Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties

With the advent of DC-powered renewable energy sources (RESs), the interest towards DC microgrid (DC mG) networks has gained attention recently. Integrating RESs has opened new challenges in handling the diversified application problems. The present work proposes an optimal operation strategy for droop-controlled islanded DC mGs considering the uncertainties involved in system variables along with the effect of correlation among them. A new point estimation technique, modified Gauss Quadrature based Point Estimate Method (GQ-PEM) is developed in this paper to model the uncertainties in load demand and solar generation. In this regard the mean and standard deviation errors of proposed method for 4-bus and 6-bus systems were minimum compared to the other existing techniques having errors of 0.00010082, 0.057165401 and 3.85333E-06, 0.059906462 respectively. Based on this a stochastic optimal power flow (SOP F) problem is formulated in DC mG environment with diversified objectives. The formulated SOP F problem is solved by new heuristic, dragonfly algorithm (DA), to obtain the optimal droop parameters for the modified 6-bus islanded DC mG test network. The suitable comparisons were made with other well-known heuristics; namely, the Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm (NSGA-II), to validate the proposed approach. In addition to that, the effect of correlations was investigated with suitable NaTaf transformation (NaT) embedded within the proposed GQ-PEM. Various simulations pertaining to optimality and correlations were carried out to assess the robustness involved in the proposed approach.

Automated summary

This paper proposes an optimal operation strategy for droop-controlled islanded DC microgrids by considering the uncertainties and correlations in system variables. A new point estimation technique is developed to model the uncertainties in load demand and solar generation. The stochastic optimal power flow problem is solved using the dragonfly algorithm to obtain the optimal droop parameters. The proposed approach is validated through comparisons with other well-known heuristics.