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| 考虑需求响应及消纳权重的售电商购售电策略 |
| The electricity purchase and sale strategy of power retailers considering demand response and consumption weight |
| 投稿时间：2024-10-18 修订日期：2025-01-12 |
| DOI： |
| 中文关键词: 售电商 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 多目标粒子群算法 |
| 英文关键词: power retailers renewable energy consumption weight user demand response two-layer optimization model time-segmented consumption weight multi-objective particle swarm optimization |
| 基金项目:国网湖北省电力有限公司(SGHBJY00PSJS2400056) |
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| 中文摘要: |
| 在新型电力市场框架下，售电商作为关键参与者，必须承担一定的可再生能源消纳权重。针对售电商消纳权重的问题，本文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商购售电策略。首先，本文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商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，并以售电商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商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本文所提改进算法和策略的有效性与优越性。 |
| 英文摘要: |
| Under the framework of the new electricity market, the power retailers, as key participants, must bear a certain weight of renewable energy consumption. To address the issue of renewable energy consumption responsibilities for power retailers, this paper proposes a power purchase and sale strategy that considers both user demand response and renewable energy consumption weight. 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 power retailers’ revenue. The lower-level model involves users shifting and reducing their loads according to the real-time prices set by the power retailers, with the objective of minimizing the user's 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 in this paper were verified through a case study analysis. |
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