Probabilistic Energy Management for Building Climate Comfort in Smart Thermal Grids with Seasonal Storage Systems

TL;DR

This paper develops a probabilistic energy management framework for interconnected building climate control systems utilizing seasonal aquifer thermal energy storage, addressing uncertainties with a novel stochastic optimization approach.

Contribution

It introduces a large-scale stochastic hybrid model and a less conservative chance-constrained optimization method for managing thermal energy in smart grids with seasonal storage.

Findings

The proposed method effectively handles private and common uncertainties.

Simulation shows improved energy management over traditional decoupled approaches.

Numerical results demonstrate the framework's advantages in real-world scenarios.

Abstract

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.