Optimal siting and sizing of distributed generation units and energy storage systems for reconfigurable distribution networks

This paper presents an integrated model for optimal siting and sizing of distributed generation (DG) and energy storage (ES) units for radial distribution networks. The developed model involves strategically determining the optimal network topology for the reconfigurable distribution networks using optimal switching actions while keeping the radial topology of the network. The model investigates the branch flow model for handling the multi-temporal optimal power flow (MTOPF) problem and integrates the MTOPF into the optimal planning problem. The developed model is represented as a standard mixed-integer quadratically constrained programming (MIQCP) model and solved by the CPLEX solver. Simultaneous siting and sizing of both DG and ES units and network reconfiguration have been simulated in this paper as a complex optimization model. The developed model aims to maximize the benefits of DG and ES units' integration while mitigating potential technical issues and enhancing overall system performance with flexible switching actions. The proposed strategy has been approved by several benchmarks, and simulation results demonstrate the superiority of the MIQCP model in handling such complex optimization problems for optimal planning of distribution networks. The simulation results on the modified IEEE 33-bus test system are demonstrated in this paper. Using the MTOPF-based optimal planning approach, the daily operational cost decreases from $148,125.262 to $134,451.206, i.e., a reduction of 9.23%, without network reconfiguration. When optimal network reconfiguration is incorporated, along with optimal siting and sizing of distributed energy resources (DERs), the cost further decreases from $146,090.461 to $133,932.247, corresponding to an 8.32% reduction. Compared to the base-case scenario, the stochastic programming approach reduces the expected cost and energy losses by 20.15% and 54%, respectively.