Modeling and control of complex building energy systems

Building energy sector is one of the important sources of energy consumption and especially in the United States, it accounts for approximately 40% of the total energy consumption. Besides energy consumption, it also contributes to CO2 emissions due to the combustion of fossil fuels for building operation. Preventive measures have to be taken in order to limit the greenhouse gas emission and meet the increasing load demand, energy efficiency and savings have been the primary objective globally. Heating, Ventilation, and air-conditioning (HVAC) system is a major source of energy consumption in buildings and is the principal building system of interest. These energy systems comprising of many subsystems with local information and heterogeneous preferences demand the need for coordination in order to perform optimally. The performance required by a typical airside HVAC system involving a large number of zones are multifaceted, involves attainment of various objectives (such as optimal supply air temperature) which requires coordination among zones. The required performance demands the need for accurate models (especially zones), control design at the individual (local-VAV (Variable Air Volume)) subsystems and a supervisory control (AHU (Air Handling Unit) level) to coordinate the individual controllers. In this thesis, an airside HVAC system is studied and the following considerations are addressed: a) A comparative evaluation among representative methods of different classes of models, such as physics-based (e.g., lumped parameter autoregressive models using simple physical relationships), data-driven (e.g., artificial neural networks, Gaussian processes) and hybrid (e.g., semi-parametric) methods for different physical zone locations; b) A framework for control of building HVAC systems using a methodology based on power shaping paradigm that exploits the passivity property of a system. The system dynamics are expressed in the Brayton-Moser (BM) form which exhibits a gradient structure with the mixed-potential function, which has the units of power. The power shaping technique is used to synthesize the controller by assigning a desired power function to the closed loop dynamics so as to make the equilibrium point asymptotically stable, and c) The BM framework and the passivity tool are further utilized for stability analysis of constrained optimization dynamics using the compositional property of passivity, illustrated with energy management problem in buildings. Also, distributed optimization (such as subgradient) techniques are used to generate the optimal setpoints for the individual local controllers and this framework is realized on a distributed control platform VOLTTRON, developed by the Pacific Northwest National Laboratory (PNNL)

Model-based predictive control methods for distributed energy resources in smart grids.

This thesis develops optimization-based techniques for the control of distributed energy resources to provide multiple services to the power network. It is divided into three parts. The first part of this thesis focuses on the development of a framework for the efficient control of a single resource that is subject to the effect of periodic stochastic uncertainties. More specifically, resources that can be described by the general class of periodic constrained linear systems are considered and a method, based on Stochastic MPC, to control the over-time-average constraint violations is developed. Finally, the effectiveness of the control framework is tested, by means of a simulation analysis, for the case of the climate control of a building. The second part of the thesis introduces the required background for the electric power grid, energy markets, and distributed energy resources providing grid support services. First, the control problem of scheduling the operation of a set of energy resources offering multiple services to the grid is formally stated as a multi-stage uncertain optimization problem. In particular, the problem is designed so as to maximize the provision of a shared tracking service while enforcing the satisfaction of the operational constraints on both the individual resources, as well as on the hosting distribution network. Two computationally tractable approximated solution methods are then presented, which are based on robust-optimization techniques and on a linearization of the power flow equations around a general linearization point. A simulation-based analysis demonstrates the capability of the proposed framework to adapt to different levels of uncertainty acting on the overall system. Finally, a quantitative and qualitative comparison between the two approximation schemes is presented and general guidelines are given. The last part of the thesis demonstrates the practical relevance of the control framework developed in Part II. In particular, the aggregation of an electrical battery system and of an office building is considered, and two case studies are investigated. The first deals with the provision of secondary frequency control in the Swiss market, whereas the second deals with the problem of dispatching the operation of an active distribution feeder characterized by the presence of stochastic prosumers. In both cases, we show how to adapt the general framework of Part II so as to accommodate the given application. Then, we design a hierarchical multi-timescale controller in order to reliably deliver the service by coordinating the controllable resources during real-time operation. The results of both experimental campaigns demonstrate the effectiveness and robustness of the control methodology against the wide range of uncertainty involved. In fact, in both cases, high-quality tracking performance could be achieved without jeopardizing the occupants' comfort in the building nor violating the operational constraints of the battery. Finally, the results also show the benefit of combining resources with complementary technical capabilities as the building and the battery