Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems
This paper presents a comprehensive long-term stochastic mixed-integer single-level single-stage nonlinear multi-objective optimization planning model for integrating Distributed Energy Resources (DERs), including wind Distributed Generations (DGs), photovoltaic (PV) DGs, stationary Battery Energy S...
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Elsevier
2025-05-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X2500046X |
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| author | Ahmad K. ALAhmad Renuga Verayiah Hussain Shareef Agileswari Ramasamy Saleh Ba-swaimi |
| author_facet | Ahmad K. ALAhmad Renuga Verayiah Hussain Shareef Agileswari Ramasamy Saleh Ba-swaimi |
| author_sort | Ahmad K. ALAhmad |
| collection | DOAJ |
| description | This paper presents a comprehensive long-term stochastic mixed-integer single-level single-stage nonlinear multi-objective optimization planning model for integrating Distributed Energy Resources (DERs), including wind Distributed Generations (DGs), photovoltaic (PV) DGs, stationary Battery Energy Storage Systems (SBESSs), and mobile Battery Energy Storage Systems (MBESSs), over a 10-year project horizon. The model evaluates the efficiency and cost-effectiveness of hybrid SBESS-MBESS systems to enhance Renewable Energy Source (RES) integration within the electric power distribution system (DS) while addressing technical, environmental, and economic objectives. It minimizes total expected planning, operation, and emission costs, power loss, and voltage deviation by determining the optimal locations and capacities for wind DGs, PV DGs, and SBESSs, and by establishing a monthly transportation schedule for MBESSs. The optimization also coordinates the charging and discharging profiles of SBESSs and MBESSs to maximize green energy utilization and minimize system costs. Monte Carlo Simulation (MCS) models uncertainties in wind speed, solar irradiation, load power, and energy prices, while the backward reduction method (BRM) mitigates computational complexities. A hybrid optimization approach combining the non-dominated sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO) with a decision-making algorithm is proposed to solve the planning problem. Simulations on a 69-bus DS demonstrate significant reductions in long-term costs (37.72 %), power loss (41.58 %), and voltage deviation (47.07 %) achieved by the hybrid SBESS-MBESS system compared to other configurations, underscoring its potential to enhance renewable energy integration and system performance in transitioning energy systems. |
| format | Article |
| id | doaj-art-e0b4adca409941529ffa665606923f0c |
| institution | OA Journals |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-e0b4adca409941529ffa665606923f0c2025-08-20T02:05:12ZengElsevierEnergy Strategy Reviews2211-467X2025-05-015910168310.1016/j.esr.2025.101683Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systemsAhmad K. ALAhmad0Renuga Verayiah1Hussain Shareef2Agileswari Ramasamy3Saleh Ba-swaimi4Institute of Power Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, MalaysiaInstitute of Power Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, MalaysiaDepartment of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates; Emirates Center for Mobility Research, United Arab Emirates University, Al Ain, United Arab Emirates; Corresponding author. Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates.Institute of Power Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, MalaysiaInstitute of Power Engineering, Universiti Tenaga Nasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia; Department of Electronic and Communications Engineering, College of Engineering & Petroleum, Hadramout University, Mukalla, YemenThis paper presents a comprehensive long-term stochastic mixed-integer single-level single-stage nonlinear multi-objective optimization planning model for integrating Distributed Energy Resources (DERs), including wind Distributed Generations (DGs), photovoltaic (PV) DGs, stationary Battery Energy Storage Systems (SBESSs), and mobile Battery Energy Storage Systems (MBESSs), over a 10-year project horizon. The model evaluates the efficiency and cost-effectiveness of hybrid SBESS-MBESS systems to enhance Renewable Energy Source (RES) integration within the electric power distribution system (DS) while addressing technical, environmental, and economic objectives. It minimizes total expected planning, operation, and emission costs, power loss, and voltage deviation by determining the optimal locations and capacities for wind DGs, PV DGs, and SBESSs, and by establishing a monthly transportation schedule for MBESSs. The optimization also coordinates the charging and discharging profiles of SBESSs and MBESSs to maximize green energy utilization and minimize system costs. Monte Carlo Simulation (MCS) models uncertainties in wind speed, solar irradiation, load power, and energy prices, while the backward reduction method (BRM) mitigates computational complexities. A hybrid optimization approach combining the non-dominated sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO) with a decision-making algorithm is proposed to solve the planning problem. Simulations on a 69-bus DS demonstrate significant reductions in long-term costs (37.72 %), power loss (41.58 %), and voltage deviation (47.07 %) achieved by the hybrid SBESS-MBESS system compared to other configurations, underscoring its potential to enhance renewable energy integration and system performance in transitioning energy systems.http://www.sciencedirect.com/science/article/pii/S2211467X2500046XRenewable energy sourcesLong-term planningDistribution systemStationary battery energy storage systemMobile battery energy storage system |
| spellingShingle | Ahmad K. ALAhmad Renuga Verayiah Hussain Shareef Agileswari Ramasamy Saleh Ba-swaimi Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems Energy Strategy Reviews Renewable energy sources Long-term planning Distribution system Stationary battery energy storage system Mobile battery energy storage system |
| title | Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems |
| title_full | Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems |
| title_fullStr | Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems |
| title_full_unstemmed | Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems |
| title_short | Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems |
| title_sort | enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources wind pv sbess mbess in distribution systems |
| topic | Renewable energy sources Long-term planning Distribution system Stationary battery energy storage system Mobile battery energy storage system |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X2500046X |
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