article · Sustainability
Flexible AC Transmission Systems (FACTS) devices enhance power system performance by improving voltage profiles, lowering power losses, and increasing operational reliability and safety. Determining the ideal type, location, and sizing for these devices presents an intricate mathematical challenge defined by mixed integer, non-linear, and non-convex constraints. Optimization approaches in the field fall into four distinct categories: classical optimization techniques, metaheuristic algorithms, analytic methods, and mixed or hybrid schemes. Classical strategies employ conventional mathematical formulations, whereas metaheuristic algorithms deploy stochastic searches capable of managing non-convex spaces. Analytic methods apply sensitivity analysis alongside gradient-based mechanisms, while hybrid tools combine multiple algorithms to boost solution accuracy. Evaluating the performance of these disparate methods assists in matching algorithms to specific network problems, while future progress depends on addressing structural complexity and integrating operational uncertainties into models.
Modern electrical grids require precise control to maintain stability, prevent power failures, and minimise costly energy losses. Correctly positioning FACTS equipment allows utilities to run transmission networks closer to their capacity limits safely. Understanding which computational methods best solve placement and sizing problems helps grid engineers design robust, efficient transmission networks without relying on expensive and risky trial-and-error installations.
Transmission system operators, electric utilities, and grid-planning software vendors can use these methodological comparisons to select optimization tools for network expansion. Because the work evaluates existing algorithmic approaches across published literature rather than testing a proprietary tool, it sits at an early conceptual stage. Real-world application would require integrating these optimization algorithms into commercial power-system planning software to handle actual operational uncertainties and field constraints.
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The use of FACTS devices in power systems has become increasingly popular in recent years, as they offer a number of benefits, including improved voltage profile, reduced power losses, and increased system reliability and safety. However, determining the optimal type, location, and size of FACTS devices can be a challenging optimization problem, as it involves mixed integer, nonlinear, and nonconvex constraints. To address this issue, researchers have applied various optimization techniques to determine the optimal configuration of FACTS devices in power systems. The paper provides an in-depth and comprehensive review of the various optimization techniques that have been used in published works in this field. The review classifies the optimization techniques into four main groups: classical optimization techniques, metaheuristic methods, analytic methods, and mixed or hybrid methods. Classical optimization techniques are conventional optimization approaches that are widely used in optimization problems. Metaheuristic methods are stochastic search algorithms that can be effective for nonconvex constraints. Analytic methods involve sensitivity analysis and gradient-based optimization techniques. Mixed or hybrid methods combine different optimization techniques to improve the solution quality. The paper also provides a performance comparison of these different optimization techniques, which can be useful in selecting an appropriate method for a specific problem. Finally, the paper offers some advice for future research in this field, such as developing new optimization techniques that can handle the complexity of the optimization problem and incorporating uncertainties into the optimization model. Overall, the paper provides a valuable resource for researchers and practitioners in the field of power systems optimization, as it summarizes the various optimization techniques that have been used to solve the FACTS optimization problem and provides insights into their performance and applicability.
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DOI: 10.3390/su15129348
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