Analysis of integration of Distributed Energy Resources (DERs) in Micro Grid under Demand Response Programs

Abstract

The microgrid is a new genre of integrating the distributed energy resources (DER) within the grid. However, literature studies with the consideration of RERs uncertainty and DRPs of grid-connected EVs integrated residential PVWT-FC-DE based community rural microgrid (MG) by employing single objective problem using ABC/PSO algorithms is missing. This work modeled a household energy management comprising of microgrid (MG) system and DRPs. Residential loads with price-based tariffs are introduced for reduction in peak load demands and energy costs. For modeling uncertainties in RERs, their stochastic nature is modeled with probabilistic method. In this paper, a joint optimization approach is proposed for the optimal planning and operation of grid-connected residential rural MG integrated to renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated to MG by including photovoltaic (PV), wind turbines (WT), fuel cell (FC) and diesel engine (DE). A multi objective optimization problem has been formulated to minimize the Operating Cost, Pollutant Treatment Cost and Carbon Emissions Cost defined as C1, C2, and C3 respectively. The load demand has been rescheduled in view of three DRPs i.e., critical peak pricing (CPP), real time electricity pricing (RTEP), time of use (TOU). Further, the EV load has also been analyzed in the form of autonomous and coordinated charging strategies. The proposed multi objective problem is transformed into a single objective problem using artificial bee colony (ABC) algorithm and the results are compared with particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different number of EVs and load data inviii terms of reducing different costs. To analyze the impact of DRPs, the residential rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs respectively. The simulation results with the total 32 test cases are formulated, while analyzing the tradeoff between ABC and PSO algorithms. The simulation analysis shows that multiple DRPs, EVs, and RERs offered substantial trade-off.