A Multi-Objective Coati Optimization Approach for Integrated DGs and D-STATCOMs in Active Distribution Networks Under Uncertainty

Abstract

The intermittent nature of distributed generators based on renewable energy sources (DGs-RESs), together with the time-varying behavior of load demand, introduces significant uncertainty into the planning and operation of active distribution networks. These uncertainties make the optimal siting and sizing of DGs-RESs and D-STATCOMs a challenging multi-objective optimization problem. This paper proposes a multi-objective Coati Optimization Algorithm (MOCOA) for the coordinated allocation of DGs-RESs and D-STATCOMs in radial distribution networks under uncertainty. The proposed framework aims to minimize total active power losses (TAPLs) and enhance the voltage stability index (VSI) while satisfying the operational constraints of the distribution system. First, the load sensitivity factor (LSF) is employed to identify the most suitable candidate buses, thereby reducing the search space and improving the computational efficiency of the optimization process. Then, MOCOA is applied to determine the optimal placement and sizing of DGs-RESs and D-STATCOMs. The uncertainties associated with load demand, solar irradiance, and wind speed are modeled using probabilistic representations, and reduced representative scenarios are considered to evaluate system performance under uncertain operating conditions. The proposed method is validated using modified IEEE 33-bus and IEEE 69-bus radial distribution networks. The simulation results demonstrate that the coordinated integration of DGs-RESs and D-STATCOMs significantly reduces TAPLs, improves the VSI, and enhances the voltage profile. In particular, increasing the number of DG/D-STATCOM units and using wind energy reduces the TAPL by 26.95% and increases the 24 h cumulative VSI from 20.16781 p.u. to 20.4162 p.u. Comparative results with other optimization techniques confirm the effectiveness, robustness, and superior performance of the proposed MOCOA for uncertainty-aware planning of active distribution networks.