Towards demand side management control using household specific Markovian models

Abstract

We devise a real-time control mechanism to aid electricity network operators facilitate effective residential Demand Side Management. Contrary to existing algorithms where individual customer behavior patterns are required or estimated, we propose a novel load management scheme for highly stochastic loads where each household possesses individually suited parameters. If the actual demand in a feeder is found to exceed its rating, selected customers are requested by the operator to lower their loads. The benefit of the proposed approach is to maximize the certainty of meeting Demand Response targets whilst minimizing the overall cost associated with the Demand Response control actions. Our contribution is a model that takes customer behavior patterns into consideration while selecting customers. We model load behavior of individual households using Markov chains and by treating the problem as a Markov Decision Process optimal control problem, we deduce an easily implementable strategy for load reduction. This strategy is then incorporated in an aggregate model that utilizes household specific transition probabilities. The practical application is explained based on real data. Our numerical results illustrate the virtue of the strategy, showing potential for the use of household specific Markovian models for Demand Side Management applications.