A Decentralized Control Strategy for Voltage Regulators and Energy Storage Devices in Active Unbalanced Distribution Systems

Abstract

This paper studies a multi-objective optimization problem that optimizes unbalanced distribution networks with high penetration of photovoltaic power generation whereby the aim is to find the optimal tap positions of voltage regulators (VRs) and locally optimized charging and discharging power profiles for energy storage devices (ESDs) based on a decentralized coordination process. The corresponding objective function is based on the voltage profile of the network and the lifetime improvement of the VRs and ESDs. The Advanced Arithmetic Optimizer (AAO) algorithm combined with the profile steering approach is used to find a near-optimal solution for the problem. The decentralized process is compared to a centralized approach for the IEEE 13 and 123 bus systems. Results show that the proposed decentralized method is able to resolve all voltage problems and find good quality solutions in lower computational time compared to the centralized approach and other optimization algorithms.

An Optimization Framework for Distributed Energy Resource Planning and Energy Management Strategy of Storage Devices and Electric Vehicles

“…The algorithm is developed to find optimal control parameters of the problem for IEEE 33 and 69 bus systems. The optimality of the solution and convergence speed of the AGWO [17] method are tested by comparing the results obtained with the other well-known algorithms, including particle swarm algorithm (PSO) [18], Grey Wolf optimizer (GWO) [19], and advanced arithmetic optimization (AAO) algorithm [20].…”

Dynamic Hunting Leadership Optimization: Algorithm and Applications

“…These algorithms rely on mathematical equations and models to create new solutions and frequently incorporate the utilization of probability and statistics. Arithmetic optimization algorithm (AOA) [38], sine cosine algorithm (SCA) [39], Advance Arithmetic optimizer (AAO) [40], and Equilibrium optimizer (EO) [41] are examples of math-based metaheuristic methods.…”