Deep learning-enabled MCMC for probabilistic state estimation in district heating grids. (15th April 2023) - Bott, Andreas; Janke, Tim; Steinke, Florian
Abstract: Flexible district heating grids form an important part of future, low-carbon energy systems. We examine probabilistic state estimation in such grids, i.e., we aim to estimate the posterior probability distribution over all grid state variables such as pressures, temperatures, and mass flows conditional on measurements of a subset of these states. Since the posterior state distribution does not belong to a standard class of probability distributions, we use Markov Chain Monte Carlo (MCMC) sampling in the space of network heat exchanges and evaluate the samples in the grid state space to estimate the posterior. Converting the heat exchange samples into grid states by solving the non-linear grid equations makes this approach computationally burdensome. However, we propose to speed it up by employing a deep neural network that is trained to approximate the solution of the exact but slow non-linear solver. This novel approach is shown to deliver highly accurate posterior distributions both for classic tree-shaped as well as meshed heating grids, at significantly reduced computational costs that are acceptable for online control. Our state estimation approach thus enables tightening the safety margins for temperature and pressure control and thereby a more efficient grid operation. Highlights: Novel approach combining classic statistical analysis and AI. The estimated state distributions are not standard distributions. MCMC and Bayesian calculus can be used to approximate state distributions. MCMC requires mapping demands to grid states in every step. Deep Neural Networks can speed this up by orders of magnitude.