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Hybrid fuel cell/gas turbine systems provide an efficient means of producing electricity from fossil fuels with ultra low emissions. However, there are many significant challenges involved in integrating the fuel cell with the gas turbine and other components of this type of system. The fuel cell and the gas turbine must maintain efficient operation and electricity production while protecting equipment during perturbations that may occur when the system is connected to the utility grid or in stand-alone mode. This paper presents recent dynamic simulation results from two laboratories focused on developing tools to aid in the design and dynamic analyses of hybrid fuel cell systems. The simulation results present the response of a carbonate fuel cell/gas turbine, or molten carbonate fuel cell/gas turbine, (MCFC/GT) hybrid system to a load demand perturbation. Initial results suggest that creative control strategies will be needed to ensure a flexible system with wide turndown and robust dynamic operation. Paper No. GT2004-53653,

Hybrid fuel cell/gas turbine systems provide an efficient means of producing electricity from fossil fuels with ultra low emissions. However, there are many significant challenges involved in integrating the fuel cell with the gas turbine and other components of this type of system. The fuel cell and the gas turbine must maintain efficient operation and electricity production while protecting equipment during perturbations that may occur when the system is connected to the utility grid or in stand-alone mode. This paper presents recent dynamic simulation results from two laboratories focused on developing tools to aid in the design and dynamic analyses of hybrid fuel cell systems. The simulation results present the response of a carbonate fuel cell/gas turbine, or molten carbonate fuel cell/gas turbine, (MCFC/GT) hybrid system to a load demand perturbation. Initial results suggest that creative control strategies will be needed to ensure a flexible system with wide turndown and robust dynamic operation. Paper No. GT2004-53653,

Gamification refers to using game attributes in a non-gaming context. Health professions educators increasingly turn to gamification to optimize students' learning outcomes. However, little is known about the concept of gamification and its possible working mechanisms. This review focused on empirical evidence for the effectiveness of gamification approaches and theoretical rationales for applying the chosen game attributes. We systematically searched multiple databases, and included all empirical studies evaluating the use of game attributes in health professions education. Of 5044 articles initially identified, 44 met the inclusion criteria. Negative outcomes for using gamification were not reported. Almost all studies included assessment attributes (n = 40), mostly in combination with conflict/challenge attributes (n = 27). Eight studies revealed that this specific combination had increased the use of the learning material, sometimes leading to improved learning outcomes. A relatively small number of studies was performed to explain mechanisms underlying the use of game attributes (n = 7). Our findings suggest that it is possible to improve learning outcomes in health professions education by using gamification, especially when employing game attributes that improve learning behaviours and attitudes towards learning. However, most studies lacked well-defined control groups and did not apply and/or report theory to understand underlying processes. Future research should clarify mechanisms underlying gamified educational interventions and explore theories that could explain the effects of these interventions on learning outcomes, using well-defined control groups, in a longitudinal way. In doing so, we can build on existing theories and gain a practical and comprehensive understanding of how to select the right game elements for the right educational context and the right type of student.

The Paris Agreement highlights the need for local climate leadership. The University Of California's approach to deep decarbonization offers lessons in efficiency, alternative fuels and electrification. Bending the emissions curve globally requires efforts that blend academic insights with practical solutions.

The introduction of fuel cell electric vehicles (FCEV) operating on hydrogen is a key strategy to mitigate pollutant emissions from the light duty vehicle (LDV) transportation sector in pursuit of air quality (AQ) improvements. Further, concomitant increases in renewable power generation could assist in achieving benefits via electrolysis-provided hydrogen as a vehicle fuel. However, it is unclear (1) reductions in emissions translate to changes in primary and secondary pollutant concentrations and (2) how effects compare to those from emissions in other transport sectors including heavy duty vehicles (HDV). This work assesses how the adoption of FCEVs in counties expected to support alternative LDV technologies affect atmospheric concentrations of ozone and fine particulate matter (PM2.5) throughout California (CA) in the year 2055 relative to a gasoline vehicle baseline. Further, impacts of reducing HDV emissions are explored to facilitate comparison among technology classes. A base year emissions inventory is grown to 2055 representing a business-as-usual progression of economic sectors, including primarily petroleum fuel consumption by LDV and HDVs. Emissions are spatially and temporally resolved and used in simulations of atmospheric chemistry and transport to evaluate distributions of primary and secondary pollutants respective to baseline. Results indicate that light-duty FCEV Cases achieve significant reductions in ozone and PM2.5 when LDV market shares reach 50–100% in early adoption counties, including areas distant from deployment sites. Reflecting a cleaner LDV baseline fleet in 2055, emissions from HDVs impact ozone and PM2.5 at comparable or greater levels than light duty FCEVs. Additionally, the importance of emissions from petroleum fuel infrastructure (PFI) activity is demonstrated in impacts on ozone and PM2.5 burdens, with large refinery complexes representing a key source of air pollution in 2055. Results presented provide insight into light duty FCEV deployment strategies that can achieve maximum reductions in ozone and PM2.5 and will assist decision makers in developing effective transportation sector AQ mitigation strategies.

In this paper, the Spatially and Temporally Resolved Energy and Environment Tool (STREET) is used in conjunction with University of California Irvine - California Institute of Technology (UCI-CIT) atmospheric chemistry and transport model to assess the impact of deploying plug-in electric vehicles and integrating wind energy into the electricity grid on urban air quality. STREET is used to generate emissions profiles associated with transportation and power generation sectors for different future cases. These profiles are then used as inputs to UCI-CIT to assess the impact of each case on urban air quality. The results show an overall improvement in 8-h averaged ozone and 24-h averaged particulate matter concentrations in the South Coast Air Basin (SoCAB) with localized increases in some cases. The most significant reductions occur northeast of the region where baseline concentrations are highest (up to 6 ppb decrease in 8-h-averaged ozone and 6 μg/m3 decrease in 24-h-averaged PM2.5). The results also indicate that, without integration of wind energy into the electricity grid, the temporal vehicle charging profile has very little to no effect on urban air quality. With the addition of wind energy to the grid mix, improvement in air quality is observed while charging at off-peak hours compared to the business as usual scenario.

This paper presents results from sensitivity studies conducted using the Distributed Generation Build-out Economic Assessment Tool (DG-BEAT). The viability of meeting commercial building loads with a stationary fuel cells is studied under different conditions of electricity pricing, dispatch strategies, and complementary technologies. Key findings support the notion that fuel cells are becoming economically viable alternatives in California, New York and Connecticut at installed costs of $7000-10,000/kW. Michigan is identified as another state well suited to fuel cell development with heat recovery. Fuel cell installations reduce net carbon emissions for commercial buildings by 20-30% when compared to local, time-resolved, grid emissions. Grid sell-back, at 50% retail price, significantly improves the economics of a base load fuel cell, but has little impact for a dispatchable system. At installed costs below $5000/kW, load following capability results in significant additional cost reductions as the generating capacity is increased beyond the building's base load requirements. Complementary technologies such as chillers and thermal storage have a pronounced impact, particularly in warmer climates. Installing fuel cells paired with electric chillers and thermal storage in Florida at buildings with exceptionally high air conditioning demands can achieve the same economic benefit as a typical New York building.

A micro-tubular solid oxide fuel cell stack model including an integrated cooling system was developed using a quasi three-dimensional, spatially resolved, transient thermodynamic, physical and electrochemical model that accounts for the complex geometrical relations between the cells and cooling-tubes. For the purpose of model evaluation, reference operating, geometrical and material properties are determined. The reference stack design is composed of 3294 cells, with a diameter of 2 mm, and 61 cooling-tubes. The stack is operated at a power density of 300 mW/cm2 and air is used as the cooling fluid inside the integrated cooling system. Regarding the performance, the reference design achieves an electrical stack efficiency of around 57% and a power output of 1.1 kW. The maximum occurring temperature of the positive electrode electrolyte negative electrode (PEN)-structure is 1369 K. As a result of a design of experiments, parameters of a best-case design are determined. The best-case design achieves a comparable power output of 1.1 kW with an electrical efficiency of 63% and a maximum occurring temperature of the PEN-structure of 1268 K. Nevertheless, the best-case design has an increased volume based on the higher diameter of 3 mm and increased spacing between the cells.

The design and deployment of DG systems requires an integrated assessment of the building and generator dynamics including the time-variant energy costs and emission factors. Static design optimizations are unable to consider the physical generator operating constraints, seasonal variability and non-coincidence in electric, heating, and cooling demands. This paper introduces the Distributed Generation Build-out Economic Assessment Tool (DG-BEAT) which combines building, utilities, and emissions databases with a library of simplified generator and building models in a user-friendly interface. Five control strategies are presented for the dynamic dispatch of distributed generation technologies at commercial buildings. The control approaches stem from the physical limitations of different generator types. Methods are also outlined for the dispatch of complementary technologies (e.g. energy storage) and accommodation of on-site renewables (e.g. solar PV) which could further improve the economic or environmental benefits of distributed generation. This paper details the methodology of sizing and dispatching distributed generation components, outlines eight databases that are employed to capture regional variations in pricing and building dynamics, and discusses the myriad of customizations available to provide a tailored analysis for a single building or national impact studies.

Due to increasing awareness of the deleterious environmental and health effects of diesel combustion emissions, major regulatory action and policy measures are focused on reducing emissions from diesel engines. Freight operations, including rail-based freight transportation, have received special attention as an industry where major change can be affected, especially in neighborhoods located near operations centers. A FORTRAN-based dynamic simulation model of an SOFC-GT (Solid Oxide Fuel Cell-Gas Turbine) system from a prior feasibility study has been adapted to analyze system operation along a representative but demanding route in southern California. In previous simulations with the model, the basic operational feasibility of the system has been demonstrated as well as the in-service operation for pre-reformed fuels. In the current study, the analysis is extended to include reformation of two fuels (diesel and natural gas) onboard the locomotive and analyses of system efficiency, fuel consumption, CO2 emission, and NOx emission that can be attained through careful thermal integration of the reformer unit. Route-averaged fuel-to-wheels system efficiencies of 60% and 52% are predicted for natural gas and diesel fuel, respectively. Additionally, SOFC-GT operation could provide (1) a reduction approaching 98% in NOx for both fuels; (2) a 54% savings in CO2 for operation on natural gas; and (3) a 30% CO2 reduction for operation on diesel fuel compared to state-of-the-art locomotive technology. These gains may be offset by design challenges, especially for the diesel case, due to the requirement for large volumes of water to support the reformation process even for medium-length freight hauling trips.

This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm.

To evaluate the dynamic operation and feasibility of designing and operating a self-sustainable hydrogen fueling station using renewable energy sources, dynamic system models have been developed for a hydrogen fueling station utilizing a proton exchange membrane (PEM) electrolyzer and fuel cell. Using fueling and power demand data from an existing public hydrogen station in Irvine, California, dynamic analyses of the self-sustainable station have been carried out. Various control strategies are developed and evaluated to determine the impacts of control strategies and renewable capacity factors on the efficiency and other performance characteristics of the station. The simulation results and analysis suggest that with careful sizing and system design, a self-sustainable hydrogen fueling station that relies completely upon renewable sources for hydrogen production, storage and dispensing is feasible. Moreover, a cost and sensitivity analysis is carried out to evaluate the levelized hydrogen cost for different station designs. The cost of the hydrogen is determined to be as low as $6.71 per kg or $9.14 per kg when the station is powered by 200 kW of wind turbines or 360 kW of PV arrays, respectively.

The objective of this paper is to extend a statistical approach to effectively provide look-ahead forecasts for cooling and electricity demand load. Our proposed model is a generalized form of a Cochrane-Orcutt estimation technique that combines a multiple linear regression model and a seasonal autoregressive moving average model. The proposed model is adaptive so that it updates forecast values every time that new information on cooling and electricity load is received. Therefore, the model can simultaneously take advantage of two statistical methods, time series, and linear regression in an adaptive way. The effectiveness of the proposed forecast model is shown through a use case. The example utilizes the proposed approach for economic dispatching of a combined cooling, heating and power (CCHP) plant at the University of California, Irvine. The results reveal the effectiveness of the proposed forecast model.

Distributed power and energy resources are now being used to meet the combined electric power, heating, and cooling demands of many buildings. The addition of on-site renewables and their accompanying intermittency and non-coincidence requires even greater dynamic performance from the distributed power and energy system. Load following generators, energy storage devices, and predictive energy management are increasingly important to achieve the simultaneous goals of increased efficiency, reduced emissions, and sustainable economics. This paper presents two optimization strategies for the dispatch of a multi-chiller cooling plant with cold-water thermal storage. The optimizations aim to reduce both costs and emissions while considering real operational constraints of a plant. The UC Irvine campus micro-grid operation between January 2009 and December 2013 serves as a case study for how improved utilization of energy storage can buffer demand transients, reduce costs and improve plant efficiency. A predictive control strategy which forecasts campus demands from weather predictions, optimizes the plant dispatch, and applies feedback control to modify the plant dispatch in real-time is compared to best-practices manual operation. The dispatch optimization and predictive control algorithms are shown to reduce annual utility bill costs by 12.0%, net energy costs by 3.61%, and improve energy efficiency by 1.56%.

In this paper, a novel decentralized algorithm is proposed to minimize power flow loss in a large-scale future grid connecting with many real-time-distributed generation systems by which power flows bi-directionally. The DC-power loss at each link is defined as the product of resistance and the square of current that can be considered as a quadratic flow cost. We employ the notion of tie-sets that reduces the complexity of the power flow loss problem by dividing a power network into a set of loops that forms a linear vector space on which the power loss problem can be formulated as a convex optimization problem. As finding a solution in each tie-set enables global optimization, we realize parallel computing within a system of independent tie-sets by integrating autonomous agents. Simulation results demonstrate the minimization of the power loss on every link by iteratively optimized power flows and show the superiority against the traditional centralized optimization scheme.

The possibility of separating and recovering CO2 in a biogas plant that co-produces electricity, hydrogen, and heat is investigated. Exploiting the ability of a molten carbonate fuel cell (MCFC) to concentrate CO2 in the anode exhaust stream reduces the energy consumption and complexity of CO2 separation techniques that would otherwise be required to remove dilute CO2 from combustion exhaust streams. Three potential CO2 concentrating configurations are numerically simulated to evaluate potential CO2 recovery rates: 1) anode oxidation and partial CO2 recirculation, 2) integration with exhaust from an internal combustion engine, and 3) series connection of molten carbonate cathodes initially fed with internal combustion engine (ICE) exhaust. Physical models have been calibrated with data acquired from an operating MCFC tri-generating plant. Results illustrate a high compatibility between hydrogen co-production and CO2 recovery with series connection of molten carbonate systems offering the best results for efficient CO2 recovery. In this case the carbon capture ratio (CCR) exceeds 73% for two systems in series and 90% for 3 MCFC in series. This remarkably high carbon recovery is possible with 1.4 MWe delivered by the ICE system and 0.9 MWe and about 350 kg day-1 of H2 delivered by the three MCFC.

The objective of this paper is to extend a statistical approach to effectively provide look-ahead forecasts for cooling and electricity demand load. Our proposed model is a generalized form of a Cochrane-Orcutt estimation technique that combines a multiple linear regression model and a seasonal autoregressive moving average model. The proposed model is adaptive so that it updates forecast values every time that new information on cooling and electricity load is received. Therefore, the model can simultaneously take advantage of two statistical methods, time series, and linear regression in an adaptive way. The effectiveness of the proposed forecast model is shown through a use case. The example utilizes the proposed approach for economic dispatching of a combined cooling, heating and power (CCHP) plant at the University of California, Irvine. The results reveal the effectiveness of the proposed forecast model.

The effects of liquefied natural gas (LNG) on pollutant emission inventories and air quality in the South Coast Air Basin (SoCAB) of California are evaluated using recent appliance emissions measurements by Lawrence Berkeley National Laboratory and the Southern California Gas Company (SoCalGas), and use of a state-of-the-art air quality model. Pollutant emissions can be impacted by LNG operation because of differences in composition and physical properties including the Wobbe index, a measure of energy delivery rate. Various LNG distribution scenarios are evaluated to determine the potential impacts of LNG. Projected penetration of LNG in the SoCalGas pipeline network in SoCAB is expected to be limited, which could cause increases in overall (area-wide) emissions of nitrogen oxides that are smaller than 0.05%. Based on the photochemical state of the South Coast Air Basin of California, any increase in NOx is expected to cause an increase in the highest local ozone concentrations, which is observed in model results. However, the magnitude of NOx emissions increases due to LNG use is determined to be within the uncertainty range of natural gas combustion sources and would not be discernible with the existing monitoring network.

High temperature fuel cells are a promising technology for ultra-low emission power generation. This paper presents a novel poly-generating system capable of greater than 80% (LHV CH4) co-production efficiency with carbon capture and liquefaction. The proposed system synergistically integrates an air separation unit providing pure oxygen to a fuel cell and liquid nitrogen to a hydrogen separation unit. Both solid oxide and molten carbonate fuel cells may be capable of this integration with additional refrigeration load needed for the molten carbonate system to condense and recirculate carbon dioxide into the cathode stream. Stack temperature control utilizes the endothermic cooling effect of internal fuel reforming. The primary characteristic of the system, converting fuel cell waste heat to produce a secondary fuel (e.g. hydrogen), portends the ultra-high efficiency while enabling additional system design and integration synergies that may reduce complexity, cost, and load following constraints. A unique controller is developed for power and thermal management of a fuel-cooled fuel cell with anode recirculation. A brief economic analysis identifies the potential revenue from each of the four product streams, electricity, hydrogen, heat, and liquid CO2, and presents a conservative, yet favorable assessment of system costs relative to market electricity and hydrogen fuel prices. © 2014 Elsevier Ltd.

The hybridization of gas turbine technology with high temperature fuel cells represents an ultra-high efficiency, ultra-low emission, fuel flexible power generation platform. The performance of past prototypes has been limited by marginal compatibility of the two primary sub-systems. This paper addresses the challenge of selecting compatible hardware by presenting a simple and robust method for bespoke hybrid system design and off-the-shelf component integration. This is the first application of detailed, spatially resolved, physical models capable of resolving off-design performance to the integration analysis of FC-GT hybrids. Static maps are produced for both turbine and fuel cell sub-systems that readily evaluate the compatibility and hybrid performance. Molten carbonate and solid oxide fuel cells are considered for hybridization with recuperated micro-turbines and larger axial flow gas turbine systems. Current state-of-the-art molten carbonate technology is shown to pair well with present micro-turbine technology in an FC bottoming cycle design achieving 74.4% LHV efficiency. Solid oxide technology demonstrates remarkable potential for integration with larger scale axial turbo-machinery to achieve greater than 75% LHV efficiency. This performance map technique closely matches results from detailed integrated hybrid system analyses, and enables quick determination of performance requirements for balance of plant design and optimization. © 2014 Elsevier B.V. All rights reserved.

The practicality of any particular distributed generation installation depends upon its ability to reduce overall energy costs. A simple yet effective economic dispatch strategy with the goal to use distributed generation to minimize the cost of building energy is developed in this work. The strategy is designed to reduce the individual utility, operations and maintenance charges that increase the cost of energy. Various electric rate structures are modeled in detail and applied with the economic dispatch strategy to simulate meeting various measured building demand dynamics for heat and power. Using the economic dispatch strategy, various modes of operation, such as electric or thermal load following, peak shaving, peak shifting, or base-load operation are simulated. The economic dispatch strategy is compared to more traditional dispatch strategies to demonstrate its effectiveness in reducing total energy costs. © 2014 Elsevier Ltd.

Fuel cell gas turbine hybrid systems have achieved ultra-high efficiency and ultra-low emissions at small scales, but have yet to demonstrate effective dynamic responsiveness or base-load cost savings. Fuel cell systems and hybrid prototypes have not utilized controls to address thermal cycling during load following operation, and have thus been relegated to the less valuable base-load and peak shaving power market. Additionally, pressurized hybrid topping cycles have exhibited increased stall/surge characteristics particularly during off-design operation. This paper evaluates additional control actuators with simple control methods capable of mitigating spatial temperature variation and stall/surge risk during load following operation of hybrid fuel cell systems. The novel use of detailed, spatially resolved, physical fuel cell and turbine models in an integrated system simulation enables the development and evaluation of these additional control methods. It is shown that the hybrid system can achieve greater dynamic response over a larger operating envelope than either individual sub-system; the fuel cell or gas turbine. Results indicate that a combined feed-forward, P-I and cascade control strategy is capable of handling moderate perturbations and achieving a 2:1 (MCFC) or 4:1 (SOFC) turndown ratio while retaining >65% fuel-to-electricity efficiency, while maintaining an acceptable stack temperature profile and stall/surge margin. © 2014 Elsevier B.V. All rights reserved.

Microbial fuel cells (MFCs) are promising for simultaneous treatment of wastewater and energy production. In this study, a mathematical model for microbial fuel cells with air cathodes was developed and demonstrated by integrating biochemical reactions, Butler-Volmer expressions and mass/charge balances. The model developed is focused on describing and understanding the steady-state polarization curves of the microbial fuel cells with various levels and methods of anode-biofilm growth with air cathodes. This polarization model combines enzyme kinetics and electrochemical kinetics, and is able to describe measured polarization curves for microbial fuel cells with different anode-biofilm growth. The MFC model developed has been verified with the experimental data collected. The simulation results provide insights into the limiting physical, chemical and electrochemical phenomena and their effects on cell performance. For example, the current MFC data demonstrated performance primarily limited by cathode electrochemical kinetics.

A dynamic internal reforming-solid oxide fuel cell system model is developed and used to simulate the coproduction of electricity and hydrogen while meeting the measured dynamic load of a typical southern California commercial building. The simulated direct internal reforming-solid oxide fuel cell (DIR-SOFC) system is controlled to become an electrical load following device that well follows the measured building load data (3-s resolution). The feasibility of the DIR-SOFC system to meet the dynamic building demand while co-producing hydrogen is demonstrated. The resulting thermal responses of the system to the electrical load dynamics as well as those dynamics associated with the filling of a hydrogen collection tank are investigated. The DIR-SOFC system model also allows for resolution of the fuel cell species and temperature distributions during these dynamics since thermal gradients are a concern for DIR-SOFC. © 2013 Elsevier B.V. All rights reserved.

The practicality of any particular distributed generation (DG) installation depends upon its ability to reduce overall energy costs. A parametric study summarizing DG performance capabilities is developed using an economic dispatch strategy that minimizes building energy costs. Various electric rate structures are considered and applied to simulate meeting various measured building demand dynamics for heat and power. A determination of whether investment in DG makes economic sense is developed using a real-time dynamic dispatch and control strategy to meet real building demand dynamics. Under the economic dispatch strategy, capacity factor is influenced by DG electrical efficiency, operations and maintenance cost, and fuel price. Under a declining block natural gas rate structure, a large local thermal demand improves DG economics. Increasing capacity for DG that produces low cost electricity increases savings, but installing further capacity beyond the average building electrical demand reduces savings. For DG that produces high cost electricity, reducing demand charges can produce savings. Heat recovery improves capacity factor and DG economics only if thermal and electrical demand is coincident and DG heat is utilized. Potential DG economic value can be improved or impaired depending upon how the utility electricity cost is determined.

To improve the reliability and the energy efficiency of datacenters, as well as to reduce infrastructure costs and environmental impacts, we demonstrated and evaluated the use of a 10 kW Proton Exchange Membrane Fuel Cell (PEMFC) stack and system for powering the servers in a data center. In this study, we designed, tested and demonstrated a PEMFC system as a Distributed Generation (DG) prime mover that has high reliability and efficiency for both steady state and dynamic operations. The 10kW PEMFC stack and system was designed to power a server rack and eliminate the power distribution system in the datacenter. The steady state electrical properties such as efficiency and polarization curves were evaluated. The ramp rate and dynamic response of the PEMFC system to server and system dynamics was also characterized and can be used to determine energy storage requirements and develop optimal control strategies to enable the dynamic load following capability.

To evaluate the dynamic operation and feasibility of designing and operating a self-sustainable hydrogen fueling station using renewable energy sources, system models for a hydrogen fueling station using a proton exchange membrane (PEM) electrolyzer and fuel cell have been developed to simulate the renewable sources and fueling dynamics together with hydrogen production and station operation. Theoretical models have been integrated to simulate station performance when subjected to measured power and fueling demand dynamics from a public fueling station and measured renewable energy supply dynamics. The theoretical models that are integrated into various self-sustainable station design configurations include a Proton Exchange Membrane (PEM) electrolyzer and PEM fuel cell, hydrogen compressor, and storage tank. The fueling dynamics and power consumption dynamics were obtained from an operating public hydrogen fueling station and implemented in the system model. Various control strategies are simulated and the station performance is determined to depend upon the way renewable power is utilized in the station. Due to the round trip efficiency penalty associated with converting electricity to hydrogen (in an electrolyzer) and vice versa (in a fuel cell), the results suggest that the station operation power should be supplied by the renewable sources directly whenever possible, and that the hydrogen fuel cell should provide power only when there is no renewable power available (the third control strategy tested in this paper). The simulated hydrogen fueling station powered by 200 kW wind turbines or 360 kW solar PV were determined to successfully operate in a self-sustainable manner while dispensing ∼25 kg of hydrogen per day. This study provides insights regarding the sizing of the station components such as renewable energy conversion devices, electrolyzer and fuel cell, and storage tank. The cost of the hydrogen was determined to be $8.01 per kg when the station is powered by 200 kW of wind turbines and operated using control strategy 3, while it increased to $20.22 per kg when the station is powered by 360 kW of PV array and operated using control strategy 3. This study provides a basis for achieving self-sustainable renewable hydrogen fueling stations. With further optimization and development, these self-sustainable renewable hydrogen fueling stations could provide valuable interconnections (especially in remote locations) throughout the hydrogen infrastructure network and further support the integration of renewable sources for vehicle fuels.

We study a counter-flow solid oxide fuel cell system and consider the challenges faced in minimizing thermal variations from the nominal operating conditions for a reasonable range of power tracking. Blower dynamics, reformer transport delays, spatial distribution of the heat generated and the resulting thermal response are among the issues considered. A novel approach, relying on partial internal reformation of the feedstock is proposed as a remedy to maintain a strong level of power tracking with minimal thermal stress to the fuel cell.

We formulate an Optimal Real-Time Power Flow (ORPF) problem that integrates renwable energy generation and energy storage. In the ORPF problem, we seek to minimize the costs of energy storage and of power generation from fossil fuel that are required to balance the loads and generation from renewable sources. We present a novel decentralized algorithm for this problem, using tie-set graph theory. Tie-set graph theory significantly reduces the complexity of the ORPF problem by dividing a power network into a set of independent loops referred to as "tie-sets." Simulation results demonstrate real-time power production responses and flow controls that lead to reliable use of battery systems and reduce the cost of using fossil fuel. © 2013 IEEE.

In this paper, we consider the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm. © 2013 AACC American Automatic Control Council.