Distribution System Performance Improvement using Meta-heuristic Optimization Algorithms

Abstract

The critical challenge for an efficient islanding operation of a distribution system having Distributed Generation (DG) is preserving the frequency and voltage stability. Contemporary load shedding schemes are inefficient and do not adequately assess the optimum amount of load to shed which results in either excessive or inadequate load shedding. Appropriate installation of renewable energy-based distributed generation units (RDGs) is one of the most important challenges and current topics of interest in the optimal functioning of modern power networks. Due to the intermittent nature of renewable energy sources, optimal allocation and sizing of RDGs, particularly photovoltaic (PV) and wind turbine (WT), remains a critical task. Additionally, maintaining frequency and voltage stability is crucial for optimal functioning of an islanded network connected to DGs. Conventional load shedding schemes do not effectively identify the optimal amount of load to shed, culminating in either excessive or insufficient load shedding. Hence, the first part of this work presents an optimal load shedding technique using Chaotic Slime Mould Algorithm (CSMA) with sinusoidal map in order to achieve greater efficiency. A constrained function with static voltage stability margin (VSM) index and total remaining load after load shedding was applied to accomplish the evaluation. A total of three islanding scenarios of IEEE 33 bus and IEEE 69 bus radial distribution systems were used as test systems to assess the efficacy of the proposed load shedding approach using MATLAB software. To identify performance enhancements, the developed method was compared to Backtrack Search Algorithm (BSA) and the original SMA. According to the results, CSMA outperforms both BSA and SMA in terms of remaining load and voltage stability margin index values in all the test systems. Moreover, the second part of this work proposes Chaotic Equilibrium Optimizer (CEO) with iterative map to achieve an optimal solution for multiple DG sizing and placement in distribution networks, as well as an optimal load shedding approach. Regarding DG placement, the objective function was to minimize total active power loss and voltage deviation of the network nodes. The proposed method was compared with Modified moth flame optimization (MMFO), Teaching learning based optimization (TLBO) and the original Equilibrium optimizer (EO). Moreover, to assess the optimal load shedding technique, a constrained function with total remaining load and static voltage stability margin (VSM) index was used. In addition, the proposed CEO algorithm is compared with some of the recent metaheuristics algorithms applied in this domain such as Grasshopper optimization algorithm (GOA), Backtrack search algorithm (BSA) and the original Equilibrium optimizer (EO). In the last part of the work, based on a new metaheuristic known as the Artificial hummingbird algorithm (AHA), this work provides a novel approach for addressing the problem of RDG planning optimization. Considering various operational constraints, the optimization problem is developed with multiple objectives including power loss reduction, voltage stability margin (VSM) enhancement, voltage deviation minimization, and yearly economic savings. Furthermore, using relevant probability distribution functions, the ambiguities related with the stochastic nature of PV and WT output powers are evaluated. The proposed algorithm was compared to two of the recent metaheuristics applied in this domain known as improved harris hawks and particle swarm optimization algorithm (HHO-PSO) and hybrid of phasor particle swarm and gravitational search algorithm (PPSOGSA). The IEEE 33-bus and 69-bus systems are assessed as the test systems in this study. According to the findings, AHA delivers superior solutions and enhances the techno-economic benefits of distribution systems in all the scenarios evaluated.