A model predictive control strategy of global optimal dispatch for a combined solar and air source heat pump heating system

IF 6.1 2区工程技术 Q2 ENERGY & FUELS

Applied Thermal Engineering Pub Date : 2024-11-03 DOI:10.1016/j.applthermaleng.2024.124778

Jing Zhao , Yawen Li , Yabing Qin , Dehan Liu , Xia Wu , Xinyu Zhang , Xiangping Cheng , Yanyuan Wu

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引用次数: 0

Abstract

Due to low-carbon transformation of heating sources in northern China, the combination of the solar heating system (SHS) and the air source heat pump (ASHP) has attracted widespread attention due to their significant low-carbon and energy-saving characteristics. Nevertheless, different outdoor meteorological parameters simultaneously affect both the SHS and the ASHP. The heat supply of the SHS is primarily influenced by solar radiation intensity, and the efficiency of the ASHP is mainly constrained by outdoor temperature. The dual uncertainty and volatility of the output capacities of these two heat sources pose significant challenges to their coordinated control. Previous research has primarily focused on traditional rule-based control (RBC) and feedback control, prioritizing the utilization of solar energy while employing ASHP to compensate for heating deficiencies. However, these methods ignore the variations in ASHP efficiency due to fluctuations in outdoor temperature, leading to low whole-system operating efficiency and limiting the flexibility and responsiveness of the control. In this paper, a model predictive control strategy of global optimal dispatch for a combined solar and ASHP (SASHP) heating system is proposed, which focuses on the flexibility and adaptability of dual heat source dispatch under different external conditions. A Temporal Convolutional Network (TCN) was used to establish a solar radiation intensity and load prediction model. A solar heat production prediction was realized by combining the mechanism model based on solar radiation intensity prediction; a two-step room temperature prediction model was established by introducing new input parameters in the load prediction. This strategy achieves globally optimal dynamic planning of the heat supply and duration of the SHS and ASHP systems by considering solar radiation, outdoor temperature, room temperature, and energy consumption, ensuring the efficient and stable operation of the system. Compared to RBC, experimental results indicated that under conditions of sufficient solar energy, the heating proportion of the SHS increased by 31.1 %, the average COP of the ASHP improved by 8.7 %, the energy-saving rate was 14.6 %, and the room temperature control was also more effective; whole-season simulation results showed an average energy-saving rate of 8.35 %.

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太阳能和空气源热泵联合供热系统的全局优化调度模型预测控制策略

由于中国北方采暖热源的低碳化改造，太阳能采暖系统（SHS）与空气源热泵（ASHP）的结合因其显著的低碳节能特性而受到广泛关注。然而，不同的室外气象参数同时影响着太阳能采暖系统和空气源热泵。SHS 的供热量主要受太阳辐射强度的影响，而 ASHP 的效率主要受室外温度的制约。这两种热源输出能力的双重不确定性和波动性给它们的协调控制带来了巨大挑战。以往的研究主要集中在传统的基于规则的控制（RBC）和反馈控制，优先利用太阳能，同时采用 ASHP 来弥补供热不足。然而，这些方法忽略了因室外温度波动而导致的 ASHP 效率变化，从而导致整个系统运行效率低下，并限制了控制的灵活性和响应能力。本文提出了太阳能和 ASHP（SASHP）联合供热系统全局最优调度的模型预测控制策略，重点关注不同外部条件下双热源调度的灵活性和适应性。利用时态卷积网络（TCN）建立了太阳辐射强度和负荷预测模型。通过结合基于太阳辐射强度预测的机制模型，实现了太阳能产热预测；通过在负荷预测中引入新的输入参数，建立了两步室温预测模型。该策略通过考虑太阳辐射、室外温度、室温和能耗，实现了 SHS 和 ASHP 系统供热量和供热时间的全局最优动态规划，确保了系统的高效稳定运行。实验结果表明，与 RBC 相比，在太阳能充足的条件下，SHS 的供热比例提高了 31.1%，ASHP 的平均 COP 提高了 8.7%，节能率为 14.6%，室温控制也更加有效；全季模拟结果显示平均节能率为 8.35%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Thermal Engineering 工程技术-工程：机械

CiteScore

11.30

自引率

15.60%

发文量

1474

审稿时长

57 days

期刊介绍： Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application. The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.