Load management in buildings

Abstract The electric power system is changing with the need for a more sustainable energy supply, a more efficient use of existing networks and the associated deployment of smart grid infrastructures. In this context, the frontiers of the electricity networks are being redefined with the emergence of consumers providing flexibility services and contributing to energy efficiency improvements. To manage the supply and demand, advanced load control techniques are increasingly required. The present chapter addresses these changes. Demand Side Management and Demand Response actions are first introduced before reviewing the different ways in which they can be implemented and the challenges of a wider deployment. Then, two advanced control techniques are addressed. Price-incentive-based DR is investigated with agent-based machine learning algorithms; the case study focuses on a neighborhood scenario where self-consumption is to be increased considering buildings with smart appliances, PV, and batteries. The second example examines Model Predictive Control within the context of thermal load management in buildings. The specificities of this optimization-based control technique are described and its potential and challenges are addressed; the application of Model Predictive Control for flexibility provision with time of use tariffs is then discussed.