Simulation of a decentralized optimal Demand Response algoritm

Demand Response will be a fundamental component in the future of smart grids, and it will be helpful to reduce peak loads and adapt elastic demands to the fluctuations of distributed generation provided by Renewable Energy. In this paper we consider customers who may operate different equipments, and an approach to Demand Response, based on maximizing the overall welfare of society (Social Welfare) with a similar methodology to classical economic theory treatment. Under certain conditions, with the help of price signals, individual optimization and social optimization can be aligned, ie, when customers selfishly optimize their own benefit, social welfare is being optimized. This fact, beyond its social and economic implications, enables a distributed technology solution where each customer is planning its consumption so as to maximize their net benefit with the information available. To do this each device (heating, washing machines, etc.) is modeled by a utility function that depends on the consumption pattern over the day and volume of consumption in that day. The electric company can then dynamically manage prices to manage demand to the benefit of the overall system. In this paper, an algorithm for Demand Response proposed in the recent literature, based on an iteration / negotiation between customers and the electric company, is implemented, and the results of the simulation of an scenario with 12 clients is presented.