Using Model Predictive Control (MPC) is a promising method for enabling grid-interactive efficient buildings. Since MPC relies on a building model and the forecasts of external disturbances to derive optimal inputs, the uncertainties due to forecast errors and model inaccuracies can deteriorate the control performance. Most existing MPC studies for the built environment are deterministic MPC without consideration of uncertainties even though several different methods (e.g., stochastic MPC) are available in the literature to deal with them. Even studies that consider forecast uncertainties often neglect model inaccuracies. Hence, in the present paper, a novel hierarchical-stochastic MPC is proposed considering forecast uncertainties and model inaccuracies, and its performance is compared with deterministic, stochastic, and hierarchical MPC for power management in a residential building with distributed energy resources. The control objective is the cost-optimal scheduling of a heat pump, a battery for energy storage, and a rooftop photovoltaic system. Measurement data is used to identify the building model. The results for one-week simulation in winter show that (1) the deterministic MPC results in an unacceptable level of temperature constraint violations in two out of five rooms; (2) the hierarchical MPC can reduce the temperature constraint violations to an acceptable level at the expense of increased cost; (3) the stochastic MPC achieves the same reduction in temperature constraint violations as the hierarchical MPC but at slightly lower costs; and (4) the new proposed hierarchical-stochastic MPC results in both lower temperature constraint violations and lower financial expenses than the use of stochastic or hierarchical MPC individually.

中文翻译：

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分布式能源电网互动多区住宅分层随机模型预测控制

使用模型预测控制 (MPC) 是实现电网交互式高效建筑的一种很有前景的方法。由于 MPC 依赖于构建模型和外部干扰的预测来得出最佳输入，因此预测误差和模型不准确导致的不确定性可能会恶化控制性能。大多数现有的针对建筑环境的 MPC 研究都是确定性 MPC，没有考虑不确定性，尽管文献中可以使用几种不同的方法（例如随机 MPC）来处理它们。即使考虑预测不确定性的研究也常常忽略模型的不准确性。因此，在本文中，考虑到预测的不确定性和模型的不准确性，提出了一种新型的分层随机 MPC，并将其性能与确定性、随机和分层 MPC 进行了比较，用于分布式能源住宅建筑的电力管理。控制目标是热泵、储能电池和屋顶光伏系统的成本最优调度。测量数据用于识别建筑模型。冬季为期一周的模拟结果表明：（1）确定性 MPC 导致五分之二的房间违反温度约束的程度达到不可接受的程度； (2)分层MPC可以将温度约束违规降低到可接受的水平，但代价是增加成本； (3) 随机 MPC 实现了与分层 MPC 相同的温度约束违规减少，但成本略低； (4) 与单独使用随机或分层 MPC 相比，新提出的分层随机 MPC 可以降低温度约束违规率并降低财务费用。