计及电压预测的主动配电网分布式电源在线优化决策方法

Online Optimization Decision Algorithm for Distributed Generations Considering Voltage Prediction in Active Distribution Network

“双碳”背景下,配电网中分布式电源的接入量逐渐增加,如何决策分布式电源的输出设定值从而使配电网经济运行成为研究的关键问题。针对分布式电源管控问题进行了深入研究,在建立时变优化模型的基础上,就现有算法未考虑当前时段分布式电源决策对未来时段分布式电源决策影响的问题,提出了一种计及电压预测的主动配电网分布式电源优化决策方法。首先根据配电网各节点负荷变动情况对负荷变动后的网络节点电压进行预测。进一步利用所预测的配电网节点电压值进行分布式电源的决策。然后利用分布式电源决策设定值的变化量预测分布式电源决策后的网络潮流状态,以此类推从而求解所有时段分布式电源的决策设定值。针对502节点配电网算例系统,利用所提在线优化决策方法对配电网最优潮流收敛性、节点电压调控收敛性以及储能电源充放电预测有效性进行了算例验证。

Under the background of“double carbon”,the access to distributed power generation in the distribution network is gradually increasing.How to decide the output setting value of distributed power generation to make the economic operation of the distribution network becomes a key issue of research.In this paper,an in-depth study on distributed power management and control is carried out.On the basis of a time-varying optimization model,an online optimization decision algorithm for distributed generations considering voltage prediction is proposed to solve the problem that the existing algorithm does not consider the influence of the decision of distributed power in the current period on the decision of distributed power in the future period.Firstly,the network node voltage after the load change should be predicted according to the load change of each node in the distribution network.The predicted node voltage values in distribution network can be further used to decide distributed power generation.Then,the change of the decision setting value of the distributed power source is used to predict the network power flow state after the decision of the distributed power source,and so on to solve the decision setting value of the distributed power source in all periods.For the 502-node distribution network example system,the online optimization decision algorithm proposed in this paper is used to verify the optimal power flow convergence of the distribution network,the convergence of node voltage regulation,and the validity of charging and discharging prediction of the energy storage power supply.