Model predictive control of a building energy system including thermal energy storage
Model predictive control of a building energy system including thermal energy storage
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DOI：
10.26868/25222708.2019.210664
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发表时间：
2020
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影响因子：
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通讯作者：
Doyun Lee;R. Ooka;S. Ikeda;W. Choi;Y. Kwak
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文献类型：
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作者：
Doyun Lee;R. Ooka;S. Ikeda;W. Choi;Y. Kwak
本文旨在验证模型预测控制（MPC）策略的有效性，办公楼的占用干扰和时变电价的比较，与传统的基于规则的控制（RBC）策略。该建筑的能源系统包括一个风冷冷水机组，分层热能存储（TES）系统，两个风机盘管，三个换热器和五个泵。通过控制五台泵的质量流量，以最小化运行成本，最佳地确定了冷却器和TES的运行。为了构建可靠但计算量小的预测模型，采用人工神经网络（ANN）和随机跳跃的随机约束差分进化（εDE-RJ）算法求解优化问题。模拟进行了四天的冷却季节与离散预测时间范围为24小时，控制时间范围为1小时的间隔。因此，与RBC相比，MPC可以节省总运营成本约8.3%，RBC优先考虑TES操作以管理热负荷。
The present paper aims to verify the effectiveness of a model predictive control (MPC) strategy for an office building subject to an occupancy disturbance and timevarying electricity pricing by comparison with the conventional rule-based control (RBC) strategy. The energy system of the building includes an air-cooled chiller, stratified thermal energy storage (TES) system, two fan coil units, three heat exchangers, and five pumps. The chiller and TES operation were optimally determined by manipulating the mass flow rate of five pumps to minimize the operation cost. In order to construct reliable but computationally light prediction models, an artificial neural network (ANN) was utilized and the epsilon constrained differential evolution with a random jumping (εDE-RJ) algorithm was employed for solving an optimization problem. The simulation was performed for four days in the cooling season with a discrete prediction time horizon of 24 h and control time horizon at 1-h intervals. As a result, the MPC could save approximately 8.3% in terms of the total operation cost in comparison to the RBC, which prioritizes the TES operation to manage the thermal load.