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Date:  Thu, February 07, 2019
Time:  3:30-4:30pm
Location:  Holmes 389
Speaker:  Kevin Davies

Abstract:

In order to achieve Hawaii's aggressive Renewable Portfolio Standard (RPS) targets, large amounts of variable renewable energy must be integrated into the electric grid. Due to land constraints in Hawaii, it is desirable to maximize the contribution of distributed rooftop photovoltaics (PV). The Hawaiian Electric Companies' Power Supply Improvement Plan (PSIP) calls fro 326 MW of additional rooftop PV by 2021, but many circuits are already at capacity and system-level operational changes are expected to become more difficult and costly. To address this issue, the recent Customer Self Supply (CSS) program eliminates grid export and Customer Grid Supply (CGS) places a lower value on exported energy to account for the implicit cost of ancillary services such as energy shifting. While there is movement towards utility-level control of distributed energy resources (DER) such as PV in the CGS+ program, operational control and visibility of the effects of PV on distribution circuits is coars and limited in scope. As a part of its efforts to support the grid integration of renewable energy, the Hawaii Natural Energy Institute (HNEI, of the School of Ocean and Earth Science and Technology) has developed a low-cost, high-resolution power monitor for use at distribution service transformers. The ultimate objective is to bring DER visibility and control to the same level of performance as central, utility-scale PV in order to maximize the contribution of DERs to RPS goals and grid resiliency. Rather than utilizing existing central, aggregated DER control topologies, we believe that the stated objective is best addressed by a hierarchical architecture with edge computing at service transformers. This seminar will introduce HNEI's power monitor, which includes current and voltage sensors, single board computer, field programmable gate array (FPGA), GPS, wireless communications, and backup power. The platform has been proposed as a field laboratory for distributed real-time and predictive modeling, grid state estimation, machine learning for PV/load disaggregation, integration of solar forecasting, passive impedance detection, fault location algorithms, non-wires alternatives, and resiliency assessment and improvement.

Bio:

Kevin Davies (PhD, Mechanical Engineering, Georgia Tech; BS, Electrical and Computer Engineering, Carnegie Mellon) specializes in distributed data acquisition, modeling, and controls for the electric grid under high penetrations of renewable energy. Dr. Davies brings pragmatic, system-level design expertise from a background in model-based systems engineering and the automotive industry. In addition to leading the development of HNEI's power monitor system, he has been instrumental in building the capabilities and outreach of HNEI's Grid System Technologies Advanced Research Team (GridSTART) with hardware-in-the-loop (HIL) test equipment, production cost modeling, and EE 396/496 courses. This seminar is part of an effort to expand collaboration and student opportunities between EE and HNEI.