Providing Flexibility in Distribution Systems by Electric Vehicles and Distributed Energy Resources in the Context of Technical Virtual Power Plants

Abstract

The energy sector has faced a growing increase in demand over the past few years, resulting from the introduction of new technologies, as well as from economic development worldwide. Similarly, energy production based on renewable sources is increasingly used to minimize fossil fuel dependency and the associated emissions. These factors have resulted in structural changes, both in topology and operation of the electrical system, leading to the appearance of distributed energy resources (DERs) technologies. On the production side, distributed generation (DG) units, that are energy sources closer to the site of the load, have become increasingly common, while on the demand side, new technological applications, such as electric vehicles with their bidirectional flow capability (Vehicle-to-Grid (V2G) technology) have changed the typical customer role. Such changes have become particularly important at the distribution network level. In this context, the concept of a Technical Virtual Power Plant (TVPP) emerges, whose fundamental operating principle consists of the aggregation of small producers and power sources into a virtual power generation network. This concept aims to enhance the flexibility of the grid, while contributing to an increase in the quality and reliability of electric energy, which also brings economic benefits. In this thesis, a stochastic mixed-integer linear programming (MLIP) optimization model was developed. The main objective was providing flexibility to the distribution system through the concept a TVPP who is responsible for aggregating, DG units and electrical vehicles (EVs) capable of supplying energy to the grid at some of the nodes of the TVPP. The model is tested using the IEEE 119 Bus test system, and results show that with the inclusion of DG units and V2G, the profit of a TVPP increases by 45.4%.