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.
Increased deployment of rooftop solar in California has resulted in the "duck curve", where there is a decrease in the midday net load followed by a very fast increase in late afternoon caused by declining solar output and a simultaneous increase in load. The large observed and even larger predicted ramp rates present a reliability concern. 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) hour-ahead scheduling process (HASP) real-time market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes was analyzed to identify trends and opportunities for the deployment of solar plus storage. In addition, a comparison of the opportunities in the day ahead and HASP real-time energy markets is presented.
The Natural Energy Laboratory of Hawaii Authority's (NELHA) campus on The Island of Hawai'i supplies resources for a number of renewable energy and aquaculture research projects. There is a growing interest at NELHA to convert the research campus to a 100% renewable, islanded microgrid to improve the resiliency of the campus for critical ocean water pumping loads and to limit the increase in the long-term cost of operations. Currently, the campus has solar array to cover some electricity needs but scaling up this system to fully meet the needs of the entire research campus will require significant changes and careful planning to minimize costs. This study will investigate least-cost solar and energy storage system sizes capable of meeting the needs of the campus. The campus is split into two major load centers that are electrically isolated and have different amounts of available land for solar installations. The value of adding an electrical transmission line if NELHA converts to a self-contained microgrid is explored by estimating the cost of resources for each load center individually and combined. Energy storage using lithium-ion and hydrogen-based technologies is investigated. For the hydrogen-based storage system, a variable efficiency and fixed efficiency representation of the electrolysis and fuel cell systems are used. Results using these two models show the importance of considering the changing performance of hydrogen systems for sizing algorithms.
The New York State Public Service Commission recently made significant changes to the compensation mechanisms for distributed energy resources, such as solar generation. The new mechanisms, called the Value of Distributed Energy Resources (VDER), alter the value proposition of potential installations. In particular, multiple time-of-generation based pricing alternatives were established, which could lead to potential benefits from pairing energy storage systems with solar installations. This paper presents the calculations to maximize revenue from a solar photovoltaic and energy storage system installation operating under the VDER pricing structures. Two systems in two different zones within the New York Independent System Operator area were modeled. The impact of AC versus DC energy storage system interconnections with solar generation resources was also explored. The results show that energy storage systems could generate significant revenue depending on the pricing alternative being targeted and the zone selected for the project.
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.
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.