Utilizing RDPSO Algorithm for Economic-Environmental Load Dispatch Modeling Considering Distributed Energy Resources

There are many factors such as long construction time, high investment cost, and low competition between energy providers to justify penetrating the distributed energy resources (DERs) such as wind turbines (WTs) and demand response programs (DRPs) in power systems. The uncertainties about WTs' power output have increased the complexity of the economic-environmental load dispatch (EELD) problem. Because it is very difficult to predict the output power of wind farms, additional costs are imposed to the EELD problem. The mean amount of wind energy density has been used to define the storage and additional costs in the developed model. In addition, the DRPs have been facing the problem in reduction of cost. Demand response programs have been considered in two approaches; in the first approach, a certain percentage of the buses' load determines the maximum amount of participation of the DRPs and in the second one, a certain capacity of these programs determines the maximum amount of their participation. The efficiency of the developed model has been analyzed by simulation results on multi-area IEEE 118 bus test system. The operational constraints in the test system, including lines limit, supply-demand balance and the generation limit of generators, WTs, and DRPs have been considered in the EELD problem. Multi-objective Random Drift Particle Swarm Optimization (MORDPSO) has been used in this study to analyze the model. The effects of DERs have been analyzed on power loss, voltage profile, and static voltage stability of the test system.