A solution strategy for distributed uncertain economic dispatch problems via scenario theory

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.

Development of Hybrid Algorithm Based on PSO and NN to Solve Economic Emission Dispatch Problem

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.

Fixed-time distributed robust optimization for economic dispatch with event-triggered intermittent control

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.