Comparison of PGAS Languages on a Linked Cell Algorithm - Bauer, Martin; Kuschel, Christian; Ritter, Daniel; Sembritzki, Klaus (PARS Mitteilungen, 2013)
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.lsinfs.compar_0

High-performance simulation of nation-sized smart grids - Kuschel, Christian; Rüde, Ulrich (International Journal of Parallel, Emergent and Distributed Systems, 2016)
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.
Keywords: cooperative bargaining; high-performance computing; market model; multi-agent system; smart grid

High-performance simulation of nation-sized smart grids in demand response scenarios - Kuschel, Christian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2017)

Multi-Energy Simulation of a Smart Grid with Optimal Local Demand and Supply Management - Kuschel, Christian; Köstler, Harald; Rüde, Ulrich (Smart Grid and Renewable Energy, 2015)
A simulation approach of a smart grid by cooperative bargaining is presented in this paper. Each participant of the smart grid determines its optimal schedule to meet its power and heating demand at minimal costs employing solar panels, fuel cells and batteries. This is done by solving a quadratic optimisation problem which takes the energy prices and the available devices into account. The energy prices are related to the demand and supply in the smart grid, so that a lower demand yields lower prices. The cooperative bargaining game is used to tune the participants’ optimal solution to obtain a Nash equilibrium. The computed solutions of the participants are validated against the capacities and structure of the smart grid by solving a multi-commodity flow problem. The presented model features multiple types of energy, so that they may be substituted to meet the participants’ demand. Furthermore, the participants may also act as supplier and not only as consumer, which allows decentralised generation of energy. The approach is validated in several experiments where effects like negative energy prices if generated energy exceeds the smart grid’s total demand and peak-shaving with even small-capacity batteries are exhibited.
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.lsinfs.multie
Keywords: Cooperative Bargaining; Pricing Model; Multi-Agent System; Multi-Commodity Flow; Smart Grid

Parallel multi-agent smart grid simulation - Kuschel, Christian; Rüde, Ulrich (Tagungsband ASIM 2016 – 23. Symposium Simulationstechnik, 2016)