Frauke Oldewurtel

Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions

One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.

Reducing peak electricity demand in building climate control using real-time pricing and model predictive control

A method to reduce peak electricity demand in building climate control by using real-time electricity pricing and applying model predictive control (MPC) is investigated. We propose to use a newly developed time-varying, hourly-based electricity tariff for end-consumers, that has been designed to truly reflect marginal costs of electricity provision, based on spot market prices as well as on electricity grid load levels, which is directly incorporated into the MPC cost function. Since this electricity tariff is only available for a limited time window into the future we use least-squares support vector machines for electricity tariff price forecasting and thus provide the MPC controller with the necessary estimated time-varying costs for the whole prediction horizon. In the given context, the hourly pricing provides an economic incentive for a building controller to react sensitively with respect to high spot market electricity prices and high grid loading, respectively. Within the proposed tariff regime, grid-friendly behaviour is rewarded. It can be shown that peak electricity demand of buildings can be significantly reduced. The here presented study is an example for the successful implementation of demand response (DR) in the field of building climate control.

A tractable approximation of chance constrained stochastic MPC based on affine disturbance feedback

This paper deals with model predictive control of uncertain linear discrete-time systems with polytopic constraints on the input and chance constraints on the states. When having polytopic constraints and bounded disturbances, the robust problem with an open-loop prediction formulation is known to be conservative. Recently, a tractable closed-loop prediction formulation was introduced, which can reduce the conservatism of the robust problem. We show that in the presence of chance constraints and stochastic disturbances, this closed-loop formulation can be used together with a tractable approximation of the chance constraints to further increase the performance while satisfying the chance constraints with the predefined probability.

Stochastic Model Predictive Control for Building Climate Control

In this brief paper, a Stochastic Model Predictive Control formulation tractable for large-scale systems is developed. The proposed formulation combines the use of Affine Disturbance Feedback, a formulation successfully applied in robust control, with a deterministic reformulation of chance constraints. A novel approximation of the resulting stochastic finite horizon optimal control problem targeted at building climate control is introduced to ensure computational tractability. This work provides a systematic approach toward finding a control formulation which is shown to be useful for the application domain of building climate control. The analysis follows two steps: 1) a small-scale example reflecting the basic behavior of a building, but being simple enough for providing insight into the behavior of the considered approaches, is used to choose a suitable formulation; and 2) the chosen formulation is then further analyzed on a large-scale example from the project OptiControl, where people from industry and other research institutions worked together to create building models for realistic controller comparison. The proposed Stochastic Model Predictive Control formulation is compared with a theoretical benchmark and shown to outperform current control practice for buildings.

A framework for and assessment of demand response and energy storage in power systems

The shift in the electricity industry from regulated monopolies to competitive markets as well as the widespread introduction of fluctuating renewable energy sources bring new challenges to power systems. Some of these challenges can be mitigated by using demand response (DR) and energy storage to provide power system services. The aim of this paper is to provide a unified framework that allows us to assess different types of DR and energy storage resources and determine which resources are best suited to which services. We focus on four resources: batteries, plug-in electric vehicles, commercial buildings, and thermostatically controlled loads. We define generic power system services in order to assess the resources. The contribution of the paper is threefold: (i) the development of a framework for assessing DR and energy storage resources; (ii) a detailed analysis of the four resources in terms of ability for providing power system services, and (iii) a comparison of the resources, including an example case for Switzerland. We find that the ability of resources to provide power system services varies largely and also depends on the implementation scenario. Generally, there is large potential to use DR and energy storage for providing power system services, but there are also challenges to be addressed, for example, adequate compensation, privacy, guaranteeing costumer service, etc.

Towards a standardized building assessment for demand response

This paper addresses the setup of an aggregation of office buildings to provide services for the electricity grid. In order to determine the energy shifting potential of different buildings for a given time of the day, a standardized assessment procedure based on Model Predictive Control and predefined price signals is proposed. The aim is to provide the aggregator with an index describing the additional energy used for a desired change in power consumption for each building and for each hour of the day, enabling him to choose a good portfolio of buildings for providing the grid service cost-effectively. We first show in an experiment on a real office building in Switzerland the possibility of shifting the electricity consumption as well as a comparison of experiments and simulations for this building case. We then use this building case to test the proposed procedure in simulation and provide an hourly analysis of the power shifting potential for different seasons.

Building control and storage management with dynamic tariffs for shaping demand response

The results from a proof-of-concept study combining modern building automation systems (BAS) with dynamic electricity tariffs are presented. The use of a building automation system that optimizes the electricity demand of a retail end-consumer while managing a local battery unit and respecting all comfort constraints, e.g., on room temperature, illuminance, and indoor air quality, is proposed. The optimization is done in a fully automated fashion, i.e. without any need of action from an external operator. The study focuses on the situation of end-consumers in the city of Zurich, Switzerland. Demand shifting as well as effects on the cost of electricity consumption for different retail consumer groups, i.e. households and offices using their typical usage profiles are assessed. In-house battery systems are introduced as additional means of electricity demand flexibility. Extensive simulations (500+ full-year simulations) are performed for different building types, battery types, and usage profiles. The overall load shifting effect on the aggregated load curve of the city of Zurich is evaluated.

Stochastic MPC Framework for Controlling the Average Constraint Violation

This technical note considers linear discrete-time systems with additive, bounded, disturbances subject to hard control input bounds and a stochastic constraint on the amount of state-constraint violation averaged over time. The amount of violations is quantified by a loss function and the averaging can be weighted, corresponding to exponential forgetting of past violations. The freedom in the choice of the loss function makes this formulation highly flexible-for instance, probabilistic constraints, or integrated chance constraints, can be enforced by an appropriate choice of the loss function. For the type of constraint considered, we develop a recursively feasible receding horizon control scheme exploiting the averaged-over-time nature by explicitly taking into account the amount of past constraint violations when determining the current control input. This leads to a significant reduction in conservatism. As a simple extension of the proposed approach we show how time-varying state-constraints can be handled within our framework. The computational complexity (online as well as offline) is comparable to existing model predictive control schemes. The effectiveness of the proposed methodology is demonstrated by means of a numerical example from building climate control.

Minimizing communication cost for demand response using state estimation

Demand Response (DR) is widely considered to be an integral part of future energy systems with high renewable energy penetration. Fast and reliable DR schemes require appropriate communication infrastructure. By limiting both the data sent and the measurements taken, the marginal cost of adding a load unit to the DR scheme can be reduced so that it becomes economically viable to utilize small loads such as hot-water boilers for DR. This paper presents an estimation and control topology for DR relying only on measurements of aggregated power and one-way communication. By using a state observer based on particle filtering and by switching individual loads based on comparing a drawn random number to a broadcast switching signal, accurate tracking of a reference signal can be achieved. In a case study it is shown that employing the proposed algorithms on a basic Smart Meter infrastructure enables the utility to significantly reduce overall cost by avoiding consumption of balancing energy.

Strongly feasible stochastic model predictive control

In this article we develop a systematic approach to enforce strong feasibility of probabilistically constrained stochastic model predictive control problems for linear discrete-time systems under affine disturbance feedback policies. Two approaches are presented, both of which capitalize and extend the machinery of invariant sets to a stochastic environment. The first approach employs an invariant set as a terminal constraint, whereas the second one constrains the first predicted state. Consequently, the second approach turns out to be completely independent of the policy in question and moreover it produces the largest feasible set amongst all admissible policies. As a result, a trade-off between computational complexity and performance can be found without compromising feasibility properties. Our results are demonstrated by means of two numerical examples.