Control algorithms to mitigate the effect of uncertainties in residential demand management
Published in Applied Energy, 2022
Recommended citation: Lankeshwara, Gayan & Sharma, Rahul & Yan, Ruifeng & Saha, Tapan K., 2022. "Control algorithms to mitigate the effect of uncertainties in residential demand management," Applied Energy, Elsevier, vol. 306(PA). https://doi.org/10.1016/j.apenergy.2021.117971
Abstract: Uncertainties at end-user and aggregator levels can be highly detrimental to the practical implementation of residential load control schemes for electricity market applications. Uncertainty factors such as end-user non-compliance, comfort violations and load set-point changes associated with the demand response aggregator are unavoidable in practice. This paper proposes a novel two-stage control algorithm for robust centralised management of aggregate residential loads which guarantee precise load set-point tracking in the presence of uncertainties occurring in real-time while ensuring that end-user thermal comfort is not compromised. The approach is underpinned by optimal selection of appliances based on an emulated supply curve followed by solving a one-step-ahead optimisation problem. Using air conditioners and water heaters as the controllable loads, the paper illustrates the effectiveness of the proposed approach in load management whilst mitigating the effects of unknown uncertainties. Further, the developed control scheme is compared with an existing industry approach. The results yield that the proposed control scheme is robust to uncertainties, preserves thermal comfort and is applicable for practical implementation under existing demand response standards.