Extending the AgentSpring/EMLab Tool to Evaluate Additional Agent Behaviour such as Electric Vehicles and Demand Side Response.

扩展 AgentSpring/EMLab 工具以评估其他代理行为，例如电动汽车和需求侧响应。

• 批准号: 2371859
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2371859
• 关键词: Extending; AgentSpring; EMLab; Tool; Evaluate

Development of a python port of the java based power EMLab(AgentSpring) allows integration of other python based power frameworks such as PowerGama and SmartNet. This new framework allows us to extend long term investment and policy modelling with short term modelling issues such as DSR , Aggregation, flexibility and EV's . Using the Agent based Modelling (ABM) and Multi agent System (MAS) paradigm for computational economics and for investigating societal effects is an important methodology that enables building a holistic understanding of any future power grid structure and, particularly, its evolution through time. Such power systems will become ever more complex with an increased penetration of Distributed Energy Resources (DER), including renewable resources with variable generation output such as wind and solar. The addition of new actors in the power environment such as Electric Vehicle (EV) Owners, flexible demand, aggregators and digital platform providers (e.g. providing P2P services) will further complicate the system with unknown interactions and strategies which will impact prices, investment behaviour and system operation (voltages, line congestion etc.) amongst other things. Understanding these causes and effects, and how future policy makers and regulators could facilitate better and more efficient interaction among the players in this ever evolving and complex system, would be extremely valuable.The aim of the work is to develop a multi-level multi-scale ABM/MAS framework to help us better identify and evaluate these effects and to enable better understanding of how rules and polices can affect energy networks and market participant behaviour.

基于Java的Power EMLab（AgentSpring）的Python端口的开发允许集成其他基于Python的Power框架，如PowerGama和SmartNet。这个新的框架使我们能够扩展长期投资和政策建模与短期建模问题，如DSR，聚合，灵活性和EV。使用基于智能体的建模（ABM）和多智能体系统（MAS）的范例计算经济学和调查的社会影响是一个重要的方法，使任何未来的电网结构，特别是它的演变建立一个整体的理解，随着时间的推移。随着分布式能源（DER）的渗透率增加，这种电力系统将变得更加复杂，包括具有可变发电输出的可再生资源，如风能和太阳能。在电力环境中增加新的参与者，如电动汽车（EV）所有者，灵活的需求，聚合器和数字平台提供商（例如提供P2P服务）将进一步复杂化系统，未知的相互作用和策略将影响价格，投资行为和系统运行（电压，线路拥堵等）。还有其他的事了解这些原因和影响，以及未来的决策者和监管者如何能够在这个不断演变和复杂的系统中促进参与者之间更好和更有效的互动，将是非常有价值的。这项工作的目的是发展一个多层次多规模的反弹道导弹/MAS框架帮助我们更好地识别和评估这些影响，并更好地理解规则和政策如何影响能源网络，市场参与者行为。