Stochastic scheduling of energy storage systems in harmonic polluted active distribution networks

Abstract

Harmonics originating from non-linear loads in distribution systems will cause undesired effects and malfunctions of equipment. It is important to use efficient techniques for eliminating the voltage and current harmonic components. However, the uncertain nature of the active distribution networks (ADN) makes it difficult to apply these techniques, efficiently. Renewable energy sources (RESs), such as wind turbines (WTs) besides load fluctuations are the most important uncertainty sources. This study employs energy storage systems (ESS) to reduce total harmonic distortion (THD) as well as the ADN operating costs. In addition to the ESS, network structure reconfiguration and reactive power scheduling is presented for a more efficient operation of ADN. Also, the uncertainty of the network is modelled by the K-means and LHS methods. This methodology is conducted under a multi-objective optimization problem, which is handled by a multi-objective particle swarm optimization (MOPSO) algorithm. Furthermore, a technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to obtain the best compromise between two objectives. The efficacy of the proposed methodology is investigated in the IEEE 33 bus test system. Also, for validating the results, a large-scale network, the IEEE 118 bus test system is surveyed, too.