A 250kW hydrogen electrolysis facility was recently installed at the Natural Energy Laboratory of Hawaii Authority's (NELHA's) campus. This facility that will begin operation in 2020 to produce hydrogen for fuel cell buses on the island to demonstrate of the application of hydrogen to decarbonize transportation. Given the size of the electrolysis station, it has the potential to significantly increase electricity costs for the campus, which is subject to energy and peak demand charges from the local utility. In this paper, we analyze the cost of hydrogen production at NELHA given the rate structure options available from the utility. Production costs are estimated using optimal versus constant scheduling of the facility to meet the buses’ demand. A model of the electrolysis station is used to capture changes in production efficiency over the power range in the optimization routine. The effects of combining the station and campus load versus standalone operation and increasing solar generation are also explored. The analyses surrounding this scenario show the importance of multiple factors on the potential profitability of hydrogen production in behind-the-meter applications and show trends that could have implications for other similar installations.
The state of California is leading the nation with respect to solar energy and storage. The California Energy Commission has mandated that starting in 2020 all new homes must be solar powered. In 2010 the California state legislature adopted an energy storage mandate AB 2514. This required California's three largest utilities to contract for an additiona11.3 GW of energy storage by 2020, coming online by 2024. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) day ahead market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes were analyzed to identify trends and opportunities for the deployment of solar plus storage.
Forward operating base (FOB) microgrids typically use diesel generators with discrete logic control to supply power. However, emerging energy storage systems can be added as spinning reserves and to increase the PV hosting capacity of microgrids to significantly reduce diesel consumption if resources are controlled appropriately. Discrete logic controllers use if/else statements to determine resource dispatch based on inputs such as net load and generator run times but do not account for the capabilities of energy storage systems explicitly. Optimal dispatch controllers could improve upon this architecture by optimizing dispatch based on forecasts of load and generation. However, optimal dispatch controllers are far less intuitive, require more processing power, and the level of potential improvement is unclear.This work seeks to address three points with regards to FOB microgrid operations. Firstly, the impact of energy storage systems on the adoption of solar generation in microgrids is discussed. Secondly, logic is added to the typical discrete controller decision tree to account for energy storage resources. Lastly, fuel savings with energy storage and solar generation using the new discrete control logic and optimal dispatch are compared based on load data measured from a real FOB. The results of these analyses show the potential impact of energy storage on fuel consumption in FOBs and gives guidance as to the appropriate control architecture for management of integrated resource microgrids.
Battery energy storage is being installed behind-the-meter to reduce electrical bills while improving power system efficiency and resiliency. This paper demonstrates the development and application of an advanced optimal control method for battery energy storage systems to maximize these benefits. We combine methods for accurately modeling the state-of-charge, temperature, and state-of-health of lithium-ion battery cells into a model predictive controller to optimally schedule charge/discharge, air-conditioning, and forced air convection power to shift a electric customer's consumption and hence reduce their electric bill. While linear state-of-health models produce linear relationships between battery usage and degradation, a non-linear, stress-factor model accounts for the compounding improvements in lifetime that can be achieved by reducing several stress factors at once. Applying this controller to a simulated system shows significant benefits from cooling-in-the-loop control and that relatively small sacrifices in bill reduction performance can yield large increases in battery life. This trade-off function is highly dependent on the battery's degradation mechanisms and what model is used to represent them.
Ceramic fiber insulation materials are used in numerous applications (e.g. aerospace, fire protection, and military) for their stability and performance in extreme environments. However, the thermal properties of these materials have not been thoroughly characterized for many of the conditions that they will be exposed to, such as high temperatures, pressures, and alternate gaseous atmospheres. The resulting uncertainty in the material properties can complicate the design of systems using these materials. In this study, the thermal conductivity of two ceramic fiber insulations, Fiberfrax T-30LR laminate and 970-H paper, was measured as a function of atmospheric temperature and compression in an air environment using the transient plane source technique. Furthermore, a model is introduced to account for changes in thermal conductivity with temperature, compression, and ambient gas. The model was tuned to the collected experimental data and results are compared. The tuned model is also compared to published data sets taken in argon, helium, and hydrogen environments and agreement is discussed.