This paper presents a sequential approach with matrix framework for solving various kinds of economic dispatch problems. The objective of the economic dispatch problems of electrical power generation is to schedule th...This paper presents a sequential approach with matrix framework for solving various kinds of economic dispatch problems. The objective of the economic dispatch problems of electrical power generation is to schedule the committed generating units output so as to meet the required load demand while satisfying the system equality and inequality constraints. This is a maiden approach developed to obtain the optimal dispatches of generating units for all possible load demands of power system in a single execution. The feasibility of the proposed method is demonstrated by solving economic load dispatch problem, combined economic and emission dispatch problem, multiarea economic dispatch problem and economic dispatch problem with multiple fuel options. The proposed methodology is tested with different scale of power systems. The generating unit operational constraints are also considered. The simulation results obtained by proposed methodology for various economic dispatch problems are compared with previous literatures in terms of solution quality. Numerical simulation results indicate an improvement in total cost saving and hence the superiority of the proposed method is also revealed for economic dispatch problems.展开更多
A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area d...A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.展开更多
文摘This paper presents a sequential approach with matrix framework for solving various kinds of economic dispatch problems. The objective of the economic dispatch problems of electrical power generation is to schedule the committed generating units output so as to meet the required load demand while satisfying the system equality and inequality constraints. This is a maiden approach developed to obtain the optimal dispatches of generating units for all possible load demands of power system in a single execution. The feasibility of the proposed method is demonstrated by solving economic load dispatch problem, combined economic and emission dispatch problem, multiarea economic dispatch problem and economic dispatch problem with multiple fuel options. The proposed methodology is tested with different scale of power systems. The generating unit operational constraints are also considered. The simulation results obtained by proposed methodology for various economic dispatch problems are compared with previous literatures in terms of solution quality. Numerical simulation results indicate an improvement in total cost saving and hence the superiority of the proposed method is also revealed for economic dispatch problems.
基金supported in part by National Key Basic Research Program of China(973 Program:2013CB228205)National Natural Science Foundation of China(51177051,51477055).
文摘A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.
