On Using Complex Event Processing for Dynamic Demand Response Optimization in Microgrid

TL;DR

This paper explores using semantic Complex Event Processing to improve real-time demand response in microgrids by detecting dynamic situations from smart meter and sensor data, enabling more effective demand-side management.

Contribution

It introduces a taxonomy of event patterns for demand response and demonstrates their application in a campus microgrid using a CEP framework.

Findings

Effective detection of demand response situations from real-time data

Enhanced adaptive demand management capabilities

Validation on USC Campus microgrid data

Abstract

Demand-side load reduction is a key benefit of Smart Grids. However, existing demand response optimization (DR) programs fail to effectively leverage the near-realtime information available from smart meters and Building Area Networks to respond dynamically to changing energy use profiles. We investigate the use of semantic Complex Event Processing (CEP) patterns to model and detect dynamic situations in a campus microgrid to facilitate adaptive DR. Our focus is on demand-side management rather than supply-side constraints. Continuous data from information sources like smart meters and building sensors are abstracted as event streams. Event patterns for situations that assist with DR are detected from them. Specifically, we offer a taxonomy of event patterns that can guide operators to define situations of interest and we illustrate its efficacy for DR by applying these patterns on realtime events in the USC Campus microgrid using our CEP framework