| 王弘利,杨 军,李高俊杰,李勇汇,肖金星,徐冰雁.考虑需求响应潜力的配电网多主体交易策略[J].电力需求侧管理,2023,25(2):50-56 |
| 考虑需求响应潜力的配电网多主体交易策略 |
| Multi-agent transaction strategy of distribution network considering demand response potential |
| 投稿时间：2022-09-27 修订日期：2022-12-17 |
| DOI：10.3969/j.issn.1009-1831.2023.02.008 |
| 中文关键词: 配电网 负荷需求响应 多主体交易 多目标优化 闵可夫斯基求和 |
| 英文关键词: distribution network load demand response multi-agent transaction multi objective optimization Minkowsky sum |
| 基金项目:国家电网有限公司科技项目（52093220000H） |
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| 中文摘要: |
| 为了促进负荷需求响应技术在配电网中的应用，提高配电网交易运行效率，提出一种考虑电力需求响应潜力的配电网多主体交易策略。首先，建立一种基于闵可夫斯基和的负荷需求响应潜力计算方法，将分布式的主动型负荷聚合为广义负荷进行统一的调度。然后，依据配电网中各个交易主体的盈利模式，建立各主体的数学模型，并分别以配电网总体收益最大和各主体收益最大为目标函数构建双层多目标优化模型。最后，利用MultiGPO算法对优化问题进行求解。结果显示，所构建的配电网模型能够在配电网整体收益最大的前提下合理分配各主体收益，从而提高配电网整体和单一个体的经济性，同时，所使用的MultiGPO 算法相比于传统算法能够获得更好的优化结果。 |
| 英文摘要: |
| In order to promote the application of load demand response technology in the distribution network and improve the efficiency of the distribution network transaction operation, a multiagent transaction strategy of the distribution network considering the load demand response potential is proposed. Firstly, a calculation method for load participation in demand response potential is established based on Minkowsky sum, the distributed active loads are aggregated into generalized loads for unified scheduling. Then,according to the profit model of each transaction entity in the distribution network, the mathematical model of each entity is established, and the two-layer multi-objective optimization model is constructed with the largest overall income of the distribution network and the largest income of each entity as the objective function. Finally, MultiGPO algorithm is used to solve the optimization problem.The results show that the constructed distribution network model can reasonably distribute the benefits of each entity under the premise of the maximum benefit of the distribution network, thereby improve the economy of the distribution network as a whole and a single individual. At the same time, the MultiGPO algorithm is compared with traditional algorithms can obtain better optimization results. |
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