{"title":"MANGOever: An optimization framework for the long-term planning and operations of integrated electric vehicle and building energy systems","authors":"Alicia Lerbinger , Siobhan Powell , Georgios Mavromatidis","doi":"10.1016/j.adapen.2024.100193","DOIUrl":null,"url":null,"abstract":"<div><div>The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for <strong>e</strong>lectric <strong>v</strong>ehicles and <strong>e</strong>nergy <strong>r</strong>etrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"16 ","pages":"Article 100193"},"PeriodicalIF":13.0000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666792424000313","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
The growing electrification of heating and mobility has increased the interdependence of these two sectors and introduced a new coupling with the electricity sector. However, existing studies on local energy planning often focus solely on solutions to meet buildings’ energy demands, neglecting or highly simplifying new mobility demands. Here, we address this gap by introducing MANGOever (Multi-stAge eNerGy Optimization for electric vehicles and energy retrofits), a comprehensive optimization framework for long-term co-planning of building energy systems and electric vehicle (EV) charging infrastructure. The framework optimizes multi-stage investments and operational strategies to minimize system costs and CO emissions over a multi-year horizon, considering the stochastic nature of EV charging based on observed driver habits and travel patterns. Applying the model to a case study of a multi-family home in Switzerland reveals significant synergies between EV charging and the management of solar photovoltaic generation. The results underscore the importance of considering habit-based EV charging behavior in the model and demonstrate how diverse EV plug-in behaviors can be leveraged to maximize the use of midday solar production and reduce emissions. These findings emphasize the need for integrated planning of these sectors to achieve a cost-effective, low-carbon energy transition.
供暖和交通日益电气化,增加了这两个部门的相互依存性,并与电力部门产生了新的联系。然而,现有的地方能源规划研究往往只关注满足建筑物能源需求的解决方案,忽视或高度简化了新的交通需求。在此,我们引入了 MANGOever(电动汽车和能源改造的多阶段 eNerGy 优化)来弥补这一不足,它是一个综合优化框架,用于建筑能源系统和电动汽车(EV)充电基础设施的长期共同规划。该框架根据观察到的驾驶员习惯和出行模式,考虑到电动汽车充电的随机性,对多阶段投资和运营策略进行优化,以在多年期限内最大限度地降低系统成本和二氧化碳排放量。将该模型应用于瑞士的一个多户住宅案例研究,发现电动汽车充电与太阳能光伏发电管理之间存在显著的协同效应。研究结果强调了在模型中考虑基于习惯的电动汽车充电行为的重要性,并展示了如何利用不同的电动汽车插电行为最大限度地利用中午的太阳能发电并减少排放。这些发现强调了对这些部门进行综合规划的必要性,以实现具有成本效益的低碳能源转型。