Battery Capacity of Deferrable Energy Demand

We investigate the ability of a homogeneous collection of deferrable energy loads to behave as a battery; that is, to absorb and release energy in a controllable fashion up to fixed and predetermined limits on volume, charge rate and discharge rate. We derive bounds on the battery capacity that can be realized and show that there are fundamental trade-offs between battery parameters. By characterizing the state trajectories under scheduling policies that emulate two illustrative batteries, we show that the trade-offs occur because the states that allow the loads to absorb and release energy at high aggregate rates are conflicting

Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method

The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling and winter heating are introduced using a V-shaped hourly total load curve. The trained LTSM model is additionally run with TmHVAC and zero irradiance inputs yielding an estimated baseload, which is representative of typical occupancy patterns. The HVAC power component is disaggregated as the difference between total and baseload power. Total power forecasts of an aggregated residential community as seen by major distribution lines are experimentally validated with a satisfactory MAPE error below 10% based on a 4-year dataset from a representative suburban community with more than 1800 homes in Kentucky, U.S. Discussions regarding the validity of the separation method based on combined considerations of fundamental physics, statistics, and human behavior are also included

Predictive Control of Buildings for Demand Response and Ancillary Services Provision

This thesis develops optimization based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for the provision of demand response and ancillary services to the electric grid. The first part of the thesis focuses on the development of the open source MATLAB toolbox OpenBuild, developed for modeling of buildings for control applications. The toolbox constructs a first-principles based model of the building thermodynamics using EnergyPlus model data. It also generates the disturbance data affecting the models and allows one to simulate various usage scenarios and building types. It enables co-simulation between MATLAB and EnergyPlus, facilitating model validation and controller testing. OpenBuild streamlines the design and deployment of predictive controllers for control applications. The second part of the thesis introduces the concept of buildings acting as virtual storages in the electric grid and providing ancillary services. The control problem (for the bidding phase) to characterize the flexibility of a building, while also participating in the intraday energy market is formulated as a multi-stage uncertain optimization problem. An approximate solution method based on a novel intraday control policy and two-stage stochastic programming is developed to solve the bidding problem. A closed loop control algorithm based on a stochastic MPC controller is developed for the online operation phase. The proposed control method is used to carry out an extensive simulation study using real data to investigate the financial benefits of office buildings providing secondary frequency control services to the grid in Switzerland. The technical feasibility of buildings providing a secondary frequency control service to the grid is also demonstrated in experiments using the experimental platform (LADR) developed in the Automatic Control Laboratory of EPFL. The experimental results validate the effectiveness of the proposed control method. The third part of the thesis develops a hierarchical method for the control of building HVAC systems for providing ancillary services to the grid. Three control layers are proposed: The local building controllers at the lowest level track the temperature set points received from the thermal flexibility controller that maximizes the flexibility of a buildingâs thermal consumption. At the highest level, the electrical flexibility controller controls the HVAC system while maximizing the flexibility provided to the grid. The two flexibility control layers are based on robust optimization methods. A control-oriented model of a typical air-based HVAC system with a thermal storage tank is developed and the efficacy of the proposed control scheme is demonstrated in simulations

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