This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devic...This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.展开更多
现有深度强化学习(deep reinforcement learning,DRL)方法在解决配电网电压优化问题时,存在信用分配难、探索效率低等问题,在模型训练速度和优化效果等方面表现欠佳。为此,结合配电网分区降损与模仿学习的思想,提出一种基于指导信号的...现有深度强化学习(deep reinforcement learning,DRL)方法在解决配电网电压优化问题时,存在信用分配难、探索效率低等问题,在模型训练速度和优化效果等方面表现欠佳。为此,结合配电网分区降损与模仿学习的思想,提出一种基于指导信号的多智能体深度确定性策略梯度(guidance signal based multi-agent deep deterministic policy gradient,GS-MADDPG)的电压优化方法。首先,将电动汽车(electric vehicles,EV)集群、分布式电源(distributed generations,DG)和无功调节装置作为决策智能体,构建强化学习优化模型。然后,通过配电网分区,解耦多智能体的外部奖励,并结合模仿学习,利用指导信号引入内部奖励,帮助智能体快速寻优。最后,基于改进IEEE33节点系统进行算例测试。结果表明,所提电压优化策略较传统DRL方法具有更高的样本利用率,实现了更稳定的收敛及更高的模型训练效率,提升了配电网电压的优化效果。展开更多
We propose novel techniques to find the optimal location,size,and power factor of distributed generation(DG) to achieve the maximum loss reduction for distribution networks.Determining the optimal DG location and size...We propose novel techniques to find the optimal location,size,and power factor of distributed generation(DG) to achieve the maximum loss reduction for distribution networks.Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation.Based on the network's total load demand,four DG sizes are selected.They are used to form energy loss curves for each bus and then for determining the optimal DG options.The study shows that by defining the energy loss minimization as the objective function,the time-varying load demand significantly affects the sizing of DG resources in distribution networks,whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other calculations.The devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method(EIM) and recently published results.Results showed that the proposed technique can provide an optimal solution with less computation.展开更多
基金supported by Borujerd Branch,Islamic Azad University Iran
文摘This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.
文摘现有深度强化学习(deep reinforcement learning,DRL)方法在解决配电网电压优化问题时,存在信用分配难、探索效率低等问题,在模型训练速度和优化效果等方面表现欠佳。为此,结合配电网分区降损与模仿学习的思想,提出一种基于指导信号的多智能体深度确定性策略梯度(guidance signal based multi-agent deep deterministic policy gradient,GS-MADDPG)的电压优化方法。首先,将电动汽车(electric vehicles,EV)集群、分布式电源(distributed generations,DG)和无功调节装置作为决策智能体,构建强化学习优化模型。然后,通过配电网分区,解耦多智能体的外部奖励,并结合模仿学习,利用指导信号引入内部奖励,帮助智能体快速寻优。最后,基于改进IEEE33节点系统进行算例测试。结果表明,所提电压优化策略较传统DRL方法具有更高的样本利用率,实现了更稳定的收敛及更高的模型训练效率,提升了配电网电压的优化效果。
文摘We propose novel techniques to find the optimal location,size,and power factor of distributed generation(DG) to achieve the maximum loss reduction for distribution networks.Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation.Based on the network's total load demand,four DG sizes are selected.They are used to form energy loss curves for each bus and then for determining the optimal DG options.The study shows that by defining the energy loss minimization as the objective function,the time-varying load demand significantly affects the sizing of DG resources in distribution networks,whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other calculations.The devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method(EIM) and recently published results.Results showed that the proposed technique can provide an optimal solution with less computation.
