A new Stochastic Hybrid Technique for DER Problem

This paper presents a new Hybrid Particle Swarm optimization with Time Varying Acceleration Coefficients (HPSOTVAC) and Bacteria Foraging Algorithm (BFA) namely (PSOTVAC/BFA) base fuzzy stochastic long term approach for determining optimum location and size of Distributed Energy Resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other and then they are introduced to a PSOTVAC/BFA algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. IEEE 30-bus radial distribution test system is used as an illustrative example to show the effectiveness of the proposed method.