Optimal Sizing and Placement of DGs to Reduce the Fuel Cost and T& D Losses by using GA & PSO optimization Algorithms

Abstract

Distributed Generators helps in limiting the expense for power to customers, calm the system clog and, give non-polluted power close to the load centers. Its ability can quantifiable and versatile, it gives voltage support. Thus, the installation and penetration of DG is an impressive issue for both the customers and the suppliers of DG. The rebuilt control markets are gradually developing with Standard Market Design. The guideline feature of the SMD is Location Marginal Pricing (LMP) plans. The proposed methodology is shown by the contextual investigation on IEEE 30 bus framework. Optimum Power Flow (OPF) method has been broadly utilized for control, planning and operation of a power system n/w. A typical OPF solution that adjusts the relevant control variables in order to optimize (maximize or minimize) the specific objective of constraints imposed by the electrical network. OPF is the ideal environment for deregulation. The primary issue in the placement of DGs is finding economically viable sites and corresponding MWs. DGs are placed based on the nodal LMPs, the Price per unit is obtained at the node for corresponding LMPs at the node. As it will yield the highest returns, a node having largest LMP is the candidate to locate the DG. GA and PSO are used to find the optimum size of DG evolutionary techniques. The targets include decreasing T& D losses and optimizing the system voltage profile, taking due account of fixed and variable costs. The problem of DG placement in deregulated environment for Optimal Power Flow (OPF) solved by the optimization techniques of Particle Swarm optimization (PSO) and Genetic Algorithm (GA). The results obtained by the two methods were compared and observed that PSO producing best suited for this problem than GA.