A hierarchical control scheme for residential air-conditioning loads to provide real-time market services under uncertainties. (1st July 2022)

• Record Type: Journal Article
• Authors: Lankeshwara, Gayan; Sharma, Rahul; Yan, Ruifeng; Saha, Tapan K.
• Is Part Of: Energy. Volume 250(2022)
• Publication Date: 2022-07-01
• Subjects: Distributed energy resources (DERs) -- Real-time market services -- Robust model predictive control (MPC) -- Demand response (DR) aggregator -- Inverter-type air conditioners; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123796
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Recently, there has been growing interest in the provision of market services from distributed energy resources (DERs). In pursuing this goal, demand response (DR) aggregators continue to face challenges in retaining privacy and comfort for end-users, mitigating scalability issues while controlling a large cohort of DERs and handling uncertainties which are inevitable in a practical setting. This paper presents an end-user privacy and comfort preserving, scalable, hierarchical control scheme for inverter-type air conditioners to provide real-time market services in the presence of uncertainties. Privacy and scalability are achieved thorough the adoption of the alternating direction method of multipliers (ADMM) framework which ensures minimal reliance on local information whilst ensuring desired reference tracking without compromising the end-user comfort. Benefiting from the proposed non-conservative robust MPC design, the local control is able to account for mismatches in outdoor temperature predictions. The overall scheme is validated using real data obtained from the Australian Energy Market operator. The results demonstrate that the proposed approach can achieve desired tracking of the reference signal while regulating indoor temperature within a narrow range (±1 °C) from the nominal set-point. Besides, the robustness to uncertainties is achieved without compromising computational performance and therefore the approach is scalable. Highlights: A hierarchical control scheme for inverter-type air conditioners to provide market services. The uncertainty in outdoor temperature predictions is considered. A combination of ADMM and robust MPC is employed. The overall approach is scalable and preserves end-user data privacy.