A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell

In recent years, accurate identification of voltage versus current (V-I) characteristics of proton exchange membrane fuel cell (PEMFC) has attracted significant attention in the literature. However, the main drawback in accurate modeling is the lack of information about the precise values of the model parameters. In this paper, in order to overcome this drawback a grouping-based global harmony search algorithm, named GGHS, is proposed for parameter identification issue. The proposed algorithm attempts to provide an efficient way in which a new harmony can be properly improvised. In order to study the capability of the proposed algorithm, the results obtained by GGHS are compared with those obtained by two versions of harmony search (HS) algorithms, three versions of particle swarm optimization (PSO) algorithms, as well as seeker optimization algorithm (SOA). Simulation results accentuate the superiority of the GGHS over the other methods.     

Transport energy demand modeling of South Korea using artificial neural network

Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025.     

A superstructure model of an isolated power supply system using renewable energy: Development and application to Jeju Island,Korea

In this study, we aim to develop a superstructure-based optimization model using mixed integer linear programming (MILP) to determine the optimal combination and sizing for a hybrid renewable energy system to be used in an isolated area. The developed model has a three-layered energy structure to reflect the current reality in which energy production and consumption sites are generally separate. A variety of economic factors, including distance between facilities and an installation area, are considered for a more accurate estimation of the total annualized cost. Two types of optimization models, i.e., with and without a battery, are proposed to evaluate the economic and technical effects of a storage device to resolve operation issues caused by intermittent resources. An application case study on Jeju Island, Korea, confirms that the proposed model is suitable for decision making at the planning stage of a renewable energy system.     

Cuckoo search algorithm with explosion operator for modeling proton exchange membrane fuel cells

Developing an accurate model is important to design and simulation of the fuel cell systems. In this work, we propose a cuckoo search algorithm with explosion operator (CS-EO) to estimate the model parameters of the proton exchange membrane fuel cells (PEMFCs). In CS-EO, the adaptive strategy of step size is adopted to enhance search ability. And the explosion operator is employed to avoid being trapped into local optima. Numerical experiments conduct on some benchmark functions indicate that CS-EO has better performance both in convergence and accuracy. The CS-EO is also applied to estimate the PEMFC model parameters and the satisfactory results reveal its effectiveness.     

Sustainable energy systems: Role of optimization modeling techniques in power generation and supply—A review

Electricity is conceivably the most multipurpose energy carrier in modern global economy, and therefore primarily linked to human and economic development. Energy sector reform is critical to sustainable energy development and includes reviewing and reforming subsidies, establishing credible regulatory frameworks, developing policy environments through regulatory interventions, and creating market-based approaches. Energy security has recently become an important policy driver and privatization of the electricity sector has secured energy supply and provided cheaper energy services in some countries in the short term, but has led to contrary effects elsewhere due to increasing competition, resulting in deferred investments in plant and infrastructure due to longer-term uncertainties. On the other hand global dependence on fossil fuels has led to the release of over 1100 GtCO₂ into the atmosphere since the mid-19th century. Currently, energy-related GHG emissions, mainly from fossil fuel combustion for heat supply, electricity generation and transport, account for around 70% of total emissions including carbon dioxide, methane and some traces of nitrous oxide. This multitude of aspects play a role in societal debate in comparing electricity generating and supply options, such as cost, GHG emissions, radiological and toxicological exposure, occupational health and safety, employment, domestic energy security, and social impressions. Energy systems engineering provides a methodological scientific framework to arrive at realistic integrated solutions to complex energy problems, by adopting a holistic, systems-based approach, especially at decision making and planning stage. Modeling and optimization found widespread applications in the study of physical and chemical systems, production planning and scheduling systems, location and transportation problems, resource allocation in financial systems, and engineering design. This article reviews the literature on power and supply sector developments and analyzes the role of modeling and optimization in this sector as well as the future prospective of optimization modeling as a tool for sustainable energy systems.     

Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm

This paper addresses an improved optimization method to enhance the energy extraction capability of fuel cell implementations. In this study, the proposed method called Dynamic Cuckoo Search Algorithm (DCSA) is tested in a stand-alone fuel cell in order to control the system power under dynamic temperature response. In the operational process, a fuel cell is connected to a load through a dc-dc boost converter, and DCSA is utilized to adjust the switching duration in dc-dc converter by using voltage, current and temperature parameters. In this way, it controls the output voltage to maximize power delivery capability at the demand-side and eliminates the drawback of conventional cuckoo search algorithm (CSA) which cannot change duty cycle under operating temperature variations. In this regard, DCSA shows a significant improvement in terms of system response and achieves a more efficient power extraction than the conventional CSA method. In order to demonstrate the system performance, the stand-alone fuel cell system is constructed in Simulink environment via a processor-in the-loop (PIL) based digital implementation and analyzed by using different optimization methods. In the analysis section, the results of the proposed method are compared with conventional methods (perturb&observe mppt, incremental conductance mppt, and particle swarm optimization). In this context, convergence speed and efficiency analysis for both methods verify that the DCSA gives original results compared to conventional methods.     

Comparison of neural network,conditional demand analysis,and engineering approaches for modeling end-use energy consumption in the residential sector

This paper investigates the use of conditional demand analysis (CDA) method to model the residential end-use energy consumption at the national level. There are several studies where CDA was used to model energy consumption at the regional level; however the CDA method had not been used to model residential energy consumption at the national level. The prediction performance and the ability to characterize the residential end-use energy consumption of the CDA model are compared with those of a neural network (NN) and an engineering based model developed earlier. The comparison of the predictions of the models indicates that CDA is capable of accurately predicting the energy consumption in the residential sector as well as the other two models. The effects of socio-economic factors are estimated using the NN and the CDA models, where possible. Due to the limited number of variables the CDA model can accommodate, its capability to evaluate these effects is found to be lower than the NN model.     

Design,modeling, and analysis of a high performance piezoelectric energy harvester for intelligent tires

Basic parameters affecting vehicle safety and performance such as pressure, temperature, friction coefficient, and contact‐patch dimensions are measured in intelligent tires via sensors that require electric power for operation and wireless communication to be synchronized to the vehicle monitoring and control system. Piezoelectric energy harvesters (PEHs) can extract a fraction of energy that is wasted as a result of deflection during rolling of tires, and this extracted energy can be used to power up sensors embedded in intelligent tires. A new design of PEH inspired from Cymbal PEHs is introduced, and its performance is evaluated in this paper. Cymbal PEHs are proven to be useful in vibration energy harvesting, and in this paper, for the first time, the modified shape of Cymbal energy harvester is used as strain‐based energy harvester for the tire application. The shape of the harvester is adjusted in a way that it can be safely embedded on the inner surface of tires. In addition to the high performance, ease of manufacturing is another advantage of this new design. A multiphysics model is developed and validated to determine the output voltage, power, and energy of the designed PEH. The modeling results indicated that the maximum output voltage, the maximum electric power, and the accumulated harvested energy are about 3.5 V, 2.8 mW, and 24 mJ/rev, respectively, which are sufficient to power two sensors. In addition, the possibility is shown to supply power to five sensors by increase in piezoelectric material thickness. The effect of rolling tire temperature on the performance of the proposed PEH is also studied.     

Scheduling coupled photovoltaic,battery and conventional energy sources to maximize profit using linear programming

To address the increase of electricity demand, the need for reducing carbon dioxide, and the reduction of available fossil fuel resources, renewable energy sources are being recruited. Specifically energy generated by photovoltaic (PV) cells is becoming one of the most promising alternatives. In this context, this paper presents an optimization model for the scheduling problem where conventional and photovoltaic sources of energy are scheduled to be delivered to satisfy energy demand. The optimization model is formulated as a Linear Program (LP) with a bounded number of variables and constraints. The respective solution can be obtained in polynomial time and provides the optimal combination or schedule of energy generated from different sources (conventional, renewable and battery storage) such that the total demand is satisfied and the profit is maximized. Numerical results demonstrate the effectiveness and the generality of the scheme.     

Fast market penetration of energy technologies in retrospect with application to clean energy futures

The fast penetration of energy technologies in the past was analyzed and applied to investigate the prospects of new energy technologies. The results show that single energy sources have obtained quite a dominant position in the past. In the USA, at one time both oil and coal each represented over half of all the yearly additions to energy capacity for more than half a century and reached a dominant position in overall energy production. Oil showed a similar dominance on a global scale. For two decades nuclear power represented one third of all the new electricity added worldwide and over 60% in the countries possessing nuclear power. In some countries nuclear grew to around half of all electricity in less than just 10 years. Applying these empirical observations to new renewables and assuming similar growth conditions as for the old technologies, the share of renewable electricity could grow from its present 19% to 60% by 2050, which would drop the baseline CO₂ emissions by 27%. The share of new renewables of all electricity would come up to 42%. The rate of adoption of these new technologies would not exceed that of oil or nuclear in the past, but they would need to dominate new electricity investments from 2030 onwards. A hypothetical fast-track case for solar photovoltaics, assuming an expansion similar to that seen in the case of nuclear and oil, would lead to a 20–25% share of all electricity in 2050. An important observation is that the fast and high penetration of energy technologies implies, in most cases, a full lock-in into these, requiring a preferential position regarding investments and a favorable long-term policy framework.     

Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

A novel and modern framework for energy modeling is developed in this paper with a focus on nuclear energy modeling and simulation. The framework combines multiphysics simulations and real data, with validation by uncertainty quantification tasks and facilitation by machine and deep learning methods. The hybrid framework is built on the basis of a wide range of physical models, real data, mathematical and statistical methods, and artificial intelligence techniques. The framework is demonstrated in different applications, including quantifying uncertainties in computer simulations, multiphysics coupling, analysis of variance using machine learning surrogate models, deep learning of time series phenomena, and propagating parametric uncertainties of nuclear data. The applications demonstrated are oriented to nuclear engineering simulations, even though majority of the methods are applicable to other energy sources (eg, renewable). Efficient utilization of this framework is expected to yield a much better understanding of the physical phenomena analyzed as well as an improvement in the performance of the energy design/model under construction.     

Transition to renewable energy systems with hydrogen as an energy carrier

Unlike the present energy system based on fossil fuels, an energy system based on renewable energy sources with hydrogen and electricity as energy carriers would be sustainable. However, the renewable energy sources in general have less emergy than the fossil fuels, and their carriers have lower net emergy. Because of that they would not be able to support continuous economic growth, and would eventually result in some kind of a steady-state economy. An early transition to renewable energy sources may prove to be beneficial in the long term, i.e., it may result in a steady state at a higher level than in the case of a transition that starts later. Once the economy starts declining it will not be able to afford transition to a more expensive energy system, and transition would only accelerate the decline. Similarly, if a transition is too fast it may weaken and drain economy too much and may result in a lower steady state. If a transition is too slow, global economy may be weakened by the problems related to utilization of fossil fuels (such as global warming and its consequences) before transition is completed and the result again would be a lower steady state. Therefore, there must be an optimal transition rate; however, its determination would require very complex models and constant monitoring and adjustment of parameters.     

Heterogeneous mixture distributions for modeling wind speed,application to the UAE

Heterogeneous mixture distributions (HTM) have not been employed for wind speed modeling of the Arabian Peninsula. In order to improve our understanding of wind energy potential in the Arabian Peninsula, HTM should be tested for the frequency analysis of wind speed. The aim of the current study is to assess the suitability of HTMs and identify the most appropriate probability distribution to model wind speed data in the UAE. Hourly mean wind speed data were used in the current study. Ten homogeneous and heterogeneous mixture distributions were used and constructed by mixing the four following probability distributions: Gamma, Weibull, Extreme value type-one, and Normal distributions. The Weibull and Kappa distributions were also employed as representatives of the conventional non-mixture distributions. Maximum Likelihood, Expectation Maximization algorithm, and Least Squares methods were employed to fit the mixture distributions. Results indicate that mixture distributions give the best fit to wind speed data for all stations. Wind speed data of five stations show strong mixture distributional characteristics. Applications of HTMs show a significant improvement in explaining the whole wind speed regime. The Weibull-Extreme value type-one mixture distribution is considered the most appropriate distribution for wind speed data in the UAE.     

A new optimization approach to energy network modeling: anthropogenic CO2 capture coupled with enhanced oil recovery

To meet next generation energy needs such as wind‐ and solar‐generated electricity, enhanced oil recovery (EOR), CO₂ capture and storage (CCS), and biofuels, the US will have to construct tens to hundreds of thousands of kilometers of new transmission lines and pipelines. Energy network models are central to optimizing these energy resources, including how best to produce, transport, and deliver energy‐related products such as oil, natural gas, electricity, and CO₂. Consequently, understanding how to model new transmission lines and pipelines is central to this process. However, current energy models use simplifying assumptions for deploying pipelines and transmission lines, leading to the design of more costly and inefficient energy networks. In this paper, we introduce a two‐stage optimization approach for modeling CCS infrastructure. We show how CO₂ pipelines with discrete capacities can be ‘linearized’ without loss of information and accuracy, therefore allowing necessarily complex energy models to be solved. We demonstrate the new approach by designing a CCS network that collects large volumes of anthropogenic CO₂ (up to 45 million tonnes of CO₂ per year) from ethylene production facilities and delivers the CO₂ to depleted oil fields to stimulate recovery through EOR. Utilization of anthropogenic CO₂ has great potential to jumpstart commercial‐scale CCS while simultaneously reducing the carbon footprint of domestic oil production. Model outputs illustrate the engineering challenge and spatial extent of CCS infrastructure, as well as the costs (or profits) of deploying CCS technology. We show that the new linearized approach is able to offer insights that other network approaches cannot reveal and how the approach can change how we develop future energy systems including transporting massive volumes of shale gas and biofuels as well as electricity transmission for wind and solar energy. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

An artificial intelligence approach to optimization of an off-grid hybrid wind/hydrogen system

Off-grid electrification of remote areas using a hybrid renewable energy scheme is a requirement to achieve the goals of sustainable development. However, the optimization and sizing for the capacity of such systems are challenging. In this regard, this study targets an improved optimization algorithm with high efficiency for optimization and long-term capacity planning of an off-grid hybrid renewable energy scheme composed of wind, fuel cell, and hydrogen storage schemes. The suggested methods are three improved versions of the global dynamic harmony search to do pitch adjustment mechanism. The objective function of this study is to reduce the total net annual cost of the system and the loss of power supply probability to a minimum. The performance of this hybrid system is examined via a simulation study, which had been performed on a remote area located in eastern Iran over a long period. The results of the three improved proposed algorithms are compared with the original global dynamic harmony search algorithm. Also, sensitivity analysis is proposed to showcase the influence of uncertainties on the system and input parameters on the algorithm. The simulation results indicate that three improved versions of the global dynamic harmony search algorithm ?nd more promising results than the original algorithm, and confirm the superior accuracy, convergence speed, and robustness of the global dynamic harmony search-II. Also, reliability level and iteration values have a considerable impact on the total net annual cost of the optimal hybrid energy system based on wind and hydrogen energy.     

Optimization of wind turbine energy and power factor with an evolutionary computation algorithm

An evolutionary computation approach for optimization of power factor and power output of wind turbines is discussed. Data-mining algorithms capture the relationships among the power output, power factor, and controllable and non-controllable variables of a 1.5 MW wind turbine. An evolutionary strategy algorithm solves the data-derived optimization model and determines optimal control settings. Computational experience has demonstrated opportunities to improve the power factor and the power output by optimizing set points of blade pitch angle and generator torque. It is shown that the pitch angle and the generator torque can be controlled to maximize the energy capture from the wind and enhance the quality of the power produced by the wind turbine with a DFIG generator. These improvements are in the presence of reactive power remedies used in modern wind turbines. The concepts proposed in this paper are illustrated with the data collected at an industrial wind farm.     

Valuing carbon assets for high-tech with application to the wind energy industry

In contrast to the traditional methods for high-tech evaluation, we introduce a new, more active idea for considering the carbon asset effect, in addition to the economic and technological considerations for strategic significance. The method proposed in this paper considers a reduced amount of carbon emissions, less than that of the current industry baseline, to be an asset that is beneficial to a firm that adopts a new technology. The measured carbon asset values vary across different technologies, in different industries and over time. The new method is applied to the valuing of wind energy technology and uses the Weibull distribution to estimate the wind energy capacity and a concrete sensitivity analysis. These applications support the validity of the new method and show that the impact of the fluctuations of carbon sinks on the values of carbon assets is significantly greater than that of volatility in the production output. The paper also presents some policy recommendations based on the results.     

Equilibrium modeling of gasification: Gibbs free energy minimization approach and its application to spouted bed and spout-fluid bed gasifiers

Spouted beds have been found in many applications, one of which is gasification. In this paper, the gasification processes of conventional and modified spouted bed gasifiers were considered. The conventional spouted bed is a central jet spouted bed, while the modified spouted beds are circular split spouted bed and spout-fluid bed. The Gibbs free energy minimization method was used to predict the composition of the producer gas. The major six components, CO, CO₂, CH₄, H₂O, H₂ and N₂, were determined in the mixture of the producer gas. The results showed that the carbon conversion in the gasification process plays an important role in the model. A modified model was developed by considering the carbon conversion in the constraint equations and in the energy balance calculation. The results from the modified model showed improvements. The higher heating values (HHV) were also calculated and compared with the ones from experiments. The agreements of the calculated and experimental values of HHV, especially in the case of the circular split spouted bed and the spout-fluid bed were observed.     

突出节能同时化管理,实现企业本质节能化

工业企业节能是全社会节能工作的重要组成部分。作为企业化管理单位的航天材料及工艺研究所在长期的节能工作实践中,形成了一套行之有效的节能管理模式,提出了节能“同时”化管理理念。