王顺江, 任守东, 于博, 鲁浦锟, 李赫, 李志伟. 智能电网下计及用户侧效益最优的负荷调控方法[J]. 现代电力, 2023, 40(1): 117-124. DOI: 10.19725/j.cnki.1007-2322.2021.0227
引用本文: 王顺江, 任守东, 于博, 鲁浦锟, 李赫, 李志伟. 智能电网下计及用户侧效益最优的负荷调控方法[J]. 现代电力, 2023, 40(1): 117-124. DOI: 10.19725/j.cnki.1007-2322.2021.0227
WANG Shunjiang, REN Shoudong, YU Bo, LU Pukun, LI He, LI Zhiwei. A Load Regulation Method Considering Optimal Consumer Side Benefits in Smart Grid[J]. Modern Electric Power, 2023, 40(1): 117-124. DOI: 10.19725/j.cnki.1007-2322.2021.0227
Citation: WANG Shunjiang, REN Shoudong, YU Bo, LU Pukun, LI He, LI Zhiwei. A Load Regulation Method Considering Optimal Consumer Side Benefits in Smart Grid[J]. Modern Electric Power, 2023, 40(1): 117-124. DOI: 10.19725/j.cnki.1007-2322.2021.0227

智能电网下计及用户侧效益最优的负荷调控方法

A Load Regulation Method Considering Optimal Consumer Side Benefits in Smart Grid

  • 摘要: 智能电网中,为调控电网峰谷期间的用电量,提高负荷用户效益,提出了一种基于博弈论的负荷调控方法。以用户侧与电网侧为博弈主体分别构建负荷模型、负荷效益模型及电网侧效益模型。设计了基于博弈机制的电价调控策略,通过多轮动态调整博弈双方的收益,协调用户侧与电网侧之间的不平衡关系。在负荷与电价的双重约束下,通过CPLEX求解器计算负荷用户收益结果与电网负荷调控结果。算例表明,相同数据条件下,与改进粒子群算法相比,所提调控方法的负荷侧支出减少了0.95%,网侧支出减少了6.14%。验证了该方法可在有效调控电网峰谷期间负荷的同时,保证用户侧效益最优。

     

    Abstract: To regulate and control the electricity consumption in smart power grid during peak-valley load period and improve the benefit of electricity consumers, a game theory-based method for controlling and regulating load was proposed. Taking the consumer side and the gird side as game-agents the load model, the load side benefit model and the grid side benefit model were constructed respectively. Based on game mechanism an electricity price regulation strategy was designed, and the earning of the two game sides was adjusted by multi-round dynamic regulation to coordinate the unbalanced relation between consumer side and grid side. Under the double restraints of load and electricity price, by means of CPLEX solver the earning of the consumer and the load regulation results of the grid were computed. Results of computing example show that under the condition of same data and comparing with the improved particle swarm optimization, by use of the proposed regulation method the expenditure at load side was reduced 0.95% and that at the grid side was reduced 6.14%. It is verified that using the proposed method not only the optimal benefit of user side can be ensured but also the load during peak-valley period can be effectively regulated and controlled.

     

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