Tomasz Tadeusz Gorecki

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 provides 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.

Guaranteeing input tracking for constrained systems: Theory and application to demand response

A method for certifying exact input trackability for constrained discrete time linear systems is introduced in this paper. A signal is assumed to be drawn from a reference set and the system must track this signal with a linear combination of its inputs. Using methods inspired from robust model predictive control, the proposed approach certifies the ability of a system to track any reference drawn from a polytopic set on a finite time horizon by solving a linear program. Optimization over a parameterization of the set of reference signals is discussed, and particular instances of parameterization of this set that result in a convex program are identified, allowing one to find the largest set of trackable signals of some class. Infinite horizon feasibility of the methods proposed is obtained through use of invariant sets, and an implicit description of such an invariant set is proposed. These results are tailored for the application of power consumption tracking for loads, where the operator of the load needs to certify in advance his ability to fulfill some requirement set by the network operator. An example of a building heating system illustrates the results.

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.

Experimental Implementation of Frequency Regulation Services Using Commercial Buildings

This paper illustrates the potential of commercial buildings to act as frequency reserves providers through an experimental demonstration conducted in a multi-zone university building. The proposed control methodology is presented in detail, 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 controlled environment.

Robust Tracking Commitment

Many engineering problems that involve hierarchical control applications, such as demand-side ancillary service provision to the power grid, can be posed as a robust 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 in exchange for a reward corresponding to the size of the reference set and the allowed margin of tracking error. If the commitment is accepted, the lower level system is required to track any reference trajectory that can be sampled from the committed set. This paper presents the framework of robust tracking commitment and a method to solve the optimal 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 uncertainty 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 frequency regulation service to the power grid.

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 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 multizone office building (45 kWp).

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.