Renewable energy and demand uncertainty-aware stochastic allocation and management of soft open points for simultaneous reduction of harmonic distortion, voltage deviations and losses

Abstract

The uncertainty of renewable energies and demand complicates the management of harmonically polluted distribution networks. Power electronics-based soft open points (SOPs) are a promising solution as they can precisely control the power flow in the network. This paper proposes a novel stochastic SOP allocation and management approach by properly optimizing its operational set points. The proposal's key emphasis is simultaneously alleviating harmonic distortion, voltage deviation, and power loss by the optimal allocation and management of the SOPs. This is realized through optimal control of active and reactive power flow and the cautious injection of harmonic currents through the allocated and managed SOPs. The proposal employs the K-means data clustering technique to discern appropriate parameters’ uncertainties, while the Cholesky decomposition method and the Nataf transformation technique are combined to handle the existing correlations amongst various uncertainties proficiently. The proposal uses the non-dominated sorting genetic algorithm II (NSGA-II) to solve the formulated optimization problem by extracting the Pareto front solutions set, while the final solution is selected using the technique of ordering the preference by similarity to the ideal solution (TOPSIS). The proposal's performance is evaluated and verified through numerical studies on modified IEEE 33 and 118 bus networks.