This paper presents an energy management framework for building climate comfort systems that are interconnected in a grid via aquifer thermal energy storage (ATES) systems in the presence of two types of uncertainty, namely private and common uncertainty sources. The ATES system is considered as a seasonal storage system that can be a heat source or sink, or a storage for thermal energy. While the private uncertainty source refers to uncertain thermal energy demand of individual buildings, the common uncertainty source describes the uncertain common resource pool (ATES) between neighbors. To this end, we develop a large-scale stochastic hybrid dynamical model to predict the thermal energy imbalance in a network of interconnected building climate comfort systems together with mutual interactions between the local ATES systems. 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 hard to solve. We then provide a computationally tractable framework based on an extension to the so-called robust randomized approach which offers a less conservative solution for a problem with multiple chance constraints. A simulation study is provided to compare three different configurations, namely: completely decoupled, centralized and move-blocking centralized solutions. In addition, we present a numerical study using a geohydrological simulation environment (MODFLOW) to illustrate the advantages of our proposed framework.
['Vahab Rostampour', 'Tamas Keviczky']
Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems