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Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

Author

Listed:
  • Xiaoling Yuan

    (College of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, China)

  • Hao Cao

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Zheng Chen

    (State Grid (Beijing) Integrated Energy Planning and D&R Institute, Beijing 100052, China)

  • Jieyan Xu

    (State Grid (Beijing) Integrated Energy Planning and D&R Institute, Beijing 100052, China)

  • Haoming Liu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Suggested Citation

  • Xiaoling Yuan & Hao Cao & Zheng Chen & Jieyan Xu & Haoming Liu, 2024. "Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System," Energies, MDPI, vol. 17(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1291-:d:1353250
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    References listed on IDEAS

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    1. Joe, Jaewan & Karava, Panagiota, 2019. "A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings," Applied Energy, Elsevier, vol. 245(C), pages 65-77.
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