy supply costs required of its consumers according to the retailer's bid price. In this paper, a combined method based on~MILP (Mixed Integer Linear Programming)~and MO (Mayfly Optimization) has been used to find the optimal point of this modeled game. To evaluate the efficiency of the proposed method, the three pricing methods FP (Fixed Pricing),~TOU (Time Of Using), and RTP (Real Time Pricing) as price-based demand response programs have been compared using the proposed algorithm. The simulation results show that among the three pricing methods for customers, the RTP pricing method has the highest profit for the retailer and the lowest cost for the aggregator.
کلیدواژه‌ها
Retailer؛ Smart grid؛ Renewable energy resources؛ Prosumers' aggregator؛ Energy procurement
مراجع
  1. Shayeghi, M. Alilou, "Multi-Objective Demand Side Management to Improve Economic and Environmental Issues of a Smart Microgrid," J. Oper. Autom. Power Eng. ,vol. 9, no. 3, pp.182-192, 2021.
  2. Babaei Shahmars, J.Salehi, N. Taghizadegan Kalantari, "Bi-Level Unit Commitment Considering Virtual Power Plants and Demand Response Programs Using Information Gap Decision Theory," J. Oper. Autom. Power Eng., vol. 9, no. 2, pp. 88-102, 2021.
  3. Gh. Azar, H. Nazaripouya, B. Khaki, C. Chu, R. Gadh, R. H. Jacobsen, "A Non-Cooperative Framework for Coordinating a Neighborhood of Distributed Prosumers," IEEE Trans. Indus. Informatics, vol. 15, pp. 2523-2534, 2019.
  4. Taherian, M. R. Aghaebrahimi, L. Baringo, S. R. Goldani, "Optimal dynamic pricing for an electricity retailer in the price-responsive environment of smart grid," Int. J. Electr. Power Energy Syst., vol. 130, 2021.
  5. Dimitroulis, M. Alamaniotis, "A fuzzy logic energy management system of on-grid electrical system for residential prosumers," Electr. Power Syst. Res., vol. 202, 2022.
  6. Wasiak, M. Szypowski, P. Kelm, R. Mienski, A. We˛dzik,´ R. Pawełek, M. Małaczek, P. Urbanek, "Innovative energy management system for low-voltage networks with distributed generation based on prosumers’ active participation," Appl. Energy, vol. 312,2022.
  7. Srilakshmi, S. P. Singh, "Energy regulation of EV using MILP for optimal operation of incentive based prosumer microgrid with uncertainty modelling," Int. J. Electr. Power Energy Syst., vol. 134, 2022.
  8. Yin, Q. Ai, Z. Li, Y. Zhang, T. Lu, "Energy management for aggregate prosumers in a virtual power plant: A robust Stackelberg game approach," Int. J. Electr. Power Energy Syst., vol. 117, 2020.
  9. Xiao, X. Wang, P. Pinson, X. Wang, Transactive Energy Based Aggregation of Prosumers as a Retailer, IEEE Trans. Smart Grid, vol. 11, pp. 3302-3312, 2020.
  10. Ferrara, A. Violi, P. Beraldi, G. Carrozzino, T. Ciano, "An integrated decision approach for energy procurement and tariff definition for prosumers aggregations," Energy Econ., vol. 97, 2021.
  11. Ma, J. Lyu, Y. Wang, J. Zhang and J. Xu, "The Prosumer Energy Management Method Based on Smart Load, IEEE Access, vol. 8, pp. 117086-117095, 2020.
  12. Mussadiq, S. Ahmed, N. Gul, J. Kim and S. M. Kim, "Priority-Based Energy Sharing and Management Among Prosumers in Smart Grids," IEEE Access, vol. 10, pp. 12179-12190, 2022.
  13. Liu, M. Cheng, X. Yu, J. Zhong , J. Lei, "Energy-Sharing Provider for PV Prosumer Clusters: A Hybrid Approach Using Stochastic Programming and Stackelberg Game," IEEE Trans. Ind. Electron., vol. 65, pp. 6740-6750, 2018.
  14. Li, Q. Li, W. Song, L. Wang, "Incentivizing distributed energy trading among prosumers: A general Nash bargaining approach," Int. J. Electr. Power Energy Syst., vol. 131, 2021.
  15. Ma, N. Liu, J. Zhang, L. Wang, "Real-Time Rolling Horizon Energy Management for the Energy-Hub-Coordinated Prosumer Community From a Cooperative Perspective," IEEE Trans. Power Syst., vol. 34, pp. 1227-1242, 2019.
  16. Morstyn, M. D. McCulloch, "Multiclass Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer Preferences," IEEE Trans. Power Syst., vol. 34, pp. 4005-4014, 2019.
  17. Khorasany, A. Najafi-Ghalelou, R. Razzaghi, "A Framework for Joint Scheduling and Power Trading of Prosumers in Transactive Markets," IEEE Trans. Sustainable Energy, vol. 12, pp. 955-965, 2021.
  18. Kanakadhurga, N. Prabaharan, "Demand response-based peer-to-peer energy trading among the prosumers and consumers," Energy Reports, vol. 7, pp. 7825-7834, 2021.
  19. Mehdinejad, H. A. Shayanfar, B. Mohammadi-Ivatloo and H. Nafisi, "Designing a Robust Decentralized Energy Transactions Framework for Active Prosumers in Peer-toPeer Local Electricity Markets," IEEE Access, vol. 10, pp. 26743-26755, 2022.
  20. Mao, D. Han, Y. Wang, X. Dong and Z. Yan, "Pricing mechanism for community prosumers in decentralized electricity market," CSEE J. Power Energy Syst., 2015.
  21. M. Mousavi, T. Barforoushi, F. H. Moghimi, "A decisionmaking model for a retailer considering a new short-term contract and flexible demands," Electr. Power Syst. Res., vol. 192, 2021.
  22. Wang , Z. Zhu, Z. Guo, "Optimal Day-Ahead DecisionMaking Scheduling of Multiple Interruptible Load Schemes for Retailer With Price Uncertainties, IEEE Access, vol. 9, pp. 102251-102263, 2021.
  23. C. Do Prado, U. Chikezie, "A Decision Model for an Electricity Retailer With Energy Storage and Virtual Bidding Under Daily and Hourly CVaR Assessment," IEEE Access, Vol. 9, pp. 106181-106191, 2021.
  24. C. do Prado, W. Qiao, "A Stochastic Bilevel Model for an Electricity Retailer in a Liberalized Distributed Renewable Energy Market," IEEE Trans. Sustainable Energy, vol. 11, pp. 2803-2812, 2020.
  25. Liu, D. Zhang and H. B. Gooi, "Data-driven decision-making strategies for electricity retailers: A deep reinforcement learning approach," CSEE J. Power Energy Syst., vol. 7, no. 2, pp. 358-367, 2021.
  26. Xu, J. Wen, Q. Hu, J. Shu, J. Lu and Z. Yang, "Energy procurement and retail pricing of electricity retailers via deep reinforcement learning with long short-term memory," CSEE J. Power Energy Syst., 2022.
  27. Nojavan, R. Nourollahi, H. Pashaei-Didani, K. Zare, "Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange," Int. J. Electr. Power Energy Syst., vol. 105, pp. 237-248, 2019.
  28. Sharifi, A. Anvari-Moghaddam, S. Hamid Fathi, V. Vahidinasab, "A bi-level model for strategic bidding of a price-maker retailer with flexible demands in day-ahead electricity market," Int. J. Electr. Power Energy Syst., vol. 121, 2020.
  29. Sun, J. Zhang, P. Zeng, W. Liu, "Energy storage configuration and day-ahead pricing strategy for electricity retailers considering demand response profit," Int. J. Electr. Power Energy Syst., vol. 136, 2022.
  30. S. Dagoumas, M. L. Polemis, "An integrated model for assessing electricity retailer’s profitability with demand response," Appl. Energy, vol. 198, pp. 49-64, 2017.
  31. Grimm, G. Orlinskaya, L. Schewe, M. Schmidt, G. Zöttl, Optimal design of retailer-prosumer electricity tariffs using bilevel optimization," Omega, vol. 102, 2021.
  32. Zervoudakis, S. Tsafarakis, "A mayfly optimization algorithm," Comput. Ind. Eng., vol. 145, 2020.
  33. Nojavan, K. Zare, "Interval optimization based performance of photovoltaic/wind/FC/electrolyzer/electric vehicles in energy price determination for customers by electricity retailer," Solar Energy, vol. 171, pp. 580-592, 2018.
  34. Zeynali, N. Rostami, A. Ahmadian, A. Elkamel, "Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model," Sustainable Cities Soc., vol. 74, 2021.
  35. Nojavan, K. Zare, "Optimal energy pricing for consumers by electricity retailer," Int. J. Electr. Power Energy Syst.,vol. 102, pp. 401-412,2018.
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