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

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.