Optimal Operation of a Building with Electricity-Heat Networks and Seasonal Storage

TL;DR

This paper investigates optimal control strategies for a building's electricity and heat networks with seasonal storage, using model predictive control to minimize costs over different prediction horizons based on real data.

Contribution

It introduces a method to determine the minimal effective prediction horizon for MPC in managing seasonal thermal energy storage systems.

Findings

A 6-day prediction horizon yields a suboptimality gap of 4.31%.

Longer horizons (42 days) increase suboptimality to 11.42%.

Using historical data to set target storage levels improves control performance.

Abstract

As seasonal thermal energy storage emerges as an efficient solution to reduce CO2 emissions of buildings, challenges appear related to its optimal operation. In a system including short-term electricity storage, long-term heat storage, and where electricity and heat networks are connected through a heat pump, it becomes crucial to operate the system on two time scales. Based on real data from a university building, we simulate the operation of such a system over a year, comparing different strategies based on model predictive control (MPC). The first objective of this paper is to determine the minimum prediction horizon to retrieve the results of the full-horizon operation problem with cost minimization. The second objective is to evaluate a method that combines MPC with setting targets on the heat storage level at the end of the prediction horizon, based on historical data. For a prediction horizon of 6 days, the suboptimality gap with the full-horizon results is 4.31%, compared to 11.42% when using a prediction horizon of 42 days and fixing the final level to be equal to the initial level, which is a common approach.