{"title":"考虑电池退化和价格套利的计划车队V2G优化的MILP框架","authors":"Daniele Martini;Michela Longo;Luca Daniel","doi":"10.1109/OJIES.2025.3613601","DOIUrl":null,"url":null,"abstract":"The scope of this article is to assess the economic viability of bidirectional charging strategies for scheduled electric bus fleets, focusing on their potential to reduce operational costs under real-world market conditions. This article presents a comprehensive and production-ready optimization framework for vehicle-to-grid integration, addressing day-ahead pricing, battery degradation, and grid constraints through a mixed-integer linear programming formulation. Unlike existing approaches that often rely on aggregated vehicle behavior, our framework preserves the full topology of the charging station, maintaining vehicle-level granularity. This enables precise evaluation of both marginal costs and savings, enhancing the model’s practical relevance and deployment potential. Results from real-world fleet schedules and Italian market data show that both controlled charging and vehicle-to-grid achieve substantially lower costs than uncontrolled charging, with savings exceeding 35% in current scenarios. However, the cost gap between vehicle-to-grid and controlled charging remains narrow. These findings indicate that as battery pack prices continue to decline and energy markets evolve, vehicle-to-grid will become increasingly favorable for scheduled fleets.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"6 ","pages":"1593-1602"},"PeriodicalIF":4.3000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176439","citationCount":"0","resultStr":"{\"title\":\"MILP Framework for V2G Optimization With Battery Degradation and Price Arbitrage in Scheduled Fleets\",\"authors\":\"Daniele Martini;Michela Longo;Luca Daniel\",\"doi\":\"10.1109/OJIES.2025.3613601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The scope of this article is to assess the economic viability of bidirectional charging strategies for scheduled electric bus fleets, focusing on their potential to reduce operational costs under real-world market conditions. This article presents a comprehensive and production-ready optimization framework for vehicle-to-grid integration, addressing day-ahead pricing, battery degradation, and grid constraints through a mixed-integer linear programming formulation. Unlike existing approaches that often rely on aggregated vehicle behavior, our framework preserves the full topology of the charging station, maintaining vehicle-level granularity. This enables precise evaluation of both marginal costs and savings, enhancing the model’s practical relevance and deployment potential. Results from real-world fleet schedules and Italian market data show that both controlled charging and vehicle-to-grid achieve substantially lower costs than uncontrolled charging, with savings exceeding 35% in current scenarios. However, the cost gap between vehicle-to-grid and controlled charging remains narrow. These findings indicate that as battery pack prices continue to decline and energy markets evolve, vehicle-to-grid will become increasingly favorable for scheduled fleets.\",\"PeriodicalId\":52675,\"journal\":{\"name\":\"IEEE Open Journal of the Industrial Electronics Society\",\"volume\":\"6 \",\"pages\":\"1593-1602\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176439\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Industrial Electronics Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11176439/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11176439/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
MILP Framework for V2G Optimization With Battery Degradation and Price Arbitrage in Scheduled Fleets
The scope of this article is to assess the economic viability of bidirectional charging strategies for scheduled electric bus fleets, focusing on their potential to reduce operational costs under real-world market conditions. This article presents a comprehensive and production-ready optimization framework for vehicle-to-grid integration, addressing day-ahead pricing, battery degradation, and grid constraints through a mixed-integer linear programming formulation. Unlike existing approaches that often rely on aggregated vehicle behavior, our framework preserves the full topology of the charging station, maintaining vehicle-level granularity. This enables precise evaluation of both marginal costs and savings, enhancing the model’s practical relevance and deployment potential. Results from real-world fleet schedules and Italian market data show that both controlled charging and vehicle-to-grid achieve substantially lower costs than uncontrolled charging, with savings exceeding 35% in current scenarios. However, the cost gap between vehicle-to-grid and controlled charging remains narrow. These findings indicate that as battery pack prices continue to decline and energy markets evolve, vehicle-to-grid will become increasingly favorable for scheduled fleets.
期刊介绍:
The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments.
Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.