| 刘骏宇,刘世件,章 勇,徐 威.考虑需求响应及消纳权重的售电公司购售电策略[J].电力需求侧管理,2025,27(6):71-77 |
| 考虑需求响应及消纳权重的售电公司购售电策略 |
| Electricity purchase and sale strategy of electricity sales companies considering demand response and consumption weight |
| 投稿时间：2025-06-15 修订日期：2025-08-29 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 06. 011 |
| 中文关键词: 售电公司 可再生能源消纳权重 用户需求响应 双层优化模型 分时段消纳权重 |
| 英文关键词: electricity sales companies renewable energy consumption weight user demand response two-layer optimization model timesegmented consumption weight |
| 基金项目:国网湖北省电力有限公司基金项目（SGHBJY00PSJS2400056） |
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| 中文摘要: |
| 针对售电公司消纳权重的问题，提出了一种考虑用户需求响应以及可再生能源消纳权重的售电公司购售电策略。首先，构建了考虑用户需求响应以及可再生能源消纳权重的售电公司购售电双层优化模型，上层模型根据风光出力特性设定不同时段的消纳权重，并以售电公司收益最大为目标建立分时段消纳责任权重的购售电决策模型。下层模型用户根据售电公司制定的实时电价进行负荷转移和负荷削减，并以用户用电综合成本最小为目标，建立用户用能优化模型；其次，利用萤火虫算法的亮度吸引力机制改进多目标粒子群算法，并利用改进的算法求解该双层优化模型；最后，通过算例分析验证了所提改进算法和策略的有效性与优越性。 |
| 英文摘要: |
| To address the issue of renewable energy consumption responsibilities for electricity sales companies, a power purchase and sale strategy that considers both user demand response and renewable energy consumption weight is proposed. Firstly, a bilevel optimization model is constructed, incorporating user demand response and renewable energy consumption weight. The upper-level model sets consumption weight for different time periods based on the characteristics of wind and solar power output, and establishes a time-segmented power purchase and sale decision model with the goal of maximizing the electricity sales companies’revenue. The lower-level model involves users shifting and reducing their loads according to the real-time prices set by the electricity sales companies, with the objective of minimizing the users’overall electricity cost, thereby creating an optimized energy usage model for users. Secondly, the luminance attraction mechanism of the firefly algorithm is used to improve the multi-objective particle swarm optimization algorithm, and the improved algorithm is used to solve the double-layer optimization model. Finally, the effectiveness and superiority of the improved algorithm and strategy proposed are verified through a case study analysis. |
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