Agent modeling of a pervasive application to enable deregulated energy markets

In a not so far future, private houses will be provided with devices that can produce renewable energy, and this will give the owners the chance of selling the unused energy to neighbors. The fact that this selling will be based on peer to peer negotiation (i.e., between single producers and single consumers), will make this market deregulated. This situation could lead to advantages for both producers, who will have an extra income for energy they would not use, and consumers, who can buy cheaper energy than the big companies' one. However, this scenario is very complex and dynamic, and without an appropriate management can lead to odd situations. This paper presents the agent-based modeling of an application to manage the negotiation among different parties producing and consuming energy. We will show that the feature of autonomy of agents well suit the requirements of the proposed scenario. Moreover, we will exploit game theory to define a strategy that try to optimize energy production and supply costs by means of negotiation and learning. By means of simulation of the different parties we will show the effectiveness of the proposed approach; the results show that applying our approach enables to reduce the price of the energy and leads to an equilibrium between expected and real prices.