Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems
For operators of smart thermal grids with seasonal storage, this framework provides a tractable solution to energy management under uncertainty, though it is an incremental extension of existing robust randomized methods.
This paper develops a probabilistic energy management framework for building climate comfort systems with aquifer thermal energy storage, addressing private and common uncertainties. The proposed method, using a robust randomized approach for multiple chance constraints, achieves less conservative solutions than decoupled or centralized approaches, as demonstrated in simulations including MODFLOW.
This paper presents an energy management framework for building climate comfort (BCC) systems interconnected in a grid via aquifer thermal energy storage (ATES) systems in the presence of two types of uncertainty (private and common). ATES can be used either as a heat source (hot well) or sink (cold well) depending on the season. We consider the uncertain thermal energy demand of individual buildings as a private uncertainty source and the uncertain common resource pool (ATES) between neighbors as a common uncertainty source. We develop a large-scale stochastic hybrid dynamical model to predict the thermal energy imbalance in a network of interconnected BCC systems together with mutual interactions between their local ATES. We formulate a finite-horizon mixed-integer quadratic optimization problem with multiple chance constraints at each sampling time, which is in general a non-convex problem and difficult to solve. We then provide a computationally tractable framework by extending the so-called robust randomized approach and offering a less conservative solution for a problem with multiple chance constraints. A simulation study is provided to compare completely decoupled, centralized and move-blocking centralized solutions. We also present a numerical study using a geohydrological simulation environment (MODFLOW) to illustrate the advantages of our proposed framework.