Various ganged heliostat concepts have been proposed in the past. The attractive aspect of ganged heliostat concepts is multiple heliostats are grouped so that pedestals, tracking drives, and other components can be shared, thus reducing the number of components. The reduction in the number of components is thought to significantly reduce cost. However, since the drives and tracking mechanisms are shared, accurate on-sun tracking of grouped heliostats becomes challenging because the angular degrees-of-freedom are now limited for the multiple number of combined heliostats. In this paper, the preliminary evaluation of the on-sun tracking of a novel tensile-based cable suspended ganged heliostat concept is provided. In this concept, multiple heliostats are attached to two guide cables. The cables are attached to rotation spreader arms which are anchored to end posts on two ends. The guide cables form a catenary which makes tracking on-sun interesting and challenging. Tracking is performed by rotating the end plates that the two cables are attached to and rotating the individual heliostats in one axis. An additional degree-of-freedom can be added by differentially tensioning the two cables, but this may be challenging to do in practice. Manual on-sun tracking was demonstrated on small-scale prototypes. The rotation arms were coarsely controlled with linear actuators, and the individual heliostats were hand-adjusted in local pitch angle and locked in place with set screws. The coarse angle adjustments showed the tracking accuracy was 3–4 milli-radians. However, with better angle control mechanisms the tracking accuracy can be drastically improved. In this paper, we provide tracking data that was collected for a day, which showed feasibility for automated on-sun tracking. The next steps are to implement better angle control mechanisms and develop tracking algorithms so that the ganged heliostats can automatically track.

Efficient hydrogen flow control is of great importance to ensure the reliable operation of an automotive fuel cell system because it is closely associated with the safety and the economic efficiency. In this study, an effective hydrogen flow control algorithm for hydrogen excess ratio control is addressed by pointing out the recovery speed and overshoot response. Unlike previous studies on the hydrogen management systems of an automotive fuel cell, this study presents an analytic hydrogen tank model which can present the characteristics of the discharge and charge of hydrogen from a type 4 hydrogen tank. To this end, a mode reference adaptive control (MRAC) based on proportional-integral (PI) control is introduced, to ensure robust hydrogen flow during the dynamic operation of fuel cell system. The MRAC was compared with the nominal PI control and PWM control in the hydrogen management system of an automotive fuel cell operating within normal conditions, under steady-state responses and transient. Based on these result, it can further demonstrate that the MRAC algorithm shows better recovery speed and tracking performance than the nominal PI, and PWM control algorithm with respect to the transient behaviors.

In geothermal heating and cooling, there exists an opportunity to improve the efficiency by utilizing non-uniform soil properties of a ground source heat exchanger during installation. This paper presents a gradient approach based upon finite element mathematics to determine an optimal distribution of heterogeneous soils with varying thermal conductivities. The numerically simulated case studies demonstrate the good performance of this algorithm to minimize the cross-talk of heat flux between pipes and maximize the overall efficiency.

Combined Cooling Heat and Power (CCHP) attained significant attention among energy professionals and academicians recently due to its superior thermal, economic and environmental benefit in comparison with conventional energy producing systems (internal combustion engine (ICE), micro-turbine, etc). Despite the abundance of literature on CCHP, only a few studies emphasized on the selection of appropriate prime mover for an economically sustainable CCHP system. Furthermore, the effect of part load efficiencies is commonly neglected during CCHP analysis. We had introduced these two new concepts of economic sustainability of specific prime mover and part load effects on efficiency to CCHP system in our previous paper. An algorithm based on hybrid load following method was utilized to determine the optimum prime mover for a particular location and weather type. No studies explored the effects of efficiency parameters and the selection strategies of prime mover in different building types for any particular location using this newly developed algorithm. Since building types dominates the electric, heating and cooling demand extensively, it is imperative to extend the prime mover selection analysis for building types for efficient CCHP operation. Economic, energy, and emission performance criteria have been utilized for the prime mover selection systems in different building types. Computer simulations were conducted for five different building categories (primary school, restaurant, small hotel, outpatient clinic and small office buildings) for each of three different types of prime movers (reciprocating internal combustion engine (ICE), micro-turbine and phosphoric acid fuel cell) in a cold climate zone (Minneapolis, MN). The simulation results of different prime movers were compared with the outcomes of a reference case (for each building in the same climate zone) that has a typical separate heating and power system. The cold climate zone (Minneapolis, MN) helped to explore the heating load effects on economic, energy, and emission performance of the buildings in comparison to other energy demands (i.e. electric and cooling demand). A hybrid load following method was executed, using improvements shown in our previous article. Performance parameters and other outcomes of this study showed that economic savings were observed for the ICE in all building types, and the micro-turbine in some building types. Internal rate of returns of ICE are 22.4%, 14.7%, 20.5%, 14.6% and 6.5% for primary school, restaurant, small hotel, outpatient clinic and small office respectively. ICE also shows highest energy savings among all three prime movers with an energy savings of 20%, 17.2%, 25.7%, 23.8% and 9.7% for primary school, restaurant, small hotel, outpatient clinic and small office respectively. For all types of prime mover based CCHP systems, lower CO 2 emission was observed for all building types. However, unlike ICE, which is preferable in terms of economic and energy savings, emission analysis shows that micro-turbine poses better emission characteristics compared to other types of prime movers. CO 2 emission for micro-turbine savings are 67.1%, 62.2%, 82%, 43.2% and 81.4% for primary school, restaurant, small hotel, outpatient clinic and small office respectively. The relationship between the power and thermal demand of the different buildings was determined to be a significant factor in CCHP system performance. A sensitivity analysis determining the effects of heat exchanger and heating coil efficiencies on the performance of CCHP systems shows that the economic performance was most sensitive to the heat exchanger efficiency, while energy consumption and emissions was most sensitive to the heating coil and boiler efficiency.

Utilities in regulated energy markets manage power generation, transmission, and delivery to consumers. Matching peak demand with peak generation is costly, and the increasing penetration of renewable energy into the grid adds complexity due to fluctuations in supply. A few options exist for addressing the task of balancing supply and demand, including demand response, energy storage, and time-varying pricing (tariffs). Arizona Public Service (APS), the largest electric utility company in Arizona, employs tariffs that charge more for electricity at certain times (on-peak periods) and a demand charge for the highest power demand throughout the billing period. Such tariffs incentivize end users to lower peak demand. Arizona State University (ASU), a public university with its largest campus in Tempe, AZ, participates in a time-of-use tariff structure with APS. Analysis in this paper shows that ASU’s 16MWdc of onsite solar capacity alone can lower its monthly electricity bills by over 10% by decreasing on-peak power demand. A novel contribution of the paper is the analysis of the value of small scale, on-campus energy storage in lowering the demand charge. Most analyses consider savings from transferring off-peak electric power to peak-electric power, but this paper considers using stored electricity solely to reduce peak demand and thus lower the demand charge. Small amounts of electricity could greatly reduce overall cost. An algorithm was developed and executed in Python to decide when on-campus storage should be charged and discharged. The critical part of the algorithm is to decide when to discharge. Deploying too early, or too late, will not change peak demand. The paper’s storage dispatch model is implemented alongside a financial model that calculates the savings in electricity bills and determines the net present value (NPV) of different storage technologies as a function of storage lifetime and installed capacity (kWh). The results show that, for all storage technologies considered, a positive NPV is realized. NPVs are very sensitive to actual power demand and thus vary from year to year. This is to be expected because the storage dispatch strategy operates on extreme values, which tend to include very rare events. This analysis uses actual data from ASU, which allows us to extend the results to other universities and commercial customers. The favorable results suggest that a smarter dispatch algorithm based on machine learning would enable further cost savings by determining what can be thought of as a shadow price of electricity.

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This paper evaluates model-based optimization processes (OP) for HVAC systems utilizing any computer algebra system (CAS), genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) is tested using data collected from an actual HVAC system. Using this optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature ( T s ), supply duct static pressure ( P s ), chilled water supply temperature ( T w ), minimum outdoor ventilation, and chilled water differential pressure set-point ( D pw ) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. This research focuses primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO 2 , etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort.

Sustainability of natural gas transmission infrastructure is highly related to the system’s ability to decrease emissions due to ruptures or leaks. Although traditionally such detection relies in alarm management system and operator’s expertise, given the system’s nature as large-scale, complex, and with vast amount of information available, such alarm generation is better suited for a fault detection system based on data-driven techniques. This would allow operators and engineers to have a better framework to address the online data being gathered. This paper presents an assessment on multiple fault-case scenarios in critical infrastructure using two different data-driven based fault detection algorithms: Principal component analysis (PCA) and its dynamic variation (DPCA). Both strategies are assessed under fault scenarios related to natural gas transmission systems including pipeline leakage due to structural failure and flow interruption due to emergency valve shut down. Performance evaluation of fault detection algorithms is carried out based on false alarm rate, detection time and misdetection rate. The development of modern alarm management frameworks would have a significant contribution in natural gas transmission systems’ safety, reliability and sustainability.

Current trends show that renewable energy production costs continue to decrease with time, so that renewable energy sources (RES) are becoming more suitable as electricity sources. In addition to their environmental benefits, RES are especially appropriate for remote areas, where the expansion of existing power grid is impractical and fuel transportation for thermal generators is too expensive. In this regard, our work studies the optimal capacity sizing for a completely green village (CGV), which is an isolated residential microgrid (MG) whose power is entirely generated by RES. In particular, we consider a neighborhood composed of smart homes that contain programmable appliances, whose operations can be interrupted or automatically scheduled in time. Though there are many works in literature that investigate MG optimal capacity sizing, to our knowledge, our work is the first that utilizes the scheduling of programmable appliance to minimize MG investment costs. To establish the effectiveness of our method, we compare an optimal MG capacity sizing algorithm that utilizes appliances’ programmability (Opt-P) with an algorithm that places appliances into operation as soon as they are ready without shifting in time or preempting their operation (NoSch-P). Our simulation results show that Opt-P reduces the investment cost by at least 42% compared to NoSch-P, when the ratio between the energy storage investment cost per kWh and the RES’ investment cost per kW is greater or equal to 10.

A low-cost rigid foam-based concentrator technology development program was funded by the DOE SunShot Initiative to meet installed cost goals of $75/m 2 vs. current costs of ∼ $200–250/m 2 . Phase 1 of the project focused on design trades and cost analyses leading to a cost-optimized self-powered autonomous tracking heliostat concept with a mirror surface area in the 100m 2 range. In Phase 2 30-year accelerated testing of the mirror modules based on ReflecTec film with 94% specular reflectivity bonded on composite foam substrate were initiated and completed in Phase 3. The tests with 15 coupons showed optical performance degradation of less than 5% in specular reflectance following 30-year equivalent UV testing and other abuse testing such as acid rain, bird dropping, thermal cycling, etc. A small scale prototype (3m×2m) heliostat design based on modular truss elements with removable mirror modules was developed in detail. In this phase components such as the dual-axis actuators were sized and selected based on wind load requirements and pointing accuracy demands were completed. Finite Element analyses for the mechanical structure with mirror modules were performed using three separate commercial codes — ANSYS, COMSOL and SolidWorks to validate the optical errors induced by wind loads on the structure up to 35 mph. Results indicated that the RMS deflections contributed to less than 0.4 mrad pointing error. Dynamic response of the heliostat indicated that the first 5 eigenmodes were in the 17–20 Hz range. The individual structure elements such as the trusses and c-rails were fabricated locally and assembled with the mirror facets in the lab for initial fit check and testing. The nine mirror facet surface errors were characterized using photogrammetry and verified using Reverse Hartmann techniques and showed to be in the order of 1 mrad or less. A three-level controller (main, gateway and heliostat) was architected and built. Tracking of the sun is done using NREL’s Sun Tracking Algorithm implemented in the gateway controller. Target-pointing vectors are calculated for each heliostat and conveyed wirelessly to the individual heliostat controllers for actuating the azimuth and elevation motors. The power subsystem consisting of solar panels and a battery provide 24V for the actuators and controller boards. The system was sized to provide adequate power for a period of 5hrs of operation when power is not available. Initial calibration will be performed with on-site camera tracking the sun’s image on a target located approximately 52m from the heliostat. Testing of the heliostat pointing under calm and windy conditions will be done to demonstrate overall performance that meet DOE targets of 4 mrad under 27mph winds. Commercialization efforts are underway to transition the design to the commercial sector. The project is well on its way to approaching overall cost targets and current estimates are approximately $90–110/m 2 and lower costs can be achieved with alternates to the film we have identified.

Reducing global emissions and meeting the electricity generation needs of urban areas are compelling energy issues. Rooftop and small-scale photovoltaic (PV) technology is a quickly growing sector of the distributed generation market. The array size chosen for a PV installation is one of the main factors affecting its ability to meet a building’s electrical needs and reduce its operational emissions. The rooftop area available for placement of PV can be a constraint on the optimization of PV array size. If the optimal array area for a specific building is larger than the area available on the rooftop, optimization using demand-matching methods is not necessary. A new parameter EUI-R has been introduced to describe the building’s annual electrical demand with respect to the building rooftop area. It is a decision-making tool presented to help a system designer choose whether to employ an optimization method or not in selecting a PV system. The EUI-R depends on 2 general parameters, building electric demand and building rooftop area, that should be easily accessible for any system designer. This paper presents an extended study of EUI-R applied to 10 commercial building types in 3 different climate zones throughout the U.S. These cities are characterized by different latitudes and varying amounts of available solar radiation. The results show a linear dependency between optimal PV size obtained with a simple demand-matching algorithm, and building rooftop area, applicable to any building type. Any distributed energy technology serving a single building should be sized based on the specific conditions unique to the building, including electric demand and physical space available.

Conventional power networks have experienced a gradual evolution from a centralized nature to distributed and localized structures. The upgrading of power system toward a smart grid is being developed to improve reliability and facilitate the integration of different types of renewable energies and improve load management. Due to different uncertainties linked to electricity supply in renewable MGs, probabilistic energy management techniques are going to be necessary to analyze the system. In this study, the short-term operation planning of a typical microgrid (MG) with diverse units for achieving the maximum profit, considering technical and economical constraints, for the next 24 hours, using gravitational search algorithm (GSA) with SPSS software is presented and the effect of wind generation in the planning is investigated. The MG consists of a diverse variety of power system components such as wind turbine, microturbine, photovoltaic, fuel cell, Hydrogen storage tank, reformer, a boiler, and electrical and thermal loads. Moreover, MG is connected to an electrical grid for exchange of power. The MG is managed and controlled through a central controller. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. Total obtained profit from the MG, considering with US electricity and natural gas prices is $5.312902×10 3 .

Finding optimal operating conditions of solar-based power and cooling systems is always a challenge. Performance of these systems is highly dependent on several important parameters, which not only impact the long-term efficiency but also its technical and economic feasibility. This paper studies the operation/configuration problem of an ammonia-water power and cooling cycle using an exergetic analysis. Thermodynamic performance of the combined cycle was addressed by using analysis of variance and multiple linear regression analysis. Modeling was done in Matlab ® , using Refprop 9.0 to calculate the thermodynamic properties of the ammonia-water mixture. Convergence issues were observed on the thermodynamic properties estimation carried out by Refprop when the stream had high ammonia mass fraction. To solve this issue an averaging algorithm was implemented online to estimate such properties using pure ammonia data and high, but stable, ammonia concentration data. After this implementation, small differences between current and reference model were seen. Optimum operating conditions were obtained using response surface technique. The response variable used was the ratio between exergetic efficiency and exergy destruction. Results showed that the response variable is mainly influenced by the ammonia concentration, pressure ratio, turbine efficiency and temperature gradient in the heat exchanger. Finally integration of the power/cooling cycle with a solar field was performed using two types of concentrated solar collectors: Linear Fresnel Collector (LFC) and Parabolic Trough Collector (PTC). The analysis showed that LFC technology can be a viable alternative for small scale applications combined with power/cooling systems.

The Solar Load Ratio (SLR) method is a performance prediction algorithm for passive solar space heating systems developed at Los Alamos National Laboratory. Based on curve fits of detailed thermal simulations of buildings, the algorithm provides fast estimation of monthly solar savings fraction for direct gain, indirect gain (water wall and concrete wall) and sunspace systems of a range of designs. Parameters are not available for passive solar heat pipe systems, which are of the isolated gain type. While modern computers have increased the speed with which detailed simulations can be performed, the quick estimates generated by the SLR method are still useful for early building design comparisons and for educational purposes. With this in mind, the objective of this project was to develop SLR predictions for heat pipe systems, which use heat pipes for one-way transport of heat into the building. A previous thermal network was used to simulate the heat pipe system with Typical Meteorological Year (TMY3) weather data for 13 locations across the US, representing ranges of winter temperature and available sunshine. A range of (nonsolar) load-to-collector ratio LCR = 1–15 W/m 2 K was tested for each location. The thermal network, along with TMY3 data, provided monthly-average-daily absorbed solar radiation and building load to calculate SLR . Losses from the solar aperture in a heat pipe system are very low compared to conventional passive solar systems, thus the load-to-collector ratio of the solar aperture was neglected in these preliminary calculations. Likewise, nighttime insulation is unnecessary for a heat pipe system, and was not considered. An optimization routine was used to determine an exponential fit (the heart of the SLR method) to simulated monthly solar savings fraction ( SSF ) across all locations and LCR values. Accuracy of SSF predicted by SLR compared to the thermal network results was evaluated. The largest errors (up to 50%) occurred for months with small heating loads (< 80 K days), which inflated SSF . Limiting the optimization to the heating season (October to March), reduced the error in SSF to an average of 4.24% and a standard deviation of 5.87%. These results expand the applications of the SLR method to heat pipe systems, and allow building designers to use this method to estimate the thermal benefits of heat pipe systems along with conventional direct gain, indirect gain and sunspace systems.

The shape of an installed solar concentrator (e.g. a heliostat) may differ from its original design due to manufacturing defects, structural/wind loads and thermal expansion. By measuring the shape of a solar concentrator, it is possible to account for the deviation in optical performance from its original design point. A method to measure concentrator shape needs to be fast, accurate, and not involve contact or interference with the reflective surface of the concentrator. State-of-the-art techniques include flux mapping, photogrammetry, and deflectometry using conventional cameras. This paper presents a study to characterise solar concentrator shapes using light-field imaging. Conventional cameras capture the light intensity of a point in a scene at a single point in the sensor, creating a two-dimensional image. A light field camera features multiple micro-lenses placed between the main lens and the sensor, providing many small images from slightly different angles in a single shot. This information is used to reconstruct the position of a light source in space. The advantage of this new technique to the ones mentioned above is that the light field camera is robust and self-contained, which allows easy-to-use application in heliostat fields. In this study, light-field camera measurements were performed with flat mirrors and a curved mirror under laboratory conditions. In order to resolve the surface of the mirror surfaces, several methods to impose contours of the mirror surface have been studied, including dirt, small water droplets, scattering of low-power laser light, and paper-marks. A wide range of camera-to-mirror distances between 43 cm and 5 m have been studied. Greater distances allow the capture of the entire surface, but decrease the precision of depth measurements. In order to obtain high precision measurements while being able to capture the entire surface, a compositing strategy has been developed, combining several light-field image measurements. The overall accuracy of the system was improved further by averaging measurements over several image frames. Subsequently, the reconstructed surface points have been fed to a ray-tracing algorithm realized in Matlab/Python. Results in this study are able to resolve the shape of small concentrators to sub-millimetre precision when taking pictures at a distance of 0.4 m.

The overcoming inclusion of biotechnology in biofuels industry involves several challenges among which are found the variety of operational cycles, the highly nonlinear behavior of the processes and the need for measurement of intermediate variables. In order to reproduce biological conversion of biodiesel production discharge products into other biofuels, experimental data from ethanol production from glycerol/glucose mixture was analyzed implementing fuzzy techniques to investigate and model the nonlinear behavior of the process. This paper presents a general methodology for TS fuzzy modeling based on a novel approach on data structured regression which consists on combination of fuzzy c-regression model and clustering using a golden search algorithm approach to adjust the proper number of membership functions to fit the model and minimize the statistic difference among the experimental data, simulated data and the Fuzzy Inference System results.

Accurate measurement of heat flux and temperature can significantly affect the energy usage in several industrial applications, including furnace operation, metal processing, fire safety tests and more. Directional Flame Thermometers, or DFTs, offer the ability to use both temperature and heat flux measurements for furnace control. Currently, analysis of dynamic temperature data from the DFTs to compute heat flux information must be performed off-line at the conclusion of data-gathering by using software tools such as IHCP1D. Availability of a near real-time algorithm for accurate reduction of the data will allow for continual monitoring of the furnace during operation. This will result in better control over the process and significant savings in energy and cost. In this paper, a filter form of the inverse heat conduction algorithm is developed for utilization in DFTs. The algorithm is based on linearized solutions of the direct heat equation, and non-linear effects introduced by temperature dependent thermal properties are accounted for by interpolating of the resulting filter coefficients. The developed method is tested through several numerical experiments and also ANSYS model. A graphical user interface is developed in LabVIEW to provide a friendly interface for the end user. The temperature data measured by thermocouples on the DFT are transmitted to the computer through data acquisition card and the developed tool in LabView display the heat flux in a near real time fashion.

Solar central receiver systems are viewed as one of the most promising concentrated solar power (CSP) technologies for power production, in which solar radiation is concentrated through large mirrors (heliostats) onto a central receiver. This is due to the fact that very high temperatures can be reached at the receiver and thus higher thermal efficiency can be achieved compared with other CSP technologies. Heliostat field layout optimization is an essential task for any solar central receiver system. In this paper, a heuristic algorithm, i.e. the differential evolution (DE), was employed to perform efficient optimization of the conventional radial staggered heliostat field layout using MATLAB. The model calculates all the required optical performance parameters at every step of the optimization process for each heliostat and consequently more reliable results are obtained as compared with many other optimization methods. Two cases were considered: one with single variable optimization and the other with multi-variable optimization. For the first case, the azimuthal spacing between the adjacent heliostats or the radial distance between the rows of heliostats were optimized independently and for the second case both of these variables were optimized simultaneously. Both cases were examined for high sun altitude angle and low altitude angle and a comparison study was performed between them to check their effect on the heliostat field efficiency. Finally, it was noted that varying the radial distance between the rows of the heliostats yields slightly better efficiency as compared with when optimizing the azimuthal spacing.

India has always been victim of power failures or blackouts and the recent July 2012 countrywide blackout is a perfect example for it. It is expected that due to the widening gap between supply and demand, such instances of power failure would occur more regularly in future. Such blackouts are also be foreseen in other parts of the world. The electricity grids in many countries are highly centralized and are mostly dependent on fossil-fuel based energy sources (coal, oil, natural gas etc). Due to the rapid rise in the living standards of developing countries such as India and China, there is an increase in demand for electricity for running various appliances, as well as for heating and air-conditioning equipment. Such an increased in demand places tremendous strain on ailing centralized grid burning fossil fuel. The use of renewable energy sources (such as solar and wind) could potentially allow large amount of demand to be met though alternative means and offset the demand on the grid. The advancement in technology has encouraged the implementation of renewable resources especially solar and wind. Hybrid power systems (HPS) that consist of these resources can significantly lower storage requirements. Furthermore, besides being cost-efficient, it is coherent to the weather conditions since solar and wind complement each other well. For highly efficient hybrid power systems to be developed, a significant degree of research must be applied to further their development. This includes tasks such as modeling these systems and applying proficient control algorithms to maximize efficiency. This paper focuses on simulation of an HPS consisting of a photovoltaic (PV) module, wind turbine (WT), and a lead acid battery through MATLAB/SIMULINK software. Moreover, a control algorithm is proposed, which leads to an efficient and autonomous operation of the HPS, along with maximizing power output from PV module and WT. The model and control system were tested using sample hourly solar radiation, temperature, and wind speed data to generate the power output from the PV module and WT, which was then processed through the proposed algorithm, to power a sample hourly load profile. The results indicate that a simple HPS can meet the type of load demand provided in an efficient and effective manner.

In recent years Computational Fluid Dynamics (CFD) simulations are increasingly used to model the air circulation and temperature environment inside the rooms of residential and office buildings to gain insight into the relative energy consumptions of various HVAC systems for cooling/heating for climate control and thermal comfort. This requires accurate simulation of turbulent flow and heat transfer for various types of ventilation systems using the Reynolds-Averaged Navier-Stokes (RANS) equations of fluid dynamics. Large Eddy Simulation (LES) or Direct Numerical Simulation (DNS) of Navier-Stokes equations is computationally intensive and expensive for simulations of this kind. As a result, vast majority of CFD simulations employ RANS equations in conjunction with a turbulence model. In order to assess the modeling requirements (mesh, numerical algorithm, turbulence model etc.) for accurate simulations, it is critical to validate the calculations against the experimental data. For this purpose, we use three well known benchmark validation cases, one for natural convection in 2D closed vertical cavity, second for forced convection in a 2D rectangular cavity and the third for mixed convection in a 2D square cavity. The simulations are performed on a number of meshes of different density using a number of turbulence models. It is found that k-epsilon two-equation turbulence model with a second-order algorithm on a reasonable mesh gives the best results. This information is then used to determine the modeling requirements (mesh, numerical algorithm, turbulence model etc.) for flows in 3D enclosures with different ventilation systems. In particular two cases are considered for which the experimental data is available. These cases are (1) air flow and heat transfer in a naturally ventilated room and (2) airflow and temperature distribution in an atrium. Good agreement with the experimental data and computations of other investigators is obtained.

In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. In a previous paper, ASME ES2010-90373, we employed a single objective genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA was significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT was employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. In this paper, we employ a multi-objective GA to optimize the flatback airfoils to achieve two objectives, namely the generation of maximum lift as well as the maximum lift to drag ratio. It is shown that the multi-objective GA optimization can generate superior flatback airfoils compared to those obtained by using single objective GA algorithm.