Learning and Control Applied to Demand Response and Electricity Distribution Networks

Abstract:

Balancing the supply and demand of electrical energy in real-time is a core task in power system operation, and this balance has traditionally been maintained by controlling generation assets. Generation from intermittent, renewable energy sources is increasing, which requires additional energy balancing capacity. An alternative to providing this additional capacity via power plants is to provide signals to loads that change their demand, which is referred to as demand response. There exists a large potential capacity for demand response using residential loads, but enabling these loads to participate in demand response requires communication and sensing capabilities as they are spatially distributed resources.

The main contribution of this dissertation is to show that advanced algorithms can leverage existing infrastructure to make energy balancing with loads feasible in the near-term, which improves the reliability, economics, and environmental impact of the power grid. Control and estimation algorithms for residential demand response are developed that address communication delays while taking into account existing measurement availability. Following this, feeder-level energy disaggregation algorithms are developed from existing infrastructure capabilities that determine the real-time air conditioning demand on a distribution feeder. This could be used to obtain a feedback signal for demand response control and estimation algorithms.