Optimal Control of Ice-Storage Air-Conditioning Systems Using Action-Dependent Heuristic Dynamic Programming

This study integrates a Radial Basis Function Neural Network (RBFNN) with Adaptive Dynamic Programming (ADP) to develop an Action-Dependent Heuristic Dynamic Programming (ADHDP) framework for the cost-effective control of an Ice Storage Air-Conditioning (IAC) system. The proposed approach aims to establish a self-learning control strategy for reducing the overall operating cost of the system. In this study, the operational states of the air-conditioning system and the ice-storage/release states of the ice-storage tank are selected as the control variables. To approximate the nonlinear cost-to-go function of the IAC scheduling problem, an RBFNN is employed as the critic-network function approximator. Its local nonlinear approximation capability enables the controller to evaluate the long-term operating costs associated with different ice-storage, ice-melting, and air-conditioning actions. As a result, the proposed ADHDP method can evaluate feasible operating actions and select the action associated with the minimum estimated cost-to-go. Furthermore, an actual IAC system is adopted as the research subject. Field operational data are collected and utilized for system modeling and analysis. Simulations are conducted to compare the Conventional Control Strategy (CCS), ADP, and the ADHDP approach, with operating cost serving as the primary evaluation index. For the investigated representative operating scenario, ADHDP achieved operating costs approximately 7.24% and 1.66% lower than CCS and ADP, respectively. These values represent case-study results under the specific operating conditions considered in this study. Moreover, the ADHDP framework exhibits stronger adaptability to varying cooling loads and electricity pricing conditions, thereby improving the operational efficiency and energy management capability of the IAC system.