Showing papers in "Energies in 2016"

Deep Neural Network Based Demand Side Short Term Load Forecasting

Abstract: In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN)-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN), a double seasonal Holt–Winters (DSHW) model and the autoregressive integrated moving average (ARIMA). The mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

The Effect of Distributed Parameters on Conducted EMI from DC-Fed Motor Drive Systems in Electric Vehicles

Abstract: The large dv/dt and di/dt outputs of power devices in DC-fed motor drive systems in electric vehicles (EVs) always introduce conducted electromagnetic interference (EMI) emissions and may lead to motor drive system energy transmission losses. The effect of distributed parameters on conducted EMI from the DC-fed high voltage motor drive systems in EVs is studied. A complete test for conducted EMI from the direct current fed(DC-fed) alternating current (AC) motor drive system in an electric vehicle (EV) under load conditions is set up to measure the conducted EMI of high voltage DC cables and the EMI noise peaks due to resonances in a frequency range of 150 kHz–108 MHz. The distributed parameters of the motor can induce bearing currents under low frequency sine wave operation. However the impedance of the distributed parameters of the motor is very high at resonance frequencies of 500 kHz and 30 MHz, and the effect of the bearing current can be ignored, so the research mainly focuses on the distributed parameters in inverters and cables at 500 kHz and 30 MHz, not the effect of distributed parameters of the motor on resonances. The corresponding equivalent circuits for differential mode (DM) and common mode (CM) EMI at resonance frequencies of 500 kHz and 30 MHz are established to determine the EMI propagation paths and analyze the effect of distributed parameters on conducted EMI. The dominant distributed parameters of elements responsible for the appearing resonances at 500 kHz and 30 MHz are determined. The effect of the dominant distributed parameters on conducted EMI are presented and verified by simulation and experiment. The conduced voltage at frequencies from 150 kHz to 108 MHz can be mitigated to below the limit level-3 of CISPR25 by changing the dominant distributed parameters.

Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

Abstract: Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.

A Review of Systems and Technologies for Smart Homes and Smart Grids

Abstract: © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs

Abstract: This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative.

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Abstract: Wind farm power production is known to be strongly affected by turbine wake effects. The purpose of this study is to develop and test a new analytical model for the prediction of wind turbine wakes and the associated power losses in wind farms. The new model is an extension of the one recently proposed by Bastankhah and Porte-Agel for the wake of stand-alone wind turbines. It satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the velocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence intensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes. Furthermore, the power production from the wind turbines is calculated using the power curve. The performance of the new analytical wind farm model is validated against power measurements and large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind directions, corresponding to a variety of full-wake and partial-wake conditions. A reasonable agreement is found between the proposed analytical model, LES data, and power measurements. Compared with a commonly used wind farm wake model, the new model shows a significant improvement in the prediction of wind farm power.

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

Abstract: This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

Quantifying the Water-Energy-Food Nexus: Current Status and Trends

Abstract: Water, energy, and food are lifelines for modern societies. The continuously rising world population, growing desires for higher living standards, and inextricable links among the three sectors make the water-energy-food (WEF) nexus a vibrant research pursuit. For the integrated delivery of WEF systems, quantifying WEF connections helps understand synergies and trade-offs across the water, energy, and food sectors, and thus is a critical initial step toward integrated WEF nexus modeling and management. However, current WEF interconnection quantifications encounter methodological hurdles. Also, existing calculation results are scattered across a wide collection of studies in multiple disciplines, which increases data collection and interpretation difficulties. To advance robust WEF nexus quantifications and further contribute to integrated WEF systems modeling and management, this study: (i) summarizes the estimate results to date on WEF interconnections; (ii) analyzes methodological and practical challenges associated with WEF interconnection calculations; and (iii) points out opportunities for enabling robust WEF nexus quantifications in the future.

A Criterion for Rating the Usability and Accuracy of the One-Diode Models for Photovoltaic Modules

Abstract: In selecting a mathematical model for simulating physical behaviours, it is important to reach an acceptable compromise between analytical complexity and achievable precision. With the aim of helping researchers and designers working in the area of photovoltaic systems to make a choice among the numerous diode-based models, a criterion for rating both the usability and accuracy of one-diode models is proposed in this paper. A three-level rating scale, which considers the ease of finding the data used by the analytical procedure, the simplicity of the mathematical tools needed to perform calculations and the accuracy achieved in calculating the current and power, is used. The proposed criterion is tested on some one-diode equivalent circuits whose analytical procedures, hypotheses and equations are minutely reviewed along with the operative steps to calculate the model parameters. To assess the achievable accuracy, the current-voltage (I-V) curves at constant solar irradiance and/or cell temperature obtained from the analysed models are compared to the characteristics issued by photovoltaic (PV) panel manufacturers and the differences of current and power are calculated. The results of the study highlight that, even if the five parameter equivalent circuits are suitable tools, different usability ratings and accuracies can be observed.

Land Suitability Analysis for Solar Farms Exploitation Using GIS and Fuzzy Analytic Hierarchy Process (FAHP)—A Case Study of Iran

Abstract: Considering the geographical location and climatic conditions of Iran, solar energy can provide a considerable portion of the energy demand for the country. This study develops a two-step framework. In the first step, the map of unsuitable regions is extracted based on the defined constraints. In the next step, in order to identify the suitability of different regions, 11 defined criteria, including solar radiation, average annual temperatures, distance from power transmission lines, distance from major roads, distance from residential area, elevation, slope, land use, average annual cloudy days, average annual humidity and average annual dusty days, are identified. The relative weights of defined criteria and sub-criteria are also determined applying fuzzy analytical hierarchy process (FAHP) technique. Next, by overlaying these criteria layers, the final map of prioritization of different regions of Iran for exploiting solar photovoltaic (PV) plants is developed. Based on Iran’s political divisions, investigation and analysis of the results have been presented for a total of 1057 districts of the country, where each district stands in one of the five defined classes of excellent, good, fair, low, and poor level. The obtained data indicate that 14.7% (237,920 km2), 17.2% (278,270 km2), 19.2% (311,767 km2), 11.3% (183,057 km2), 1.8% (30,549 km2) and 35.8% (580,264 km2) of Iran’s area are positioned as excellent, good, fair, low, poor and unsuitable areas, respectively. Moreover, Kerman, Yazd, Fars, Sisitan and Baluchestan, Southern Khorasan and Isfahan are included in the regions as the most excellent suitable provinces for exploiting solar PV plants.

Optimal Power Flow Using the Jaya Algorithm

Abstract: This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

The Environmental Biorefinery: Using Microalgae to Remediate Wastewater, a Win-Win Paradigm

Abstract: Microalgae have been shown to be a source of multiple bio-based products ranging from high value molecules to commodities. Along with their potential to produce a large variety of products, microalgae can also be used for the depollution of wastewaters of different origins (urban, industrial, and agricultural). This paper is focused on the importance of harnessing the bioremediation capacity of microalgae to treat wastewaters in order to develop the microalgae industry (especially the microalgae biofuel industry) and to find other alternatives to the classic wastewater treatment processes. The current research on the potential of microalgae to treat a specific wastewater or a targeted pollutant is reviewed and discussed. Then, both strategies of selecting the best microalgae strain to treat a specific wastewater or pollutant and using a natural or an artificial consortium to perform the treatment will be detailed. The process options for treating wastewaters using microalgae will be discussed up to the final valorization of the biomass. The last part is dedicated to the challenges which research need to address in order to develop the potential of microalgae to treat wastewaters.

Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges

Abstract: Current logistics and transportation (LT and (b) strategic, planning and operational issues associated with “standard” EVs and with hydrogen-based EVs. The paper also analyzes how the introduction of EVs in L&T systems generates new variants of the well-known Vehicle Routing Problem, one of the most studied optimization problems in the L&T field, and proposes the use of metaheuristics and simheuristics as the most efficient way to deal with these complex optimization problems.

Review of Physicochemical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

Abstract: A power transformer outage has a dramatic financial consequence not only for electric power systems utilities but also for interconnected customers. The service reliability of this important asset largely depends upon the condition of the oil-paper insulation. Therefore, by keeping the qualities of oil-paper insulation system in pristine condition, the maintenance planners can reduce the decline rate of internal faults. Accurate diagnostic methods for analyzing the condition of transformers are therefore essential. Currently, there are various electrical and physicochemical diagnostic techniques available for insulation condition monitoring of power transformers. This paper is aimed at the description, analysis and interpretation of modern physicochemical diagnostics techniques for assessing insulation condition in aged transformers. Since fields and laboratory experiences have shown that transformer oil contains about 70% of diagnostic information, the physicochemical analyses of oil samples can therefore be extremely useful in monitoring the condition of power transformers.

Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study

Abstract: This paper presents an optimal dispatch model of an ice storage air-conditioning system for participants to quickly and accurately perform energy saving and demand response, and to avoid the over contact with electricity price peak. The schedule planning for an ice storage air-conditioning system of demand response is mainly to transfer energy consumption from the peak load to the partial-peak or off-peak load. Least Squares Regression (LSR) is used to obtain the polynomial function for the cooling capacity and the cost of power consumption with a real ice storage air-conditioning system. Based on the dynamic electricity pricing, the requirements of cooling loads, and all technical constraints, the dispatch model of the ice-storage air-conditioning system is formulated to minimize the operation cost. The Improved Ripple Bee Swarm Optimization (IRBSO) algorithm is proposed to solve the dispatch model of the ice storage air-conditioning system in a daily schedule on summer. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the demand response of ice storage air-conditioning systems to demonstrate the optimization of its energy savings and operational efficiency and offering greater energy efficiency.

Diagnostic Measurements for Power Transformers

Abstract: With the increasing age of the primary equipment of the electrical grids there exists also an increasing need to know its internal condition. For this purpose, off- and online diagnostic methods and systems for power transformers have been developed in recent years. Online monitoring is used continuously during operation and offers possibilities to record the relevant stresses which can affect the lifetime. The evaluation of these data offers the possibility of detecting oncoming faults early. In comparison to this, offline methods require disconnecting the transformer from the electrical grid and are used during planned inspections or when the transformer is already failure suspicious. This contribution presents the status and current trends of different diagnostic techniques of power transformers. It provides significant tutorial elements, backed up by case studies, results and some analysis. The broadness and improvements of the presented diagnostic techniques show that the power transformer is not anymore a black box that does not allow a view into its internal condition. Reliable and accurate condition assessment is possible leading to more efficient maintenance strategies.

The BioSCWG Project: Understanding the Trade-Offs in the Process and Thermal Design of Hydrogen and Synthetic Natural Gas Production

Abstract: This article presents a summary of the main findings from a collaborative research project between Aalto University in Finland and partner universities. A comparative process synthesis, modelling and thermal assessment was conducted for the production of Bio-synthetic natural gas (SNG) and hydrogen from supercritical water refining of a lipid extracted algae feedstock integrated with onsite heat and power generation. The developed reactor models for product gas composition, yield and thermal demand were validated and showed conformity with reported experimental results, and the balance of plant units were designed based on established technologies or state-of-the-art pilot operations. The poly-generative cases illustrated the thermo-chemical constraints and design trade-offs presented by key process parameters such as plant organic throughput, supercritical water refining temperature, nature of desirable coproducts, downstream indirect production and heat recovery scenarios. The evaluated cases favoring hydrogen production at 5 wt. % solid content and 600 °C conversion temperature allowed higher gross syngas and CHP production. However, mainly due to the higher utility demands the net syngas production remained lower compared to the cases favoring BioSNG production. The latter case, at 450 °C reactor temperature, 18 wt. % solid content and presence of downstream indirect production recorded 66.5%, 66.2% and 57.2% energetic, fuel-equivalent and exergetic efficiencies respectively.

A Review of CO2-Enhanced Oil Recovery with a Simulated Sensitivity Analysis

Abstract: This paper reports on a comprehensive study of the CO2-EOR (Enhanced oil recovery) process, a detailed literature review and a numerical modelling study. According to past studies, CO2 injection can recover additional oil from reservoirs by reservoir pressure increment, oil swelling, the reduction of oil viscosity and density and the vaporization of oil hydrocarbons. Therefore, CO2-EOR can be used to enhance the two major oil recovery mechanisms in the field: miscible and immiscible oil recovery, which can be further increased by increasing the amount of CO2 injected, applying innovative flood design and well placement, improving the mobility ratio, extending miscibility, and controlling reservoir depth and temperature. A 3-D numerical model was developed using the CO2-Prophet simulator to examine the effective factors in the CO2-EOR process. According to that, in pure CO2 injection, oil production generally exhibits increasing trends with increasing CO2 injection rate and volume (in HCPV (Hydrocarbon pore volume)) and reservoir temperature. In the WAG (Water alternating gas) process, oil production generally increases with increasing CO2 and water injection rates, the total amount of flood injected in HCPV and the distance between the injection wells, and reduces with WAG flood ratio and initial reservoir pressure. Compared to other factors, the water injection rate creates the minimum influence on oil production, and the CO2 injection rate, flood volume and distance between the flood wells have almost equally important influence on oil production.

A Review of Wave-to-Wire Models for Wave Energy Converters

Abstract: Control of wave energy converters (WECs) has been very often limited to hydrodynamic control to absorb the maximum energy possible from ocean waves. This generally ignores or significantly simplifies the performance of real power take-off (PTO) systems. However, including all the required dynamics and constraints in the control problem may considerably vary the control strategy and the power output. Therefore, this paper considers the incorporation into the model of all the conversion stages from ocean waves to the electricity network, referred to as wave-to-wire (W2W) models, and identifies the necessary components and their dynamics and constraints, including grid constraints. In addition, the paper identifies different control inputs for the different components of the PTO system and how these inputs are articulated to the dynamics of the system. Examples of pneumatic, hydraulic, mechanical or magnetic transmission systems driving a rotary electrical generator, and linear electric generators are provided.

Scrap Tyre Management Pathways and Their Use as a Fuel—A Review

Abstract: This article provides a review of different methods for managing waste tyres Around 15 billion scrap tyres make their way into the environmental cycle each year, so there is an extreme demand to manage and mitigate the environmental impact which occurs from landfilling and burning Numerous approaches are targeted to recycle and reuse the tyre rubber in various applications Among them, one of the most important methods for sustainable environmental stewardship is converting tyre rubber components into bio-oil In this study, scrap tyre management techniques including landfill, retreading, recycling, combustion, and conversion to liquid fuels was reviewed (including gasification, hydrothermal liquefaction, and pyrolysis) The effects of parameters such as reactor types, pyrolysis temperature, and catalyst on the oil, gas and solid products in pyrolysis process were investigated

Cost Engineering Techniques and Their Applicability for Cost Estimation of Organic Rankine Cycle Systems

Abstract: The potential of organic Rankine cycle (ORC) systems is acknowledged by both considerable research and development efforts and an increasing number of applications. Most research aims at improving ORC systems through technical performance optimization of various cycle architectures and working fluids. The assessment and optimization of technical feasibility is at the core of ORC development. Nonetheless, economic feasibility is often decisive when it comes down to considering practical instalments, and therefore an increasing number of publications include an estimate of the costs of the designed ORC system. Various methods are used to estimate ORC costs but the resulting values are rarely discussed with respect to accuracy and validity. The aim of this paper is to provide insight into the methods used to estimate these costs and open the discussion about the interpretation of these results. A review of cost engineering practices shows there has been a long tradition of industrial cost estimation. Several techniques have been developed, but the expected accuracy range of the best techniques used in research varies between 10% and 30%. The quality of the estimates could be improved by establishing up-to-date correlations for the ORC industry in particular. Secondly, the rapidly growing ORC cost literature is briefly reviewed. A graph summarizing the estimated ORC investment costs displays a pattern of decreasing costs for increasing power output. Knowledge on the actual costs of real ORC modules and projects remains scarce. Finally, the investment costs of a known heat recovery ORC system are discussed and the methodologies and accuracies of several approaches are demonstrated using this case as benchmark. The best results are obtained with factorial estimation techniques such as the module costing technique, but the accuracies may diverge by up to +30%. Development of correlations and multiplication factors for ORC technology in particular is likely to improve the quality of the estimates.

Effects of Reynolds Number on the Energy Conversion and Near-Wake Dynamics of a High Solidity Vertical-Axis Cross-Flow Turbine

Abstract: Experiments were performed with a large laboratory-scale high solidity cross-flow turbine to investigate Reynolds number effects on performance and wake characteristics and to establish scale thresholds for physical and numerical modeling of individual devices and arrays. It was demonstrated that the performance of the cross-flow turbine becomes essentially R e -independent at a Reynolds number based on the rotor diameter R eD ≈ 106 or an approximate average Reynolds number based on the blade chord length R ec ≈ 2 × 105 . A simple model that calculates the peak torque coefficient from static foil data and cross-flow turbine kinematics was shown to be a reasonable predictor for Reynolds number dependence of an actual cross-flow turbine operating under dynamic conditions. Mean velocity and turbulence measurements in the near-wake showed subtle differences over the range of R e investigated. However, when transport terms for the streamwise momentum and mean kinetic energy were calculated, a similar R e threshold was revealed. These results imply that physical model studies of cross-flow turbines should achieve R eD ∼ 106 to properly approximate both the performance and wake dynamics of full-scale devices and arrays.

Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting

Abstract: The combustion of fossil fuels in the transport sector leads to an aggravation of the air quality along city roads and highways. Urban air quality is a serious problem nowadays as the number of vehicles increases on a yearly basis. With stricter Euro emission regulations, vehicle manufacturers are not meeting the imposed limits and are also disregarding the non-exhaust emissions. This paper highlights the relevance of non-exhaust emissions of passenger vehicles, both conventional (diesel and petrol) or electric vehicles (EV), on air quality levels in an urban environment in Belgium. An environmental life cycle assessment was carried out based on a real-world emission model for passenger cars and fuel refinery data. A cut-off was applied to the models to highlight what emissions, both from the refinery to the exhaust and electricity production for EV, do actually occur within Belgium’s borders. Results show that not much progress has been made from Euro 4 to 6 for conventional vehicles. Electric vehicles pose the best alternative solution as a more environmentally friendly means of transportation. The analysis results target policy makers with the intention that regulations and policies would be developed in the future and target the characterization of non-exhaust emissions from vehicles. These results indicate that EVs offer a valid solution for addressing the urban air quality issue and that non-exhaust emissions should be addressed in future regulatory steps as they dominate the impact spectrum.

Perovskite Solar Cells: Progress and Advancements

Abstract: Organic–inorganic hybrid perovskite solar cells (PSCs) have emerged as a new class of optoelectronic semiconductors that revolutionized the photovoltaic research in the recent years. The perovskite solar cells present numerous advantages include unique electronic structure, bandgap tunability, superior charge transport properties, facile processing, and low cost. Perovskite solar cells have demonstrated unprecedented progress in efficiency and its architecture evolved over the period of the last 5–6 years, achieving a high power conversion efficiency of about 22% in 2016, serving as a promising candidate with the potential to replace the existing commercial PV technologies. This review discusses the progress of perovskite solar cells focusing on aspects such as superior electronic properties and unique features of halide perovskite materials compared to that of conventional light absorbing semiconductors. The review also presents a brief overview of device architectures, fabrication methods, and interface engineering of perovskite solar cells. The last part of the review elaborates on the major challenges such as hysteresis and stability issues in perovskite solar cells that serve as a bottleneck for successful commercialization of this promising PV technology.

A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery

Abstract: A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation.

The Energy and Environmental Performance of Ground-Mounted Photovoltaic Systems—A Timely Update

Abstract: Given photovoltaics’ (PVs) constant improvements in terms of material usage and energy efficiency, this paper provides a timely update on their life-cycle energy and environmental performance. Single-crystalline Si (sc-Si), multi-crystalline Si (mc-Si), cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS) systems are analysed, considering the actual country of production and adapting the input electricity mix accordingly. Energy pay-back time (EPBT) results for fixed-tilt ground mounted installations range from 0.5 years for CdTe PV at high-irradiation (2300 kWh/(m2·yr)) to 2.8 years for sc-Si PV at low-irradiation (1000 kWh/(m2·yr)), with corresponding quality-adjusted energy return on investment (EROIPE-eq) values ranging from over 60 to ~10. Global warming potential (GWP) per kWhel averages out at ~30 g(CO2-eq), with lower values (down to ~10 g) for CdTe PV at high irradiation, and up to ~80 g for Chinese sc-Si PV at low irradiation. In general, results point to CdTe PV as the best performing technology from an environmental life-cycle perspective, also showing a remarkable improvement for current production modules in comparison with previous generations. Finally, we determined that one-axis tracking installations can improve the environmental profile of PV systems by approximately 10% for most impact metrics.

Quantitative Model and Metrics of Electrical Grids’ Resilience Evaluated at a Power Distribution Level

Abstract: This paper presents a framework to systematically measure and assess power grids’ resilience with a focus on performance as perceived by customers at the power distribution level. The proposed framework considers an analogous measure of availability as a basic metric for resilience and defines other key resilience-related concepts and metrics, such as resistance and brittleness. This framework also provides a measurement for the degree of functional dependency of loads on power grids and demonstrates how the concepts of resilience and dependency are inherently related. It also discusses the implications of considering human-centered processes as fundamental constituting components of infrastructure systems. Thanks to its quantitative nature, the proposed resilience framework enables the creation of tools to evaluate power grids’ performance as a lifeline and to assess the effects of plans for optimal electrical power infrastructure deployment and operation. The discussion is supported by practical examples and empirical records from field damage assessments conducted after recent notable natural disasters.

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

Abstract: Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naive autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigacion en Energia SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity.

Optimal Siting of Charging Stations for Electric Vehicles Based on Fuzzy Delphi and Hybrid Multi-Criteria Decision Making Approaches from an Extended Sustainability Perspective

Abstract: Optimal siting of electric vehicle charging stations (EVCSs) is crucial to the sustainable development of electric vehicle systems. Considering the defects of previous heuristic optimization models in tackling subjective factors, this paper employs a multi-criteria decision-making (MCDM) framework to address the issue of EVCS siting. The initial criteria for optimal EVCS siting are selected from extended sustainability theory, and the vital sub-criteria are further determined by using a fuzzy Delphi method (FDM), which consists of four pillars: economy, society, environment and technology perspectives. To tolerate vagueness and ambiguity of subjective factors and human judgment, a fuzzy Grey relation analysis (GRA)-VIKOR method is employed to determine the optimal EVCS site, which also improves the conventional aggregating function of fuzzy Vlsekriterijumska Optimizacijia I Kompromisno Resenje (VIKOR). Moreover, to integrate the subjective opinions as well as objective information, experts’ ratings and Shannon entropy method are employed to determine combination weights. Then, the applicability of proposed framework is demonstrated by an empirical study of five EVCS site alternatives in Tianjin. The results show that A3 is selected as the optimal site for EVCS, and sub-criteria affiliated with environment obtain much more attentions than that of other sub-criteria. Moreover, sensitivity analysis indicates the selection results remains stable no matter how sub-criteria weights are changed, which verifies the robustness and effectiveness of proposed model and evaluation results. This study provides a comprehensive and effective method for optimal siting of EVCS and also innovates the weights determination and distance calculation for conventional fuzzy VIKOR.

Chemical Analysis of Different Parts of Date Palm (Phoenix dactylifera L.) Using Ultimate, Proximate and Thermo-Gravimetric Techniques for Energy Production

Abstract: The objective of the study was to analyze chemical structure of date palm (Phoenix dactylifera L) by employing ultimate, proximate and thermo-gravimetric techniques Samples from different anatomical parts of date palm, namely trunk, frond base, frond midrib, leaflets, coir, fruit stem, date stone, and fruit empty bunches were considered for the experiments Based on the findings in this work palm leaflet samples gave the highest amount of extractives content (329%), followed by date palm stone specimens with 315% Cellulose content values of 328% and 475% were obtained for date palm stone and palm coir samples, respectively Overall the hemicellulose contents of all samples were relatively similar to those of typical wood or non-wood lignocellulosic materials with the two exceptions of palm coir and palm leaflets Both palm coir and palm leaflet specimens had 126% and 161% hemicellulose content Volatile matter values of 743% and 875% were determined for leaflets and fruit empty bunch samples The ash content of the samples ranged from 14% for date stone to 152% for palm leaflet samples The thermal decomposition was completed below a temperature of 500 °C with an exception of those samples taken from palm leaflets Taken together the data indicate that date palm stone and palm coir revealed could be more viable for renewable energy production than the other specimens considered in this work