Self-learning demand side management for a heterogeneous cluster of devices with binary control actions

Abstract

Finding an optimal planning for a large cluster of devices with binary control actions is a challenging task for both centralized and distributed approaches. This is certainly the case when a significant fraction of devices in the cluster has binary control actions, since the resulting optimization problem belongs to NP-hard integer programming. A distributed approach can be a good solution to address this problem. Good performance however, often relies on the presence of local intelligence, such as planning and prediction at device or household level. In this work we apply a self-learning agent-based demand side management approach to a heterogeneous cluster of devices with binary control actions. The required local intelligence is limited to a state estimation and local comfort and constraint checking. Each device is represented by an individual agent communicating a bid function to a virtual energy market. In the approach the aggregated energy and power constraints of a cluster of devices are learned, independent of the type and number of devices. The aggregated constraints are estimated based upon the aggregated bid functions. These constraints are used to determine an optimal control signal managing the cluster. The approach has been evaluated in two distinct scenario's including devices with binary control actions, showing that the self-learning approach converges within 12 days to obtain 80 % of the maximum optimization potential, with a generic approach that requires limited intelligence at device level.