Key objectives for each of the project areas:
Sensing, measurement, and data processing are needed to make utilities aware of consumption needs and enable them to effectively manage demand while operating within safety and occupant constraints. Heating, ventilation, and air-conditioning (HVAC) systems account for approximately 50% of the building load and have untapped dispatch potential. The proposed approach has two key objectives:
- develop low-cost sensors, exploiting additive manufacturing techniques, to monitor the building environment and electrical characteristics of HVAC equipment, and
- develop algorithms to use building-level data to provide utility-scale visibility of grid reliability and localized weather monitoring.
Measuring flow (to optimize thermal delivery) and current (for load disaggregation) cost-effectively can significantly improve the efficiency and grid-responsive controllability of HVAC systems. These measurements are currently not available in most buildings because of the high cost of deployment. Our solution uses novel piezoelectric materials, additive manufacturing and improved connectivity with loads to develop integrated, low-cost, easily deployable solutions in buildings. It will develop cost-effective platform technologies in partnership with industry (e.g., compatibility with ASHRAE 201) and original equipment manufacturers (OEMs) to deliver commercial products with a path toward low-cost deployment (return on investment of <2 years for small/medium-size commercial buildings).
Transmission & Distribution
At the transmission level, to address the problem of ever-increasing fast transient swings in the future grids with predominant power converter-based generation and load, this project will develop advanced PMU algorithms for ultra-fast transient measurement during disturbances, and integrate PMU algorithms into optical transducers for high-accuracy steady state monitoring. The ultimate objective is to achieve transient stability prediction and control during the critical first swing—a key objective outlined in the Sensing & Measurement Chapter of the MYPP. The inability of existing Protection Class PMUs to accurately monitor system transient behaviors has been identified as a key challenge by the industry. Similarly, Meter Class PMUs are subject to instrumentation (PT and CT) errors, which can cause dynamic line rating estimation errors of over 40%.
Key attributes of interest for grid asset health monitoring applications include
- device stability for compatibility with potentially harsh environments (elevated temperatures, chemically corrosive, high voltage)
- compatibility with low-cost wireless and/or passive interrogation methodologies to allow for multi-point and large-area monitoring without the need for electrical wires to the sensing location, and
- elimination of electrical wires at the sensing location for compatibility with electrified interfaces as well as flammable gases and fluids typically utilized for insulation media (e.g. insulation oil).
Current state-of-the art solutions for sensing (of either outgas by-products or asset aging, or catastrophic failures such as arc and ground faults) range in costs on the order of $10,000-$100,000, which is cost-prohibitive for 100% monitoring adoption. On the performance side, sensitivity ranges lack accuracy and precision of detection, which are necessary for proactive monitoring and actionable information. The primary emphasis of our effort will be focused on sensing platforms with attributes that are best-suited for broad applicability across the entire current and future grid asset monitoring application space and for which the DOE national laboratory system is uniquely capable of developing and demonstrating in close partnership with industry and other strategic partners. These device platforms are highly versatile and can be functionalized for a broad range of parameters of interest spanning temperature, chemistry, magnetic field, voltage, etc. through a combination of device design and integration with engineered functional sensing materials. At the same time, we identified feasible goals of reducing developed sensors costs by at least an order of magnitude compared to current state of the art.