Optimal Placement of Battery Swapping Stations for Power Quality Improvement: A Novel Multi Techno-Economic Objective Function Approach - Al-Zaidi, Waleed Khalid Mahmood; Inan, Aslan (MDPI, 2024)
In recent years, battery swapping stations have become increasingly popular in smart energy networks. Its advantages include reducing the time required for recharging energy, balancing the grid's load, and extending the battery's lifespan. Despite efforts focused on the placement and operation of battery swapping stations (BSSs), there is still a lack of a comprehensive and systematic examination that covers all aspects of both the economic and technical aspects of the power network. This encompasses considerations such as power quality and reliability, particularly in terms of where these stations should be located. This paper introduces a novel framework for strategically positioning BSS within smart microgrids that integrate distributed energy resources (DERs). It takes into account various technical factors (such as reliability and power quality) and economic factors (like the cost of generation and operation), which have been overlooked in the previous research. To achieve this goal, a unique hybrid optimization strategy is developed, incorporating a combination of epsilon-constraint and lexicographic (DECL) optimization methods. This approach tackles a multi-objective challenge, treating factors like the number, locations, and sizes of BSS as independent variables, while operational costs and technical power quality metrics are considered dependent variables. To validate this approach, it is tested on standard benchmark distribution power networks such as IEEE 33, 69, and 118 bus systems. The simulation results, showcasing the strengths and capabilities of this innovative strategy, are compared to the findings of previous research studies.
Keywords: battery swapping station (BSS); optimal placements; power quality indices; hybrid DECL multi-objective optimization; DISTRIBUTION-SYSTEMS; RECONFIGURATION; REDUCTION; OPERATION; ALGORITHM; Energy & Fuels