Bi-Level Optimized Peak Shaving Strategy for Temperature-Controlled Loads Considering Low User Perception Based on Dispatchable Potential Assessment and Similarity Clustering
Yu Yang1,2, Niu Yunjing1,2, Pang Qiwen1,2, Li Junwei1,2, Xiang Xiaoping1,2
1. Hebei Key Laboratory of Distributed Energy Storage and Micro-Grid North China Electric Power University Baoding 071003 China; 2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Baoding 071003 China
Abstract：With the continuous expansion of the peak-valley difference in power systems, the demand for flexible regulation capabilities of the system has become increasingly prominent. Thermostatically controlled load (TCL) flexible resources, can effectively assist in peak shaving. To enhance its flexible peak-shaving capability and address the mismatch between grid peak-shaving demands and user willingness in demand response, this paper proposes a TCL Bi-level optimized peak-shaving strategy that considers user low perception based on schedulable potential assessment and similarity clustering. Firstly, combining operational characteristics, a TCL schedulable potential assessment method based on the IIAO-Trans-BiGRU combined model is proposed, and the assessment results are subjected to similarity clustering to reduce variable redundancy. Secondly, leveraging the potential assessment outcomes, a TCL Bi-level optimized peak-shaving strategy that considers user low perception is proposed. The upper grid side takes peak-shaving demands into account to develop a day-ahead scheduling plan for TCL clusters, aiming to minimize the load peak-valley difference and grid losses, while the lower user side, based on the upper-level scheduling plan, considers user willingness and integrates economy, comfort, and technology to build a low perception cost peak shaving target model for intra-day control of individual TCLs. Meanwhile, to further balance the needs of both the grid side and the user side, a dual closed-loop feedback mechanism based on model predictive control is designed. By real-time feedback of grid peak-shaving status and user low perception status information, dynamic adjustments are achieved through intra-day rolling optimization, thereby enhancing the flexibility of TCL peak-shaving scheduling. Simulation comparisons across multiple scenarios show that the proposed strategy outperforms existing methods significantly in algorithm solving and objective optimization: comparing the IIAO-Trans-BiGRU model with 5 other models such as Trans-BiGRU confirms the strategy can accurately evaluate the dispatchable potential of TCLs; comparing with scenarios where the grid's peak-shaving demand mismatches users' willingness proves the two-layer peak-shaving model can effectively balance the two; and in terms of solution schemes, comparison with the closed-loop feedback mechanism of conventional MPC verifies that the double closed-loop feedback mechanism can effectively enhance the flexible peak-shaving capability of TCLs.
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2026, Vol. 41 
