An Intelligent Decision Support System for Efficient Scheduling of Smart Home Appliances in a Smart Grid

Student thesis: Doctoral Thesis


Many new Demand-Side Integration (DSI) load scheduling strategies are emerging for energy management in smart grids. The focus of this thesis is upon smart homes and end-user DSI participation using Intelligent Decision Support Systems (IDSSs). The use of IDSSs can assist consumers to respond effectively to Real-Time Pricing (RTP) tariffs and other DSI pricing signals relayed to customers by utilities. For many DSI load management strategies, mathematical optimization and metaheuristic strategies have previously been suggested. For an end-consumer IDSS, these may not be acceptable from a computational complexity perspective, due to the non-deterministic polynomial hardness (NP-hardness) of the scheduling problem encountered by the IDSS. In this thesis, an efficient polynomial-time heuristic algorithm for scheduling residential smart home appliances across a receding time horizon is proposed. The heuristic algorithm is extensively evaluated using a generic cost model for electricity prices and a variety of representative smart home configurations. Results indicate that, when compared to an exact optimal algorithm, the proposed heuristic algorithm consistently produces results which are very close to optimal at a fraction of the computing cost. A prototype of the heuristic algorithm is implemented on a resource-constrained embedded processor, and testing and validation confirm its suitability for co-location on a smart meter. The final sections of the thesis are therefore concerned with the performance of multiple implementations of the heuristic from the perspective of a utility company. This thesis concludes that the proposed heuristic algorithm is a good candidate for the large-scale deployment of residential consumer oriented DSI and could be deployed as a useful and low-cost extension of an Advanced Metering Infrastructure (AMI) in smart grids.
Date of Award10 Jan 2018
Original languageEnglish
Awarding Institution
  • Teesside University
SupervisorMichael Short (Supervisor)

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