【Applied Energy最新原创论文】计及柔性负荷的空调系统光伏产能动态跟踪模型预测控制策略

(1) 针对暖通空调系统精细化控制提出了光伏产能动态跟踪模型预测控制策略

(2) 提出的控制策略考虑了光伏输出的变化规律

(3) 采用灰狼优化算法对长短期记忆神经网络进行参数优化

(4) 通过不同控制策略之间的比较说明了所提出策略的有效性

(5) 提出了一种新的负荷波动评价指标—“波动率”

针对太阳能发电不确定性和不可控性的特点，本研究提出了一种计及柔性负荷的暖通空调系统模型预测控制策略以实现暖通空调系统的能耗实时有效地追踪光伏产能，解决了太阳能并网发电导致的电网输出功率不平衡的问题。

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer.

Keywords：

Model predictive control

Flexible regulation of HVAC systems

Flexible load

Demand-side management