Abstract

Renewable energy sources introduce uncertainty regarding generated power in smart grids. For instance, power that is generated by wind turbines is time-varying and dependent on the weather. Electric vehicles will become increasingly important in the development of smart grids with a high penetration of renewables, because their flexibility makes it possible to charge their batteries when renewable supply is available. Charging of electric vehicles can be challenging, however, because of uncertainty in renewable supply and the potentially large number of vehicles involved. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator, reduces cost of customers and reduces consumption of conventionally-generated power.

Abstract

Renewable energy sources introduce uncertainty regarding generated power in smart grids. For instance, power that is generated by wind turbines is time-varying and dependent on the weather. Electric vehicles will become increasingly important in the development of smart grids with a high penetration of renewables, because their flexibility makes it possible to charge their batteries when renewable supply is available. Charging of electric vehicles can be challenging, however, because of uncertainty in renewable supply and the potentially large number of vehicles involved. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator, reduces cost of customers and reduces consumption of conventionally-generated power.

Abstract

The deployment of Electrical Vehicles (EV) is not taking place at the expected rate. Consumers’ tendency to value losses higher than gains works against electric vehicles when comparing them to traditional solutions. In this context, added value services may support business models with additional incomes, business differentiation or appealing mobility concepts. In the frame of the Green eMotion FP7 project, added value services will be proposed and their implementation in backend systems studied, in order to permit a widespread and sustainable deployment of EVs. Services and functionalities are delimited by business models defining the overall stakeholder relationship framework. Therefore, the latter will have direct influence, not only at economical level but also in technical aspects of the implementation of added value services. EV services are classified in two main groups: those provided by the EVs to the network and those provided by service providers to end-users. EVs characteristics make them especially suitable for service oriented business, while ICT solutions appear as key enablers of new sustainable mobility concepts. It is very important to decide which services and how these services need to be implemented to allow a wide range of business models to be applied. Interoperability is another essential aspect when dealing with EVs, since all systems involved in service provision should be able to communicate with each other. This will allow EV users to have transparent and efficient driving experiences, together with lower cost solutions. New advances in communication standards definition and in interoperability assessment (COTEVOS FP7 EU project) are currently going on and will tackle this challenge. EC FP7