Publications

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Implications of conducting hardware-in-the-loop testing of a specific hydrokinetic turbine on controllable motor-generator sets or electromechanical emulation machines (EEMs) are explored. The emulator control dynamic equations are presented, methods for scaling turbine parameters are developed and evaluated, and experimental results are presented from three EEMs programmed to emulate the same vertical-axis fixed-pitch turbine. Although hardware platforms and control implementations varied, results show that each EEM is successful in emulating the turbine model at different power levels, thus demonstrating the general feasibility of the approach. However, performance of motor control under torque command, current command, or speed command differed. In a demonstration of the intended use of an EEM for evaluating a hydrokinetic turbine implementation, a power takeoff controller tracks the maximum power-point of the turbine in response to turbulence. Utilizing realistic inflow conditions and control laws, the emulator dynamic speed response is shown to agree well at low frequencies with numerical simulation but to deviate at high frequencies.

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Increasing the penetration of distributed renewable sources, including photovoltaic (PV) sources, poses technical challenges for grid management. The grid has been optimized over decades to rely upon large centralized power plants with well-established feedback controls, but now non-dispatchable, renewable sources are displacing these controllable generators. This one-year study was funded by the Department of Energy (DOE) SunShot program and is intended to better utilize those variable resources by providing electric utilities with the tools to implement frequency regulation and primary frequency reserves using aggregated renewable resources, known as a virtual power plant. The goal is to eventually enable the integration of 100s of Gigawatts into US power systems.

The goal of this effort was to apply four potential control analysis/design approaches to the design of distributed grid control systems to address the impact of latency and communications uncertainty with high penetrations of photovoltaic (PV) generation. The four techniques considered were: optimal fixed structure control; Nyquist stability criterion; vector Lyapunov analysis; and Hamiltonian design methods. A reduced order model of the Western Electricity Coordinating Council (WECC) developed for the Matlab Power Systems Toolbox (PST) was employed for the study, as well as representative smaller systems (e.g., a two-area, three-area, and four-area power system). Excellent results were obtained with the optimal fixed structure approach, and the methodology we developed was published in a journal article. This approach is promising because it offers a method for designing optimal control systems with the feedback signals available from Phasor Measurement Unit (PMU) data as opposed to full state feedback or the design of an observer. The Nyquist approach inherently handles time delay and incorporates performance guarantees (e.g., gain and phase margin). We developed a technique that works for moderate sized systems, but the approach does not scale well to extremely large system because of computational complexity. The vector Lyapunov approach was applied to a two area model to demonstrate the utility for modeling communications uncertainty. Application to large power systems requires a method to automatically expand/contract the state space and partition the system so that communications uncertainty can be considered. The Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) design methodology was selected to investigate grid systems for energy storage requirements to support high penetration of variable or stochastic generation (such as wind and PV) and loads. This method was applied to several small system models.

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Increasing the penetration of distributed renewable sources, including photovoltaic (PV) generators, poses technical challenges for grid management. The grid has been optimized over decades to rely on large centralized power plants with well-established feedback controls. Conventional generators provide relatively constant dispatchable power and help to regulate both voltage and frequency. In contrast, photovoltaic (PV) power is variable, is only as predictable as the weather, and provides no control action. Thus, as conventional generation is displaced by PV power, utility operation stake holders are concerned about managing fluctuations in grid voltage and frequency. Furthermore, since the operation of these distributed resources are bound by certain rules that require they stop delivering power when measured voltage or frequency deviate from the nominal operating point, there are also concerns that a single grid event may cause a large fraction of generation to turn off, triggering a black out or break-up of an electric power system.

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