PVT-蓄热住宅建筑能量管理的不确定性认知模型预测控制
Uncertainty-Cognizant Model Predictive Control for Energy Management of Residential Buildings with PVT and Thermal Energy Storage
摘要：
建筑业占全球能源消耗的近40%。这揭示了在建筑物中利用可再生能源以实现气候目标的绝佳机会。在这种情况下，本文提供了一种建筑能源系统，包括热泵，热能储存系统以及并网光伏热（PVT）收集器，以最小的运营成本提供电力和热能需求的建筑物。为此，本文开发了一种随机模型预测控制（MPC）策略，以最佳地确定整个建筑能源系统的设定点，同时考虑与PVT能量生成相关的不确定性。该系统使建筑物能够1-将其电力需求从高峰时间转移到非高峰时间，2-将电力出售给电网以进行能源套利。
英文摘要：
The building sector accounts for almost 40 percent of the global energy consumption. This reveals a great opportunity to exploit renewable energy resources in buildings to achieve the climate target. In this context, this paper offers a building energy system embracing a heat pump, a thermal energy storage system along with grid-connected photovoltaic thermal (PVT) collectors to supply both electric and thermal energy demands of the building with minimum operating cost. To this end, the paper develops a stochastic model predictive control (MPC) strategy to optimally determine the set-point of the whole building energy system while accounting for the uncertainties associated with the PVT energy generation. This system enables the building to 1-shift its electric demand from high-peak to off-peak hours and 2- sell electricity to the grid to make energy arbitrage.
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