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投稿时间：2020-03-18
投稿时间：2020-03-18
中文摘要: 基于改进的粒子群算法(PSO)和遗传算法(GA)的动态经济调度模型, 充分考虑风电、火力发电的成本及碳排放交易的利润, 并通过PSO结合GA模型, 计算出了风力发电和火电在一定时间内的实际发电量, 能够满足负荷需求; 并且根据碳交易的制度, 在保证低碳发展的同时, 获取了最低的经济成本。改进算法的运用, 提高了算法的效率, 保证了结果的稳定, 并且避免了陷入局部最优解的问题。实验结果证明了模型的可行性。
Abstract:As the carbon trading market has been initially established and the low-carbon economy has gradually entered the era.How to achieve wind power access and carbon trading, and to achieve flexible scheduling is very important.The model has considered the costs of wind and thermal power generation, as well as the profits of carbon trading, and established a low-carbon economic scheduling model function with multiple uncertainties.The actual power generation of wind power and thermal power in a certain period is calculated by PSO combined with GA model, which can meet the load demand.By the carbon trading system, low-carbon development is guaranteed while achieving the lowest economic costs.Improvised algorithm applications improve the efficiency of the algorithm, make the results stable, and avoid the problem of getting into the local optimal solution.The experimental results prove the feasibility of the model.
keywords: economic dispatch carbon emission wind power prediction particle swarm optimization genetic algorithm
文章编号：20221002 中图分类号：TM734 文献标志码：
基金项目:
引用文本：
胡清清,曹渝昆.一种基于改进PSO和GA的动态低碳调度方法[J].上海电力大学学报,2022,38(1):9-16.
HU Qingqing,CAO Yukun.Dynamic Low-carbon Dispatching Model Based on Improving PSO and GA[J].Journal of Shanghai University of Electric Power,2022,38(1):9-16.
胡清清,曹渝昆.一种基于改进PSO和GA的动态低碳调度方法[J].上海电力大学学报,2022,38(1):9-16.
HU Qingqing,CAO Yukun.Dynamic Low-carbon Dispatching Model Based on Improving PSO and GA[J].Journal of Shanghai University of Electric Power,2022,38(1):9-16.