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【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

AEii国际应用能源  · 公众号  ·  · 2024-01-26 11:51

正文

原文信息

Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads

原文链接:

https://www.sciencedirect.com/science/article/abs/pii/S0306261923017944

Highlights

(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”

Research gap

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。

摘要

光伏发电(photovoltaic, PV)具有间歇性、不稳定性、随机性和不可控性。因此,采用太阳能并网发电会导致电网系统电压波动和功率不平衡等一系列问题。暖通空调系统(heating, ventilation, and air conditioning, HVAC)负荷作为典型的柔性负荷,其调度与调节可以帮助光伏并网的电网系统实现稳定灵活运行。本文提出了一种计及柔性负荷的暖通空调系统光伏产能动态跟踪模型预测控制策略,该策略旨在有效解决光伏并网导致的电网系统出力不稳定的问题。特别是建立了基于灰狼优化算法优化的长短期记忆神经网络(long short-term memory neural network optimized by the grey wolf optimization algorithm,GWO-LSTM)的太阳辐射预测模型,有效地提高了模型预测精度。计及柔性负荷的暖通空调系统控制策略的成本函数考虑了光伏输出规律和人体热舒适模型,在考虑光伏输出的可变性和保证人体热舒适的前提下,采用遗传算法(genetic algorithm,GA)动态求解最优控制参数。本文以典型光伏发电并网系统的办公大楼为研究对象,建立了动态调控模型,根据实测数据和相关气象参数利用TRNSYS仿真平台进行验证。除此以外,本文提出了一种新的评价指标—“波动率”来评价电网净负荷波动。通过TRNSYS仿真平台进行模拟计算,其结果显示,计及柔性负荷的模型预测控制的暖通空调系统能耗曲线与光伏发电曲线具有较强的相似性,净负载波动率为2.63。与未考虑柔性负荷的模型预测控制结果相比,电网净负荷波动率降低了47.08%,夏季节能率达到10.89%。

更多关于"Air-conditioning system"的文章请见: https://www.sciencedirect.com/search?qs=air-conditioning%20system&pub=Applied%20Energy&cid=271429

Abstr act

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

Graphics

图1 图形摘要

图2 MPC逻辑图

图3 GWO-LSTM算法流程图







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