loading page

A Self-constraint Model Predictive Control Method via Air Conditioner Clusters for Min-level Generation Following Service
  • +1
  • Yunfeng Ma,
  • Chao Zhang,
  • Bangkun Ding,
  • Zengqiang Mi
Yunfeng Ma
North China Electric Power University

Corresponding Author:[email protected]

Author Profile
Chao Zhang
The University of Manchester
Author Profile
Bangkun Ding
North China Electric Power University - Baoding Campus
Author Profile
Zengqiang Mi
North China Electric Power University - Baoding Campus
Author Profile


As renewable power generation increases in distribution networks, the real-time power balance is becoming a tough challenge. Unlike simple peak-load shedding or demand turn-down scenarios, generation following requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limitations and execution performance of ACCs when providing such services. Accordingly, this paper proposes a self-constraint MPC that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency-based power allocation, and real-time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved by containing the prior factors to model the constraints and cost functions. Once the optimised following signals are obtained, a real-time model predictive controller for generation following task execution is employed. Simulations are conducted to verify the feasibility and effectiveness of the proposed method.
29 Aug 2023Submitted to IET Generation, Transmission & Distribution
30 Aug 2023Submission Checks Completed
30 Aug 2023Assigned to Editor
30 Aug 2023Review(s) Completed, Editorial Evaluation Pending
10 Sep 2023Reviewer(s) Assigned
06 Oct 2023Editorial Decision: Revise Major
20 Oct 20231st Revision Received
26 Oct 2023Submission Checks Completed
26 Oct 2023Assigned to Editor
26 Oct 2023Review(s) Completed, Editorial Evaluation Pending
26 Oct 2023Reviewer(s) Assigned
14 Nov 2023Editorial Decision: Accept