article · Journal of Electrical and Computer Engineering
This research evaluated the performance of six voltage stability indices within a multiobjective optimal power flow framework for modern power systems. The study used a modified IEEE 30-bus system, incorporating thermal, wind, solar, and hybrid wind-hydro generators, under both normal and contingency conditions. The Multiobjective Mayfly Algorithm (MOMA) was employed for optimisation, with Fuzzy Decision-Making determining best compromise solutions. The findings indicate that the Voltage Collapse Proximity Index (VCPI) and Novel Line Stability Index (NLSI) offered the best-optimised system performance, leading to reduced generation costs, lower transmission losses, and faster simulation times. VCPI also supported the highest penetration of renewable energy sources. Furthermore, the MOMA algorithm proved more effective than several other multiobjective algorithms.
Optimising power system operations is crucial for ensuring reliable and cost-effective electricity supply. This research identifies superior methods for managing voltage stability and integrating renewable energy, which can lead to more efficient grids, lower energy costs, and a greater reliance on sustainable power sources.
This research provides foundational insights for developing advanced software tools for power system operators and planners. The identified superior voltage stability indices and optimisation algorithms could be integrated into energy management systems to improve grid efficiency, reduce operational costs, and enhance the integration of renewable energy sources. This appears to be applied research, tested in a simulated environment, suggesting it is several steps away from direct real-world deployment.
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The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.
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DOI: 10.1155/2022/7456333
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