A substation planning method that accounts for the widespread introduction of distributed generators(DGs)in a low-carbon economy is proposed.With the proliferation of DGs,the capacity that DGs contribute to the distri...A substation planning method that accounts for the widespread introduction of distributed generators(DGs)in a low-carbon economy is proposed.With the proliferation of DGs,the capacity that DGs contribute to the distribution network has become increasingly important.The capacity of a DG is expressed as a capacity credit(CC)that can be evaluated according to the principle that the reliability index is unchanged before and after the introduction of the DG.A method that employs a weighted Voronoi diagram is proposed for substation planning considering CC.A low-carbon evaluation objective function is added to the substation planning model to evaluate the contribution of DGs to a low-carbon economy.A case study is analyzed to demonstrate the practicality of the proposed method.展开更多
Multi-agent reinforcement learning holds tremendous potential for revolutionizing intelligent systems across diverse domains.However,it is also concomitant with a set of formidable challenges,which include the effecti...Multi-agent reinforcement learning holds tremendous potential for revolutionizing intelligent systems across diverse domains.However,it is also concomitant with a set of formidable challenges,which include the effective allocation of credit values to each agent,real-time collaboration among heterogeneous agents,and an appropriate reward function to guide agent behavior.To handle these issues,we propose an innovative solution named the Graph Attention Counterfactual Multiagent Actor–Critic algorithm(GACMAC).This algorithm encompasses several key components:First,it employs a multiagent actor–critic framework along with counterfactual baselines to assess the individual actions of each agent.Second,it integrates a graph attention network to enhance real-time collaboration among agents,enabling heterogeneous agents to effectively share information during handling tasks.Third,it incorporates prior human knowledge through a potential-based reward shaping method,thereby elevating the convergence speed and stability of the algorithm.We tested our algorithm on the StarCraft Multi-Agent Challenge(SMAC)platform,which is a recognized platform for testing multiagent algorithms,and our algorithm achieved a win rate of over 95%on the platform,comparable to the current state-of-the-art multi-agent controllers.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.51477116).
文摘A substation planning method that accounts for the widespread introduction of distributed generators(DGs)in a low-carbon economy is proposed.With the proliferation of DGs,the capacity that DGs contribute to the distribution network has become increasingly important.The capacity of a DG is expressed as a capacity credit(CC)that can be evaluated according to the principle that the reliability index is unchanged before and after the introduction of the DG.A method that employs a weighted Voronoi diagram is proposed for substation planning considering CC.A low-carbon evaluation objective function is added to the substation planning model to evaluate the contribution of DGs to a low-carbon economy.A case study is analyzed to demonstrate the practicality of the proposed method.
文摘Multi-agent reinforcement learning holds tremendous potential for revolutionizing intelligent systems across diverse domains.However,it is also concomitant with a set of formidable challenges,which include the effective allocation of credit values to each agent,real-time collaboration among heterogeneous agents,and an appropriate reward function to guide agent behavior.To handle these issues,we propose an innovative solution named the Graph Attention Counterfactual Multiagent Actor–Critic algorithm(GACMAC).This algorithm encompasses several key components:First,it employs a multiagent actor–critic framework along with counterfactual baselines to assess the individual actions of each agent.Second,it integrates a graph attention network to enhance real-time collaboration among agents,enabling heterogeneous agents to effectively share information during handling tasks.Third,it incorporates prior human knowledge through a potential-based reward shaping method,thereby elevating the convergence speed and stability of the algorithm.We tested our algorithm on the StarCraft Multi-Agent Challenge(SMAC)platform,which is a recognized platform for testing multiagent algorithms,and our algorithm achieved a win rate of over 95%on the platform,comparable to the current state-of-the-art multi-agent controllers.
