{"title":"电动汽车与太阳能光伏系统集成配电网中电池储能技术的技术经济分析","authors":"Chukwuemeka Emmanuel Okafor , Saheed Lekan Gbadamosi , Senthil Krishnamurthy , Mukovhe Ratshitanga , Prathaban Moodley","doi":"10.1016/j.egyr.2025.06.008","DOIUrl":null,"url":null,"abstract":"<div><div>This study presents a simulation, optimization, and assessment of economic impacts of a grid-connected solar PV system with electric vehicles (EVs) and various battery energy storage systems (BESS) for distribution network systems. Four optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Direct Search Optimization (DSO), and Grey Wolf Optimizer (GWO) were deployed in determining the optimal parameters for energy storage deployment in the distribution networks integrated with EVs and solar photovoltaic (PV) systems. Two real-world distribution network systems were modeled for time-based (quasi-dynamic) simulations: the 30-bus South African distribution network and the 49-bus distribution network of Baghdad City, Iraq. These models were used to evaluate the impacts of integrating solar PV, electric vehicles (EVs), and battery energy storage systems (BESS) on voltage profiles and active power losses. The simulation results were further optimized to determine the optimal BESS placement within the proposed networks using GA, PSO, DSO and GWO techniques. An economic analysis was conducted to optimize the integration of BESS with EVs and solar photovoltaic (PV) systems. The study considered various battery storage configurations, including grid + time-dependent loads (TDLs), grid + PVs + TDLs, grid + PVs + EVs + TDLs, grid + PVs + TDLs + Li-ion BESS, grid + PVs + TDLs + Surrette BESS, and grid + PVs + TDLs + Trojan BESS. These configurations were analyzed to assess their impact on power losses and voltage deviations within the distribution networks. The results showed promising outcomes, with the total Net Present Cost (NPC) ranging from $8.41 million to $10.03 million across all configurations. Li-ion BESS emerged as the most cost-effective option, achieving the lowest annualized cost. The PSO algorithm outperformed the GA in optimization, determining an optimal BESS size of 0.1126 MW. Systems utilizing Li-ion BESS consistently demonstrated superior performance to those with Surrette and Trojan BESS, achieving the lowest net energy purchases, peak demands, and energy costs while generating significant revenue from excess energy sales to the grid. Li-ion BESS is the most cost-effective and sustainable solution due to its high efficiency, energy density, and long lifecycle. For the real-world 30-bus South African distribution network, PSO and GWO are preferred due to their efficient utilization of BESS, achieving optimal performance with lower storage investment. For the larger 49 bus network, GWO offers a more balanced and robust solution, effectively maintaining acceptable power losses and voltage quality through appropriate BESS allocation.</div></div>","PeriodicalId":11798,"journal":{"name":"Energy Reports","volume":"14 ","pages":"Pages 579-599"},"PeriodicalIF":4.7000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Techno-economic analysis of battery storage technologies in distribution networks with integrated electric vehicles and solar PV systems\",\"authors\":\"Chukwuemeka Emmanuel Okafor , Saheed Lekan Gbadamosi , Senthil Krishnamurthy , Mukovhe Ratshitanga , Prathaban Moodley\",\"doi\":\"10.1016/j.egyr.2025.06.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study presents a simulation, optimization, and assessment of economic impacts of a grid-connected solar PV system with electric vehicles (EVs) and various battery energy storage systems (BESS) for distribution network systems. Four optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Direct Search Optimization (DSO), and Grey Wolf Optimizer (GWO) were deployed in determining the optimal parameters for energy storage deployment in the distribution networks integrated with EVs and solar photovoltaic (PV) systems. Two real-world distribution network systems were modeled for time-based (quasi-dynamic) simulations: the 30-bus South African distribution network and the 49-bus distribution network of Baghdad City, Iraq. These models were used to evaluate the impacts of integrating solar PV, electric vehicles (EVs), and battery energy storage systems (BESS) on voltage profiles and active power losses. The simulation results were further optimized to determine the optimal BESS placement within the proposed networks using GA, PSO, DSO and GWO techniques. An economic analysis was conducted to optimize the integration of BESS with EVs and solar photovoltaic (PV) systems. The study considered various battery storage configurations, including grid + time-dependent loads (TDLs), grid + PVs + TDLs, grid + PVs + EVs + TDLs, grid + PVs + TDLs + Li-ion BESS, grid + PVs + TDLs + Surrette BESS, and grid + PVs + TDLs + Trojan BESS. These configurations were analyzed to assess their impact on power losses and voltage deviations within the distribution networks. The results showed promising outcomes, with the total Net Present Cost (NPC) ranging from $8.41 million to $10.03 million across all configurations. Li-ion BESS emerged as the most cost-effective option, achieving the lowest annualized cost. The PSO algorithm outperformed the GA in optimization, determining an optimal BESS size of 0.1126 MW. Systems utilizing Li-ion BESS consistently demonstrated superior performance to those with Surrette and Trojan BESS, achieving the lowest net energy purchases, peak demands, and energy costs while generating significant revenue from excess energy sales to the grid. Li-ion BESS is the most cost-effective and sustainable solution due to its high efficiency, energy density, and long lifecycle. For the real-world 30-bus South African distribution network, PSO and GWO are preferred due to their efficient utilization of BESS, achieving optimal performance with lower storage investment. For the larger 49 bus network, GWO offers a more balanced and robust solution, effectively maintaining acceptable power losses and voltage quality through appropriate BESS allocation.</div></div>\",\"PeriodicalId\":11798,\"journal\":{\"name\":\"Energy Reports\",\"volume\":\"14 \",\"pages\":\"Pages 579-599\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Reports\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352484725003774\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Reports","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352484725003774","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Techno-economic analysis of battery storage technologies in distribution networks with integrated electric vehicles and solar PV systems
This study presents a simulation, optimization, and assessment of economic impacts of a grid-connected solar PV system with electric vehicles (EVs) and various battery energy storage systems (BESS) for distribution network systems. Four optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Direct Search Optimization (DSO), and Grey Wolf Optimizer (GWO) were deployed in determining the optimal parameters for energy storage deployment in the distribution networks integrated with EVs and solar photovoltaic (PV) systems. Two real-world distribution network systems were modeled for time-based (quasi-dynamic) simulations: the 30-bus South African distribution network and the 49-bus distribution network of Baghdad City, Iraq. These models were used to evaluate the impacts of integrating solar PV, electric vehicles (EVs), and battery energy storage systems (BESS) on voltage profiles and active power losses. The simulation results were further optimized to determine the optimal BESS placement within the proposed networks using GA, PSO, DSO and GWO techniques. An economic analysis was conducted to optimize the integration of BESS with EVs and solar photovoltaic (PV) systems. The study considered various battery storage configurations, including grid + time-dependent loads (TDLs), grid + PVs + TDLs, grid + PVs + EVs + TDLs, grid + PVs + TDLs + Li-ion BESS, grid + PVs + TDLs + Surrette BESS, and grid + PVs + TDLs + Trojan BESS. These configurations were analyzed to assess their impact on power losses and voltage deviations within the distribution networks. The results showed promising outcomes, with the total Net Present Cost (NPC) ranging from $8.41 million to $10.03 million across all configurations. Li-ion BESS emerged as the most cost-effective option, achieving the lowest annualized cost. The PSO algorithm outperformed the GA in optimization, determining an optimal BESS size of 0.1126 MW. Systems utilizing Li-ion BESS consistently demonstrated superior performance to those with Surrette and Trojan BESS, achieving the lowest net energy purchases, peak demands, and energy costs while generating significant revenue from excess energy sales to the grid. Li-ion BESS is the most cost-effective and sustainable solution due to its high efficiency, energy density, and long lifecycle. For the real-world 30-bus South African distribution network, PSO and GWO are preferred due to their efficient utilization of BESS, achieving optimal performance with lower storage investment. For the larger 49 bus network, GWO offers a more balanced and robust solution, effectively maintaining acceptable power losses and voltage quality through appropriate BESS allocation.
期刊介绍:
Energy Reports is a new online multidisciplinary open access journal which focuses on publishing new research in the area of Energy with a rapid review and publication time. Energy Reports will be open to direct submissions and also to submissions from other Elsevier Energy journals, whose Editors have determined that Energy Reports would be a better fit.