Improving microgrid hosting capacity: A two-stage BONMIN solver-based framework for battery storage allocation and operational energy management strategy

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Vydáno v:PLoS One vol. 20, no. 5 (May 2025), p. e0323525
Hlavní autor: Ali, Ziad M
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Public Library of Science
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100 1 |a Ali, Ziad M 
245 1 |a Improving microgrid hosting capacity: A two-stage BONMIN solver-based framework for battery storage allocation and operational energy management strategy 
260 |b Public Library of Science  |c May 2025 
513 |a Journal Article 
520 3 |a The growing concerns over fossil fuel dependency, environmental impacts, and escalating energy expenses highlight the critical importance of enhancing energy system efficiency. This study presents a dual-phase optimization approach for improving grid-connected microgrid (μG) operations, focusing on Sodium-Sulfur (NaS) and Sodium Nickel Chloride (Na-NiCl₂) battery storage systems. The problem was structured as a mixed-integer nonlinear programming (MINLP) model and resolved using GAMS software with its embedded open-source BONMIN solver. The initial phase establishes optimal battery storage system (BSS) allocation methods to optimize renewable energy source (RES) self-consumption (SC), increase hosting capacity (HC), and minimize operational expenses. Building on these results, the second phase develops optimal microgrid operational strategies to reduce total operating costs further. The research evaluates five scenarios with incrementally increasing the number of BSSs, ranging from one to five units. Through this systematic analysis, the work demonstrates that both the quantity and type of BSS units significantly impact μG operating costs. The most efficient configuration emerged in Case 3, where three Na-NiCl₂ BSS units achieved a 32.35% reduction in operating costs. Additionally, the integration of BSS demonstrated notable improvements in both HC and SC rates. 
653 |a Energy management 
653 |a Integer programming 
653 |a Distributed generation 
653 |a Clean technology 
653 |a Emissions 
653 |a Optimization techniques 
653 |a Sulfur 
653 |a Air pollution 
653 |a Solvers 
653 |a Nickel chloride 
653 |a Energy storage 
653 |a Outdoor air quality 
653 |a Operating costs 
653 |a Energy resources 
653 |a Energy consumption 
653 |a Sodium 
653 |a Climate change 
653 |a Nonlinear programming 
653 |a Innovations 
653 |a Electricity 
653 |a Fossil fuels 
653 |a Storage systems 
653 |a Energy industry 
653 |a Costs 
653 |a Carbon 
653 |a Environmental impact 
653 |a Renewable energy sources 
653 |a Renewable energy 
653 |a Renewable resources 
653 |a Sustainability 
653 |a Optimization 
653 |a Linear programming 
653 |a Mixed integer 
653 |a Alternative energy sources 
653 |a Economic 
773 0 |t PLoS One  |g vol. 20, no. 5 (May 2025), p. e0323525 
786 0 |d ProQuest  |t Health & Medical Collection 
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