Maximizing local renewable energy consumption by shifting flexible electrical loads in time and space

Abstract

Worldwide efforts facing global climate change issues and the rise of renewable energy sources are leading to significant changes in the energy sector. G[e]oGreen is a SmartGrids ERA-NET project that aims at bringing another approach to energy balance and overall power system stability. The unpredictable nature of renewable energy sources leads to power peaks in the distribution network which are correlated in time and space and therefore within regions load conditions on the grid will vary. One approach to cope with these fluctuations is the massive deployment of energy storage systems but also the temporal and spatial shifting of energy consumption is possible but not widely used at the moment. Introducing a cell concept of mobile consumers, it considers consumption mobility both in terms of time and space. In particular, electric vehicles and Data Centers’ (DC) processing tasks, as typical cases of mobile consumers and their impact on the power grid, improved energy usage efficiency, grid stability and peak shaving are considered.

First simulations of the 18 G[e]oGreen cells and the described use-case were performed. The aim was to simulate electric vehicles in uncontrolled charging mode and to analyse the developed use-case within applicability for the optimization algorithm.

The analysis of uncontrolled charging of EVs show that 67 % of all EVs arrive at their charging point with state-of-charges (SOCs) above 80 % and another 25 % of vehicles have SOCs between 50 and 80 %, which leads to a considerable potential for controlled charging and optimization strategies.

The developed use-case features sufficient imbalance in generation and consumption of electrical power as well in time and geographical terms. This is the essential basis for the ongoing development of the optimization algorithm. Along with this and the described simulation environment, which allows full control of simulated consumers, further development and research on optimization algorithms for load shifting in time as well as geographical terms can be done.