Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization
In the competitive energy market, energy retailers are facing the uncertainties of both energy price and demand, which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market. Particularly, the attractive multi-energy retail p...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10159548/ |
| _version_ | 1797342352197550080 |
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| author | Hongjun Gao Hongjin Pan Rui An Hao Xiao Yanhong Yang Shuaijia He Junyong Liu |
| author_facet | Hongjun Gao Hongjin Pan Rui An Hao Xiao Yanhong Yang Shuaijia He Junyong Liu |
| author_sort | Hongjun Gao |
| collection | DOAJ |
| description | In the competitive energy market, energy retailers are facing the uncertainties of both energy price and demand, which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market. Particularly, the attractive multi-energy retail packages are the key for retailers to increase their benefit. Therefore, combined with incentive means and price signals, five types of multi-energy retail packages such as peak-valley time-of-use (TOU) price package and day-night bundled price package are designed in this paper for retailers. The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower (MLMF) Stackelberg game, in which retailers are leaders and end-users are followers. Retailers make decisions to maximize the profit considering the conditional value at risk (CVaR) while end-users optimize the satisfaction of both energy comfort and economy. Besides, a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization (PSO) algorithm and CPLEX solver are applied to solve the optimization model for each participant (retailer or end-user). Numeral results show that the designed retail packages can increase the overall profit of retailers, and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction. |
| first_indexed | 2024-03-08T10:32:03Z |
| format | Article |
| id | doaj.art-fe1eeb6a66f7441599512eae93dcf0af |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-03-08T10:32:03Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-fe1eeb6a66f7441599512eae93dcf0af2024-01-27T00:03:13ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202024-01-0112122523710.35833/MPCE.2022.00080810159548Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package OptimizationHongjun Gao0Hongjin Pan1Rui An2Hao Xiao3Yanhong Yang4Shuaijia He5Junyong Liu6College of Electrical Engineering, Sichuan University,Chengdu,China,610065Electric Power Research Institute,State Grid Jiangsu Electrir: Power Compny,Nanjing,Chaina,211100College of Electrical Engineering, Sichuan University,Chengdu,China,610065Institute of Electrical Engineering, Chinese Academy of Sciences,Beijing,China,100190Institute of Electrical Engineering, Chinese Academy of Sciences,Beijing,China,100190College of Electrical Engineering, Sichuan University,Chengdu,China,610065College of Electrical Engineering, Sichuan University,Chengdu,China,610065In the competitive energy market, energy retailers are facing the uncertainties of both energy price and demand, which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market. Particularly, the attractive multi-energy retail packages are the key for retailers to increase their benefit. Therefore, combined with incentive means and price signals, five types of multi-energy retail packages such as peak-valley time-of-use (TOU) price package and day-night bundled price package are designed in this paper for retailers. The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower (MLMF) Stackelberg game, in which retailers are leaders and end-users are followers. Retailers make decisions to maximize the profit considering the conditional value at risk (CVaR) while end-users optimize the satisfaction of both energy comfort and economy. Besides, a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization (PSO) algorithm and CPLEX solver are applied to solve the optimization model for each participant (retailer or end-user). Numeral results show that the designed retail packages can increase the overall profit of retailers, and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.https://ieeexplore.ieee.org/document/10159548/Conditional value at risk (CVaR)energy retailermulti-energy retail package designmulti-leader multi-follower (MLMF) Stackelberg gamesatisfaction |
| spellingShingle | Hongjun Gao Hongjin Pan Rui An Hao Xiao Yanhong Yang Shuaijia He Junyong Liu Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization Journal of Modern Power Systems and Clean Energy Conditional value at risk (CVaR) energy retailer multi-energy retail package design multi-leader multi-follower (MLMF) Stackelberg game satisfaction |
| title | Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization |
| title_full | Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization |
| title_fullStr | Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization |
| title_full_unstemmed | Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization |
| title_short | Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization |
| title_sort | bi level multi leader multi follower stackelberg game model for multi energy retail package optimization |
| topic | Conditional value at risk (CVaR) energy retailer multi-energy retail package design multi-leader multi-follower (MLMF) Stackelberg game satisfaction |
| url | https://ieeexplore.ieee.org/document/10159548/ |
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