In modern society, the energy problem has become increasingly prominent. In order to achieve sustainable and efficient energy utilization, microgrid technology came into being. Microgrid is a small power system with a...
详细信息
We present an innovative partially observable Markov decision process (POMDP) modelling method for the reactive power optimization process of the active distribution network (ADN) under the high permeability of the di...
详细信息
We present an innovative partially observable Markov decision process (POMDP) modelling method for the reactive power optimization process of the active distribution network (ADN) under the high permeability of the distributed generation. This model is tolerant of the uncertainty resulting from data uncertainty. We believe that the belief state space in the POMDP model corresponds to the state space in the Markov decision process (MDP) model, and we apply the multi-agent actor-attention-critic (MAAC) reinforcementlearning (RL) algorithm to the proposed model. This technique extracts the most effective information with the highest quality from the huge historical measurement database, hence enhancing the learning effectiveness of agents and the stability of the optimization strategy. We simulate reactive power optimization in a modified IEEE-33 nodes ADN and a modified IEEE-123nodes ADN. The simulation demonstrates the stability and economic superiority of the proposed approach under varying degrees of data uncertainty relative to previous RL algorithms based on the MDP model. The simulation demonstrates that the proposed POMDP model is more appropriate for the real operation of the partially observable distribution network than the MDP models. And the optimal strategy obtained by the proposed MAAC algorithm is reliable with deteriorating data quality.
This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the sy...
详细信息
ISBN:
(纸本)9798350377040;9798350377033
This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the system under variable environments. In order to cope with the uncertainty in the distributed photovoltaic power generation process, this paper designs a self-decision control algorithm to achieve real-time monitoring and optimization control of photovoltaic components, inverters and grid interfaces through multi-agent collaboration. Each agent represents a different control unit, which is independent and cooperative with each other, and continuously trains under the reinforcementlearning framework to improve the overall performance. The model verification based on the simulation platform shows that the algorithm can significantly improve the response speed and power generation efficiency of the photovoltaic system under different weather and load conditions. The data analysis results show that after adopting this control strategy, the output fluctuation of photovoltaic power generation is reduced by 15%, the inverter efficiency is improved by 12%, the system stability is enhanced, and the impact on the power grid is effectively reduced. In addition, the algorithm has good generalization performance and can be applied to large-scale distributed photovoltaic networks.
暂无评论