|
|
考虑用户理性及市场竞价的电热能源代理商运营优化研究
|
Abstract:
在能源互联网发展和电力市场改革的背景下，研究电热能源代理商(ETEA)在为智慧园区用户制定能源价格的同时如何优化电能量市场竞价，具有重要的理论和实践意义。本文首先采用K-Means聚类算法，对电能量市场多个时段的报价和报量进行多场景生成，并通过概率加权法获得典型出清价格，为ETEA制定平均电价和保守电能量市场交互价格提供科学依据。在此基础上，考虑用户理性，通过构建用户价格敏感性模型，将能源用户分为绝对理性、有限理性和非理性三类。基于此，本文建立了ETEA与多类型用户之间的主从博弈定价调度模型，并结合启发式算法与商业求解器Gurobi进行求解。随后，本文基于K-Means聚类结果，构建了多场景下ETEA参与电能量市场策略购售电的MPEC竞标模型，并通过KKT条件与强对偶定理转换为MILP模型，进一步优化其与电能量市场的交互成本。算例结果表明，本文提出的考虑用户理性区分的定价调度及电能量市场策略竞标综合优化模型能够显著提升ETEA的收益，从而推动能源互联网的发展。
In the context of energy internet development and electricity market reform, optimizing the electricity and heat energy agent (ETEA) strategies for setting energy prices for smart park users while participating in electricity market bidding holds significant theoretical and practical value. This study employs the K-Means clustering algorithm to generate multiple scenarios of electricity market bids and offers across various time periods, deriving typical clearing prices through probability weighting. These results provide a scientific basis for ETEA to establish average electricity prices and conservative market interaction prices. Building on this, a user price sensitivity model is constructed to classify energy users into three categories: absolutely rational, boundedly rational, and irrational. Subsequently, a leader-follower game-based pricing and scheduling model is developed to describe the interactions between ETEA and these user types, solved using a combination of heuristic algorithms and the commercial solver Gurobi. Further, a multi-scenario MPEC bidding model for ETEA’s strategic power trading in electricity markets is formulated based on the K-Means clustering results. The model is converted into a MILP formulation using KKT conditions and the strong duality theorem to optimize ETEA’s market interaction costs. Case studies demonstrate that the proposed integrated optimization model, which accounts for user rationality differentiation in pricing and scheduling as well as strategic bidding in electricity markets, significantly enhances ETEA’s revenue, thereby advancing the development of the energy internet.
| [1] | Wang, J., Zhong, H., Ma, Z., Xia, Q. and Kang, C. (2017) Review and Prospect of Integrated Demand Response in the Multi-Energy System. Applied Energy, 202, 772-782. https://doi.org/10.1016/j.apenergy.2017.05.150 |
| [2] | Huang, K., Fu, M. and Ding, X. (2023) Security and Economic Integration Scheduling of Electricity-Heat Integrated Energy System. IEEE Access, 11, 112236-112247. https://doi.org/10.1109/access.2023.3322428 |
| [3] | Liu, X. (2022) Optimal Scheduling Strategy of Electricity‐Heat‐Hydrogen Integrated Energy System under Different Operating Modes. International Journal of Energy Research, 46, 12901-12925. https://doi.org/10.1002/er.8063 |
| [4] | Pan, C., Jin, T., Li, N., Wang, G., Hou, X. and Gu, Y. (2023) Multi-Objective and Two-Stage Optimization Study of Integrated Energy Systems Considering P2G and Integrated Demand Responses. Energy, 270, Article 126846. https://doi.org/10.1016/j.energy.2023.126846 |
| [5] | Mansouri, S.A., Ahmarinejad, A., Sheidaei, F., Javadi, M.S., Rezaee Jordehi, A., Esmaeel Nezhad, A., et al. (2022) A Multi-Stage Joint Planning and Operation Model for Energy Hubs Considering Integrated Demand Response Programs. International Journal of Electrical Power & Energy Systems, 140, Article 108103. https://doi.org/10.1016/j.ijepes.2022.108103 |
| [6] | Li, Y., Wang, B., Yang, Z., Li, J. and Li, G. (2022) Optimal Scheduling of Integrated Demand Response-Enabled Community-Integrated Energy Systems in Uncertain Environments. IEEE Transactions on Industry Applications, 58, 2640-2651. https://doi.org/10.1109/tia.2021.3106573 |
| [7] | Li, R., Yan, X. and Liu, N. (2022) Hybrid Energy Sharing Considering Network Cost for Prosumers in Integrated Energy Systems. Applied Energy, 323, Article 119627. https://doi.org/10.1016/j.apenergy.2022.119627 |
| [8] | Ning, J. and Xiong, L. (2024) Analysis of the Dynamic Evolution Process of the Digital Transformation of Renewable Energy Enterprises Based on the Cooperative and Evolutionary Game Model. Energy, 288, Article 129758. https://doi.org/10.1016/j.energy.2023.129758 |
| [9] | Wang, H., Zheng, T., Sun, W. and Khan, M.Q. (2023) Research on the Pricing Strategy of Park Electric Vehicle Agent Considering Carbon Trading. Applied Energy, 340, Article 121017. https://doi.org/10.1016/j.apenergy.2023.121017 |
| [10] | Cao, J., Yang, D. and Dehghanian, P. (2023) Co-Optimization of Multiple Virtual Power Plants Considering Electricity-Heat-Carbon Trading: A Stackelberg Game Strategy. International Journal of Electrical Power & Energy Systems, 153, Article 109294. https://doi.org/10.1016/j.ijepes.2023.109294 |
| [11] | 帅轩越, 马志程, 王秀丽, 郭慧, 张晗. 基于主从博弈理论的共享储能与综合能源微网优化运行研究[J]. 电网技术, 2023, 47(2): 679-687. |
| [12] | Ruiz, C. and Conejo, A.J. (2009) Pool Strategy of a Producer with Endogenous Formation of Locational Marginal Prices. IEEE Transactions on Power Systems, 24, 1855-1866. https://doi.org/10.1109/tpwrs.2009.2030378 |
| [13] | Wang, H., Zhao, A., Khan, M.Q. and Sun, W. (2024) Optimal Operation of Energy Hub Considering Reward-Punishment Ladder Carbon Trading and Electrothermal Demand Coupling. Energy, 286, Article 129571. https://doi.org/10.1016/j.energy.2023.129571 |
