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

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  • O., Yugeswar Reddy
  • J., Jithendranath
  • Chakraborty, Ajoy Kumar
  • Guerrero, Josep M.

Abstract

With the advent of DC-powered renewable energy sources (RESs), the interest towards DC microgrid (DCmG) 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 DCmGs 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 (SOPF) problem is formulated in DCmG environment with diversified objectives. The formulated SOPF problem is solved by new heuristic, dragonfly algorithm (DA), to obtain the optimal droop parameters for the modified 6-bus islanded DCmG 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.

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  • O., Yugeswar Reddy & J., Jithendranath & Chakraborty, Ajoy Kumar & Guerrero, Josep M., 2022. "Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties," Applied Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921013726
    DOI: 10.1016/j.apenergy.2021.118090
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    5. Mohamed A. M. Shaheen & Hany M. Hasanien & Said F. Mekhamer & Mohammed H. Qais & Saad Alghuwainem & Zia Ullah & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-23, August.
    6. Mohamed S. Hashish & Hany M. Hasanien & Haoran Ji & Abdulaziz Alkuhayli & Mohammed Alharbi & Tlenshiyeva Akmaral & Rania A. Turky & Francisco Jurado & Ahmed O. Badr, 2023. "Monte Carlo Simulation and a Clustering Technique for Solving the Probabilistic Optimal Power Flow Problem for Hybrid Renewable Energy Systems," Sustainability, MDPI, vol. 15(1), pages 1-25, January.

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