Predictive Control methods for Building Control and Demand Response

This thesis studies advanced control techniques for the control of building heating and cooling systems to provide demand response services to the power network. It is divided in three parts. The first one introduces the MATLAB toolbox OpenBuild which aims at facilitating the design and validation of predictive controllers for building systems. In particular, the toolbox constructs models of building that are appropriate for use in predictive controllers, based on standard building description data files. It can also generate input data for these models that allows to test controllers in a variety of weather and usage scenarios. Finally, it offers co-simulation capability between MATLAB and EnergyPlus in order to test the controllers in a trusted simulation environment, making it a useful tool for control engineers and researchers who want to design and test building controllers in realistic simulation scenarios. In the second part, the problem of robust tracking commitment is formulated: it consists of a multi-stage robust optimization problem for systems subject to uncertainty where the set where the uncertainty lies is part of the decision variables. This problem formulation is inspired by the need to characterize how an energy system can modify its electric power consumption over time in order to procure a service to the power network, for example Demand Response or Reserve Provision. A method is proposed to solve this problem where the key idea is to modulate the uncertainty set as the image of a fixed uncertainty set by a modifier function, which allows to embed the modifier function in the controller and by doing so convert the problem into a standard robust optimization problem. The applicability of this framework is demonstrated in simulation on a problem of reserve provision by a building. We finally detail how to derive infinite horizon guarantees for the robust tracking commitment problem. The third part of thesis reports the experimental works that have been conducted on the Laboratoire d'Automatique Demand Response (LADR) platform, a living lab equipped with sensors and a controllable heating system. These experiments implement the algorithms developed in the second part of the thesis to characterize the LADR platform flexibility and demonstrate the closed-loop control of a building heating system providing secondary frequency control to the Swiss power network. In the experiments, we highlight the importance of being able to adjust the power consumption baseline around which the flexibility is offered in the intraday market and show how flexibility and comfort trade off

Constrained bundle methods with inexact minimization applied to the energy regulation provision problem

We consider a class of large scale robust optimization problems. While the robust optimization literature often relies on structural assumptions to reformulate the problem in a tractable form using duality, this method is not always applicable and can result in problems which are very large. We propose an alternative way of solving such problems by applying a constrained bundle method. The originality of the method lies in the fact that the minimization steps in the bundle method are solved approximately using the alternating direction method of multipliers. Numerical results from a power grid regulation problem are presented and support the relevance of the approach

Robust Tracking Commitment with Application to Demand Response

Many engineering problems that involve hierarchical control applications, such as demand side ancillary service provision to the power grid, can be posed as an optimal tracking commitment problem. In this setting, the lower- level controller commits a set of possible reference trajectories over a finite horizon to an external entity, which requires guaranteed tracking of any reference trajectory that can be sampled from the committed set, with an allowed deviation, in exchange for a payment corresponding to the size of the reference set. This paper presents a method to solve the optimal tracking commitment problem for constrained linear systems subject to uncertain disturbance and reference signals. The proposed method allows tractable computations via convex optimization for conic representable reference sets and lends itself to distributed solution methods. We demonstrate the proposed method in a simulation based case study with a commercial building that offers a frequency regulation service to the power grid

OpenBuild : An Integrated Simulation Environment for Building Control

This paper introduces the OpenBuild toolbox for MATLAB. OpenBuild is a toolbox for advanced controller design for buildings heating ventilation and air conditioning systems, with emphasis on Model Predictive Control. It pro- vides researchers in the control community the ability to test algorithms on a wide range of realistic simulation scenarios, by providing most of the data needed to perform simulation and optimization. It combines the convenience of controller design in MATLAB with the simulation capabilities of the building simulation software EnergyPlus. It includes a building modeling tool to construct linear state-space models of building thermodynamics based on building description data, making it useful for design of optimal controllers requiring a good prediction model, as well as providing the input data necessary for simulation such as weather, occupancy and internal gains data. The ability to co-simulate the building between MATLAB and EnergyPlus enables fast prototyping and validation of the models and controllers. This paper presents the working principles and functionality of OpenBuild

Model Predictive Control for Market-Based Demand Response Participation

In this study, we investigate the maximum possible profit for a commercial office building participating in New York’s Day-Ahead Demand Response (DADR) program. We formulate an optimal control problem, assuming perfect knowledge of future weather, occupancy, and day-ahead electricity price predictions to examine this potential benefit. Then, a practical control strategy based upon the framework of Model Predictive Control (MPC) is proposed, which enables a building to participate in the DADR program. The controller decides once every day, whether or not to participate in the Demand Response (DR) event, and then optimizes the electric consumption to increase savings. A simulation study is carried out using a building model extracted from an EnergyPlus model, real measured weather data, and real day-ahead spot market price data for New York. Savings in the range of 23% to 33% are reported

Cooperative guidance of UAVs for area exploration with final target allocation

Fulfillment of complex missions, such as zone watching or multiple target tracking by an UAV can prove very demanding in terms of vehicle ability. A potential way of lessening these demands consists in splitting the initial task into several complementary subtasks. Each subtask can then be completed by one vehicle of a fleet whose cooperation must guarantee the satisfaction of the whole mission. In this paper, zone watching is defined as a cooperative problem where a number of autonomous vehicles must explore a wide area in a limited amount of time. In addition to zone coverage, the dynamical allocation of exit locations is considered. A model predictive control approach is adopted in which the requirements of the mission are specified as cost functions. Simulation results are presented to illustrate the behaviour of the fleet

OpenBuildNet Framework for Distributed Co-Simulation of Smart Energy Systems

The complexity and diversity of future energy systems will require co-simulation solutions that enable the integration of tools from multiple domains for research and development. We introduce an open-source framework, OpenBuildNet, for distributed co-simulation of large-scale smart energy systems. Using a loose-coupling approach to co-simulate parallel processes, it can leverage and seamlessly integrate specialized simulation and computation tools in a common platform. Users can therefore benefit from the capabilities of state-of-the-art and widely used tools in each domain. OpenBuildNet is scalable and highly flexible as it uses a decentralized architecture, message-based communication, and peer-to-peer data exchange between subsystem nodes. It also provides a set of easy-to-use software tools tailored for researchers and engineers. This paper presents the architecture and tool suite of OpenBuildNet, and demonstrates its usefulness in a case study of controlling multiple buildings for demand response

OpenBuildNet Framework for Distributed Co-Simulation of Smart Energy Systems

The complexity and diversity of future energy systems will require co-simulation solutions that enable the integration of tools from multiple domains for research and development. We introduce an open-source framework, OpenBuildNet, for distributed co-simulation of large-scale smart energy systems. Using a loose-coupling approach to co-simulate parallel processes, it can leverage and seamlessly integrate specialized simulation and computation tools in a common platform. Users can therefore benefit from the capabilities of state-of-the-art and widely used tools in each domain. OpenBuildNet is scalable and highly flexible as it uses a decentralized architecture, message-based communication, and peer-to-peer data exchange between subsystem nodes. It also provides a set of easy-to-use software tools tailored for researchers and engineers. This paper presents the architecture and tool suite of OpenBuildNet, and demonstrates its usefulness in a case study of controlling multiple buildings for demand response

Experimental Implementation of Frequency Regulation Service Using Commercial Buildings

Frequency control reserves are a fundamental mechanism in the current electric power system to preserve the balance between demand and generation of active power at all times. The traditional approach is to exploit conventional power plants for frequency reserves. However, due to the increasing penetration of renewable energies, the demand side is expected to play an essential role in the near future.This paper illustrates the potential of commercial buildings as frequency reserves providers through an experimental demonstration conducted in a multi-zone university building. The proposed control methodology, that represents a first step to the full accomplishment of this task, is presented in details,including the control architecture, the controller design, model identification and hardware description. Finally, the effectiveness of the presented approach is tested by means of simulations and experiments in a very controlled environment

Dispatching active distribution networks through electrochemical storage systems and demand side management

In this paper, the problem of dispatching the operation of a distribution feeder comprising a set of heterogeneous resources is investigated. In particular, the main objective is to track a 5-minute resolution trajectory, called the \textit{dispatch plan} that is computed one day before the beginning of operation. During real-time operation, due to the stochasticity of part of the resources in the feeder portfolio, tracking errors need to be absorbed in order to track the committed dispatch plan. This is achieved by modulating the power consumption of a grid-connected battery energy storage system (BESS) and of the HVAC system of a commercial controllable building (CB). To this end, a hierarchical multi-time-scale controller is designed to coordinate the two entities while requiring a minimal communication infrastructure. The effectiveness of the proposed control framework is demonstrated by means of a set of full-day experimental results on the 20kV distribution feeder of the EPFL campus that is comprised of: 1) a set of uncontrollable resources represented by 5 office buildings (350kWp) and a roof-top PV installation (90kWp) 2) a set of controllable resources, namely, a grid-connected BESS (720kVA-500kWh), and a fully-occupied multi-zone office building (45 kWp)