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Summary: In the pulp and paper industry, black liquor, which is a biomass resource, is burned to produce electricity. Black liquor is concentrated to 21 wt% water through an evaporator before being burned in a boiler. For the evaporator, a multiple‐effect evaporator (MEE) is mainly used, but it requires a large amount of energy and cost. Therefore, it is crucial to reduce energy and cost of evaporation process. Hence, this study suggested a novel process model that integrated mechanical vapor recompression (MVR) with MEE to increase the energy efficiency. The suggested MVR‐assisted evaporation process was composed of preheating and evaporation processes to effectively concentrate black liquor. In addition, it reduced the steam consumption of MEE by using MVR, which uses relatively inexpensive electric energy in the pre‐evaporation process. In the simulation results, the steam, electricity consumption, and the latent heat recovered from the secondary vapor of the suggested process were quantitatively analyzed to verify the energy efficiency. The results indicate that the proposed process increases substantial energy efficiency compared to the conventional process. Then, the appropriateness of the suggested process was evaluated by the techno‐economic analysis. The total annualized cost (TAC) is determined for both current and potential future economic benefits. TAC of the MVR‐assisted MEE configuration can be reduced by up to 77.54%. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

The need for sustainable integrated energy systems to mitigate environmental impact is hindered by challenges in fluctuating demand, trading reliability, and trustworthiness. This paper proposes an innovative approach to tackle these challenges by introducing a blockchain-based integrated energy system trading model with smart contracts. It is intricately linked with the operation of carbon capture and storage (CCS) technology and power-to-gas (P2G) equipment. The CCS-P2G-coupled operation principle is first outlined, followed by the presentation of a comprehensive system model. The peer-to-peer (P2P) energy trading algorithm is enhanced using the Bloom filtering technique. Leveraging smart contracts, a distributed energy trading mechanism is employed, resulting in a meticulously constructed integrated energy system trading model for CCS-P2G-coupled operation. The proposed model is rigorously evaluated for energy trading efficiency and system performance, revealing significant improvements compared to prior studies and showcasing substantial cumulative benefits. Three operation scenarios are examined, with experimental results highlighting the model's superior carbon emission reduction capacity. This study introduces an innovative paradigm for trading and managing integrated energy systems, holding potential implications for the sustainable development and decarbonization transition of future energy systems. [ABSTRACT FROM AUTHOR]

Summary: The intermittent, random, and uncertain characteristics of the energy harvested from solar or wind are threatening the commercial efficiency and reliability of power system. With the continuous expansion of renewable energy installation scale, it has become a new challenge to improve the level of renewable energy accommodation and reduce the rate of renewable energy abandonment. Power system production simulation provides basis for the planning and development of renewable energy to assess the capacity of renewable energy accommodation in the power grid accurately. Traditional power system production simulation ignores sequential variation characteristics of load and new energy power output, thus bringing non‐negligible errors. Moreover, most reported studies ignore the impact of power source location on the level of renewable energy accommodation. This paper is therefore prepared with the objective to establish a time‐series production simulation model of multi‐energy power system and carry out multipoint layout planning and design of multi‐energy power supply. To achieve this objective, extensive literature reviews are conducted with more attention given to recent studies on the field. On the basis of the traditional production simulation model, this paper adds the power flow constraints of the transmission network, and proposes a time‐series production simulation model. A multipoint layout planning and design method for multi‐energy power systems is proposed to give full play to the synergistic effects of natural resources, to achieve optimal resource allocation, and to provide a theoretical basis for improving the consumption of wind and renewable energy. The method is simulated with the actual meteorological and operation data of a province power grid in Northwest China. The simulation results of different scenarios are compared and analyzed, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

Summary: The internet of things (IoT) is a distributed heterogeneous network of lightweight nodes with very minimal power and storage. The IoT energy system for smart applications such as smart grid, smart building, and smart transportations depends on the IoT architecture, determining the high or low‐energy consumption levels. Most of the IoT objects are power‐driven by batteries with short life spans that require replacement. The replacement phase is tedious; hence this paper comprehensively discussed the IoT energy system, energy resources, and energy storage as these three elements are crucial to enable energy efficiency for the IoT applications. In comparison to the battery‐powered solutions, the scavenging of infinite quantities of energy makes the IoT systems robust. Therefore, this paper further elaborates on understanding the current situation in terms of renewable energy harvesting for these low‐energy systems. The IoT energy storage highlighted in this paper includes fuel cell, lithium battery, and supercapacitor technology. This paper also provides the findings for IoT energy system challenges and open issues in management and storage in terms of bidirectional, continuity, autonomy, fluctuation, conversion, consumption, integration, multifunction, and stability. This paper will assist researchers in more quality yet practical energy usage and savings, better IoT system architecture, and smart application sustainability. [ABSTRACT FROM AUTHOR]

Stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations. [ABSTRACT FROM AUTHOR]

As the integration of distributed energy into the power grid continues to rise, the significance of electricity transactions in promoting renewable energy consumption grows substantially. This necessitates the establishment of a robust electricity trading framework to facilitate reliable and trustworthy transactions for prosumers. This paper introduces an innovative blockchain-based electricity trading framework. Within this framework, we present a decentralized collaborative model training approach aimed at predicting the power generation of distributed photovoltaic power stations. Simultaneously, we propose a graph-based algorithm for efficiently matching producers and consumers. Our experiments, conducted on the Hyperledger Fabric blockchain platform using real-world datasets, demonstrate enhanced prediction accuracy compared to existing models. The framework effectively handles transactions within one second, sustaining a sending rate of up to 200 transactions per second. The outcomes of this study not only surpass the performance of existing prediction models but also provide valuable insights for the development of trading frameworks in actual production environments. The proposed framework stands as a reference point for constructing reliable trading infrastructures, thereby contributing to the acceleration of green electricity trading. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

The paper investigates the interplay between clean energy, environmental pollution reduction, and economic growth in the Gulf Cooperation Council (GCC) countries from 1980 to 2019, utilizing the autoregressive distributed lag (ARDL) method. The study underscores the global transition towards cleaner energy sources and its implications for the region. In assessing the determinants of economic growth, the findings reveal a positive and significant short- and long-term impact of energy production, with energy consumption exhibiting a positive and significant effect solely in the long term. The absence of a confirmed relationship in the short term is notable. Causality tests demonstrate a significant link from both energy consumption types to environmental pollution, alongside causal relationships from urbanization, energy production, and consumption to economic growth. Moreover, the results include the pivotal role of energy production in driving GDP growth, caution in short-term energy consumption effects, and significant causal links between energy consumption and environmental pollution. The recommendations to incentivize sustainable energy production, engage in long-term energy consumption planning, and adopt integrated urbanization policies provide actionable insights for policymakers. These suggestions are aimed at guiding the GCC countries in balancing the pursuit of economic growth with environmental sustainability—a delicate equilibrium that necessitates careful consideration of various factors. [ABSTRACT FROM AUTHOR]

The academic and policy-oriented literature increasingly quantifies the wider effects of energy policy on the macroeconomy. However, the spillovers from economic policies to energy use are less frequently recognised, meaning that many policymakers who strive to improve productivity whilst simultaneously targeting emission reduction fail to consider these interactions. This paper addresses this issue by using simulation results generated by introducing an exogenous increase in labour productivity in a computable general equilibrium model calibrated with data from the United Kingdom. Theoretical analysis suggests that increasing labour productivity can have positive or negative effects on employment levels and energy use. However, the simulation results show that in the context of a developed open economy, improved labour efficiency will increase employment, a key policy objective, but simultaneously increase energy use. A key policy implication is that this work highlights the need for policy frameworks that explicitly acknowledge and quantify the interconnections between national economic policy strategies and energy policy objectives. Secondly, it shows that in practice, policies to reduce carbon emissions need to be strengthened alongside policies to improve productivity successfully implemented. [ABSTRACT FROM AUTHOR]

Summary: The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak‐to‐average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions. [ABSTRACT FROM AUTHOR]

Summary: Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort toward the provision of uninterrupted power to the end‐users is strongly dependent on an effective and fault‐resilient Operation & Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust prognostic maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Artificial Intelligence (AI) algorithms are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges, including data‐related issues, such as the availability of quality data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML‐based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies the key future research directions to further enhance the prognostics maintenance procedures. [ABSTRACT FROM AUTHOR]

Energy consumption from biofuels against fossil fuels over the past few years has increased. This is due to the availability of these resources for production of different forms of energy, and the environmental benefit in the utilization of these resources. Ethanol fuel production from biomass is a complex process of known challenges in the area of handling, optimizing, and future forecasting. The existence of modelling techniques like artificial intelligence (AI) is, therefore, necessary in the design, handling, and optimization of bioethanol production. The flexibility and high accuracy of artificial neural network (ANN), a machine learning technique, to solve intricate processes is beneficial in modelling pretreatment, fermentation, and conversion stages of a bioethanol production system. This paper reviews various AI techniques in bioethanol production giving emphasis on published articles in the past decade. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS. [ABSTRACT FROM AUTHOR]

Summary: Computationally intelligent energy forecasting methods for appropriate energy management at the consumer/producer side have a positive impact on the preservation of energy and play a constructive role in tackling global climate change. The energy production and consumption are very high worldwide, demanding intelligent methods with real‐world implementation potentials for appropriate energy management. In this paper, we survey the existing intelligent load forecasting (ILF) systems, highlight their advantages and downsides, and briefly discuss the workflow of the employed literature. Furthermore, we debate on the existing load forecasting datasets and their features along with a brief overview of the challenges confronted by researchers using these datasets. Distinct from previous survey papers, we provide a detailed review of performance evaluation metrics and comparison of employed methods for energy load forecasting, thereby concluding the need of efficient, effective, and adoptable ILF methods functional in real‐world scenarios. Finally, we assess the employed techniques and deliver future research opportunities based on the derived conclusions from existing research works. This paper delivers the overall energy forecasting literature in a compact form with possible future insights for researchers working in ILF domain. [ABSTRACT FROM AUTHOR]

Summary: During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs. [ABSTRACT FROM AUTHOR]

Energy decision-makers have considered solutions to meet the demand for affordable and highly reliable energy due to population growth and technological advancement. One of these possibilities is the utilization of several energy carriers in one system as an energy hub. Instead of optimizing a single energy carrier, the energy hub optimizes a system with many energy carriers, including electricity, natural gas, and local heat, by use of its converters and storages. In this paper, a novel and new general framework is proposed to evaluate and compare both series and parallel connections of n hub to analyze cost and reliability aspects by considering coupling matrices. In order to compare the outcomes of the connections of several comparable hubs in both series and parallel modes, new indices are proposed and evaluated from the aspect of evaluating the amount of energy not supplied and the amount of energy input to each hub. Additionally, simulations are run for a variety of scenarios in order to better assess the proposed model and investigate each type of connection by evaluating the proposed performance indices. The results show that in all the examined scenarios, the total cost of the energy carriers in the series mode (link in the output) is lower than in the parallel mode (link in the input). [ABSTRACT FROM AUTHOR]

The battery pack performance and expected lifespan are crucial in electric vehicle applications. Balancing the charge on a battery pack connected in series and parallel is crucial due to manufacturing discrepancies and distinct performance of each cell in a standard battery pack. In this paper, a switched-resistor passive balancing-based method is proposed for balancing cells in a battery management system (BMS). The value of the available voltage at the battery cell terminals is balanced using resistors in an electrical circuit, and the excess voltage is eliminated. The cell balancing outcome demonstrates that the electrical circuit can maintain an even voltage across each cell. The procedure of balancing involves individually adjusting each cell's level of charge. Passive balancing releases energy as heat by draining charge from cells that have too much charge. A passive cell balancer is a cost-effective solution and easy to install, but due to thermal loss from a resistor, it has a low energy efficiency for cell balancing and necessitates a lengthy balancing process. This passive cell balancer is an effective and reliable method for low-power devices and portable applications such as electrical vehicles. The power limits during charging and discharging are estimated using the bisection method. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a prediction‐based optimization strategy (POS) for the Energy Management System to balance the use of diesel generator (DG) and emergency battery (EB) in the microgrid. The POS is developed by combing two operating strategies, the "predictive analysis" and "optimal operation" in each scheduling period for the microgrid. Based on the predicted microgrid state and energy demand, a multi‐objective mixed‐integer nonlinear programming model (MOMINP) is constructed to minimize the fuel consumption and the regularization of battery charge/discharge subject to the practical constraints in the microgrid. This paper proposes a detailed scheme to deal with the multiple objectives and nonlinear constraints in the MOMINP, then the MOMINP is successfully converted into a mixed‐integer linear programming model (MILP). And an adjustment strategy is designed to obtain the near‐optimal solution of the MOMINP based on the optimal solution of the MILP solved by using the CPLEX Optimizer. Experimental results show that in a basic scheduling period, the working time of DG in the POS‐softmax regression strategy is shorter than the current operation, and the fuel consumption reduction ratio is about 15.3% with the same battery SoC value at the end of the scheduling. At the same time, the fuel consumption in the POS‐accurate prediction strategy can be reduced by up to 54.9% compared with the POS‐softmax regression strategy and can be reduced by 61.8% compared to the current operation. Based on the comparative analysis of the actual case data of a micro‐grid in 6 months, it can be seen that on average the POS works better than the current operation, with an approximately 23.6% decrease in the objective function and an additional 16.2% decrease with an accurate prediction. [ABSTRACT FROM AUTHOR]

Summary: A cavity structure effectively decreases the heat loss of a receiver. This paper applies the widely used collector analysis method to derive the total heat loss coefficient and efficiency factor for a cavity receiver. Based on the derived factors, this paper investigates the effect of different parameters, such as absorber surface temperature and emissivity, on thermal performance. The absorber surface emissivity significantly affects the total heat loss coefficient at high temperatures. The effect of the absorber surface temperature on the total heat loss coefficient is significant, while the effect of the ambient temperature on the total heat loss coefficient can be ignored. The present results can help to determine the testing conditions for the cavity receiver by using molten salt as the heat transfer fluid. [ABSTRACT FROM AUTHOR]

Summary Retrofitting depleted oil wells to extract geothermal energy is considered as one of the promising proposals to extend the overall economic life of oil and gas well. For successful implementation of this initiative, a comprehensive overview covering all aspects of geothermal energy extraction from abandoned oil well should be taken into account including technical, economic considerations as well as regulations and policies of respective local governments. Unfortunately, most reported studies have been focused only on one or two aspects, primarily on technical and economic aspects. Little or no study has focused on the policy sector. Moreover, these findings have been scattered, creating difficulties to extract essential information and dragging further development of the technology. This paper is therefore prepared with the objective to provide a comprehensive overview on the geothermal energy extraction from abandoned oil well, technical challenges in its implementation, economical consideration on the conversion of the well and government policy on energy especially geothermal energy and regulation on the utilization of abandoned oil well. To achieve this objective, extensive literature reviews are conducted with more attention given to recent studies on the field. Challenges on the development of this technology are discussed from technical, economic, and policy perspectives. Based on the identified challenges, required research and development as well as necessary policies for further advancement of this technology are outlined and discussed. By providing this comprehensive information, this review paper may serve as a good foundation and guidelines on the conversion of abandoned oil wells into geothermal energy wells. [ABSTRACT FROM AUTHOR]

Summary: At the present day, the issue of the further development of energy economics, industry, and other areas are quite urgent, taking into account the environmental component and not excluding the economic component of this problem. It is possible to reduce the negative impact on the environment by switching to biofuel. In addition to the environmental benefits, biofuels are beneficial in use, also to the companies themselves, as it is more cost‐efficient. The production and use of energy by the companies involve distributed generation. The purpose of the paper is to analyse the economic prospects of replacing distributed generation with biofuel. Results: within the framework of the study, the concepts of distributed generation, biofuel, its main types, as well as some of the advantages of biofuel are defined. Some economic aspects are considered, mainly in the direction of biofuel‐related benefits. In addition, the paper considers the distributed generation market development and the use of biofuel in Russia in comparison with other countries, the main problems were identified, and the main prospects in this direction were considered. Prospects for further research: due to the subsequent study of biofuel usage, taking into account economic and environmental aspects. The applied value of the material: the paper is of practical value in the framework of generalising data on the distributed generation market and the bioenergy market in the Russian Federation and foreign countries. [ABSTRACT FROM AUTHOR]

Nowadays, consumption of different energy carriers is increasing due to the division of community activities in various sectors such as residential, commercial, and industrial. The energy hub concept is used to meet the demands of different energy carriers in these sections. In this paper, a new method for energy management of a microgrid is presented in an intelligent network based on three types of commercial, residential, and industrial energy hubs. In the energy hubs, a wide range of components, including renewable and nonrenewable generation units, converters, storage devices, intelligent parking lots consisting of electric vehicles, P2G units, and cogeneration units, are used to supply electrical, heating, cooling, and natural gas energies. Some parameters like renewable generations, energy demands, and arriving and departure time of electric vehicles are considered to be uncertain, and a relevant method is applied to provide a near-realistic profile for them. In addition, a demand response paradigm has also been proposed for three types of electrical, heating, and cooling demands at the hub output side. Therefore, in this method, a mixed integer linear programming model is proposed based on benefit and reduction of emission caused by the activity of gas-burning units for short-term planning and obtaining an optimal solution for generation and sending loads in a distribution network containing energy hubs. To evaluate the performance of the proposed modeling and structure, the presented approach is applied on a modified 33-bus IEEE test network. According to the results of the energy management model, it is possible to significantly increase benefits and also obtain a smoother consumption pattern in consumption with financial incentives. [ABSTRACT FROM AUTHOR]

Summary: Shading is one of the cases that has effectivity on the household energy consumption. Shadings can be used either in horizontal or vertical form adjacent to the building's window in order to control the sunlight. In this paper, a multi‐objective optimization is conducted to simultaneously reduce the annual electricity consumption and augment the electricity production with the application of photovoltaic shadings installed over the building window. To initiate the multi‐objective optimization, four design variables based on the positioning and geometry of photovoltaic shading are selected and test samples are simulated through EnergyPlus software. The model is three‐floor building located in Tehran, Iran. Next, the Morris sensitivity analysis (MSA) is implemented to calculate the extent to which each design variable affects the objective functions. Afterwards, the relation between the design variables and objective functions is extracted through the regression modeling using GMDH type‐ANN and the equations extracted are then utilized as inputs for genetic algorithm to conduct a Pareto‐based optimization. The results demonstrate that the maximum sunlight reception is attributed to the PV shading in southern direction of the building and its optimal tilt angle is 19.6 considering an annual period. There are slight changes to the tilt angle while seasonal period is considered for the calculations. The results also show that the use of solar shadings can reduce electricity consumption by approximately 4 to 11, 1 to 4, and 10 to 22% for an annual period, winter and summer season, respectively. At the end, the numerical data for the city of Tehran is compared to the cities of Canberra and Macapa. [ABSTRACT FROM AUTHOR]

Summary: The need to increase the share of renewable energy supply in buildings has been a subject of concern for many authors. Renewable energies can offset building electrical and thermal energy loads, thus reducing constrains on national grid networks. The aim of this paper is to construct, model and optimize a hybrid renewable energy supply system under transient conditions. The supply system consists of solar‐PV panels and vertical axis wind turbines, while batteries are used for energy storage. TRNSYS software is used to model the energy demand of an office building that operates under the average standard building as specified in EnergyPlus software. In order to determine the optimal size of the energy supply system an Artificial Neural Network (ANN) combined with a Sobol algorithm are employed. Two objective functions are developed to: (a) minimize the cost of purchasing solar‐PV panels and wind turbines, and (b) minimize the electricity required from the national grid network. The simulation and optimization results show that the best condition is achieved with 461 panels and one vertical axis wind turbine. At this best condition, the amount of electricity required from the national grid network is 5.55 MWh/year, and this will cost 2.4 $/h. Moreover, 3% of electricity will be obtained from the grid, 31% from batteries and 66% are supplied directly through panels and turbines. [ABSTRACT FROM AUTHOR]

Summary: Phase change materials (PCMs) attached to building envelopes have been widely investigated, but there are rare studies to evaluate the influence of micro‐environments of PCMs. This paper, therefore, aims to study the action mechanism of PCMs under the micro‐environment systematically and provides the optimization of PCMs parameters, to realize a further reduction in building energy use. Simulation results indicate that a big difference in the heat transfer of envelopes appears in different orientations due to differences in solar radiation. Then, the optimal transition temperature Tp and amount of use of PCMs increase with the increase in solar radiation intensity Isr. Compared with other envelopes, the west‐facing wall and roof need higher Tp and amount of PCMs, under Isr from 400 to 1000 W/m2 the optimal Tp increases from 28 to 34°C with a difference high up to 6°C. The result also shows that the building energy‐saving of optimal PCMs parameters would rise by 16.6% compared to the fixed PCMs parameters. [ABSTRACT FROM AUTHOR]

Summary: Today, it has become imperative to design these systems at optimum size to make reliable and minimize the total costs of renewable energy systems. There are many studies about size optimization in the literature. However, few studies have approached the problem with a heuristic method at different levels of reliability and compared the results with commercial software. This paper aims to fill this gap by proposing an artificial bee colony (ABC) algorithm with different reliability ratios for renewable energy systems size optimization and comparing the results of the proposed method with the results of the HOMER Pro software. The current methodology is validated with a real case study on the load demand of a school building (latitude 41.37, longitude 27.3) in Turkey. The peak and average load per day for the school building are 7.4 kW and 51.8 kW, respectively. The proposed method has presented 14.71%, 14.36%, and 6.23%, respectively, more economical solutions than HOMER Pro software for hybrid photovoltaic (PV)/wind/battery, PV/battery, and wind/battery renewable energy system scenarios. In addition, the ABC algorithms with different reliability ratios (%0, %0.2, %1, and %3) are used to analyze the effect on the system cost of the loss of power supply probability. According to the results, the hybrid renewable energy system consisting of 230 PV panels, 6 wind turbines, and 34 batteries is the most economical option with a total annual cost of $42 717.58 compared to stand‐alone systems with a reliability %0 ratio. It has been seen that increasing the loss of power supply probability reduces system costs. Consequently, this study shows that using the ABC algorithm in size optimization of renewable energy systems yields accelerates the decision‐making process and more optimized solutions. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes the optimal design for fast electric vehicle charging stations for the capacity configuration of components and the power scheduling strategy. The proposed approach is the artificial transgender longicorn algorithm, which is a simple and powerful controller. The optimal capacity of the system components and the optimal power scheduling approach are determined. First, an electric vehicle charging simulation model is built considering the demand response, which dynamically modified the charging expectation based on the electricity price at the time of use. Second, based on the system design, a multi‐objective optimization model is proposed with minimal objectives for the electricity cost and polluting emissions. Finally, a scenario analysis is carried out and it is concluded that renewable energy supplies, connection to the utility grid and demand response may help improve the performance of the optimization objectives. By then, the performance of the proposed technique is implemented in the MATLAB platform and compared with several existing approaches. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicle virtual energy storage technology can effectively improve the utilization of renewable energy. Aiming at the impact of the uncertainty of electric vehicle on the power grid, an optimized dispatching method of hybrid energy storage systems based on multiobjective optimization in the scenario of tracking plan output is proposed in this paper. The predicted value of the photovoltaic power obtained by the particle swarm optimization (PSO)‐back propagation (BP) neural network is used to formulate the planned output of photovoltaic power generation, and the principle component analysis algorithm is used to extract the main features affecting photovoltaic power generation to further improve the prediction accuracy of photovoltaic output power. From the perspective of the service life of electric vehicles, a two‐stage optimal control method of hybrid energy storage systems based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to achieve energy distributions between electric vehicles and supercapacitors. Fully consider the benefits of electric vehicle users and the capacity of tracking plans, a multiobjective optimization model of hybrid energy storage systems to track planned output is established, and the nondominated sorted genetic algorithm‐III is adopted to solve the model. The validity of the model is verified by a simulation test of actual operating data of a business park in China. The simulation results show that after the optimized control, the average absolute error of the deviation power reduces from 1.092 to 0.0528 MW, power fluctuating times of electric vehicles decreases from 151 to 80, and the daily income benefit increases from $404.468 to $483.116 in the cloudy day. The method proposed in this paper can effectively improve the controllability of renewable energy, and provide a theoretical basis for the application of electric vehicle virtual energy storage technology. [ABSTRACT FROM AUTHOR]

Summary: Energy consumption increases all over the world as a result of industrialization in recent years. In this context, it has become important to meet increasing energy demand. It is significant to find alternative energy sources because of limited nonrenewable energy sources such as coal, petroleum, and natural gas. At this point, renewable energy sources have an important role to meet energy requirement. As a result, decision and policy making for energy is completely critical issue for countries. Various technical, economic, environmental, and social criteria are used to solve energy policy and decision‐making problems such as evaluation of energy projects, selection among energy alternatives, power plant site selection, and determining energy policy. Multicriteria decision‐making (MCDM) methods are used as effective tools to help decision makers while solving energy problems. There are a large number of studies including articles, conference papers, and book chapters, which examine energy problems by using MCDM methods in the literature. Therefore, analyzing these studies according to different features such as publication year, MCDM method, document type, statistical analyses, country, and published journal can be beneficial for leading researchers who study in energy decision‐making field. In this study, published papers, which use traditional MCDM methods to handle energy problems, are examined systematically. By the way, it is aimed to lead to researchers regarding MCDM methods which are utilized in energy applications. It also conducted some statistical analyses to obtain trends between years and MCDM methods. Besides, recent developments in energy field are presented through this comprehensive literature review. [ABSTRACT FROM AUTHOR]

Summary: With the increasing penetration level of renewable energy in the integrated energy system (IES), water electrolysis for hydrogen production (WEHP) technology has become an effective solution to increase the capacity of renewable energy utilization. Therefore, this paper proposes to take WEHP and hydrogen fuel cell (HFC) as flexible resources to participate in the optimal scheduling of IES under grid‐connected/off‐grid operation mode. Firstly, focusing on the production, storage and utilization of hydrogen energy, a unified operation model of WEHP module is built by analyzing the differences of the three WEHP technologies. And then, "co‐generation" model of HFC, the residual capacity and transportation cost model of hydrogen storage device (HSD) are built. Second, by analyzing the uncertainty of light intensity and wind speed, a prediction error correction method is proposed to improve the prediction data accuracy of renewable energy power generation. Third, taking the lowest daily operation cost as the objective function, the optimal scheduling model of electricity‐heat‐hydrogen integrated energy system under grid‐connected/off‐grid operation mode is constructed. On this basis, the operation scheduling model of electricity‐heat‐hydrogen integrated energy system is transformed into a mixed integer linear model by piecewise linearization method, and the model is solved by CPLEX solver. Finally, an example is given to verify the rationality of the proposed scheduling model in the grid‐connected/off‐grid operation mode. On one hand, reasonable optimal scheduling of multiple WEHP combination is helpful to reduce the daily operation cost of IES. On the other hand, by correcting the prediction data deviation of renewable energy power generation, the equipment output of electricity‐heat‐hydrogen integrated energy system can be more reasonable, which is helpful to reduce the daily operation cost and improve the operation reliability. The effects of sensitive factors such as the penetration level of renewable energy, the relative fluctuation of electricity price and hydrogen price on the operation cost of optimal scheduling scheme are further analyzed, which provides a theoretical reference for the optimal scheduling of IES. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large‐scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid‐connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval‐based formulation, for optimal scheduling of microgrids considering battery and pumped‐hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped‐hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval‐based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval‐based formulation for the optimal scheduling of microgrid with pumped‐hydro and battery energy storage under uncertainty. [ABSTRACT FROM AUTHOR]

Summary: The iodine‐sulfur (I‐S) cycle is one of the most promising thermochemical cycles to produce carbon‐free hydrogen on a large scale. The employment of an efficient internal heat exchange network is very essential for the efficiency improvement of the I‐S cycle, however, detailed research on this topic is seldomly reported. To enrich the existing research content, an I‐S cycle‐based hydrogen production system with an internal heat exchange network is proposed in this paper using Aspen Plus. The internal heat exchange network is designed by following the energy cascade utilization principle, and three sets of different heat transfer constraints corresponding to different temperature zones are imposed in the design process. The simulation results show that for the proposed I‐S system, more than half of the system energy consumption is used by the distillation processes of two acid solutions, and more than 40% of the system energy consumption is used by the HI concentration and distillation process. Using the internal heat exchange network, about 422 kJ of waste heat (for 1 mol of hydrogen production) can be recovered and the system thermal efficiency is improved by about 4.9%. In addition, an efficiency improvement of about 11.8% can be achieved when the waste heat from the condensers of two distillation columns is completely recovered. In general, the thermal efficiency of the proposed I‐S system is estimated to be in the range of 15.8% to 49.8%, and after adopting several common waste heat recovery measures, the system can achieve a promising thermal efficiency of approximately 36.7%. [ABSTRACT FROM AUTHOR]

Summary: The unsophisticated configuration of plants with magnetohydrodynamic liquid metal and no dynamic parts has made them cost‐effective and sustainable compared to the turbine‐based types. This paper proposes an innovative liquid metal magnetohydrodynamic‐based system for electricity, cooling, and hydrogen production utilizing a parabolic solar unit and waste exhaust gases as renewable heat sources, a double effect absorption cooling cycle, and a proton exchange membrane electrolyzer. Hence, an environmentally‐friendly system is devised and analyzed thermodynamically towards a lower emission footprint. The results of the thermodynamic analysis reveal that 71.5 kW, 10.26 m3/day, and 834.9 kW, power, hydrogen, and cooling are produced by the designed unit, respectively, with exergy efficiency and energy utilization factor of 45.28% and 40.27%. Also, the overall destruction of the exergy is obtained 527.4 kW, the main cause of which is the receiver due to high heat loss to the environment. The output of the parametric study indicates that the energy efficiency of the present plant improves by reducing the biogas temperature or by increasing the effectiveness of the solution heat exchanger and mass flow rate of the magnetohydrodynamic stream. [ABSTRACT FROM AUTHOR]

Summary: This paper reports the thermodynamic efficiency analysis of the SnO2/SnO redox cycle conducted using the HSC Chemistry software. A theoretical process model is developed and used, including reduction and oxidation cells, heaters, separators, and an ideal CO/O2 fuel cell. The thermodynamic calculations are performed by varying the reduction temperature (Tred) from 1500 to 2000 K, assuming a steady gas‐to‐gas heat recovery effectiveness (εgg) equal to 0.5, and oxidation temperature (Toxd) equal to 900 K. The obtained results indicate that as Tred increases from 1500 to 2000 K, the requirement of ṅinert reduces from 17 000 to 12 mol/s, respectively. The total thermal energy demand of the cycle is significantly affected by the energy needed to heat the inert/O2 gas mixture (from Toxd to separator‐1 temperature) and inert sweep gas (from separator‐1 temperature to Tred). A considerable reduction in Q̇TC (by 171 500.2 kW) and Q̇solar (by 189 259.6 kW) is noted due to the surge in Tred from 1500 to 2000 K. The SnO2/SnO CDS redox cycle attains the solar‐to‐fuel energy conversion efficiency is 16.7% at Tred of 2000 K. [ABSTRACT FROM AUTHOR]

SUMMARY Changing conditions in biomass and energy markets require the pulp and paper industry to improve energy efficiency and find new opportunities in biorefinery implementation. Considering the expected changes in the pulp mill environment and the variety of potential technology pathways, flexibility should be a strong advantage for pulp mills. In this context, flexibility is defined as the ability of the pulp mill to respond to changing conditions. The aim of this article is to show the potential value of flexibility in the planning of pulp mill energy and biorefinery projects and to demonstrate how this value can be incorporated into models for optimal strategic planning of such investments. The paper discusses the requirements on the optimization models in order to adequately capture the value of flexibility. It is suggested that key elements of the optimization model are multiple points in time where investment decisions can be made as well as multiple scenarios representing possible energy price changes over time. The use of a systematic optimization methodology that incorporates these model features is illustrated by a case study, which includes opportunities for district heating cooperation as well as for lignin extraction and valorization. A quantitative valuation of flexibility is provided for this case study. The study also demonstrates how optimal investment decisions for a pulp mill today are influenced by expected future changes in the markets for energy and bioproducts. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: The incessant growing of the world's energy consumption and associated greenhouse gases emissions have created tremendous problems to be solved by today's and future generations. As the building sector is one of the biggest energy consumers, reducing its energy consumption is now mandatory. Being able to conceive and built efficient buildings, to effectively manage and operate them, and to rapidly renovate the existing building stock is a challenging task. Neural networks models open new possibilities to address this problem. This paper offers a comprehensive review of the studies that use neural networks for energy‐related applications in the building sector focusing on their application and on the technical characteristics of the network (ie, learning algorithm, number of layers, number of neurons, inputs and output variables, and performance criteria). On the basis of this review, limitations concerning the use of neural networks in the building sector along with existing research gaps and future research directions are identified. [ABSTRACT FROM AUTHOR]

Summary: To promote the sustainable development of the iron and steel industry (ISI), evaluation methods and influencing factors of energy efficiency have been studied in the past decades. A systematic review of these methodologies and influencing factors is presented in this paper. Initially, this paper states the development, current situation, and energy consumption of the world's ISI. Then, the definition of energy efficiency is described. After that, the evaluation methods are categorized as single‐factor methods (including these indicators of thermodynamic, thermos‐physical, thermo‐economic, and economic) and multifactor methods (including parametric and nonparametric methods). Furthermore, typical evaluation methods are highlighted. Thermal analysis method and exergy analysis method are routinely combined to optimize the energy utilization. Data envelopment analysis (DEA) and stochastic frontier analysis (SFA) are widely concerned and well developed to gain a better economic benefit. Malmquist index method is often associated with DEA to analyze the technical process and return to scale. Some emerging methods are proposed, for example, the three‐stage DEA method together with DEA and SFA. Finally, the influencing factors of energy efficiency in the ISI are discussed, including industrial structure, technological level, energy consumption structure, price, and so on. It is suggested that optimizing energy structure and improving technology level are effective measures to improve the energy efficiency in the ISI. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with innovative approaches to renewable energy sources computation methodologies, which provide more refined results than the classical alternatives. Such refinements provide additional improvements especially for replacement of fossil energy usages that emit greenhouse gas (GHG) into the atmosphere leading to climate change impact. Current knowledge gap among each renewable energy source calculation is rather missing fundamentals of plausible, rational, and logical explanations for the interpretation of results. In the literature, there are rather complicated and mechanically applicable methodologies, which require input and output measurement data match with missing physical explanations. The view taken in this review paper is to concentrate on quite plausible, logical, rational, and effectively applicable innovative energy calculation methodologies with simplistic fundamentals. For this purpose, a set of renewable energy methodological approaches is revisited with their innovative structures concerning solar, wind, hydro, current, and geothermal energy resources. With the increase in the renewable energy utilizations to combat the undesirable impacts of global warming and climate change, there is a need for better models that will include physical environmental conditions and data properties in the probabilistic, statistical, stochastic, logical, and rational senses leading to refined and more reliable estimations with application examples in the text. Finally, new research directions are also recommended for more refined innovative energy system calculations. [ABSTRACT FROM AUTHOR]

Summary: Research on thermoelectric geometry optimization has focused on stand‐alone thermoelectric devices utilizing energy analysis. This paper considers the multi‐parameter optimization of a solar‐powered thermoelectric generator using exergy analysis since it is more comprehensive than energy analysis. Results indicate that the energy and exergy efficiencies are minimally affected by very high optical concentration ratio values. Also, for achieving maximum performance, larger thermoelectric leg cross‐sectional area to length ratios should be used, the load resistance connected must be kept at an optimum value of 0.06 Ω, and an optimal hot junction temperature of 489.8 K are required to obtain maximum performance from the device. Finally, the thermoelement area to length ratio is increased by 10% for maximum system performance. [ABSTRACT FROM AUTHOR]

Summary: Increased green energy presence in distribution power networks can resolve rising energy demands while also reducing fossil fuel consumption, as well as providing economic and technical merits. However, the quantity of power delivered to customers cannot be allowed to take precedence over the quality of that electricity. This is because the high penetration of the renewable‐based power generation comes at the cost of power quality (PQ) deterioration, especially at the point of common coupling (PCC), owing to nonlinear characteristics of renewable‐based distributed generation (DG) systems and pre‐contamination of modern distribution grids. So, the function of PQ improvement technologies unavoidably comes into the picture when the hosting capacity (HC) of renewable energy systems is constrained by any of the grid PQ performance indices. This paper investigates the application of a hybrid active power filter (HAPF) to improve the harmonic‐constrained HC (HC‐HC) of renewable DG systems in harmonically polluted distribution systems. A shunt type HAPF designing to achieve maximum HC‐HC is considered as an optimization problem and solved by the firefly algorithm under several constraints the system particularly related to individual and total harmonic distortion limits in line current and voltage at PCC, harmonic currents overloading of distribution feeder. Aside from harmonics, constraints related to voltage rise and drop limits, as well as power factor, are also taken into account to ensure the optimal use of the HAPF as a compensator. The proposed HAPF‐based HC‐HC improvement approach's findings and efficacy are compared to those acquired from one of the literature's recently proposed techniques. Besides, the study is extended for HC‐HC enhancement by HAPF, under changing the utility‐side's background voltage distortion and the load‐side's nonlinearity level conditions. The performed extended investigation of the system shows that HC‐HC enhancing the effectiveness of the proposed approach is rather less susceptible to utility or customer side contribution to background distortion at the PCC. Highlights: Hybrid harmonic filtering is investigated for enhancing the harmonic‐constrained hosting capacity of distributed generation in distorted grid conditions.A hybrid filter has been found as a better alternative to achieve an advanced level of renewable energy penetration in distorted distribution grids.Along with increasing the penetration level of distributed generation the harmonic distortion is also substantially reduced instead of being at the verge of the standard limit.With the increase in background voltage distortion, the percentage enhancement in HC‐HC degrades.High load‐side's nonlinearity also results in a relatively lower enhancement in HC‐HC.Background nonlinearity negatively influences the percentage of HC enhancement more than the load‐side's nonlinearity.Though the utility, as well as customer‐side nonlinearity, affect the performance of the hybrid filter also yet the variation is significantly lesser than the purely passive filtering‐based approach. [ABSTRACT FROM AUTHOR]

Significant advancements in developing earth-air heat exchanger models have been detected in the past several decades. It is worth mentioning that this type of device takes advantage of the Earth's constant temperature to cool or heat spaces in buildings so that the identification of its most appropriate geometric configurations to reduce energy consumption is still an actual challenge. In this context, the present paper is focused on the geometric evaluation of several earth-air heat exchangers arrangements according to the Constructal Design method. The performance indicators are the minimization of its soil volume occupation, the minimization of its airflow pressure drop, and the maximization of its thermal potential. Therefore, from a straight duct named Reference Installation, 26 complex geometries have been outlined here using the numerical-analytical investigation. Many ideas emerged from this study: the use of serpentine with low spacing between ducts reduced nearly 39% of the soil volume occupied by the device compared to Reference Installation, showing its applicability in urban regions. In addition, configurations with few curves benefited the decrease of air pressure drop, allowing a performance 30% superior to the most complex shapes. Instead, complex designs can be recommended for thermal potential increase, although the influence of the different configurations over this indicator was not substantial since the maximum improvement achieved between the best and worst shapes proved to be around 6%. Finally, when the three performance indicators are concomitantly considered, several complex geometries reached an overall performance superior to the Reference Installation. [ABSTRACT FROM AUTHOR]

Several mathematical models have been developed in the past to consider reliability when designing energy systems. However, these models focus on the system reliability which only considers the operability of the system. Hence, a reliability indicator called supply reliability is introduced to explicitly assess an energy system's reliability meeting the desired energy demand. Besides, previous models can only allocate equipment with identical sizes and equipment reliability. This could overdesign systems and ignore the possibility of allocating a combination of different equipment sizes and reliability. This paper presents a combinatory matrix methodology for allocating equipment with different sizes and reliabilities to improve supply reliability of an energy system design. This combinatory matrix method is a novel feature that allows decision-makers to efficiently evaluate various configurations of equipment based on size and reliability. A case study consisting of a power distribution network is first presented and solved as a pedagogical example to illustrate the methodology and features of the proposed approach. Then, a large-scale power system case study in Malaysia combining existing conventional power equipment and a palm-based bioelectricity supply chain is solved. Based on the optimized results, the redundancy allocated increased the energy generated from the bioelectricity supply chain by 74.14% and cost by 82.96% to achieve the desired energy demand at high supply reliability. This allows decision-makers to determine the cost associated with improving supply reliability for an energy system. [ABSTRACT FROM AUTHOR]

Summary: The overall energy conversion efficiency and the power regulation performance of the volatile renewable energy alkaline water electrolyzer (AWE) hydrogen production system need to be improved. Efficient use of heat in the electrolysis process and optimization of the control strategy are technically feasible. According to the concept of heat exchange utilization, this paper proposes a series parallel modular structure of AWE, which realizes heat exchange and utilization between modules. Based on the electrothermal characteristics of AWE and the power fluctuation characteristics of volatile energy, the function positioning and control strategy of the module are proposed. Theoretical analysis, together with a case study, has been conducted to show the overall efficiency and power regulation characteristics of the traditional AWE, the traditional combined AWE and the proposed new AWE under various working scenarios. Research study results show that the proposed new structure and control strategy can effectively improve the overall energy efficiency and static as well as dynamic power regulation characteristics of the electrolyzer, with the energy efficiency increased up to 16%. Although the static power range decreases slightly in some conditions, it always maintains the ability of wide range regulation. The findings can provide reference for the research and development of new AWE that improve the power response capability and the comprehensive utilization of energy under the premise of ensuring a certain power adjustment range. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with the development and operation of a mechanical vapor recompression (MVR) desalination system with improved energy efficiency in harnessing wind energy, which is non‐dispatchable. Its design, construction, and operation details are presented in this paper. Especially, the main focus of developing the system was on the operation of the system in conjunction with variable loads of new and renewable power sources, in particular, varying wind power. That is, the present work has been carried out to assess the feasibility of its operation in light of capacity modulation to match the power generated under various wind speeds. Optimal operation modes of the system were studied, in which single‐ and double‐effect operations were analyzed for their improvement in energy efficiency. The compression ratio of the proposed MVR system was 1.55 at an inverter speed of 55 Hz, which agreed well with its design value. Operation of the main heat exchanger remained stable within the limits of its operable range, although the temperature differences in the main heat exchanger did not remain constant because of the pressure variations in the evaporator. The daily freshwater yield was between 28 and 51 tons. The power consumption per ton of freshwater produced was about 43 kW for a single effect and about 23 kW for a double effect, which is about twice as efficient. [ABSTRACT FROM AUTHOR]

Summary: The transition to an intermittent energy production from renewable energy sources (RES) increases the complexity of providing reliable energy supply in Germany. Yet the growing number of RES impede the necessary effort to control the system. The introduction of digital or smart energy systems is often proclaimed as a logical next step towards coping with this rising complexity. It seems convenient that the manufacturing industry, as one of the major energy consumers, is currently also in a process of a digital transition, the fourth industrial revolution. This paper explores the current state of expert discourse on the role of a digitized industry as a potential enabler for the energy transition using Germany as a case study. For this purpose, we gathered qualitative data through semistructured interviews among industry managers and energy researchers. We identified the three major areas in the expert discourse of industry's future potentials: (1) increasing transparency in the energy system, (2) providing demand flexibility, and (3) increasing energy efficiency. In this paper, we address the internal barriers and explore industry's reluctance to interact with the energy system in order to initiate a transition. [ABSTRACT FROM AUTHOR]

Summary: Key performance indicators (KPIs) are powerful tools that industries can use not only to monitor their activities but also to highlight their unexploited potential. Energy‐based KPIs are nowadays mostly used to evaluate industrial process performances. However, these indicators might present some limitations and might give misleading results in some circumstances. An example is represented by industrial processes that make use of different energy forms (eg, electricity and heat) and of different material inputs, and that are therefore difficult to compare in terms of energy. A further example can be found in the Carnot engine that, despite being ideal, can have quite low energy efficiency (eg, the energy efficiency of a Carnot engine working between 700 and 300 K is 57%), suggesting that its performance can be improved. The use of exergy‐based KPIs allows us to overcome many of the limitations of energy‐based indicators. The exergy efficiency of Carnot engines is 100%, clearly indicating that the system cannot be further improved. Moreover, the use of specific exergy consumption instead of specific energy consumption to monitor the performance of a process allows one to take into account possible differences in quality of material and energy streams. In the present work, exergy‐based KPIs for industrial use are reviewed. The paper outlines advantages and limitations of the reviewed indicators, with the scope of promoting their use in industry. A systematic use of exergy‐based KPIs not only gives a meaningful representation of process performances in terms of resource use but it can also direct efforts to improve the processes. To better understand their meaning under different circumstances, the revised indicators are applied to 3 industrial processes. In this paper, we reviewed exergy‐based indicators used to evaluate industrial performance that are found in literature. The use of these indicators is further illustrated by applying them to three industrial processes. [ABSTRACT FROM AUTHOR]

Summary: In a recent perspective paper, Dincer's 6‐step approach in teaching thermodynamics was explained with reference to Bryton and refrigeration cycles. In the present study the benefits of this approach was assessed with nutrition, as a distant example. One of the best advantages of using this guided tool was found as its functionality in avoiding the analogy mistakes and pointing the spots where further research is needed. [ABSTRACT FROM AUTHOR]

Summary: To make manufacturing systems more energy cost‐efficient, significant research proposes adopting energy‐‐aware production scheduling with on‐site renewable energy generation systems and battery energy storage systems. Depending on the different system configurations and various practical scenarios, the effect of those proposals would vary on energy efficiency and time‐related performance of the system because of the complex dynamics and trade‐offs in the manufacturing process. This paper proposes a mathematical optimization‐based systematic approach to comprehensively evaluate and analyze the effect on energy cost and makespan by energy‐aware production scheduling, on‐site solar power generation, and battery energy storage in various configurations. Numerical experiments are designed to demonstrate how the presented optimization models can be used to investigate the cost‐saving and performance degradation that results from different configurations. Based on our numerical experiment, using only energy‐aware scheduling can reduce the energy cost by 23% and the total cost by 7%. Simultaneous use of all three efforts saves 36% of energy cost and 15% of the total cost, which comprises the energy and time‐related makespan costs. A sensitivity analysis is performed to evaluate the practical benefits of renewable energy generation and battery energy storage under different generation and storage capacities. [ABSTRACT FROM AUTHOR]