Search

Electric vehicles (EVs) are widely regarded as valuable assets in the smart grid as distributed energy resources in addition to their primary transportation function. However, connecting EVs to the distribution network and recharging the EV batteries without any control may overload the transformers and cables during peak hours when the penetration of EVs is relatively high. In this study, a two level hierarchical control method for integrating EVs into the distribution network is proposed to coordinate the self-interests and operational constraints of two actors, the EV owner and Distribution system operator (DSO), facilitated by the introduction of the fleet operator (FO) and the grid capacity market operator (CMO). Unlike the typical hierarchical control system where the upper level controller commands the low level unit to execute the actions, in this study, market based control are applied both in the upper and low level of the hierarchical system. Specifically, in the upper level of the hierarchy, distribution system operator uses market based control to coordinate the fleet operator׳s power schedule. In the low level of the hierarchy, the fleet operator use market based control to allocate the charging power to the individual EVs, by using market based control, the proposed method considers the flexibility of EVs through the presence of the response-weighting factor to the shadow price sent out by the FO. Furthermore, to fully demonstrate the coordination behavior of the proposed control strategy, we built a multi-agent system (MAS) that is based on the co-simulation environment of JACK, Matlab and Simulink. A use case of the MAS and the results of running the system are presented to intuitively illustrate the effectiveness of the proposed solutions. [ABSTRACT FROM AUTHOR]

• A multi-time scale energy management for virtual battery-based prosumer was presented. • Proposal of a virtual battery model to describe aggregated electric vehicles flexibilities. • Combination of MPC and virtual battery model to determine prosumers' real-time power. • Comparison of the different models in terms of modelling accuracy and cost variation. The increasing number of small and medium-sized prosumers with distributed energy resources (DERs) has led to the need for innovative operational strategies at the distribution system level. Among them, microgrid (MG) energy management for peer-to-peer power sharing between prosumers is a promising approach. In this study, we develop a multi-time scale optimization method for virtual battery model-based prosumer energy management. First, a day-ahead optimal scheduling model is established based on an integrated virtual battery model that aims to minimize the total dispatching cost of a prosumer-oriented MG without impacting the privacy of individual end-prosumers. Then, close to real-time operation, model predictive control is applied to minimize the deviation between the real-time power and the day-ahead optimal schedule over the control horizon for prosumers with energy storage resources. The simulation results show that the proposed approach employing the integrated virtual battery model to quantitatively characterize the resource flexibility of prosumers yields a good optimization performance and high computing efficiency, compared to those of the approaches modelling traditional DER flexibilities. [ABSTRACT FROM AUTHOR]