High-performance simulation of nation-sized smart grids

Abstract

This paper develops a large scale simulation approach for smart grids with a fine temporal resolution. This permits a detailed simulation of every single smart grid participant facilitating real-time estimates on a realistic scale about load distribution, demand and supply. This is especially interesting when considering the sustainability and efficiency of renewable energy generation. The smart grid model is based on a multi-agent system. Each agent is modelled by an optimisation problem which minimises its costs to meet a prescribed demand. In order to bestow some flexibility to each agent, it can generate energy, by e.g. solar panels, or store energy. All agents participate in a cooperative bargaining game to compute their respective optimal solution. By doing so, the energy prices are dynamically adapted to the agents’ solutions so that an energy market is formed. This bargaining game is designed that a unique Nash equilibrium exists. The computations to obtain the equilibrium point are performed in parallel. To this end, a synchronisation scheme is derived to ensure convergence of the proposed parallel bargaining algorithm. The smart grid structure and capacity limitations are also taken into account. The solutions of the bargaining game are compliant with the smart grid’s capacities by solving a modified network flow problem and forcing the bargaining solution within the capacity limitations in an iterative procedure. The correctness of the presented parallel algorithm is validated in tests along with a parallel performance evaluation. As a proof of concept of the practical applicability of this approach, a smart grid with over 30 million private households and multiple energy types is simulated.