Optimal STATCOM allocation using mixed integer distributed ant colony optimisation

Abstract

The global demand for electricity is rising rapidly, mainly due to the speeding up of industrialisation, the rapid growth of urbanisation, and the gradual integration of renewable energy sources in the last decades. As a result, the electrical power grids are required to operate at a high capacity near critical power angles and voltage limits, which increases the risk of affecting the electrical grid's voltage stability. This challenge is particularly significant as existing power grids evolve towards greater sustainability through the incorporation of renewable energy and the pursuit of net-zero carbon emissions, a goal actively supported and regulated by the UK government to achieve by 2050. However, enhancing the voltage stability of electrical grids is crucial to enabling the higher penetration of renewable energy resources, such as wind turbines, which can affect voltage stability and may compromise grid stability. In this environment, the Static Synchronous Compensator (STATCOM) plays an important role in enhancing the power system voltage stability by generating and absorbing reactive power; thus, it is essential to allocate the STATCOM at the optimal location with the optimal size in the grid to get the greatest results out of it. This thesis presents a novel optimisation approach to optimise the STATCOM allocation using Mixed Integer Distributed Ant Colony Optimisation (MIDACO) to enhance the voltage stability at minimum STATCOM installation cost. The key contribution of this thesis is providing the first attempt at utilising MIDACO in the load flow analysis for optimal STATCOM allocation and validates the MIDACO-based optimisation approach through its application to three different standard IEEE test systems: 14-Bus, 57-Bus, and 118-Bus. In addition to that, this thesis shows the impact of the different MIDACO parameters on the optimal STATCOM allocation for each test system and compares the MIDACO performance with three well-known metaheuristics algorithms: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Artificial Bee Colony (ABC). The results showed that MIDACO outperformed the other metaheuristics algorithms in the comparison, offering a fast and accurate tool for STATCOM allocation optimisation problems at different electrical grid sizes.