A novel stochastic framework based on PEM-DPSO for optimal operation of microgrids with demand response

Abstract

This article assesses the optimal operation and management of the microgrids considering the dispatchable units, demand response (DR) and uncertainty effects. A new formulation is developed which not only models the DR benefits, but also handles the air pollutions due to the power units operation. Moreover, a new term is added to the objective function which incorporates the customers discomfort due to the DR plans. The overall social welfare objective function consists of DGs operation and storage costs, the cost of utilizing the hydrogen tank and other DG resources, the cost of pollutions and the cost of discomfort function. In order to solve the above nonlinear problem, a new optimization method based on particle swarm optimization and a novel three-phase modification method is introduced. The discomfort behavior is modeled using the Taguchi function to convert the DR time interval usages with real cost. In order to have a practical analysis, the time of use (TOC) electricity tariff is also deployed by the upstream network in the DR programs. The proposed method is then implemented on a sample network and validated in four different scenarios of operation. The simulation results show the high performance, and effectiveness of the proposed formulation for the DR problem in the microgrids. It is seen that the DR program with optimal power scheduling could reduce around 30 % in operating costs and about 12 % in the air pollution. In addition, it is seen that although considering the emission as an objective function can increase the operation costs, but the DR program can play a significant role in mitigating the additional operating cost. Moreover, both cost and emission functions increased by modeling the uncertainty effects through the proposed point estimation approach.