Probabilistic multi-objective energy management of a distribution system considering reactive power injection by voltage source converters

Uncertainties and variability of renewable generation pose scheduling and operational challenges at the distribution level. Further, the introduction of electric vehicle charging load modifies the existing load demand pattern and introduces additional uncertainties. Therefore, an efficient energy management approach, considering input uncertainties, and involving active and reactive power dispatch is imperative for a safe, profitable, and environment-friendly operation. A multi-objective energy management scheme in a probabilistic framework is proposed in this work. The energy management scheme aims to maximize the expected daily profit, improve voltage stability, and reduce emissions from power generation activities. Probabilistic models of input uncertainties (renewable generation, load demand, demand for electric vehicles, grid energy price) are formulated by adopting the “Hong’s point estimate method” and incorporated into the problem. Renewable units are interfaced using voltage source converters. Voltage source converters can simultaneously regulate active and reactive power injections to the network, which is explored in this work. The energy management scheme involves optimally coordinating activities like active and reactive power procurement from renewable units, energy management of batteries, optimal control of a smart transformer, and implementation of a demand response program. The multi-objective problem is modelled in the fuzzy domain and solved using a multi-stage approach involving linear programming, dynamic programming, and particle swarm optimization. A thirty-three-bus distribution network is used in simulation studies for validation. Studies confirm that the proposed method increases the profit by \(\sim 67.25\%\), reduces emission by \(\sim 17.08\%\), and improves the voltage stability by \(\sim 41.66\%\).