Asset management optimization for generation, transmission and energy storage in electric power systems
This research paper introduces three distinct optimization models that aim to enhance asset management and energy storage systems. The first model takes into consideration asset aging, end-of-life, and decommissioning while focusing on generation and transmission expansion planning. The second model underscores the optimization of transmission line maintenance strategy by prioritizing assets effectively. Lastly, the third model optimizes an integrated gas-solar-battery energy storage system to ensure maximum efficiency and productivity. The purpose of this study is to analyze existing literature, identify research gaps, and set research goals. The study uses mixed-integer linear programming, analytical hierarchy process, and heuristic methodology to examine base case scenarios, carry out comparative analyses, and conduct sensitivity analyses. The results are presented through tables, figures, and detailed explanations after conducting computational experiments on Garver 6-bus and IEEE RTS 24-bus systems. According to the study, the asset management optimization model is more accurate in estimating costs for generation and transmission planning, considering asset aging, end-of-life, and decommissioning. The AHP framework also provides an enhanced asset prioritization technique for the optimization of transmission line maintenance strategies. The model prioritizes maintenance and retrofitting as the asset approaches end-of-life, avoiding replacement. The final model uses a single-stage strategic and operational technique to optimally configure the best solar and battery combination to integrate with a gas-powered generation unit at the point of the system subject to variable locational marginal pricing and photovoltaic generation. Results show that this technique improves cost performance over the heuristic model and makes extensive use of the battery state of charge. The study discusses theoretical and practical implications and identifies several opportunities for future research.
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