A stochastic multi-objective optimization framework for distribution feeder reconfiguration in the presence of renewable energy sources and energy storages

摘要

In this paper, a multi-objective optimization framework is proposed to solve the distribution feeder reconfiguration (DFR) problem and its operation considering the demand response (DR) program, renewable energy sources (RES's), and electrical energy storages (EES's). The proposed model is implemented on 33-bus and 118-bus radial distribution systems while the uncertainties of RES's output power, load demand and electricity price are taken into account. The Monte Carlo simulation approach is used to generate scenarios while the backward scenario reduction approach is used to reduce the number of scenarios. The studied problem is modeled using the Epsilon-constrained method as a two objective problem and it is solved in the form of five case studies using the GUROBI solver in GAMS software. Our analysis of the results shows that reducing losses and increasing system reliability increases the production of local generation units, thereby increasing the operating costs. In addition, simulation results demonstrate that considering the dynamic topology reduced losses by 9.73% and increased reliability by 4.7%. The results also show that using the DR program reduces LMP by about 20% during peak hour.