The project aims at developing a distributed nonlinear controller for transient stability enhancement. The new control layer will actuate on distributed energy storage systems, be robust to uncertainties in modelling and capable of compensating input time-delay while independent of operating conditions. Furthermore, the robust controller will not require exact knowledge of the system dynamics. Second, bad data analytics based on the innovation approach and cross-layered information provided by distributed software-defined network will be developed. The bad data analytics will consider the inherent interdependencies of the physical processes while providing a countermeasure. Third, an adaptive distributed robust machine learning approach will be developed. The overwhelming majority of supervised machine learning methods require large amounts of carefully labeled training data that is representative of the data distribution to be seen under test.
The ultimate goal of the project is to help the electric grid become more reliable even when a large amount of electricity is generated from green, but intermittent – sources such as solar and wind. To deal with this intermittency, inexpensive source of energy storage are required. Instead of investing in batteries, this project seeks to obtain cheap storage by manipulating power demand in consumer loads through intelligent decision-making algorithms. By varying power demand up and down from what a load would nominally consume, the load can be made to behave like a battery, effectively creating a source of Virtual Energy Storage (VES). This kind of virtual storage is cheaper than batteries since it is a software-based solution; little additional hardware is needed. Another aspect of the project is to develop decision-making algorithms to cope with operational issues faced by the power distribution networks (that deliver electricity to neighborhoods) due to increasing use of intermittent solar power.
Buildings account for 45 percent of the total energy consumption in the United States (U.S.), and maintaining indoor climate, which includes heating, cooling, and ventilation, accounts for approximately half of that energy consumption. A low-cost option for reducing building energy usage is intelligent climate control, moving away from the prevalent “design for steady-state conditions” philosophy into one that exploits the constantly changing conditions a building operates in due to its occupants and the weather. The potential for intelligent climate control has been recognized for many years, especially for commercial buildings that have the requisite sensors and actuators. In particular, control algorithms that make decisions using real-time optimization have been shown to be highly promising. In spite of its promise, such “model-optimization” based control technologies have not been widely adopted by industry. The reason for this lack of translation to practice is the lack of autonomy of existing algorithms. Not only do they require expert human involvement in model creation, which have to be tuned for each building manually, they do not provide guarantees about the quality of real-time decisions. Addressing these weaknesses will lead to the wider adoption of intelligent building climate control technologies, which will contribute to the technological edge U.S. industries enjoy, and reduce the nation’s energy usage.
A dynamic network consists of interacting dynamic sub-systems. Such networks occur in many domains: living cells, financial markets, the Internet and the power grid are some examples. Heating, ventilation and air conditioning (HAVC) systems in buildings can also be modeled through dynamic networks since each room’s climate depends on that of nearby spaces. Knowledge of such dynamic network models is essential to design and deploy control strategies devoted to the improvement of energy efficiency and occupant comfort. Yet, in practice the structure and dynamics of these networks are either unknown or imprecisely known. For instance, information on the thermal interaction among rooms is difficult to obtain from laws of physics due to the complexity of the physical processes involved. The goal of this project is to formulate algorithms for the identification of dynamic sparse network models from measured data. The research results will support the study of advanced controls for HVAC systems to reduce their energy use and to provide demand-side flexibility to the power grid. Since buildings consume 75% of the nation’s electricity, improvement of energy efficiency through smart building control systems will contribute to the sustainability of the nation’s energy system.