In this paper,an uncertain economic dispatch problem(EDP)is considered for a group of coopertive agents.First,let each agent extract a set of samples(scenarios)from the uncertain set,and then a scenario EDP is obtaine...In this paper,an uncertain economic dispatch problem(EDP)is considered for a group of coopertive agents.First,let each agent extract a set of samples(scenarios)from the uncertain set,and then a scenario EDP is obtained using these scenarios.Based on the scenario theory,a prior certifcation is provided to evaluate the probabilistic feasibility of the scenario solution for uncertain EDP.To facilitate the computational task,a distributed solution strategy is proposed by the alternating direction method of multipliers(ADMM)and a fnite-time consensus strategy.Moreover,the distributed strategy can solve the scenario problem over a weight-balanced directed graph.Finally,the proposed solution strategy is applied to an EDP for a power system involving wind power plants.展开更多
The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessi...The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.展开更多
The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fue...The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.展开更多
This paper proposes a fixed-time distributed robust optimization approach for solving economic dispatch problems.Based on an integral sliding mode control scheme,the proposed multi-agent system converges to an optimal...This paper proposes a fixed-time distributed robust optimization approach for solving economic dispatch problems.Based on an integral sliding mode control scheme,the proposed multi-agent system converges to an optimal solution to an economic dispatch problem before a fixed time.In addition,the proposed multi-agent system can suppress the disturbance in a fixed time.To reduce the cost of sliding mode controls,we propose a distributed event-triggered intermittent control which reduces the sliding mode control time by setting a control triggering rule on the basis of two boundary functions of a Lyapunov function.The simulation results of three power systems illustrate the characteristics and effectiveness of the theoretical results.展开更多
文摘In this paper,an uncertain economic dispatch problem(EDP)is considered for a group of coopertive agents.First,let each agent extract a set of samples(scenarios)from the uncertain set,and then a scenario EDP is obtained using these scenarios.Based on the scenario theory,a prior certifcation is provided to evaluate the probabilistic feasibility of the scenario solution for uncertain EDP.To facilitate the computational task,a distributed solution strategy is proposed by the alternating direction method of multipliers(ADMM)and a fnite-time consensus strategy.Moreover,the distributed strategy can solve the scenario problem over a weight-balanced directed graph.Finally,the proposed solution strategy is applied to an EDP for a power system involving wind power plants.
基金funded by Guangxi Science and Technology Base and Talent Special Project,grant number GuiKeAD20159077Foundation of Guilin University of Technology,grant number GLUTQD2018001.
文摘The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.
文摘The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.
基金supported by the National Natural Science Foundation of China(Grant No.62173308)the Natural Science Foundation of Zhejiang Province of China(Grant Nos.LR20F030001 and LD19A010001)Jinhua Science and Technology Project(Grant No.2022-1-042)。
文摘This paper proposes a fixed-time distributed robust optimization approach for solving economic dispatch problems.Based on an integral sliding mode control scheme,the proposed multi-agent system converges to an optimal solution to an economic dispatch problem before a fixed time.In addition,the proposed multi-agent system can suppress the disturbance in a fixed time.To reduce the cost of sliding mode controls,we propose a distributed event-triggered intermittent control which reduces the sliding mode control time by setting a control triggering rule on the basis of two boundary functions of a Lyapunov function.The simulation results of three power systems illustrate the characteristics and effectiveness of the theoretical results.
