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Abstract: This paper addresses a comprehensive efficiency evaluation model for electrical distribution system. Concerning the efficiency of different resources including energy, infrastructure and human resources, the overall distribution system efficiency is determined on the basis of relevant efficiency indicators. The employed efficiency indicators comprising loss, utilization factor, power factor, load factor, energy not supplied and human resource productivity express the efficiency of the above-mentioned resources. The proposed model takes into account the effect of certain external factors on the performance of distribution system. It has been shown that how external factors such as governance, social behavior and urban planning can directly influence the efficiency of the system. The correlation between efficiency indicators and external factors is derived using artificial neural network and a sensitivity analysis is conducted to illuminate the path of efficiency increment through managing the effective external factors. These indices can be used for improvement of the system performance in system planning. An empirical study is addressed here based on a real world distribution network to investigate the performance of the proposed model. [Copyright &y& Elsevier]

The co-gasification of bituminous coal (BC) and industrial sludge (IS) in the downdraft fixed bed gasifier (DFBG) was simulated by a validated steady-state model structured using the Aspen Plus software. The gasification process was decoupled into the drying, pyrolysis, partial oxidation, and reforming and reduction, and then modeled. Furthermore, the sensible heat recovery of syngas was also considered to realize the energy optimization of DFBG gasification system. The sensitivity analysis was performed to investigate the influences of the gasification temperature, air equivalent ratio, and steam to BC-IS blends ratio (S/BC-IS) on the co-gasification characteristics. The energy distributions of the whole process were completed by calculating energy input and output. Results showed that, boosting gasification temperature could improve the syngas content and gasification performance. However, as air equivalent ratio increased, the H 2 and CO contents, and gasification performance constantly reduced. A higher S/BC-IS was beneficial for H 2 production, but impaired CO content and cold gas efficiency. In addition, the calculations of energy balance indicated that the best cold gas efficiency of the blends could be obtained when the BC blending ratio was 0.7. [Display omitted] • Co-gasification of bituminous coal and industrial sludge was studied by Aspen Plus. • Co-gasification process was uncoupled into four stage, and performed optimization. • Effects of temperature, S/BC-IS, and ER on co-gasification performance were analyzed. • Co-gasification was coupled with sensible heat recovery, and analyzed by Sankey diagram. [ABSTRACT FROM AUTHOR]

Complexity in energy systems is increasing. In this context, we investigate if, and if so under which circumstances, more complex models are superior for providing a sound basis for decision making processes. On the one hand, energy system analysts keep increasing model complexity with the growing availability of data. On the other hand, decision makers tend to rely on the results of these ever more complex models. We focus our investigation on assessing the security of electricity supply with two different models associated with different levels of complexity: deterministic capacity balances and probabilistic simulations. We then abstract our findings by introducing a mathematical framework to determine the optimal level of detail for a model. With this, we demonstrate that, under the realistic assumptions made, the optimal model design is not reached by ever increasing model complexity. We summarize our findings as complexity dilemma: the more sophisticated the prevailing research question, the greater the need to depict the details of the underlying system, leading to more complex models. However, the accuracy of complex models highly depends on the quality of input data. Uncertainties of these input data and the costs for conducting sensitivity analyses, in turn, are high for complex models. • Transferring insights from complexity science to energy system analyses. • Deriving optimal levels of model complexity to serve as basis for policy-relevant decisions. • Demonstrating the complexity dilemma for the case of security of electricity supply assessments. • For policy makers, main findings imply not to unconditionally rely on the accuracy of complex models. [ABSTRACT FROM AUTHOR]

Coupled migration and retention of suspended injection particles and reservoir particles can severely damage the formation, especially for weakly consolidated sandstone geothermal reservoirs. Understanding their migration and retention is significant to prevent undesired formation damage. The forces acted on these particles were calculated, and then a comprehensive simulation model was established to analyze the coupled particle migration and retention. Massive detachment of reservoir particles, formation of wormhole-like preferential flow paths, and retention of the injected suspended particles are identified as three successive stages during geothermal energy exploitation via water recycling. The mobile reservoir particles play a leading role in the first two stages, while the injected suspended particles mainly affect the last stage. Sensitivity analysis indicates that the high injection-production pressure difference and low concentration of injected suspended particles are conducive to form preferential flow paths, but a severe local reservoir blockage may occur under high mobile reservoir particles. CO 2 can effectively reduce reservoir damage caused by particle migration due to its high mobility and low drag force. Although the region of reservoir particle detachment is large during geothermal energy exploitation via CO 2 recycling, more preferential flow paths can form to reduce the formation blockage caused by particle migration. [Display omitted] • Critical velocities of clay and sand particles detachment are calculated. • A model for coupled migration-precipitation of solid particles is built. • Migration-precipitation of reservoir particle forms preferential flow paths. • Injected suspended particles affects reservoir blockage in a long-time operation. • CO 2 injection can reduce reservoir blockage in geothermal energy exploitation. [ABSTRACT FROM AUTHOR]

Fuel cells are an efficient, environmentally friendly and renewable energy device, which is synonymous with the green age. Microfluidic fuel cells are an excellent solution to the energy needs of portable electronic devices. In the present study, both vapor- and liquid-feed modes are established, which are combined with physical fields. Moreover, the accuracy of the models is verified. According to the results, the vapor-feed microfluidic fuel cell achieves higher tolerance to low flow rates while realizing a much higher energy efficiency than the liquid-feed microfluidic fuel cell. In addition, the performance of these microfluidic fuel cells is significantly improved by increasing the electrolyte and fuel concentrations. In terms of fuel utilization, the vapor-feed mode performs much better than that of liquid-feed mode no matter what parameters are changed, which shows the superiority of vapor-feed microfluidic fuel cell. Sensitivity analysis shows that either the electrolyte or fuel concentration significantly affects the power output of these microfluidic fuel cells. Although their growth can improve the power output of the fuel cells, they have adverse effects on energy efficiency. Conclusions indicate that the vapor-feed microfluidic fuel cell is more suitable for portable electronic products, and it is worthy of further development. [Display omitted] • Microfluidic fuel cell models with different fuel supply modes are established. • Vapor-feed mode are highlighted through comprehensive comparison. • Energy efficiency is evaluated by fuel utilization and exergy efficiency. • Sensitivity analysis is used to study the vapor-feed microfluidic fuel cell. [ABSTRACT FROM AUTHOR]

HOMER software is a powerful tool for modeling and optimization of hybrid energy system (HES). The main two default control strategies in HOMER are load following (LF) and cycle charging (CC) strategies. In these strategies, the decision to use the generator or battery at each time step is made based on the lowest-cost choice. Therefore, these strategies are difficult to be implemented in practice especially in countries with continuous fuel price fluctuations. In this study, a new dispatch strategy based on HOMER-MATLAB Link Controller for an isolated wind/diesel/battery HES is proposed to overcome the limitations of the default HOMER strategies. A detailed technical, economic, and greenhouse gas emission analysis is presented for the system under LF, CC, and the proposed dispatch strategies. Besides offering more realistic optimization, the results show that the proposed strategy offers the best economic and environmental performance with a net present cost of $56473 and annual CO 2 emissions of 6838 kg. Furthermore, the sensitivity analysis reveals that the proposed strategy is not affected by the fuel price variation, in opposite to LF, and CC strategies which is affected dramatically by this variation. The findings are of paramount importance towards more realistic and efficient energy management strategies. • A new dispatch strategy for wind/diesel/battery hybrid energy systems using HOMER-MATLAB Link Controller is proposed. • Overcome HOMER default strategies limitation in dealing with continuous fuel price fluctuations. • The proposed strategy is compared with HOMER default strategies in terms of techno-economic and environmental performance. • Sensitivity analysis is conducted by considering variations in critical parameters. • The proposed strategy is more realistic and has better economic and environmental performance than HOMER default strategies. [ABSTRACT FROM AUTHOR]

The development of an agri-biomass supply chain optimization model and decision support tools have a critical role to play in the success of large-scale agri-biomass utilization. A multidisciplinary approach that incorporates operational research, geographic information systems, mathematical modeling, technical economic analysis and sensitivity analysis was developed to optimize agri-biomass supply chain management and feedstock supply. It applies the model to a case study of Shandong Province's Dezhou City, and this enables it to illustrate the factors that affect supply costs. The optimal agri-biomass supply costs were 180.98 CNY/t. Of the costs, transportation related cost and purchase cost were found to be the most significant components of agri-biomass supply costs, and labor cost was the largest component of operating costs. The results showed the agri-biomass supply chain is profitable compared with the actual situation in Shandong Province. Sensitivity analysis results demonstrated that the optimal agri-biomass supply chain infrastructure was sensitive to changes in agri-biomass unit collection cost, agri-biomass unit transportation cost, and agri-biomass demand. The paper concluded that it is worthwhile to exploit economies of scale to reduce agri-biomass supply costs. • A MILP model is developed for agri-biomass supply chain optimization. • The decision-making development tool integrates a multidisciplinary approach. • The model is applied to a case study in China. • Sensitivity analysis is used to quantify how parameters contribute to variations in results. [ABSTRACT FROM AUTHOR]

Venetian blinds are widely used to improve the performance of window systems, while their energy-saving potentials vary with different materials. This paper aims to undertake a standardized evaluation of the annual energy-saving potential of venetian blinds in residential buildings, and identify the most influential parameters for design optimization. To achieve this goal, nine parameters were chosen to establish venetian blinds models in WINDOW. Then, 9072 shading models were coupled with building models for annual energy consumption simulation in EnergyPlus, where the shading schedule was derived from questionnaires. Further, an experiment was conducted to verify the reliability of the joint simulation between WINDOW and EnergyPlus. Finally, annual energy performance (AEP) was adopted for a standardized energy efficiency evaluation of various venetian blinds products, and the sensitivity analysis of AEP to various parameters was conducted to identify the key parameters. The results indicated that by adjusting the key parameters of the venetian blinds, cooling energy saving can reach 44% and the heating energy saving can be up to 65%. The significant energy saving potential of venetian blinds indicates the prospect and importance of their use in residential buildings. • A standardized evaluation method for venetian blinds was proposed. • The accuracy of joint-simulation of WINDOW and EnergyPlus was validated. • Conductivity of venetian blinds has few impacts on their energy performance. • Solar reflectance and transmittance are two key parameters for energy savings. • 65% heating energy consumptions can be saved by adjusting material parameters. [ABSTRACT FROM AUTHOR]

Power-to-gas system produces synthetic methane through two-step processes: H 2 production via water electrolysis and methanation of CO 2. The technology valorizes CO 2 as a useful commodity from biogas plants. Biogas upgrading technologies have the capacity to capture CO 2 and supply it as a source of carbon to methanation processes hence to produce synthetic methane. In this study, we have carried out a techno-economic assessment of biomethane production and estimation of its production costs via the integration of landfill biogas to methanation process. The study assessed four different scenarios, scenario 1: water-scrubbing, scenario 2: membrane, scenario 3: direct methanation and scenario 4: methanation with membrane. Production costs in case of power-to-gas scenarios 3&4 are competitive with standard upgrading scenarios 1&2 for electricity price below 20 €/MWh. Presence of electrolyzer unit contributes to more than 75% of the total biomethane production costs. Scenarios 1&2 are insensitive to changes in electricity prices as well as to operating time, but scenarios 3&4 are extremely sensitive to both electricity prices variation and operating time. Increase in the plant size and vol% of CH 4 in raw biogas presented shorter payback period in case of scenarios 1&2 due to lower specific production costs. • Landfill biogas conversion to biomethane is investigated. • Captured landfill CO 2 is valorized in methanation process to produce SNG. • Electricity price has no impact on production costs in case of standard upgrading. • Methanation process is energy intensive due to presence of electrolyzer unit. • Power-to-gas technologies are extremely sensitive to operating time of sub-systems. [ABSTRACT FROM AUTHOR]

Dynamic change of edge of sand detachment (ESD) is still unclear during the depressurization mining of gas hydrate reservoirs. Considering multi-physics such as the depose of NGH and its impact on the change of formation temperature and conductivity, a novel numerical method is provided which could be utilized to simulate and analyze the behavior of the edge of sand detachment (ESD). The simulation results show that the expansion of ESD has mainly consists of three phases, which has a tendency to be fast first, then slow, and finally tend to the ESD limit (ESDL). In our basic model referring to Shenhu field, the ESD rapidly reached around 44 m from day 0–360 days, then the increase rate slowed from 360 days to 720 days, and finally stabilized at 58 m (ESDL) after 720 days. In addition, the expansive rate of ESD and the ESD limit (ESDL) have great effect on the sand productions. The sand production rate increases rapidly and then reach peak in a rushed stage of ESD expansion, then decrease significantly and tend to be zero when ESD expands to the ESDL in NGH reservoirs. Based on the prioritization analysis of influencing factors, for a NGH reservoir with lower hydrate saturation, higher absolute permeability, higher initial reservoir temperature, lower bottom hole pressure, ESD expands much more fast and the ESDL is much larger. Our work could shed light on the dynamic behavior of ESD for the development of NGH reservoirs, and the finding of ESDL is helpful to determine the safe development area and facilitates adjustment on the sand controlling arrangements. [Display omitted] • There is an upper ESD limit (ESDL) during depressurization mining of NGH reservoirs. • The expansive rate of ESD and ESDL have great effect on the sand productions. • The ESDL could be reduced by increasing bottom hole pressure in low-saturated NGH reservoirs. [ABSTRACT FROM AUTHOR]

The levelized cost of energy (LCOE) is a widespread metric that doesn't disfavor any power generation or saving technology. In this paper, a stochastic model of the LCOE is provided. The proposed model includes all main parameters to be applied on renewable, non-renewable and energy saving systems. These parameters were modeled through non-homogeneous Poisson process as well as Weibull functions. Besides, a continuous optimization model is proposed to calculate the optimal LCOE under the constraints of main Capex/Opex expenditures. Under such conditions, the optimal value of the levelized cost of energy is obtained, which captures parameters' influence on this metric. Afterwards, these parameters' influences on the LCOE were assessed through a sensitivity analysis. • The stochastic model of the LCOE developed fits the behavior of parameters over the time. • The model is a first order economic assessment of cost competitiveness of energy generation and saving technologies. • The optimal LCOE is calculated under Capex/Opex constraints as well as the technology's parameters. [ABSTRACT FROM AUTHOR]

The use of static calculation models in building energy audits typically requires estimation of power loads, as appliance load monitoring is both expensive and intrusive. Small power loads are particularly difficult to monitor in detail and so, with increasing proportional contribution to overall building energy consumption, constitute a significant source of uncertainty in estimations. Little research has been conducted on how uncertainty in audit estimations (particularly of small power loads) should be handled and to what extent this impacts on what information is of most value in reducing audit uncertainties. The Morris method for sensitivity analysis has been adapted for small power load estimation to identify which inputs in static calculation models the output estimations are most sensitive to. The method is applied to the TM22 and TM54 audit calculation models from the Chartered Institute of Building Service Engineers (CIBSE); widely used in industry. Results demonstrate how the weight of input uncertainties differs according to the calculation model used. By providing uncertainty estimation in the audit process, auditors can choose an optimal energy assessment strategy for small power load estimations whilst also better understanding the significance of input uncertainty within the different energy calculation models. • Sensitivity analysis for assessment of energy audit input data quality. • Ranking influence of appliance type, number and use of total small power load. • Adaptation procedure to align sensitivity analysis to static energy audit models. [ABSTRACT FROM AUTHOR]

Abstract: We consider a microgrid for energy distribution, with a local consumer, a renewable generator (wind turbine) and a storage facility (battery), connected to the external grid via a transformer. We propose a 2 steps-ahead reinforcement learning algorithm to plan the battery scheduling, which plays a key role in the achievement of the consumer goals. The underlying framework is one of multi-criteria decision-making by an individual consumer who has the goals of increasing the utilization rate of the battery during high electricity demand (so as to decrease the electricity purchase from the external grid) and increasing the utilization rate of the wind turbine for local use (so as to increase the consumer independence from the external grid). Predictions of available wind power feed the reinforcement learning algorithm for selecting the optimal battery scheduling actions. The embedded learning mechanism allows to enhance the consumer knowledge about the optimal actions for battery scheduling under different time-dependent environmental conditions. The developed framework gives the capability to intelligent consumers to learn the stochastic environment and make use of the experience to select optimal energy management actions. [Copyright &y& Elsevier]

Abstract: In this paper, WESN (wavelet echo state network) with a novel ESN-based reconstruction stage is applied to both STLF (short-term load forecasting) and STTF (short-term temperature forecasting). Wavelet transform is used as the front stage for multi-resolution decomposition of load or temperature time series. ESNs function as forecasters for decomposed components. A modified shuffled frog leaping algorithm is used for optimizing ESNs. Both one-hour and 24-h ahead predictions are studied where the number of inputs are kept minimum. The performance of the proposed WESN-based load forecasters are investigated for three cases as the predicted temperature input is fed by actual temperatures, output of the WESN-based temperature forecasters and noisy temperatures. Effects of temperature errors on load forecasts are studied locally by sensitivity analysis. Hourly loads and temperatures of a North-American electric utility are used for this study. First, results of the proposed forecasters are compared with those of ESN-based forecasters that have previously shown high capability as stand-alone forecasters. Next, the WESN-based forecasters are compared with other models either previously tested on the data used here or to be rebuilt for testing on these data. Comparisons reveal significant improvements on accuracy of both STLF and STTF using the proposed forecasters. [Copyright &y& Elsevier]

Abstract: Energy and exergy analyses are used to evaluate the Zero emission coal (ZEC) system. Energy and exergy losses in each unit of the system are calculated and depicted with Sankey diagrams of energy flow and exergy flow when the system operates at a benchmark condition. Sensitivity analysis is carried out to investigate the effects of different operating parameters including H2 recycling ratio (R h), calcium to carbon ratio (R ctc) and fuel utilization factor (U f) on the energy efficiency (E en), exergy efficiency (E ex), total energy efficiency (E ten), total exergy efficiency (E tex) and carbon dioxide (CO2) sequestration ratio (R cs) of this system. The energy loss in the steam turbine (ST) is 35.2% which is the maximum among all the units and the maximum exergy loss of 37.0% occurs in the solid oxide fuel cell (SOFC). R h at 0.75 and U f at 0.8 are recommended as the optimum operation parameters of the system based on the sensitivity analysis. R cs increases with increasing R ctc. R ctc at 1.5 is recommended to maintain a high CO2 sequestration ratio with acceptable economic penalty. With these optimized parameters, E en of 36.2%, E ten of 46.8%, E ex of 35.7%, E tex of 46.2% and R cs of 87.4% can be achieved by the proposed system. [Copyright &y& Elsevier]

Abstract: The analysis framework discussed and employed in this paper utilizes the recent recognition that exergy is a form of environmental free energy to provide a fundamental basis for valuing environmental interactions independent from their secondary impacts. The framework is comprised of two separate components: (1) environmental exergy analysis and (2) anthropocentric sensitivity analysis. Environmental exergy analysis is based on fundamental thermodynamic principles and analysis techniques. It extends the principles of technical exergy analysis to the environment in order to quantify the location, magnitudes, and types of environmental impact—state change, alteration of natural transfers, and destruction change. Anthropocentric sensitivity analysis is based on the concepts of anthropocentric value and anthropocentric sensitivity. It enables the results of environmental exergy analysis to be interpreted for decision making, but at the expense of introducing some subjectivity into the framework. A key attribute of the framework is its ability to evaluate the environmental performance of energy systems on a level playing field, regardless of the specifics of the systems—i.e., resources consumed, products and by-products produced, or system size and time scale. The utility of the analysis framework for decision making is demonstrated in this paper through application to three example energy systems. [Copyright &y& Elsevier]

Abstract: A Multi-Criteria Decision Analysis (MCDA) tool was designed and used to support the evaluation of different electricity production scenarios. The MCDA tool is implemented in a user-friendly Excel worksheet and uses information obtained from a mixed integer optimization model, to produce a set of optimal schemes under different assumptions. Given the input, the MCDA allowed ranking different scenarios relying on their performance on 13 criteria covering economic, job market, quality of life of local populations, technical and environmental issues. The MCDA tool was used by a group of experts and academics with background in economics, engineering and environment. Regarding the totality of results, both the most and least expensive scenarios ranked first the same amount of times. These scenarios were, respectively, “Coal”, relying mainly in new coal power plants and “Maximum Renewable”, relying mainly in new wind and hydro power facilities. The opinions were divided towards these two solutions with different fundamental characteristics: “Maximum Renewable” with costs higher than “Coal” but leading to substantial reduction of the external energy dependency. Sensitivity analysis suggests that, although the costs are regarded as the most important criterion, those who had different rankings in their preferences have different attitudes towards other criteria. [Copyright &y& Elsevier]

Abstract: China''s annual crude steel production in 2010 was 638.7 Mt accounting for nearly half of the world''s annual crude steel production in the same year. Around 461 TWh of electricity and 14,872 PJ of fuel were consumed to produce this quantity of steel. We identified and analyzed 23 energy efficiency technologies and measures applicable to the processes in China''s iron and steel industry. Using a bottom-up electricity CSC (Conservation Supply Curve) model, the cumulative cost-effective electricity savings potential for the Chinese iron and steel industry for 2010–2030 is estimated to be 251 TWh, and the total technical electricity saving potential is 416 TWh. The CO2 emissions reduction associated with cost-effective electricity savings is 139 Mt CO2 and the CO2 emission reduction associated with technical electricity saving potential is 237 Mt CO2. The FCSC (Fuel CSC) model for the Chinese iron and steel industry shows cumulative cost-effective fuel savings potential of 11,999 PJ, and the total technical fuel saving potential is 12,139. The CO2 emissions reduction associated with cost-effective and technical fuel savings is 1191 Mt CO2 and 1205 Mt CO2, respectively. In addition, a sensitivity analysis with respect to the discount rate used is conducted. [Copyright &y& Elsevier]

Abstract: This paper reports experimental testing of an adsorption dishwasher employing different desiccants, such as 13X zeolite, microporous silica gel and SAPO-34 zeolite. Thermodynamic comparison of the selected adsorbents was carried out on the basis of the experimental measurement of the main thermo-physical parameters, such as specific heat, adsorption equilibrium curves and sorption enthalpy. A sensitivity analysis on the adsorption dishwasher parameters was carried out adopting full factorial design (FFD) on a modified dishwasher prototype. Finally, the actual energetic performance for the optimized configuration were experimentally evaluated returning a consumed electrical energy of 0.636 kWh, which is 41% lower than that of standard cycle performed by a standard dishwasher with energy label A. [Copyright &y& Elsevier]

Abstract: In this paper, a parallel double-effect or heat integrated organosilicon monomer distillation scheme is simulatively proposed with the aid of computer simulation to research the effect of heat integration on exergy loss of distillation process. The x–y diagrams of three groups of key components are drawn to confirm the feasibility of heat coupling. The total exergy loss of this scheme is calculated. The result shows that the heat integrated scheme presents 40.41% less exergy loss than the normal scheme. To make a further optimization, a sensitivity analysis is done to seek opportunities of further improving the operation parameters. The exergy loss distribution through trays of the low removal column is drawn to verify a large proportion of exergy loss in rectifying section. Then the operation condition of the condenser is changed to reduce the exergy loss from trays. [Copyright &y& Elsevier]

Abstract: Projections of energy demand are important for energy security supply and low carbon futures, and usually rely on final energy consumption trends methods, limiting the opportunity for future options. Methods supported by energy services are much preferred to estimate future energy demand, since they are better suited to accomplish end-users needs. Final energy can then be assessed through complementary tools, as technological models, resulting in deeper knowledge on the relation between energy services and technology options. This paper presents a bottom-up methodology to project detailed energy end-uses demand in the Portuguese residential buildings until 2050, aiming to identify the parameters governing energy services demand uncertainty, through a sensitivity analysis. The partial equilibrium TIMES (The Integrated MARKAL-EFOM System) model was used to assess technology options and final energy needs for the range of parameters variations for each end-use, allowing to conclude on the impact of uncertainty of energy services demand in final energy. Main results show that technology can overweight behavioral practices and lifestyle changes for some end-uses as in space heating and lighting. Nevertheless, important focus should be given to uncertain parameters related with consumer behavior, especially those on heating and other electric end-uses, as thermal comfort and equipment''s use. [Copyright &y& Elsevier]

Abstract: The increase in fluctuating power production requires an increase in flexibility in the system as well. Flexibility can be found in generation technologies with fast response times or in storage options. In the transport sector, the proportion of electric drive vehicles is expected to increase over the next decade or two. These vehicles can provide some of the flexibility needed in the power system, in terms of both flexible demand and electricity storage. However, what are the batteries worth to the power system? And does the value depend on battery capacity? This article presents an analysis of the integrated power and transport system, focusing on the sensitivity of the power system configuration according to battery capacity and price of the electric drive vehicle. The value of different battery capacities is estimated, given that the batteries are used for both driving and storage. Likewise, the prices at which the electric drive vehicles become of interest to the power system are found. Smart charge, including the opportunity to discharge (vehicle-to-grid) is used in all scenarios. Analyses show that the marginal benefits decrease the larger the battery. For very high battery prices, large batteries imply that diesel vehicles are preferable to electric drive vehicles. [Copyright &y& Elsevier]

Abstract: This study investigated the energy productivity and input sensitivity of sweet sorghum (Sorghum bicolor (L.) Moench), as compared with cotton and maize, at two coastal saline-alkali sites in Shandong Province, China. The data were collected quantitatively using a questionnaire for face-to-face interviewing of 116 farmers. The sorghum showed a lower energy input requirement than cotton and maize. The energy productivity indices were 12.4 and 9.2 for sweet sorghum, which were much higher than for cotton (3.1) at Wudi and maize (8.4) at Wendeng, respectively. Nitrogen fertilisation contributed the highest proportions (between 56% and 69% for the three crops) to the total energy consumption, as compared with the seed, labour, machinery, diesel fuel, phosphorus and potassium fertilisers, irrigation water, plastic film, and pesticides. All of the crops consumed much more non-renewable energy than renewable energy. According to the Cobb–Douglas production function, the diesel and nitrogen fertiliser energy inputs had a significantly positive impact on the sweet sorghum energy output. The energy crop showed an increasing return to scale, with cotton showing a constant and maize a decreasing return. These findings indicate that the sweet sorghum energy output is possible to be increased with further technical research and development. [Copyright &y& Elsevier]

Abstract: The amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R 2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned. [Copyright &y& Elsevier]

Abstract: This paper reviews the information available on the impurities encountered in stationary fuel cell systems, their effects on the fuel cells, and the maximum allowable concentrations of select impurities suggested by manufacturers and researchers. A generic model of a molten carbonate fuel cell-based power plant operating on digester and landfill gas has been developed; it includes a gas processing unit, followed by a fuel cell system. The model includes the key impurity removal steps to enable predictions of impurity breakthrough, component sizing, and utility needs. These data, along with process efficiency results from the model, were subsequently used to calculate the cost of electricity. Sensitivity analyses were conducted to correlate the concentrations of key impurities in the fuel gas feedstock to the cost of electricity. [Copyright &y& Elsevier]

Abstract: In this study the amount of energy consumption and its relation with yield in producing two cultivars of plum, Ghatreh Tala and Shablon cultivars, in Tehran province of Iran were studied. For each one of plum cultivars, data were obtained from 100 farmers in the form of personal interview using structured questionnaire method in Tehran province of Iran. Total input energy was found to be 192,652.55 and 168,783.94 MJ ha−1 for Ghatreh Tala and Shablon cultivars production. In Ghatreh Tala and Shablon cultivars production among input energy sources, electricity energy had the highest share with shares of 79.07% and 80.28% of total input energy respectively. Cobb–Douglass production function was used to determine a relation between input energies and yield in both productions. Results indicated that by using Cobb–Douglass production function to determine mathematical relationship between energy input and yield, human labor energy had the highest impact on yield for both productions. Sensitivity analysis indicated that among the energy inputs human labor energy had the highest MPP value for both plum cultivars productions. The benefit-cost ratios from Ghatreh Tala and Shablon productions were calculated to be 4.18 and 2.46, respectively. [Copyright &y& Elsevier]

Abstract: Peukert et al. recently published (Int. J. Chem. Kinet. 2010; 43: 107–119) the results of a series of shock tube measurements on the thermal decomposition of cyclohexane (c-C6H12) and 1-hexene (1-C6H12). The experimental data included 16 and 23 series, respectively, of H-atom profiles measured behind reflected shock waves by applying the ARAS technique (temperature range 1250–1550 K, pressure range 1.48–2.13 bar). Sensitivity analysis carried out at the experimental conditions revealed that the rate coefficients of the following six reactions have a high influence on the simulated H-atom profiles: R1: c-C6H12 = 1-C6H12, R2: 1-C6H12 = C3H5 + C3H7, R4: C3H5 = aC3H4 + H; R5: C3H7 = C2H4 + CH3; R6: C3H7 = C3H6 + H; R8: C3H5 + H = C3H6. The measured data of Peukert et al. were re-analysed together with the measurement results of Fernandes et al. (J. Phys. Chem. A 2005; 109: 1063–1070) for the rate coefficient of reaction , the decomposition of allyl radicals. The optimization resulted in the following Arrhenius parameters: R1: A = 2.441 × 1019, E/R = 52,820; R2: A = 3.539 × 1018, E/R = 42,499; R4: A = 8.563 × 1019, n = −3.665, E/R = 13,825 (high pressure limit); R4: A = 7.676 × 1031 n = −3.120, E/R = 40,323 (low pressure limit); R5: A = 3.600 × 1012, E/R = 10699; R6: A = 1.248 × 1017, E/R = 28,538; R8: A = 6212 × 1013, E/R = −970. The rate parameters above are in cm3, mol, s, and K units. Data analysis resulted in the covariance matrix of all these parameters. The standard deviations of the rate coefficients were converted to temperature dependent uncertainty parameter f(T). These uncertainty parameters were typically f = 0.1 for reaction , f = 0.1–0.3 for reaction , below 0.5 for reaction in the temperature range of 1250–1380 K, and above 1 for reactions . [Copyright &y& Elsevier]

Abstract: Thermoeconomic models, which combine the concept of cost in the field of economics and the concept of exergy in the field of thermodynamics, provide a possibility of optimizing complex energy-generating systems to achieve a best balance between thermodynamic efficiency and economic cost (including investment cost and operation cost). For the first time, operation optimization on a 300 MW coal-fired power plant located in Yiyang (Hunan Province, China) is accomplished based on the structure theory of thermoeconomic. Two optimization strategies, global optimization and local optimization, are successively realized on the power plant. Both strategies aim to minimize the total annual cost of the plant, and a 2.5% reduction in the total annual cost and a 3.5% reduction in the total investment cost are achieved. In addition, the costs of products of almost all units after optimization processes decrease obviously. It is worth noting that local optimization proposed in this paper attains almost the same performance as global optimization but with faster speed. Furthermore, sensitivities of optimal operation parameters with respect to external environmental parameters and the sensitivity of the objective function (the total annual cost) with respect to decision variables (e.g., the equipment efficiency) are presented. [Copyright &y& Elsevier]

Abstract: The most thermodynamically favorable operating conditions at which glycerol can be converted into hydrogen with maximum hydrogen yield by autothermal reforming using supercritical water were identified in a previous paper. As a second part of the study, a conceptual design based on energy integration and exergy analysis of the whole process has been performed. In the proposed scheme, the huge pressure energy of the gas product just at the outlet of the reforming reactor is converted into electrical power and a fraction of the expanded gas used to provide energy support for the process by burning it in a furnace, if needed. By using the optimal conditions found in the previous work, a severe deficit of energy arises in the process. Thus, both water-to-glycerol and oxygen-to-glycerol mole ratios at which thermoneutral conditions are achieved in the reformer are computed by burning all the product gas from the reformer, both for pure and pretreated crude glycerol, at different reforming and preheating temperatures. The pressure used is 240 atm. The effects of the main operating parameters are investigated by sensitivity analysis to identify optimal conditions to maximize power production under autothermal conditions, evaluating the results by energy and exergy analyses. The computations are made with the aid of AspenPlus™, using the predictive Soave–Redlich–Kwong equation of state as the thermodynamic method in the simulation of the supercritical region. [Copyright &y& Elsevier]

Abstract: Recently in electricity markets, a massive focus has been made on setting up opportunities for participating demand side. Such opportunities, also known as demand response (DR) options, are triggered by either a grid reliability problem or high electricity prices. Two important challenges that market operators are facing are appropriate designing and reasonable pricing of DR options. In this paper, time-of-use program (TOU) as a prevalent time-varying program is modeled linearly based on own and cross elasticity definition. In order to decide on TOU rates, a stochastic model is proposed in which the optimum TOU rates are determined based on grid reliability index set by the operator. Expected Load Not Supplied (ELNS) is used to evaluate reliability of the power system in each hour. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) problem and solved using CPLEX solver. The validity of the method is tested over the IEEE 24-bus test system. In this regard, the impact of the proposed pricing method on system load profile; operational costs and required capacity of up- and down-spinning reserve as well as improvement of load factor is demonstrated. Also the sensitivity of the results to elasticity coefficients is investigated. [Copyright &y& Elsevier]

Abstract: In the future, district heating companies in Sweden must adapt to energy efficiency measures in buildings and variable fuel and electricity prices. Swedish district heating demands are expected to decrease by 1–2% per year and electricity price variations seem to be more unpredictable in the future. A cost-optimisation model of a Swedish local district heating system is constructed using the optimisation modelling tool MODEST. A scenario for heat demand changes due to increased energy efficiency in buildings, combined with the addition of new buildings, is studied along with a sensitivity analysis for electricity price variations. Despite fears that heat demand reductions will decrease co-generation of clean electricity and cause increased global emissions, the results show that anticipated heat demand changes do not increase the studied system''s primary energy use or global CO2 emissions. The results further indicate that the heat production plants and the fuels used within the system have crucial importance for the environmental impact of district heat use. Results also show that low seasonal variations in electricity price levels with relatively low winter prices promote the use of electric heat pumps. High winter prices on the other hand promote co-generation of heat and electricity in CHP plants. [Copyright &y& Elsevier]

Abstract: In recent years the domestic energy management has become a non-trivial task as the number of energy sources and system components involved have increased, and all components have to operate coordinately in order to maximize global efficiency measures. In this paper a methodology is presented for finding the optimal size of the main components for a solar thermal system where particular attention is given to the optimization framework. The use of the PSO (particle swarm optimization) algorithm is proposed and the results obtained are compared with a GA (genetic algorithm) solution. Further, the relative influence of certain system parameters on the optimal configuration is investigated by means of a sensitivity analysis where the size of the collector is shown to have the greatest influence on all main output quantities while the size of the auxiliary power unit presents a relatively small influence on the solution. Finally, it is demonstrated that the accurate sizing of the energy components is necessary to minimize the energy consumption and cost of installation, while maximizing the solar fraction. The proposed methodology is shown to successfully solve the problem [Copyright &y& Elsevier]

Abstract: A 3D numerical model for gas production from hydrate reservoirs is developed, which considers kinetics of dissociation, heat and multiphase fluid flow. Three components (gas, water and hydrate) and three phases (gas, water and hydrate) are considered in the model. The equations are spatially discretized by a finite difference method. IMPES method is used to solve the mass balance equations and temperature is solved implicitly. Based on the model, gas production from Class I hydrate reservoir (with underlying free gas) under constant bottom-hole pressure was simulated. The sensitivity analysis of the factors including development parameters and reservoir parameters were performed. Results show that hydrate dissociation rate increases with the increases of initial reservoir temperature. The larger the absolute permeability is, the higher the hydrate dissociation rate is. Hydrate dissociation rate is significantly improved, when dissociation rate constant increases. However, higher initial reservoir pressure and bottom-hole pressure will lead to lower hydrate dissociation rate. Research results provide theoretical support for hydrate production. [Copyright &y& Elsevier]

Thermal conductivity is a key parameter for evaluating wellbore heat losses which plays an important role in determining the efficiency of steam injection processes. In this study, an unsteady formation heat-transfer model was established and a cost-effective in situ method by using stochastic approximation method based on well-log temperature data was presented. The proposed method was able to estimate the thermal conductivity and the volumetric heat capacity of geological formation simultaneously under the in situ conditions. The feasibility of the present method was assessed by a sample test, the results of which shown that the thermal conductivity and the volumetric heat capacity could be obtained with the relative errors of −0.21% and −0.32%, respectively. In addition, three field tests were conducted based on the easily obtainable well-log temperature data from the steam injection wells. It was found that the relative errors of thermal conductivity for the three field tests were within ±0.6%, demonstrating the excellent performance of the proposed method for calculating thermal conductivity. The relative errors of volumetric heat capacity ranged from −6.1% to −14.2% for the three field tests. Sensitivity analysis indicated that this was due to the low correlation between the volumetric heat capacity and the wellbore temperature, which was used to generate the judgment criterion. [Copyright &y& Elsevier]

Simultaneous multi-variable gradient-based optimization with multi-start is performed on a 300 MWe wet-recycling pressurized oxy-coal combustion process with carbon capture and sequestration. The model accounts for realistic component behavior including heat losses, steam leaks, pressure drops, cycle irreversibilities, and other technoeconomical considerations. The optimization study involves 16 variables, three of which are integer valued, and 10 constraints with the objective of maximizing thermal efficiency. The solution procedure follows active inequality constraints which are identified by thermodynamic-based analysis to facilitate convergence. Results of the multi-variable optimization are compared to a pressure sensitivity analysis similar to those performed in literature; the base-case of both assessments performed here is a favorable solution found in literature. Significant cycle performance improvements are obtained compared to this literature design at a much lower operating pressure and with moderate changes in the other operating variables. The effect of the variables on the cycle performance and on the constraints are analyzed and explained to obtain increased understanding of the actual behavior of the system. This study reflects the importance of simultaneous multi-variable optimization in revealing the system characteristics and uncovering the favorable solutions with higher efficiency than the atmospheric operation or those obtained by single variable sensitivity analysis. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to provide a critical overview of sensitivity analyses within input-output techniques applied to energy-related CO2 emissions. A classification of those applications is presented and their main advantages and disadvantages are discussed. It is argued that sensitivity analyses within input-output techniques are very relevant to obtain a “map” of hot-spots of the production system, i.e., to identify the transactions between sectors which lead to a large impact on the generation of energy-related CO2 emissions. Possible extensions of this approach are discussed. [Copyright &y& Elsevier]

Abstract: In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha−1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (R 2) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production. [Copyright &y& Elsevier]

Abstract: ICL (Indirect coal liquefaction), an alternative fuel-supplying technology, has drawn much attention and caused considerable debate in China’s energy sector. The hurdles to its development include the high risk of investment into large-scale installations, the high CO2 emissions and water resource consumption. A comprehensive assessment of ICL is urgently needed. This study provides an economic assessment and a technical analysis based on process simulations. To address the future challenge of curbing CO2 emissions, three absorption methods are compared for capturing the CO2 released from the ICL process: DMC (a novel absorbent), MEA and Rectisol. The comparative results suggest that physical absorbents, represented by Rectisol and DMC, have a remarkable advantage over chemical absorption processes, represented by MEA. The Rectisol process costs the least, while the DMC process is close to the same level. As a novel absorbent, DMC has the potential to be widely used in the future. The economic analysis of ICL predicted a high capital cost of over 35 billion yuan and an overall product cost of approximately 3800 yuan/ton for the baseline. In addition, via a sensitivity analysis, coal price, electricity price and capacity factor were identified as the three most influential factors affecting the overall product cost. [Copyright &y& Elsevier]

Abstract: This paper examines the energy use patterns and relationship between energy input and yield for grape production in Malayer region of Hamadan Province. Data from 50 farmers were collected using a face-to-face questionnaire method. In the surveyed vineyards, average yield and energy consumption were calculated as 18,530 kgha−1 and 45,213.66 MJha−1, respectively. Among input energy sources, fertilizers, electricity and farmyard manure contained highest energy shares with 37.25%, 19%, and 17.84%, respectively. The energy ratio and energy productivity were found to be 4.95 and 0.42 kgMJ−1. Three econometric models were developed to estimate the impact of energy inputs on yield. The results revealed that impact of chemical, fertilizer and water on yield were significant at 1% probability level. Also, indirect and non-renewable energies were found to be rather high. Sensitivity analysis indicated that among the inputs, chemical has the highest MPP value of energy inputs. RTS (returns to scale) values for grape yield was found to be 2.15; thus, there prevailed an IRS of grape for estimated model. The net return was found to be positive as 2810.56 $ha−1 for grape. The benefit-cost ratio was calculated as 2.08. [Copyright &y& Elsevier]

Abstract: The aims of this study were to determine the energy consumption and evaluation of inputs sensitivity for soybean production in Kordkuy county of Iran. The data used in this study were obtained from 32 farmers using a face-to-face questionnaire base of random sampling method. The sensitivity of energy inputs was estimated using the marginal physical productivity (MPP) method and partial regression coefficients on soybean yield. The results indicated that the total input and output energy use was to be 18,026.50 and 71,228.86 MJ ha−1 respectively. With 66.67%, the diesel fuel was the highest within the energy equivalents and followed by chemical fertilizers and water for irrigation with 14.32% and 6.18% respectively. The input–output ratio was found as 4.62 (used efficiency). The share of direct, indirect, renewable and non-renewable was 74%, 26%, 14% and 86% respectively. The econometric model estimation emphasized that the seed was significantly positive on yield. The sensitivity analysis indicated the MPP value of 2.42 for seed, indicates that with an additional use of 1 MJ of seed energy would lead to an increase in yield by 2.42. The impact of direct, indirect and non-renewable energies on yield was significant. [Copyright &y& Elsevier]

Abstract: The objective of this study is to present a new method for determination of the optimal bidding strategies among generating companies (GenCo) in the electricity markets using agent-based approach and numerical sensitivity analysis (NSA). While agent-based approach provides for decision making, NSA can help with identifying the critical control points that lead to proper decisions to be taken by GenCos. To achieve the objective, the pricing mechanism used for settling the electricity market and determining the GenCos rewards is locational marginal pricing (LMP) and the sensitivity of each GenCo reward with respect to its bid is analyzed, then, the optimal strategy is determined. An example and a case study are used to illustrate the efficiency of the proposed method. The LMPs and allocated generations of GenCos show that the proposed method leads GenCos to learn a strategic manner and, as a result, increase prices and maximize their rewards. To validate the proposed method, the results from this study are compared with those available in the literature. The comparison of results shows an improved simulation time by 8.16 percent and total reward of market by 2.46 percent. [Copyright &y& Elsevier]

Abstract: The study was carried out on energy requirement and energy input–output relationship of rice production in Guilan province of Iran. Data were collected from 105 farmers with face-to-face questionnaire method. The research results revealed rice production consumed a total energy of 39333 MJ ha−1 which fuel energy use was 46% followed by chemical fertilizer (36%), seed (8%) and biocide (6%), respectively. The share of direct, indirect, renewable and non-renewable energies was 49%, 51%, 11% and 89% respectively. The energy use efficiency and energy productivity were found as 1.53, 0.09 kg MJ−1, respectively. The econometric model was developed using Cobb–Douglas type function and results showed that fuel and machinery energy inputs contributed significantly to the yield. The results of sensitivity analysis of the energy inputs showed that the MPP value of fuel was the highest (0.93), followed by machinery (0.23), biocide (0.17) and seed (0.15) energy inputs. Economic analysis indicated that total cost of production was 3156 $ ha−1. Gross and net return were 1642 $ ha−1 and 940 $ ha−1, respectively and the benefit-cost ratio was calculated 1.29. Mainly, large farms (more than 1 ha) had better management and were more successful in energy use and economic performance. [Copyright &y& Elsevier]

Abstract: The growing worldwide demand for less polluting shapes of energy have led to a renewed interest in the use of Micro Combined Heat and Power (Micro CHP) technologies in the residential sector. Micro CHP have been introduced around Iran recently, and expected to diffuse more and more. In this paper, technical and economic studies for the use of Micro CHP in the different climate zones of Iran are executed. These zones are categorized in to five; Tehran, Rasht, Bandar Abbas, Ardebil and Yazd, based on weather conditions. Later on using an economic model, both annual energy savings and percentage of system profitability in each zone are calculated as well as reduction in annual emissions. It should be mentioned that, for economic calculations, gas and electricity price are determined using a sensitivity analysis. This analysis indicated that profitability of Micro CHP systems are sensitive to energy prices, as well as hours needed for heating room in each climate zones. The analysis results show that Ardebil with 38 million RLS (with electricity buyback) annual energy saving is recognized as the best option for installing Micro CHP. On the other hand, Bandar Abbas with 2.5 million RLS annual energy saving is not suitable. [Copyright &y& Elsevier]

Abstract: Identifying the building parameters that significantly impact energy performance is an important step for enabling the reduction of the heating and cooling energy loads of apartment buildings in the design stage. Implementing passive design techniques for these buildings is not a simple task in most dense cities; their energy performance usually depends on uncertainties in the local climate and many building parameters, such as window size, zone height, and features of materials. For this paper, a sensitivity analysis was performed to determine the most significant parameters for buildings in hot-humid climates by considering the design of an existing apartment building in Izmir, Turkey. The Monte Carlo method is selected for sensitivity and uncertainty analyses with the Latin hypercube sampling (LHC) technique. The results show that the sensitivity of parameters in apartment buildings varies based on the purpose of the energy loads and locations in the building, such as the ground, intermediate, and top floors. In addition, the total window area, the heat transfer coefficient (U) and the solar heat gain coefficient (SHGC) of the glazing based on the orientation have the most considerable influence on the energy performance of apartment buildings in hot-humid climates. [Copyright &y& Elsevier]

Abstract: Renewable energy is considered by many policy-makers to contribute to achieving at least three major policy goals: the energy goal, the environmental goal, and the economic goal (3E goals). As an innovation-oriented island country with scarce natural resources, Taiwan announced the Sustainable Energy Policy Principles in 2008 that stated that Taiwan’s renewable energy policy should accomplish the 3E goals. Several studies point out that specific renewable energy policy goals lead to specific renewable energy sources and technologies because each type of renewable energy has different features. In order to achieve the renewable energy policy goals, this research aims to examine how different policy goals lead to corresponding renewable energy sources. The relative importance of each goal is evaluated by using analytic hierarchy process (AHP). The weight of each policy goal is adjusted separately to construct policy scenarios by the sensitivity analysis. According to the results, non-pumped storage hydropower, wind energy, and solar energy are three sources that could meet the three policy goals at the same time. [Copyright &y& Elsevier]

Abstract: This study was carried out on energy consumption patterns in different sizes of farms for corn silage production. Data were collected from 40 producers, using face to face questionnaire method in Karaj city. The survey studies indicated that the most important energy inputs were machinery and chemical fertilizers with 42% and 28% of total energy input, respectively. The total consumption energy was 68,928 MJ ha−1 where the output was 148,380 MJ ha−1. The results showed that farms with more than 10 ha use the least amount of total energy per hectare. The energy ratio, energy productivity, specific energy and net energy were 2.27, 0.28 kg MJ−1, 3.76 MJ kg−1 and 79,452 MJ ha−1 respectively. Share of direct and indirect energy was 25% and 75% respectively. The econometric model was developed using Cobb–Douglas type function. The elasticity coefficients of diesel fuel, chemical fertilizers, machinery and seed were found to be statistically significant. The MPP (Marginal Physical Productivity) value showed the high impact of seed and chemical fertilizers energy inputs on corn silage yield. The economic analysis showed average total cost of production as 1973 $ ha−1 that was higher in large farms; however more yield led to better benefit to cost ratio. The GHG emissions were indicated the high CO2 output in machinery production. [Copyright &y& Elsevier]

Abstract: Steam methane reforming (SMR) is one of the most promising processes for hydrogen production. Several studies have demonstrated its advantages from the economic viewpoint. Nowadays process development is based on technical and economical aspects; however, in the near future, the environmental impact will play a significant role in the design of such processes. In this paper, an SMR process is studied from the viewpoint of overall environmental impact, using an exergoenvironmental analysis. This analysis presents the combination of exergy analysis and life cycle assessment. Components where chemical reactions occur are the most important plant components from the exergoenvironmental point of view, because, in general, there is a high environmental impact associated with these components. This is mainly caused by the exergy destruction within the components, and this in turn is mainly due to the chemical reactions. The obtained results show that the largest potential for reducing the overall environmental impact is associated with the combustion reactor, the steam reformer, the hydrogen separation unit and the major heat exchangers. The environmental impact in these components can mainly be reduced by improving their exergetic efficiency. A sensitivity analysis for some important exergoenvironmental variables is also presented in the paper. [Copyright &y& Elsevier]

Abstract: One way of producing nearly CO2 free electricity is by using biomass as a combustible. In many cases, removal of CO2 in biomass grown is almost the same as the emissions for the bioelectricity production at the power plant. For this reason, bioelectricity is generally considered CO2 neutral. For large-scale biomass electricity generation two alternatives can be considered: biomass-only fired power plants, or cofiring in an existing coal power plant. Among other factors, two important aspects should be analyzed in order to choose between the two options. Firstly, which is the most appealing alternative if their Greenhouse Gases (GHG) Emissions savings are taken into account. Secondly, which biomass resource is the best, if the highest impact reduction is sought. In order to quantify all the GHG emissions related to each system, a Life Cycle Assessment (LCA) methodology has been performed and all the processes involved in each alternative have been assessed in a cradle-to-grave manner. Sensitivity analyses of the most dominant parameters affecting GHG emissions, and comparisons between the obtained results, have also been carried out. [Copyright &y& Elsevier]

Abstract: In this paper, the performance of customer-owned distributed generation (DG) units is quantified from different perspectives through an uncertainty study. A Monte Carlo-based method is applied to assess the stochastic operation of the customer-owned DG units in the power distribution system. Several cases are studied to analyze the impact on system performance of using such generators, with the emphasis on benefits. The results of the studied cases show that proper operation of customer-owned DG units placed close to significant consumption centers offers several benefits which lead to significant energy savings and improvement in the performance indices while maintaining the cost-effectiveness. Furthermore, based on the energy demand, different electricity price scenarios considering a cost sensitivity analysis are performed to indicate how the variations in electricity price influence each scenario’s feasibility. It is concluded that implementation of a proper energy purchase policy, and allocating the benefits of DG units to the owners, improves the economic performance of their investments and encourages customer DG developers to connect DG to the distribution network. [Copyright &y& Elsevier]

Abstract: The analysis framework introduced in this paper utilizes the recognition that exergy is a form of environmental free energy to provide a fundamental basis for valuing environmental interactions independent from their secondary impacts (e.g., global warming, photochemical smog). In order to extend exergy to analyze environmental interactions, modifications are required to the traditional representation of the environment and definition of the dead state used in technical exergy analysis. These are accomplished through a combination of logical extensions and use of non-equilibrium thermodynamic principles. The framework is comprised of two separate components: (1) environmental exergy analysis and (2) anthropocentric sensitivity analysis. Environmental exergy analysis is based on fundamental thermodynamic principles and analysis techniques. It extends the principles of technical exergy analysis to the environment in order to quantify the locations, magnitudes, and types of environmental impact—state change, alteration of natural transfers, and destruction change. Anthropocentric sensitivity analysis is based on the concepts of anthropocentric value and anthropocentric sensitivity. It enables the results of environmental exergy analysis to be interpreted for decision making, but at the expense of introducing some subjectivity into the framework. A key attribute of the framework is its ability to evaluate and compare the environmental performance of energy systems on a level playing field, regardless of the specifics of the systems—resources, by-products, sizes, time scales. [Copyright &y& Elsevier]