The continued advancement of lithium ion batteries for transportation applications requires addressing two key challenges: increasing energy density and providing fast charging capabilities. The first of these challenges can be met in part through the use of thicker electrodes, which reduce the electrochemically inactive mass of the cell. However, implementation of thick electrodes inherently presents a trade-off with respect to fast charging capabilities. As thickness is increased, transport limitations exert greater influence on battery performance and reduce the ability of the battery to meet aggressive charge conditions. This trade-off can manifest over multiple length scales. At the particle-scale, interactions between solid diffusion and reaction kinetics influence the effective storage of lithium within the active material. At the electrode scale, diffusion limitations can lead to local variations in salt concentrations and electric potential. These short-range and long-range effects can combine to influence local current and heat generation. In the present work, a pseudo-2D lithium ion battery model is applied to understand how active material particle size, porosity, and electrode thickness impact local field variables, current, heat generation, and cell capacity within a single cell stack. COMSOL Multiphysics 5.2 is used to implement the pseudo-2D model of a lithium ion battery consisting of a graphite negative electrode, polymer separator, and lithium transition metal oxide positive electrode. Lithium hexafluorophosphate (LiPF 6 ) in 1:1 ethylene carbonate (EC) and diethylene carbonate (DEC) was used as the electrolyte. The model was built assuming that the active particles are representative spherical particles. The governing equations and boundary conditions were set following the common Newman model. Cell response under varied combinations of charge and discharge cycling is assessed for rates of 1C and 5C. Aggressive charge and discharge conditions lead to locally elevated C-rates and attendant increases in local heat generation. These variations can be impacted in part by tailoring electrode structures. To this end, results for parametric studies of active material particle size, porosity, and electrode thickness are presented and discussed.

The use of solid particles as a heat-transfer fluid and thermal storage media for concentrating solar power is a promising candidate for meeting levelized cost of electricity (LCOE) targets for next-generation CSP concepts. Meeting these cost targets for a given system concept will require optimization of the particle heat-transfer fluid with simultaneous consideration of all system components and operating conditions. This paper explores the trade-offs in system operating conditions and particle thermophysical properties on the levelized cost of electricity through parametric analysis. A steady-state modeling methodology for design point simulations dispatched against typical meteorological year (TMY) data is presented, which includes computationally efficient submodels of a falling particle receiver, moving packed-bed heat exchanger, storage bin, particle lift, and recompression supercritical CO 2 (sCO 2 ) cycle. The components selected for the baseline system configuration presents the most near-term realization of a particle-based CSP system that has been developed to date. However, the methodology could be extended to consider alternative particle receiver and heat exchanger concepts. The detailed system-level model coupled to component cost models is capable of propagating component design and performance information directly into the plant performance and economics. The system-level model is used to investigate how the levelized cost of electricity varies with changes in particle absorptivity, hot storage bin temperature, heat exchanger approach temperature, and sCO 2 cycle operating parameters. Trade-offs in system capital cost and solar-to-electric efficiency due to changes in the size of the heliostat field, storage bins, primary heat exchanger, and receiver efficiency are observed. Optimal system operating conditions are reported, which approach levelized costs of electricity of $0.06 kW e −1 hr −1 .

Concentrating solar power (CSP) technology is moving toward high-temperature and high-performance design. One technology approach is to explore high-temperature heat-transfer fluids and storage, integrated with a high-efficiency power cycle such as the supercritical carbon dioxide (s-CO 2 ) Brayton power cycle. The s-CO 2 Brayton power system has great potential to enable the future CSP system to achieve high solar-to-electricity conversion efficiency and to reduce the cost of power generation. Solid particles have been proposed as a possible high-temperature heat-transfer medium that is inexpensive and stable at high temperatures above 1,000°C. The particle/heat exchanger provides a connection between the particles and s-CO 2 fluid in the emerging s-CO 2 power cycles in order to meet CSP power-cycle performance targets of 50% thermal-to-electric efficiency, and dry cooling at an ambient temperature of 40°C. The development goals for a particle/s-CO 2 heat exchanger are to heat s-CO 2 to ≥720°C and to use direct thermal storage with low-cost, stable solid particles. This paper presents heat-transfer modeling to inform the particle/s-CO 2 heat-exchanger design and assess design tradeoffs. The heat-transfer process was modeled based on a particle/s-CO 2 counterflow configuration. Empirical heat-transfer correlations for the fluidized bed and s-CO 2 were used in calculating the heat-transfer area and optimizing the tube layout. A 2-D computational fluid-dynamics simulation was applied for particle distribution and fluidization characterization. The operating conditions were studied from the heat-transfer analysis, and cost was estimated from the sizing of the heat exchanger. The paper shows the path in achieving the cost and performance objectives for a heat-exchanger design.

Particle-based concentrating solar power (CSP) plants have been proposed to increase operating temperature for integration with higher efficiency power cycles using supercritical carbon dioxide (sCO 2 ). The majority of research to date has focused on the development of high-efficiency and high-temperature particle solar thermal receivers. However, system realization will require the design of a particle/sCO 2 heat exchanger as well for delivering thermal energy to the power-cycle working fluid. Recent work has identified moving packed-bed heat exchangers as low-cost alternatives to fluidized-bed heat exchangers, which require additional pumps to fluidize the particles and recuperators to capture the lost heat. However, the reduced heat transfer between the particles and the walls of moving packed-bed heat exchangers, compared to fluidized beds, causes concern with adequately sizing components to meet the thermal duty. Models of moving packed-bed heat exchangers are not currently capable of exploring the design trade-offs in particle size, operating temperature, and residence time. The present work provides a predictive numerical model based on literature correlations capable of designing moving packed-bed heat exchangers as well as investigating the effects of particle size, operating temperature, and particle velocity (residence time). Furthermore, the development of a reliable design tool for moving packed-bed heat exchangers must be validated by predicting experimental results in the operating regime of interest. An experimental system is designed to provide the data necessary for model validation and/or to identify where deficiencies or new constitutive relations are needed.

The paper investigates the performance of a combined heat and power system by means of a fully dynamic numerical approach. An ad-hoc library for the simulation of energy conversion systems is developed under the OpenModelica open source platform; the library includes the main components that usually equip a Combined Heat and Power (CHP) system and they can be connected as they are logically connected in the real plant. Each component is modelled by means of equations and correlations that calculate their performance on a time dependent basis. Therefore, many configurations can be evaluated not only in terms of cumulative annual results or average performance, but the instantaneous behavior can be investigated. The numerical library is constructed using the lumped and distributed parameter approach and it is validated by comparing the numerical results with the measured values over a one-year time period. The prediction capabilities of the proposed numerical approach are evaluated by simulating a case study of a health spa. This case study is selected since it is characterized by significant requirements of both thermal and electric energy. The comparison demonstrated that the calculated results are in good agreement with the measurements in terms of both annual values and distribution over the reference period. Furthermore, an optimization algorithm is adopted and linked to the developed library in order to estimate the best size of different components of the CHP system according to a number of constraints. This feature is particularly important when addressing the energy efficiency of a complete system that is depending on a number of interdependent variables. Therefore, the case study is investigated by accounting also for additional technologies that can be further enhance the performance of the system both in terms of energy consumption and economic investment. In particular, the numerical model is used to optimized the CHP energy efficiency by estimating the best trade-off between the reduction of the energy purchased and the overall cost of the system. The application of PV panels and electric energy accumulators is also investigated and the simulation demonstrates that the size of the cogeneration unit equal to 48 kW, the number of PV panels of 299 and the battery capacity of 45 kWh provide the lowest amount of energy purchased, while the best return of investment is obtained by the CHP unit of 40 kW along with 109 PV panels and a battery of 40 kWh.

With the proposed Clean Power Plan for regulating carbon emissions from the power sector in the U.S, policymakers are likely to use a cost optimization framework to plan for future scenarios and implementation strategies. The modeling framework introduced in this paper would help such policymakers to make the appropriate investment decisions for the power sector. This paper applies an analytical model and an optimization model to investigate the implications of coimplementing an emission cap and a Renewable Portfolio Standards (RPS) policy for the U.S. Northeast. A simplified analytical model is specified and the first order optimality conditions are derived. The results from the analytical model are verified by running simulations using LP-CEM, a linear programming-based supply cost optimization model. The LP-CEM simulation results are analyzed under the recently proposed Clean Power Plan emissions cap rules and RPS scenarios for the U.S. Northeast region. The marginal abatement cost estimates, derived from a limited set of LP-CEM runs, are analyzed and compared to the theoretical results. For encouraging renewables generation, an RPS instrument is cost-effective at higher policy targets, while an emissions cap instrument is cost-effective at lower policy targets. For CO2 emissions reduction, an emissions cap instrument is found be cost-effective for all policy targets. There is a trade-off between emissions levels and supply costs when the two instruments are co-implemented.

In an effort to increase thermal energy storage densities and turbine inlet temperatures in concentrating solar power (CSP) systems, focus on energy storage media has shifted from molten salts to solid particles. These solid particles are stable at temperatures far greater than that of molten salts, allowing the use of efficient high-temperature turbines in the power cycle. Furthermore, many of the solid particles under development store heat via reversible chemical reactions (thermochemical energy storage, TCES) in addition to the heat they store as sensible energy. The heat-storing reaction is often the thermal reduction of a metal oxide. If coupled to an Air-Brayton system, wherein air is used as the turbine working fluid, the subsequent extraction of both reaction and sensible heat, as well as the transfer of heat to the working fluid, can be accomplished in a direct-contact, counter-flow reoxidation reactor. However, there are several design challenges unique to such a reactor, such as maintaining requisite residence times for reactions to occur, particle conveying and mitigation of entrainment, and the balance of kinetics and heat transfer rates to achieve reactor outlet temperatures in excess of 1200 °C. In this paper, insights to addressing these challenges are offered, and design and operational tradeoffs that arise in this highly-coupled system are introduced and discussed.

As wind energy is established as a sustainable alternative source of electricity, very large-scale wind farms with hundreds of turbines are becoming increasingly common. For the optimal design of wind farm layouts, the number of decision variables is at least twice the number of turbines (e.g., the Cartesian coordinates of each turbine). As the number of turbines increases, the computational cost incurred by the optimization solver to converge to a satisfactory solution increases as well. This issue represents a serious limitation in the computer-aided design of large wind farms. Moreover, the wind farm domains are typically highly constrained including land-availability and proximity constraints. These non-linear constraints increase the complexity of the optimization problem and decrease the likelihood of obtaining even a feasible solution. Several approaches have been proposed for micrositing of wind turbines, including random searches, mixed-integer programs, and metaheuristics. Each of these methods has its own trade-off between the quality of optimized layouts and the computational cost of obtaining the solution. In this paper, we demonstrate the capability of non-linear mathematical programming for optimizing very large-scale wind farms by leveraging explicit, analytical derivatives for the objective and constraint functions, thus overcoming the aforementioned limitations while also providing convergence and local optimality guarantees. For that purpose, two large farms with hundreds of turbines and significant land-use constraints are solved on a standard personal computer.

The success of grid scale energy storage hinges on our ability to solve real problems economically. By mapping energy storage physics to application economics, this paper offers a technology neutral look at how energy storage can solve real problems. A value analytics methodology was developed that combines the physics of energy storage, application power commands, and market-specific economic constructs. This approach evaluates and optimizes the value of energy storage for specific projects by providing insight into the tradeoffs between the lifecycle costs and revenues. These analytics calculate the net present value (NPV) and internal rate of return (IRR) of select energy storage assets, markets, and applications by considering these key factors: • Duty profiles and control strategies • Market economics and revenue streams • Asset performance at cell, module, and system levels • Price projections including balance of plant (BOP) • Cycle and calendar life • Project length and financing terms The value analytics methodology combines three model streams. The first takes a high fidelity load profile (for example, the power output of a building, wind turbine, or solar farm), imposes a specific control strategy, and calculates revenue streams in a selected market. From this first model stream a storage power command is generated. This power command is fed into the second model stream that calculates the required size and price of a given storage type. Finally, a third model stream uses empirical life models to calculate degradation rates, replacement intervals, and maintenance costs. These are rolled up into a project specific financial analysis that forecasts project NPV and IRR. The underlying engine for this methodology is a large performance and price database of over 100 commercial and emerging energy storage assets that spans a wide range of technologies from ultracapacitors and flywheels to lead acid and lithium-ion batteries. The physics based performance of each asset is captured as an equivalent circuit model. These models are exercised to create performance envelops that describe the rate dependent power capability as a function of the type, amount, and age of installed storage. The energy storage value analytics described in this paper can be used to test key sensitivities. This methodology has been applied to standalone energy storage systems as well as the combination of energy storage with renewables and distributed power generation. As shown, the methodology is relevant for an even wider range of applications. Several solution maps will be shared that reveal, by market segment, the energy storage type, amount, and application that create the greatest customer value. This type of informed design and dispatch will solve real problems, create new value streams, and open new markets for grid scale energy storage.

The present study analyses the influence that market conditions have on determining optimum molten salt solar tower plants with storage that maximize profits (in terms of plant configuration, sizing and operation) for a location in South Africa. Three different scenarios based on incentive programs and local wholesale electricity prices are considered. A multi-objective optimization modeling approach was followed, showing the trade-off curves between minimizing investment and maximizing profits when varying critical sizerelated parameters (such as nameplate capacity, solar multiple and storage capacity) together with power-cycle design and operating specifications including dynamic start-up curves and different storage dispatchability strategies. Results are shown by means of a comparative analysis between optimal plants found for each scenario, highlighting the value that storage has under the current two-tier tariff scheme, and the relevance of designing a suitable policy for technology development. Lastly, a final analysis is performed with regards of the indicators used for economic evaluation of power plants, by comparing the differences between optimum designs found when using the levelized cost of electricity solely as performance indicator instead of cash-flows and profit-based indicators, such as the internal rate of return.

This paper evaluates cost and performance tradeoffs of alternative supercritical carbon dioxide (s-CO2) closed-loop Brayton cycle configurations with a concentrated solar heat source. Alternative s-CO2 power cycle configurations include simple, recompression, cascaded, and partial cooling cycles. Results show that the simple closed-loop Brayton cycle yielded the lowest power-block component costs while allowing variable temperature differentials across the s-CO2 heating source, depending on the level of recuperation. Lower temperature differentials led to higher sensible storage costs, but cycle configurations with lower temperature differentials (higher recuperation) yielded higher cycle efficiencies and lower solar collector and receiver costs. The cycles with higher efficiencies (simple recuperated, recompression, and partial cooling) yielded the lowest overall solar and power-block component costs for a prescribed power output.

We investigate losses and costs associated with direct steam generation via parabolidal dish concentrators and steam transport to a central steam Rankine power cycle for electricity generation. This study presents a power plant model that accounts for the effects of shading, steam transport, energy conversion at the power block, and capital costs of land and pipework. The pipe network topology used was optimised using a genetic algorithm based on evolution of minimal spanning trees connecting all dishes to a central power block. Optimal pipe sizing of the network is determined by considering the trade-off of frictional losses against thermal losses and material costs. Weather data provides input for the solar resource, and shading is calculated using an established numerical model. The plant model is used to determine the collector layout for which the effective annual revenue is maximised. Results show that the optimal rectangular layout is closely spaced in the North-south direction, along which most of the pipe links run, while East-west spacing is less important. The annual thermal performance of the optimised dish field on a per-unit-area basis is then compared to simulation of a parabolic trough employed for the same purpose. A detailed breakdown of the thermal analysis used forms the basis of comparison between the collector types, giving the overall advantage and a comparison of various sources of loss. We demonstrate that dish fields can collect approximately 49% more thermal energy annually per unit collector area than a trough system employed for the same purpose.

All current commercial CSP plants operate at relatively low thermodynamic efficiency due to lower temperatures than similar conventional plants and due to the fact that they all employ Rankine conversion cycles. We present here an investigation on the effects of adding a bottoming steam power cycle to a hybrid CSP plant based on a Small Particle Heat Exchange Receiver (SPHER) driving a gas turbine as the primary cycle. Due to the high operating temperature of the SPHER being considered (over 1000 Celsius), the exhaust air from the primary Brayton cycle still contains a tremendous amount of exergy. While in the previous analysis this fluid was only used in a recuperator to preheat the Brayton working fluid, the current analysis explores the potential power and efficiency gains from instead directing the exhaust fluid through a heat exchanger to power a Rankine steam cycle. Not only do we expect the efficiency of this model to be competitive with conventional power plants, but the water consumption per kilowatt-hour will also be reduced by nearly two thirds as compared to most existing concentrating solar thermal power plants as a benefit of having air as the primary working fluid, which eliminates the condensation step present in Rankine-cycle systems. Coupling a new steam cycle model with the gas-turbine CSP model previously developed at SDSU, a wide range of cases were run to explore options for maximizing both power and efficiency from the proposed CSP combined cycle gas turbine (CCGT) plant. Due to the generalized nature of the bottoming cycle modeling, and the varying nature of solar power, special consideration had to be given to the behavior of the heat exchanger and Rankine cycle in off-design scenarios. The trade-offs of removing the recuperator for preheating the primary fluid are compared to potential overall power and efficiency gains in the combined cycle case.

One of the clean energy initiatives at Missouri S&T is an electric shuttle bus service, the Ebus. It provides valuable operational data for a fleet-type electric vehicle (EV) operating over a fixed route. The primary aim of this study is to use the daily operational data obtained from the Ebus in order to formulate an optimal driving strategy. Existing research efforts to improve EVs focus on improvements to the architecture and the energy management strategy. However, they fail to provide the driver with an optimal driving strategy leading to suboptimal use of the stored battery energy. This shortcoming was addressed here by implementing a multi-objective approach to find an optimal driving strategy for an electric bus. The driving strategy was taken to comprise two parts: a constant trip speed and an acceleration value to achieve that speed. From the operational data, the efficiency and power consumption of the electric motor were computed for different speeds. By assuming the entire trip was executed at a constant speed, the range for each speed was calculated. The speeds were ranked based on their corresponding ranges. Then, to achieve the optimal speed, the acceleration duration and energy consumption for different acceleration values were computed. The values were ranked based on the trade-off between duration and energy. The choice of driving strategy (exact speed and acceleration values) is left to the driver since different strategies would be needed for different road conditions. This multi-objective approach gives flexibility to the driver and promotes optimal use of the stored battery energy, thereby enhancing the energy efficiency and range of the Ebus. It can be easily implemented in other electric vehicles as well.

Research of advanced power cycles has shown supercritical carbon dioxide power cycles may have thermal efficiency benefits relative to steam cycles at temperatures around 500–700°C. To realize these benefits for CSP, it is necessary to increase the maximum outlet temperature of current tower designs. Research at NREL is investigating a concept that uses high-pressure supercritical carbon dioxide as the heat transfer fluid to achieve a 650°C receiver outlet temperature. At these operating conditions, creep becomes an important factor in the design of a tubular receiver and contemporary design assumptions for both solar and traditional boiler applications must be revisited and revised. This paper discusses lessons learned for high-pressure, high-temperature tubular receiver design. An analysis of a simplified receiver tube is discussed, and the results show the limiting stress mechanisms in the tube and the impact on the maximum allowable flux as design parameters vary. Results of this preliminary analysis indicate an underlying trade-off between tube thickness and the maximum allowable flux on the tube. Future work will expand the scope of design variables considered and attempt to optimize the design based on cost and performance metrics.

Smart meters have become increasingly common as an approach to benchmark and assess building energy use. In this paper, we explore what type of metering infrastructure may be required to derive value from the application of smart meters in the commercial and industrial sectors. As an example, we find that sole reliance upon a site-level smart meter—which has been the focus of most existing deployment models—provides sufficient data to extract summary statistics about how the energy use of a given site may compare to a typical ‘average’ site, but such installations fail to provide adequate detail about where the energy use is occurring or why any discrepancies might be occurring. To resolve these issues, we install a multi-tier advanced metering infrastructure (AMI) at a mixed use (industrial and commercial) campus. We use this AMI deployment to gain insight at different levels of the consumption hierarchy—from sites to buildings, panels, sub-panels, and end loads. The paper concludes by discussing the trade-offs associated with such augmented metering at each level within the hierarchy, with a view towards providing guidelines for AMI deployment at other industrial and commercial campuses.

The reduction in the power output fluctuation of grid-connected wind turbine-generator systems is strongly required to further increase their total installed capacity in Japan. This study focuses on limiting the maximum electric power output by changing the set point of the power output control as a reduction technique. The influence of limiting the maximum electric power output of a 2 MW-system, which adopts the variable speed operation, on the power output fluctuation characteristic is analyzed through numerical simulation conducted by using an observed field wind data. The focus is on the power output fluctuation, which is important for management of commercial power systems including power plants, of the 2 MW-system with six cases of the maximum electric power output. The results show that limiting the maximum electric power output does not have an influence on the power output fluctuation characteristic and control performance during the pitch angle operation at high wind speeds. However, the year-round simulation reveals that limiting the maximum electric power output brings a tradeoff between the reduction in the power output fluctuation and the generating performance.

The impacts of the U.S. transportation and electricity generation sectors include air emissions and water consumption. Information and communication technologies (ICT) such as advanced video teleconferencing have the potential to displace some activities that have historically required transportation. While ICT can reduce environmental impacts compared to transportation options in many cases, there are non-obvious environmental trade-offs associated with replacing transportation with ICT. These tradeoffs are the consequence of many factors, including the particular local electricity mix, meeting duration, number of meeting participants, travel distances, travel modes, motive transport conversion technologies, and transport fuels. Identifying and quantifying these trade-offs is the focus of this research. For this study, a nomenclature and methodology were developed to compare environmental trade-offs associated with transportation and ICT. The nomenclature was designed to facilitate side-by-side comparison of the environmental impacts of travel and ICT and to allow expansion of the nomenclature for future study. The methodology considered a variety of conversion technologies for motive transport including spark-ignition, compression-ignition, fuel cells, and electric motors. Both conventional and developing fuels were considered including gasoline, ethanol, diesel, biodiesel, natural gas, hydrogen, and electricity. Likewise, electricity consumption for ICT included both traditional and developing electricity generation technologies. Carbon dioxide emissions and water consumption for ICT were assessed for comparison with transportation in a case study that demonstrated use of this methodology by considering three distinct scenarios for a particular business meeting: 1. Two meeting attendees travel to the meeting by diesel city bus while two travel in a private vehicle. 2. All four meeting attendees travel by private vehicle powered by compressed natural gas. 3. The four meeting attendees do not travel, but instead meet their clients virtually via ICT. The case study analyzed in this manuscript considers only the water and carbon dioxide impacts, but the nomenclature developed allows future expansion for analysis of other greenhouse gases. The three scenarios revealed that, compared to short travel distances, use of ICT does not always generate fewer carbon dioxide emissions. Depending on the mode of electricity generation, travel proved to be preferable from an emissions standpoint for scenarios in which travel distances were small. However, in cases that required long distances to travel, ICT often allowed businesses and individuals to reduce their environmental impacts, especially if electric power generation in that location utilized large amounts of relatively low-emissions technologies such as hydroelectric dams, wind, solar, and nuclear. Finally, it should be noted that, in addition to comparing ICT and travel impacts, this methodology can be used to calculate the environmental tradeoffs of various transportation options when travel is a necessity.

The importance of considering energy security and resource efficiency in policies across the Department of Defense (DoD) has recently been emphasized. DoD-sponsored research reports repeatedly encourage consideration of energy efficiency and energy security in all decision-making processes, including those that pertain to Forward Operating Military Base Camp (FOB) equipment. But while energy security and efficiency are valued in the general sense, for FOBs, energy and water are strategically vital and sources of vulnerability. There are incredibly high human and capital costs associated with transporting fuel and water within hostile war zones, where convoys are routinely subjected to mortal danger. A volumetric framework for evaluating field equipment is proposed that simultaneously addresses the broad DoD-wide goals of bolstering energy security and energy efficiency, as well as the immediate and strategically significant goal of “taking [fuel and water delivery] trucks off the road.” The metrics associated with this framework focus on the volume of field equipment itself, as well as the volumes of fuel and water that are either consumed or saved by the equipment over time. For example, solar panels require additional up-front volume to be brought to the FOB, but then spare future volumes of fuel that need not be shipped. Using a volumetric framework described herein, the tradeoffs between up-front and marginal volumetric requirements can be rigorously assessed. Existing metrics, such as those that focus only on equipment lifecycle cost, are unsuitable for evaluating field equipment for FOBs primarily because they are extremely difficult to accurately quantify. By instead focusing on fuel and water volumes that are more easily quantified, decision-makers might gain important insights about the relevant trade-offs and strategic implications of fielding equipment on FOBs. The proposed metrics are compared to existing metrics with respect to their effectiveness for quantifying the true values of energy- and water-saving devices on FOBs in a strategically relevant way. A suite of volume-based metrics is developed by adapting existing economic metrics, including return on investment (ROI), payback period, and net present value (NPV), for a volume-based rather than cost-based analysis. Use of the volumetric framework is illustrated for a few different technologies.

The HLRP method is combined with optimization techniques to determine the best combination of a tri-generation system composed of a hybrid fuel cell system and alternative energy sources such as wind and solar energy. Solar thermal and photovoltaic systems with short term storage are used in this study. A small commercial site in Southern Europe is investigated. The HLRP method is based on using the ratio of the thermal load to the required power as the scaling parameter to analyze a hybrid fuel cell based tri-generation system in terms of performance parameters. The performance parameters, energy utilization factor, carbon dioxide and exergy destruction per total energy load, are reported in a series of graphs. The HLRP analysis is based on ideal performance of the power producing devices and practical performance parameters of all heat transfer devices. The alternative energy systems included in this analysis include state-of-the art performance measures. The previous publications on the HLRP method have focused on its development with short examples that illustrate how it operates as an effective, initial screening tool and limited cost factors. In this paper the application of the HLRP method as an early screening tool to determine the general outline of an efficient, total energy solution is detailed. The tool will be used to compare different solutions based on the fuel consumption (energy utilization factor), carbon dioxide production, exergy destruction and costs. The tradeoffs of reducing costs or increasing carbon dioxide production are illustrated. The solutions are based on a process using regression curves of the generalized graphs. The correlation coefficient for these regression relationships is greater than 0.996. In addition, refinement of the HLRP technique that reports carbon dioxide production and exergy destruction in terms of total energy rather than thermal loads has been adopted. Combining the generalized performance of the fuel consuming components with renewable energy sources leads to a solution based on balancing cost, minimizing fuel consumption or carbon dioxide production.