Author:

Keywords:

grey-box, Modelica, buildings, model predictive control, flexibility, energy efficiency

Abstract:

Improving the energy efficiency of building energy systems is a key challenge for the mitigation of climate change. In particular, bad controlnbsp;operation often causes large energy efficiency losses, both in new and old buildings.nbsp;implementation of model predictive control (MPC) in buildings could enable an improved thermal comfort, lower operational costs and lower CO2 emissions. Moreover, such a controller can offer services to the energy market by using the flexibility of the building energy system to shift its loads. Unfortunately, MPC has not yet been applied to many buildings.nbsp;The main reason is the large implementation effort, in particular for developing the control model. The objective of this work is to develop and demonstrate a tool chain for automated deployment of MPC innbsp;based on data-driven, grey-box building models. The tool chain serves two purposes in order to facilitate the transition to a low-carbon society: * energy efficient building operation and * optimal use of building flexibility. In the first part of this work, the multi-disciplinary Modelica library IDEAS hasnbsp;developed in collaboration with researchers of different departments at KU Leuven. The aim of IDEAS is to investigate buildings, thermal and electrical systems at district level. With IDEAS we have quantified photovoltaic curtailing losses in dwellings and proposed simple rule-based controllers to reduce these losses. However, these rule-based controllers have their limits, specifically when larger thermal energy storage capacities are available. A model predictive controller can predict and anticipatenbsp;is expected to outperform rule-based control when the time constants or the degrees of freedom of the system increase. One of the main bottlenecks for the implementation of MPC innbsp;is the development of an appropriate system model. In a second part of this work, a grey-box buildings toolbox is developed with the aim of facilitating and even automating this process. The choice for grey-box models, as opposed to black-box models, results in physical models with interpretable parameters. This is a major advantagenbsp;regard tonbsp;model development and validation. In anbsp;part of this work, a methodology is proposed to quantify the flexibility of a building. The methodology makes use of a grey-box model and returns both the amount of electricity that can be shifted and the associated costs for the building operator. The method is applied to the office building of 3E in Brussels, the KKnbsp;This building has a hybrid heating system composed of a condensing gas boiler and two air/waternbsp;pumps. The results reveal a high variability of both the amount of flexibility and the associated costs. While most of the day, the KKnbsp;can deliver flexibility at a lower cost than the imbalance price in the Belgian power system, there are several hours where the flexibility is more expensive. Finally, an MPC has been implemented in the KK building. Tonbsp;end, a tool chain is developednbsp;covers all requirednbsp;for applying MPC to real buildings.nbsp;grey-box buildings toolbox is at the core ofnbsp;tool chain. Other elements are the forecasting of disturbances, a state estimation, the configuration ofnbsp;optimal control problem with an appropriate objective function and constraints and the handling of monitoring data and control signals. While many of thesenbsp;can still be substantially improved, this tool chain sets the field for a cost-effective roll-out ofnbsp;in buildings. The MPC for the KK building controls the thermal power of the gas boiler and both heat pumps.nbsp;In comparison with the conventional control system, thenbsp;reduces the operational costs for heating by 30% to 40%. The savings are realised by a combination of a much earlier start-up (pre-heating of the building), use of the heat pumps instead of the gas boilernbsp;a drastic reduction of the supply water temperature once thenbsp;has reached its temperature set point. Whilenbsp;of these savings can possibly be realised bynbsp;the conventional control, others cannot. The anticipation of MPC on the buildingnbsp;and expected disturbances makes this conceptnbsp;to beat. With the developed tool chain, this work hopes to bring the implementation of MPC in buildings a significant step closer to reality.