| 引用本文: | 彭 茜,王爱娟,李峻阳,刘万平.基于高效遗传算法的电网需求侧调度优化研究及其
收敛性分析[J].电力系统保护与控制,2022,50(6):33-42.[点击复制] |
| PENG Qian,WANG Aijuan,LI Junyang,LIU Wanping.Optimization of the demand side dispatching of a power grid based on an efficient genetic algorithm and its convergence analysis[J].Power System Protection and Control,2022,50(6):33-42[点击复制] |
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| 摘要: |
| 智能电网中大功率电器飙升及智能终端的普及，导致需求侧用电负荷增加所造成用电困难的问题。从分布式发电、市电以及居民用电三个角度考虑需求侧调度场景，并对其构建分时电价模型。随后，通过引入居民舒适度、用电经济度和负载方差三个衡量调度性能函数，构建出一种基于调度性能函数的加权优化目标模型。考虑到复杂多方的分时电价模型参与调度，提出了一种改进的遗传算法对需求侧进行用电调度来最小化目标函数。该算法通过加入精英选择策略和进化逆转操作，可有效地减少算法迭代次数，以取得目标函数最优值。然后，从理论上对所改进的遗传算法进行收敛性证明。最后，通过算例仿真验证了算法的有效性，并在满足居民用电舒适度的同时降低了31.29%的用电成本。 |
| 关键词: 遗传算法 智能电网 分时电价模型 需求侧调度 |
| DOI：DOI: 10.19783/j.cnki.pspc.210671 |
| 投稿时间：2021-06-05修订日期：2021-11-10 |
| 基金项目:国家自然科学基金项目资助(62103070)；重庆市教育科学技术研究项目资助(KJQN202001120)；重庆理工大学研究生创新项目资助(clgycx20203111) |
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| Optimization of the demand side dispatching of a power grid based on an efficient genetic algorithm and its convergence analysis |
| PENG Qian,WANG Aijuan,LI Junyang,LIU Wanping |
| (School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China) |
| Abstract: |
| There has been a great increase in the number of high-power electrical appliances on the smart grid. Together with the popularization of smart terminals, and the increasing power consumption from the demand-side, this has brought the difficulties of power consumption to consumers. In this paper, the demand side scheduling scenario is considered from the three aspects of distributed generation, utility power and residential power consumption. Their time-sharing price models are constructed. Then, we introduce three functions to measure dispatching performance: resident comfort, electricity consumption economy and load variance. We also construct a weighted optimization objective model based on the dispatching performance function. Given that a complex multi-party time-sharing electricity price model participates in the dispatching, we propose an improved genetic algorithm to dispatch electricity consumption of demand side to minimize the objective function. Here additional elite selection strategies and evolutionary reversal operations are added. This can effectively reduce the iteration time and find an optimal value. Then, the convergence of the proposed algorithm is proved theoretically. Finally, the effectiveness of the algorithm is verified by simulation, and the power consumption cost is reduced by 31.29% while meeting the comfort of the resident power consumption.
This work is supported by the National Natural Science Foundation of China (No. 62103070). |
| Key words: genetic algorithm smart grid time-sharing electricity price model demand-side dispatch |
