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Research has revealed the existence of an energy-efficiency gap – the difference between optimal and actual energy end-use, suggesting that energy efficiency can be improved. Energy management (EnM) is a means for improving industrial energy efficiency. However, due to various barriers, the full potential of EnM is not realised. Several studies have addressed drivers and barriers to energy efficiency but few to EnM. This study aims to identify EnM practices, the most important perceived drivers and barriers for EnM, and relations among them in the energy-intensive Swedish pulp and paper industry (PPI), which has the longest experience internationally of practising EnM systems, and has worked according to the standards since 2004. Our results show that, altogether, the PPI works regularly and continuously with EnM, with a clear division of responsibilities. The highest maturity for EnM practices was for energy policy, followed by organization, investments, and performance measurement. The study also shows that communication between middle management and operations personnel has potential for improvement. The most important categories of drivers were economic, whereas for barriers they were organizational. Nevertheless, knowledge-related barriers and drivers were amongst the most important, suggesting that the absorptive capacity for energy issues could be improved. Image 1029 • Energy is managed continuously, with clear policies and responsibilities. • The main drivers are economical while main barriers are organizational. • Knowledge related barriers and drivers are amongst the next most important. • Employees' absorptive capacity for energy issues could be improved. • Link between energy policy and performance measurement could be improved. [ABSTRACT FROM AUTHOR]

Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

Increasing energy prices and market volatilities force energy intensive industries to implement energy management systems in order to ensure competitiveness. In this paper, we focus on the strategic level of energy management, specifically, on the analysis of the internal and external conditions that form the basis for strategic development. For this, we selected the case of the Austrian paper and pulp industry. We applied a hybrid method by combining an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process which was based on a survey of expert opinion. The results show that cost-related factors predominate. According to our analysis, the four most important factors are all directly linked to energy costs, energy efficiency, and the energy market. Experts pay little attention to environmental issues or to energy market volatilities. Apart from that, the combination of an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process promises to be a valuable tool for strategic energy management. This is especially true for energy intensive companies and/or sectors, since it enables decision makers to take strategic decisions based on a systematic understanding of the main issues. [ABSTRACT FROM AUTHOR]

Globally, small and medium-sized enterprises (SMEs) constitute a significant industrial segment. However, their energy-efficiency potential has remained largely untapped due to several barriers. The prevailing approaches on energy optimization in the energy-intensive SMEs are focused on the technological improvements at the operational level and lack a strategic approach. This study developed an energy management system (EnMS) for SMEs by integrating technical and managerial energy saving activities (ESAs), suggested in energy management literature. The proposed EnMS adopted a PDCA (Plan-Do-Check-Act) process approach for consistent energy efficiency and cleaner production. For the validation of the proposed EnMS, a case of small-sized Indian paper mill was selected. The case exhibited the ramp of continuous improvement in the energy efficiency at the mill by illustrating two PDCA cycles. Step I (Plan) involved creating energy management roles with management support, energy auditing, identifying energy-saving opportunities and developing action plan; step II (Do) was about creating communication, awareness and motivation before the implementation of the action plan; step III (Check) involved periodic monitoring, analysis and reporting of the energy key performance indicators (KPIs), and benchmarking enterprise energy performance. The case analysis showed a 35% reduction in specific energy consumption (SEC) of one of the Paper Machine's (PM) vacuum system by the implementation of ESAs for vacuum optimization, generating an annual energy-saving of 0.3 GJ/t. In step IV, the subsequent PDCA cycle was triggered by the biannual management review for exploring the new energy saving opportunities at the enterprise. The next improvement cycle targeted at steam and condensate system optimization in another PM resulted in an additional energy cost saving of USD 26,900. [ABSTRACT FROM AUTHOR]

Plug-in electrical vehicles (PEVs) are introduced as a compatible transportation system for the environment. Manufacturing technology of electric vehicles (EVs) could bring an opportunity to implement them as energy storages. By expanding the use of renewable energy sources (RESs), the role of energy storage system is highlighted to overcome power generation fluctuations. Integrating PEVs and RESs could be profitable for both PEV and RESs owners. In this paper, a structure of renewable energy sources based micro grid (RMG) is considered. The proposed RMG has been equipped with a parking lot in order to control and aggregate PEVs. This paper investigates the optimal energy management problem of the RMG with the presence of PEVs. The objective of the RMG owner is to minimize the cost through generating power with its local generators and trading energy with the power market considering the market price. Also, the RMG could incentive PEV owners to take part in the demand response (DR) programs as a flexible load. It could bring profit for both PEVs and RMG owners. The existence uncertainties are modeled in the scenario-based framework. Three case studies are analyzed to display the effectiveness of the proposed model. As a result, utilization of the parking lot has decreased the cost of RMG about 40%. Additionally, implementing DR program during the charging process of PEVs could bring extra profit for both RMG and PEVs owners. The results have shown the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Investments in energy efficiency often remain undecided upon and there is a large potential for enhancing energy efficiency in the industrial and commercial sectors. Within this context, the aim of the research project M_Key "Management as a key driver of energy performance" is to better understand how large-scale energy consumer (LSEC) companies make energy-efficiency investment decisions. With about 35% of the total Swiss electricity consumption, LSECs are an important target group for energy-efficiency programmes in Switzerland. The goal of this paper is to present and describe the main findings of M_Key (Management as Key for Energy Performance) research project. For the first time, these findings extensively depict large-scale energy consumers in Switzerland. They highlight the low level of energy management in Swiss LSECs and of the resources allocated to tackle energy issues. Due to a lack of monitoring and control tools, companies have difficulty assessing their energy-efficiency investments. Energy issues are not generally a priority for companies, with a few exceptions, mainly in large firms. Requirements from national or cantonal energy policies have a significant role (and in the case of SMEs a key role) in inciting companies to action. When energy efficiency is perceived as strategic, companies tend to have a high level of energy management. The better the energy management system is, the more likely the chances are for a positive decision on energy-efficiency investment. Another important conclusion is that the more strategic a project or investment is considered to be, the less restrictive are the financial criteria applied. [ABSTRACT FROM AUTHOR]

Demand response plays an important role in helping us achieve clean energy and climate goals. It offers a variety of environmental benefits, such as reducing energy usage, offsetting the need for fossil-fueled power plants, and helping to manage system challenges from increased wind and solar energy. However, DR suffers from difficulties associated with optimization model complexity. Especially, when the DR problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard problem. In order to solve this problem, a global optimization approach that combines a particle swarm optimization (PSO) algorithm with a sequential quadratic programming (SQP) local optimizer is proposed in this paper. This allows us to incorporate the global search characteristics of PSO algorithms with the local search capabilities of SQP algorithms. This unique combination increase the possibility of a successful global solution by balancing the exploration-exploitation trade-off and is particularly effective atsolving MINLP problems. Application of the approach in residential energy management indicates that the proposed algorithm is effective atresolving the MINLP DR problem. • A complex mixed nonlinear integer models is proposed and applied to a residential scenario. • A hybrid algorithm was implemented to solve the non-convex MINLP, and can obtain near optimal solution. • The performance of proposed algorithm is better for solving demand response related MINLP than commercial solver Knitro. [ABSTRACT FROM AUTHOR]

Home energy consumption is increasing, and renewable energy sources are being deployed. The use of renewable energy sources as an alternative to fossil fuels and nuclear energy is a significant opportunity for reducing CO 2 emissions. However, carbon emissions from electricity generation are dependent on the type of renewable energy source and the quantity of the electricity that is produced. With the emergence of the smart grid, residents are able to reduce their electricity consumption by shedding the power usage in their homes. The objective of this study is to smooth power peak demand and diminish CO 2 emissions. This paper describes the power shedding benefits of reducing energy consumption and CO 2 emissions. This reduction of CO 2 emissions eliminates the need for auxiliary power plants in the grid in the case of peak demand points and the need to switch to renewable energy sources. In this study, we prove that emerging information and communication technology can reduce energy use and CO 2 emissions. Wireless communication architecture is introduced in this study to increase grid flexibility and rapidity in the data flow among all of the components in the proposed system. Our proposed system with local renewable generation can reduce CO 2 emissions nearly 72% of all CO 2 emissions when all appliances are supplied with the grid. With our load shedding algorithm, the CO 2 emissions decrease nearly 91%. The yearly estimation shows that our system can reduce 62% of CO 2 emissions and 37% of consumed power. In this paper, we introduce an efficient architecture of a smart home energy management system and propose a shedding algorithm for home energy usage. This system is based on wireless communication among home appliances, a home management and control system, a grid management and control system and domestic renewable energy sources. Our proposed system, which employs wireless communication, domestic renewable generation and a power shedding algorithm, can operate in conjunction with future smart grids by applying real-time data, such as peak demand points. Numerous real data sets permitted the development of some of the statistics in the system results. [ABSTRACT FROM AUTHOR]

Machining workshop is a widely distributed manufacturing system that consumes massive energy in a low efficiency. In recent years, the increasing pressure from energy price and environmental directive have forced machinery manufacturers to address energy efficiency monitoring and management to improve economic benefits and environmental performance. However, due to the complicated energy flow and dynamic energy changes of the machining workshop, machinery manufacturers still lack an effective method to monitor and manage the energy efficiency in a practical manner. To this end, this paper proposes an energy efficiency monitoring and management system with the support of the newly emerging Internet of Things (IoT) technology. Firstly, the energy characteristics and energy efficiency indicators of the machining workshop are analyzed and defined. Then the framework of the IoT based energy efficiency monitoring and management system is proposed. The key approaches for energy efficiency monitoring and management are then illustrated. Finally, an industrial application is demonstrated to validate the effectiveness and benefits of the proposed system. With the application of the proposed system, potential opportunities for energy consumption decrement and energy efficiency improvement can be identified. Machinery manufacturers can easily reduce energy consumption and energy cost by managing the machining process. [ABSTRACT FROM AUTHOR]

Abstract The more efficient use of energy is a policy issue worldwide. Smart use of technology and management of information is becoming crucial for increasing energy efficiency. The 7th Framework Program of the European Union supported international research to fulfill the potential of Information and Communications Technologies for Energy Efficiency. Using Social Network Analysis, this paper analyzes this research network and explores the links and influences in the cross-country knowledge flows generated. The analysis identifies some important differences in the position that countries hold on the network. Results highlight the importance of countries acting as hubs and gatekeepers in the promotion of research and the improvement of energy efficiency. Highlights • The network of the 7th Framework Programme on Information and Communication Technologies for energy efficiency is studied. • Countries with a central position in the research network benefit from energy efficiency gains. • A fast access to knowledge does not guarantee efficiency improvements. • Climate-resilient countries tend to have more adaptation in terms of efficiency. [ABSTRACT FROM AUTHOR]

In today's manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision-makers to prioritize green manufacturing. The Internet of Things paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from manufacturing processes can be collected easily, and then analyzed, to improve energy-aware decision-making. Relying on a comprehensive literature review and on experts' insight, this paper contributes to the understanding of energy-efficient production management practices that are enhanced and enabled by the Internet of Things technology. In addition, it discusses the benefits that can be obtained thanks to adopting such management practices. Eventually, a framework is presented to support the integration of gathered energy data into a company's information technology tools and platforms. This is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage on such data in order to improve energy efficiency, and therefore competitiveness, of manufacturing companies. With the outcomes of this paper, energy managers can approach the Internet of Things adoption in a benefit-driven manner, addressing those energy management practices that are more aligned with company maturity, measurable data and available information systems and tools. [ABSTRACT FROM AUTHOR]

Energy management systems are considered as appropriate instruments to identify and realize energy efficiency potentials in industrial plants. Studies point out a largely untapped potential for energy efficiency in industry through the adoption of systematic energy management. We suggest introducing qualitative and quantitative modelling tools into energy management to facilitate a focused process to set priorities and identify effective measures. This paper describes the case of voestalpine Austria Draht GmbH, an Austrian wire rolling mill. voestalpine Austria Draht GmbH at present is implementing a knowledge based energy management system. Within the work described in this paper, first an overview of energy flows using Sankey diagrams was elaborated. Then a qualitative systems analysis was developed for the plant and a gap analysis done for priority areas. Subsequently physical input–output models for several energy intensive units were prepared. The paper describes the models for the cooling water system and product cooling. This study revealed a significant potential to save electricity by optimizing the control of cooling water pumps along the milling train and to recover heat from the final products. 2 GWh of electricity can be saved annually. The biggest single energy loss in the mill is the heat from air cooling the products on hook conveyors. These heat flows contain about 30–50 % of the energy input for heating up the billets. Following a theoretical analysis of the potential, a demonstration plant was developed and pilot tests were run to characterize the realistic potential of heat recovery on the hook conveyor. Based on the test results, feasibility studies for large scale plants were carried out. As a result, 0.8 GWh of heat can be recovered annually. The method was found useful because it helped to structure the existing information with a reasonable effort mostly by the operators themselves in their work and to build a conceptual model of energy flows and energy efficiency of the total plant. [ABSTRACT FROM AUTHOR]

Reducing water consumption and increasing energy efficiency are emerging as two key requirements to move towards a more sustainable mining industry. However, the two targets can be in conflict as water management initiatives often lead to an increase in energy consumption. On the other hand, some water initiatives may lead to reduction in energy consumption, leading to synergy between energy and water efficiency initiatives. To maximise energy and water sustainability in mine water management, it is essential that synergy and trade-off potentials between the water and energy targets related to water initiatives are recognised. Limited research has been conducted to develop a tool or approach to consider water and energy impacts of water initiatives in a coupled manner. This paper presents a protocol to recognise water-energy synergy and trade-off potentials. The protocol is demonstrated for three case study mine sites. The results of this paper show that a particular water management option can hold a synergy potential for one mine but a trade-off potential for another mine. The rigour of the approach captures cases where water management options are predicted to be synergistic, but are in fact shown to be a trade-off according to the results. It is concluded that the use of this protocol can provide insights about synergy and trade-off potentials between water and energy targets of mine sites subject to water management options. This is an innovative approach to more holistically assess water management option impacts. [ABSTRACT FROM AUTHOR]

Stochastic models for predicting human behavior have become an essential part of the development of demand planning strategies, as well as a high-resolution base information for building simulation software. Due to the close relationship between human presence and consumption, occupancy patterns allow for the recognition of activity peaks, and subsequently, potential maximum demand hours. This contributes to the improvement of control strategies, which combined with the active participation of consumers will drive to major energy savings. In this paper, a novel behavior model for nine economic sectors in Spain has been developed using a Markov Chain methodology that can easily be extrapolated to other locations. The model can generate daily occupancy profiles with a 10-min resolution for the selected sectors, distinguishing between the type of day and type of working hours. The results, which have been validated and compared with other works showing good accuracy, have highlighted the characteristic patterns and maximum occupancy hours of each studied sector. Furthermore, these simulated profiles have been used as input datasets for the estimation of consumption in some selected sectors, illustrating the potential link that can be established between occupancy profiles and energy usage by means of different modeling techniques. [ABSTRACT FROM AUTHOR]

The regional energy management body has a large amount of regional industrial companies’ energy consumption data. It can evaluate the energy utilization of listed regional industrial companies based on the total data and, then, find the key points for understanding the resources usage patterns, identifying the problematic companies, and establishing good energy consumption practices. This paper reviews the research progress on big data analysis and industrial energy efficiency evaluation and focuses on the energy efficiency evaluation methods based on energy consumption process analysis and big data mining approach. Based on K-means and multi-dimensional association rules algorithm, to analyze the characteristics of regional energy consumption in different industries and companies, we cluster single industry in K-means and finding their levels of water and energy consumption. This classification provided us a reference point to identify the industries and companies to focus on and locate the bad consumption practices and environmental performance. Then, multi-dimensional association rules are used to find the correlation of processes, companies and energy efficiency to guide the energy conservation in regional energy monitor. The output of our research is a working Big Data analytics platform and the results generated from advance analytics techniques applied specifically to solve regional energy efficiency problems. [ABSTRACT FROM AUTHOR]

Fuel poverty is a recognized and increasing problem in several European countries. A growing body of literature covers this topic, but dedicated analysis for Portugal are scarce despite the high perception of this condition. This paper contributes to fill this knowledge gap focusing on a European southern city while bringing new datasets and analysis to the assessment of this topic; consumer groups identification and to policy discussion. Daily electricity smart meters' registries were combined with socio-economic data, collected from door-to-door surveys, to understand the extent and the determinants of energy consumption for two contrasting consumer groups (herein called fuel poverty and fuel obesity groups). The analysis is based on the amount and annual profile of electricity consumption and was complemented with building energy simulations for relevant building typologies in those groups, to identify heating and cooling thermal performance gaps. The existence of these gaps allowed confirming and/or discarding the initial hypothesis of the poverty or obesity conditions. Results disclose socio-economic variables, as income, and consumers' behavior as key determinants of electricity consumption. It was identified a severe lack of thermal comfort levels inside households of both groups, either in cooling (98% for fuel poverty and 87% for fuel obesity) and heating seasons (98% for fuel poverty and 94% for fuel obesity). Major conclusion refers that electricity consumption cannot be used alone to segment consumer groups. This assessment may serve to support energy policy measures and instruments targeted to different consumers' groups. For example, distinct campaigns and differentiated incentives may apply to achieve energy efficiency and reduction while keep or improve indoor comfort levels. [ABSTRACT FROM AUTHOR]

The special volume on green and sustainable chemistry and engineering has fourteen papers that were considered relevant to the present day issues and discussion, such as adequate use of raw materials and efficient energy, besides considering renewable sources for materials and energy; and changing economical canons towards circular economy. Businesses, governments and Society are facing a number of challenges to tread the sustainability path and provide wellbeing for future generations. This special volume relevance provides discussions and contributions to foster that desirable future. Chemicals are ubiquitous in everyday activities. Their widespread presence provides benefits to societies’ wellbeing, but can have some deleterious effects. To counteract such effect, green engineering and sustainable assessment in industrial processes have been gathering momentum in the last thirty years. Green chemistry, green engineering, eco-efficiency, and sustainability are becoming a necessity for assessing and managing products and processes in the chemical industry. This special volume presents fourteen articles related to sustainable resource and energy use (five articles), circular economy (one article), cleaner production and sustainable process assessment (five article), and innovation in chemical products (three articles). Green and sustainable chemistry, as well as sustainable chemical engineering and renewable energy sources are required to foster and consolidate a transition towards more sustainable societies. This special volume present current trends in chemistry and chemical engineering, such as sustainable resource and energy use, circular economy, cleaner production and sustainable process assessment, and innovation in chemical products. This special volume provides insights in this direction and complementing other efforts towards such transition. [ABSTRACT FROM AUTHOR]

Measuring and targeting energy performances of manufacturing plants and their subsystems is the first, critical step to understand their energy behavior, to identify energy management opportunities and to evaluate energy savings. In addition, this activity has become always more important in the last decades, as Energy Management Systems and energy management practices based on continuous improvement and people engagement have been largely adopted, requiring the introduction of new energy consumption control systems capable to identify the standard operative energy behavior and performance of systems and machines and their energy baseline, to point out contingent deviations of energy performances from the baseline and to identify possible causes and clearly attribute responsibilities of such deviations. In this context, several attempts have been presented in literature trying to identify methods to create Energy Performance Indicators so as to satisfy these requirements, but most of them are focused either on a physical or on economical definition of the problem (and therefore either on a process/appliance level or on an aggregated level), neglecting the necessity to develop an integrated system to combine those two approaches to the problem and deliver appropriate information to each hierarchical level and function of the company. In addition, even when these indicators are identified, a proper system to monitor and control their evolution over time is neglected. In this paper, a novel methodology to manage Energy Performance by developing, analyzing and maintaining Energy Performance Indicators in manufacturing plants is presented, which has been created to fill this gap and taking into account ISO 50001:2011 and ISO 50006:2014 requirements, in order to be as general and widely applicable as possible. The proposed methodology allows an immediate identification of manufacturing plant's energy performance deviations through Energy Performance Indicators monitoring over time and an easy association of causes and responsibilities to such deviations, in order to allow companies to rapidly react defining action plans and resetting targets if necessary; it has also been validated through an industrial case study, which will eventually be presented. [ABSTRACT FROM AUTHOR]

Literature reviewed suggests energy maturity models are in their infancy in the energy management sector, with little practical guidance for their implementation in multi-site organisations. In addressing this gap, this paper presents the development and implementation of an Energy Management Maturity Model for multi-site industrial organisations with a global presence, considered as a fundamental step towards continuous improvement and optimal energy efficiency. The developed maturity model provides a global view of the overall network readiness for engaging in energy efficiency by adapting and enhancing existing ‘site focused’ maturity models to cater for a multi-site industrial organisation. The model enables two-way communication between global and local energy management teams; not only are the individual sites benchmarked but the global energy management team receives feedback and a gap analysis on their performance from the network of sites perspective. The evaluation framework created around the maturity model supports automated prioritization of elements with larger deviations from the site and network median score. In parallel it provides the global energy management team with direction on where the organisation needs to focus central efforts to support the individual sites. The maturity model enables the evaluation of key non-technical aspects of energy management required for continuous improvement on a multi-site and global scale. [ABSTRACT FROM AUTHOR]

Reducing energy use is essential for sustainable manufacturing. Monitoring existing rates of energy consumption in various plants is important and analysis and benchmarking them are gaining more attention as prerequisites for any energy management activities. This paper proposes a novel methodology for energy use analysis and benchmarking of manufacturing lines. A method for energy use data processing and analysis of machines in a production line is presented. It relates equipment energy data and corresponding machine states which allows extensive analysis for every measured piece of equipment. A local benchmarking concept has been developed. It is composed of six main steps including detailed analysis of the constituents of typical machining cycles and calculation of average energy consumption for different equipment states. The concept of local energy benchmarking is utilized to meet the challenges of collecting and comparing data from different plants with different operational variants. The developed methodology was applied to a real case study of engine blocks machining line at a major automotive Original Equipment Manufacturer (OEM) plant. Main categories of equipment consuming energy in the entire line were identified and compared for the first time, and total energy consumption per engine block was obtained as well as performing detailed analysis of the constituent CNC machines. Results reflect energy use, percentages of energy waste and values/efficiencies of benchmarking as well as lost production opportunities. It was also shown that average energy consumptions for different equipment states (productive and non-productive) are close, which highlights the importance of optimized process planning and scheduling as well as better utilization of equipment to improve energy use efficiency. The proposed methodology provides a practical decision support tool that can be readily applied by energy management practitioners in industry to assess energy use at the current state of the system. It also guides improvement efforts and identifies potential energy savings. This helps setting realistic targets in individual plants, at the manufacturing line level, for low-cost energy saving projects. [ABSTRACT FROM AUTHOR]

In Serbia, a developing country, energy consumption per capita is higher than in most other countries in the region. Implementation of energy management practices is therefore necessary to cut energy consumption and CO 2 emissions. This paper gives an insight into how energy management is implemented in Serbian manufacturing (focusing on food processing and manufacturing of non-metallic mineral products, defined as priority industrial sectors). No similar study for Serbian manufacturing has been published. The study was carried out using an on-line questionnaire based on the ISO 50001 requirements. Questions were grouped into 16 categories and presented according to the PDCA (Plan-Do-Check-Act) cycle. The proposed methodology has not been used by other researchers for similar problems. The survey was conducted in 52 organizations and lasted from May to December 2015. We found that the average level of requirements fulfillment in priority industrial sectors in Serbia is 59.05%. Full implementation was found in only 5.8% of participating organizations. Average implementation of the PLAN phase (establishing basics for energy management) is 61.87%, implementation of the DO phase (realization of energy management processes) is 59.98%, implementation of the CHECK phase (monitoring and measurement of energy performance) is 59.61%, and finally, implementation of the ACT phase (review and improvement of energy management) is only 35.34%. The study results offer scientific data for improving national policy and education on energy management in Serbia. [ABSTRACT FROM AUTHOR]

Abstract: The EU has established so-called 20–20–20 targets, which in relation to energy mean that each Member State shall improve energy intensity levels by 3.3% annually, leading to a reduced primary energy use of 20% by the year 2020, calculated from a projected level based on the primary energy use in 2005. Sweden has established a less ambitious target of 1.7% annual energy intensity improvement through 2020. The aim of this paper is to evaluate, ex-ante, the EU 2020 primary energy target for the Swedish industrial sector. An applied backcasting methodology is used. The assessment made in this paper is that actions that lead to between 31.6 and 33.2 TWh/year reductions in energy end-use are needed if the EU target is to be achieved. Results from this paper shows that the current energy policy instruments are not sufficient to the EU or Swedish targets. Estimations in this paper are that a primary energy target of about 22.3 TWh/year is reasonable. The paper concludes by presenting a roadmap on how the Swedish 2020 target can be achieved through: i) energy management; ii) energy-efficient technology; and iii) energy supply measures, with an approximate cost of 280–300 MEUR or 75–80 kWh per public EUR. Three major additional policy measures are needed compared with the current policy: including all energy carriers, not just electricity, in the Swedish long-term agreements program PFE; setting up networks; and making it possible for third parties, i.e., industry, to deliver excess heat into the monopolized Swedish district heating grids. [Copyright &y& Elsevier]

This paper presents Slovenian experiences related to a customized educational solution called EUREM (European Energy Manager) involving the training of energy experts in the field of energy efficiency. The EUREM training program is offered in 27 countries around the world and covers nearly all the energy-relevant issues that can arise in public and private companies. The final output of the practical training is a company-specific prefeasibility study that deals with energy weak points and energy efficiency measures that have a high implementation potential. The presented curriculum of the EUREM training program provides a solid basis for increasing the knowledge and skills of energy managers and was developed with an emphasis on practical experiences related to the deployment, demonstration, measurement and verification of energy-efficient solutions in industry and buildings. Regular surveys and communications involving former training participants indicate that the EUREM training program has stimulated Slovenian energy managers to make significant energy savings in their companies. Around 55% of the Slovenian EUREM training participants confirmed that in addition to their energy concepts, they had also realized additional energy efficiency projects after they finished the training course. Based on the experience gained during the past 8 years of the training program's implementation in Slovenia we can conclude that the energy experts really appreciated their practical education and training with on-the-job support from professional coaches. The future challenges of the education and training program are related to follow-up activities, the development of interactive tools and the curriculum's customization to meet the constantly growing needs of energy experts from industry. [ABSTRACT FROM AUTHOR]

To ensure green manufacturing, the energy consumption of production processes should be transparent and minimized. Also, to achieve the desired level of energy consumption awareness and efficiency improvements, energy use should be measured in more detail and linked to production data. In this scenario, real-time monitoring of energy consumption represents an essential step to increasing energy awareness, efficiency and the support of energy-aware production processes. This paper seeks to provide a way to achieve multi-level awareness of the energy used during production processes. The multi-level awareness of energy consumption means identifying the amount of energy used, CO 2 emitted, and the cost of the energy used at operation, product, and order level. This multi-level awareness is achieved by integrating energy usage data with production data at the operational level. Furthermore, energy sources need to be considered to define the amount of CO 2 that is emitted from the production process for each product. A pilot study was carried out to integrate electrical energy data, production data and scheduling data in real time to achieve the multi-level awareness of energy used in production. The results show that integrating energy with production data enables factories to provide specific energy consumption information for decision makers at the factory level, as well as for the consumers and the regulators. This integration of energy and production data is achieved efficiently when there is a high level of standardization of production processes and the availability of detailed energy usage data. [ABSTRACT FROM AUTHOR]

Abstract: Even enterprises with very advanced management systems seldom monitor efficiency of energy usage and materials flows within their processes and have, therefore, difficulty to effectively manage their resource efficiency. This paper is built upon experiences from a pilot implementation of a new tool for promotion of energy management and cleaner production, developed within the Energy Management and Performance Related Savings Scheme project to promote an effective energy management system in industrial enterprises. The combination of soft managerial approaches (Monitoring and Targeting technical services) with financing provided on a no cure, no pay basis provided by Energy Service Companies, is the foundation for this new tool for promotion of energy management and cleaner production on no cure, no pay basis. It enabled us to test the introduction of cleaner production projects with no technical or financial risks for enterprises or for the need of financing from the side of the enterprises. Consequently, it removed some of the important barriers for implementing cleaner production. This paper distinguishes between auditing and accounting approaches in the promotion of cleaner production and argues that auditing approaches are not sufficient for completion of the learning needed for integration of cleaner production into the practice of an enterprise. [Copyright &y& Elsevier]

Reduction of fuel consumption (FC) and emissions are an indispensable part of automotive industry in recent years, which caused Plug-in Hybrid electric Vehicle (PHEV) be taken into consideration. The main objective of this paper is developing a predictive optimized control strategy based on traffic condition prediction for minimizing FC and emissions. For this purpose, initially traffic condition is predicted by utilizing driving cycle classification based on its specifications. Then, control strategy based on fuzzy logic controller (FLC) is developed for various driving conditions which their membership function (MF) parameters and rules are tuned by employing genetic algorithm (GA). In the next step, by recognition and prediction of upcoming traffic condition, control strategy is switched between optimized FLCs to enhance the optimal power split between sources and manage the internal combustion engine (ICE) to work in the vicinity of its optimal condition. Finally, the effects of control strategy, mass and aerodynamic parameters on PHEV performance and energy usage of components are investigated. Simulation results indicate that proposed approach in real world driving condition reduces emissions and FC significantly. [ABSTRACT FROM AUTHOR]

Educational sector is growing drastically as a result of upward trend of population. These schools require tremendous quantities of energy units to be generated and consumed. As a result, this will impact negatively on the environment by increasing the carbon emissions and depletion of non-renewable energy resources. Moreover, the harsh climate condition contributes substantially to increase the energy consumption in various weather zones in the world. Therefore, several energy efficiency plans have been developed to improve the energy performance of the schools facilities globally. Hence, this paper discusses the energy management program as a sustainable strategy that applicable on the schools. This program is designed for the schools to improve the energy efficiency, indoor environmental quality and adopting the best management practices during the operating stages. This research has developed a customized energy management program for one of the schools in UAE. It has concluded the findings by using computerized hourly thermal modeling. The energy model has evaluated the proposed energy conservation measures and the best practices that were generated by the energy management program steps. This case shows that the energy performance could be improved substantially by 35%. Thus, the energy use index of the audited school that adopted the energy management program has reduced from 438 kWh/m 2 /year to 285 kWh/m 2 /year. The proposed energy management program has been articulated in this study as a consecutive and linear progression. This methodology presents several steps to mitigate the carbon emission by addressing the sustainability dimensions; economic, environment and social aspects. This study discusses the steps and the work flow of developing the energy management program and the anticipated outcomes for the school's buildings. The program considers practical ECMs along with the students and teachers participation and interaction. Furthermore, it highlights the success criteria that assure the sustainability of the energy efficiency at the operating life cycle of school's facilities. [ABSTRACT FROM AUTHOR]

Given the self-declared energy and environmental protection objectives of many car manufacturers responsible companies must use energy-efficient production technologies. The running costs of production sites are a decisive factor in the overhead of automotive production. Therefore, it is important for many operators to reduce those costs in a sustainable way. For this purpose, they attempt to reduce both the energy consumption costs and CO 2 emissions of production systems. However, most components of the energy costs are determined during the very early phases of the product creation process. To achieve an estimation of the energy consumption in this early planning stage, a new suitable forecasting method is required. Therefore, this paper presents an approach for determining the prospective energy consumption by using Methods-Energy Measurement combined with defined energy reference cycles for manufacturing technologies. With this approach, key performance indicators are defined, and the production technologies are divided into basic energy consumers. The average energy values will be determined empirically by using the defined energy reference cycles. With these data, the energy demands for different scenarios are calculated. Thereby, it is possible to estimate the potential energy consumption of complex manufacturing cells. The designed method is validated within a case study of a modern automotive body shop. In summary, the approach contributes to identify and realize sustainable solutions in advance and thus increase the profitability of a factory. [ABSTRACT FROM AUTHOR]

In this paper, an online mixed-integer optimal energy management strategy is proposed for connected hybrid electric vehicles. Firstly, a predictive framework is constructed based on the backpropagation neural network, aiming to predict the future information utilizing the connected vehicle technology. Subsequently, for the mixed-integer programming problem in the predictive horizon, a novel optimal algorithm is proposed in the predictive framework. Finally, the proposed strategy is verified under both simulation and hardware-in-the-loop system environments. The results show that the proposed strategy reduces fuel consumption by 25.34% and 1.13% compared with the rule-based EMS and equivalent consumption minimization strategy (ECMS)-based EMS, and reduces fuel consumption by 25.79% and 1.78% compared with the rule-based EMS and ECMS-based EMS in two typical conditions. The proposed strategy can reduce 84% computation time than the particle swarm optimization-based EMS in the same typical condition. Using real-word conditions, the proposed strategy can reduce fuel consumption by 9.2% compared with ECMS-based EMS. The proposed strategy achieved satisfactory results in a hard-in-loop experiment. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the method and results of a survey of 27 of the 33 Australian universities teaching engineering education in late 2007, undertaken by The Natural Edge Project (hosted by Griffith University and the Australian National University) and supported by the National Framework for Energy Efficiency. This survey aimed to ascertain the extent of energy efficiency (EE) education, and to identify preferred methods to assist in increasing the extent to which EE education is embedded in engineering curriculum. In this paper the context for the survey is supported by a summary of the key results from a variety of surveys undertaken over the last decade internationally. The paper concludes that EE education across universities and engineering disciplines in Australia is currently highly variable and ad hoc. Based on the results of the survey; this paper highlights a number of preferred options to support educators to embed sustainability within engineering programs, and future opportunities for monitoring EE, within the context of engineering education for sustainable development (EESD). [Copyright &y& Elsevier]

Energy hubs as a multi-carrier energy system with various energy carriers and conversions have been integrated with smart grids to increase the efficiency, flexibility, and reliability of the network. Today, scheduling multi-energy hubs (MEH) is one of the challenging issues in smart grids. In this paper, the optimal schedule of interconnected MEHs based on the robust optimization in the presence of electric vehicles (EVs) is proposed. A robust optimization approach is considered to deal with uncertainties of electrical price, renewable energy resources (RESs) generations, and electrical loads. In this paper, MEHs network include commercial, industrial, and residential energy hubs. The energy consumption of MEHs consist of electrical, water, gas, and heat. RESs, demand response (DR) programs, heat and electrical energy storage devices are used to supply the energy demand. The aim of the proposed model is reducing the total cost of operation and carbon emission. The price-based demand response program is applied to shift the power consumption from on-peak to off-peak times in response to the price signal. Finally, the problem is solved for the three cases with CPLEX under YALMIP MATLAB. The results illustrate the effectiveness of the proposed model to achieve optimal scheduling of MEHs. The proposed problem is organized in three scenarios, among which the scenario I determines optimal scheduling of MEHs, using robust optimization method. The scenario II investigates the problem with deterministic model. The scenario III evaluates the problem applying robust optimization method without DR program and energy storage device consideration in autonomous EHs. The numerical results show that the total operation cost is increased 15.46% in scenario I compared with the scenario II due to applying the worst-case of uncertainties in robust optimization approach. • An optimal scheduling of Multi-Energy Hubs is proposed with energy costs and emission. • Electrical, heat, gas and water demands, RESs, storages, and EVs is considered with DR. • A robust optimization is used to cover uncertainties of price, generation of RESs and loads. • Three interconnected EHs as MEHs including commercial, industrial and residential sectors. • The proposed problem is optimized in three cases and the results are compared with each other. [ABSTRACT FROM AUTHOR]

One of the main barriers for the wide penetration of fuel cell electric vehicles is the lack of proper infrastructures for hydrogen transportation that hinders the implantation of refuelling stations. This barrier could be overcome by deploying onsite hydrogen generators based on mature electrolysis and hydrogen storage technologies. This way, the necessity of hydrogen transportation is avoided. In addition, electrolysers can be onsite supplied by means of renewable generators like photovoltaic panels, while the produced hydrogen can also be destined to generate electricity through fuel cells thus obtaining a monetary revenue. Thereby, the economy of the system may be improved in order to make viable this kind of infrastructures. However, the optimal coordination of the different assets is challenging and requires the use of energy management tools to pursue the optimal performance of the installation. In this kind of infrastructures, the energy management problem is performed under substantial uncertainties; moreover, these unknown parameters have a very different character. Thus, while energy pricing and renewable generation can be forecasted using conventional techniques, refuelling demand is highly unpredictable. To this end, this paper proposes a novel stochastic-interval model for the optimal scheduling of photovoltaic-assisted refuelling stations. The new proposal uses interval notation to model the inherent uncertainty of renewable generation and energy pricing, while the vehicle demand is modelled using a more suitable approach based on scenarios. In this regard, a comprehensive stochastic model for fuel cell electric vehicles is developed, which is based on reported driving behaviour and common characteristics of commercial vehicles. To solve the problem subjected to uncertainties, an iterative solution methodology is developed which allows adopting risk-seeker and risk-averse operational strategies. A case study is analysed to validate the new proposal and discussing the importance of the different economic activities that can be exploited in refuelling stations. Results reveal the importance of selling energy to the grid in order to complement the revenues obtained from refuelling; however, this process is highly impacted by uncertainties and the operational strategy, observing variations up to 50% in the total profit depending on the strategy adopted. [ABSTRACT FROM AUTHOR]

Due to the widespread deployment of distributed energy resources, renewable energies and battery energy storage, the peer to peer (P2P) energy trading schematic has gained the staple attention for improving the energy efficiency and energy flexibility of power grids. This is while, smart demand response programming (DRP) is considered as the bridge between these two indicators of smart grid. Moreover, the subtle point of proliferating P2P schematics is the regulation towards the maximization of social welfare leading to economic profitability of customers and owner's of microgrid and, eventually, reduction of pollutant emission of fossil fuels. Also, uncertainty, aroused by electrical consumption and renewable energy resources, is the core of every considerations, which has to be dealt with intelligent algorithms for strengthening the stability of transactions. On the other hand, compatibility with upper grid's regulations, i.e. power loss and voltage deviation, along with determining fair price of energy trading are the subjects of P2P-based tactics. Therefore, this paper proposes a P2P-based transactive energy sharing architecture, as two stage mixed integer non-linear programming, using smart DRP integrated with machine learning approach, i.e. radial basis neural network. Firstly, the uncertainty of electrical demand and renewable energies are relaxed through short term forecasting. Doing so, the day-ahead transactions of peers are obtained based on their energy management objective, targeting the energy reliability of customers, which energy not supplied criterion has to be equal to zero. Then, participation of customers in DRP, cost of customers, revenue of microgrid's owner and transactions of real time programming are optimally acquired based on Pareto front technique. Also, the simulations are conducted on IEEE 85 bus test system to realize the considerations. The results convey that the profitability of customers and owners is tied with the implementation of smart DRP and accurate forecasting of uncertain variables. In addition, the maximum improvements towards maximizing the revenue of owners and minimizing the cost of customers take place at hours which the electrical consumption is shifted from peaks to off-peaks and mid-peaks, certifying the performance of proposed methodology. [ABSTRACT FROM AUTHOR]

Current research points to a large energy efficiency potential in industry which is still left unexploited. One of the most promising means of reducing energy consumption and related energy costs is implementing an energy management. This paper provides a systematic review of existing academic journal publications on energy management in industry. Five essential key elements of an energy management based on overarching themes are identified within the body of literature (strategy/planning, implementation/operation, controlling, organization and culture) and the specific findings relating to each key element are synthesized. Subsequently a conceptual framework of an energy management is developed which illustrates that a comprehensive approach is necessary in order to effectively exploit the existing energy efficiency potential. Finally implications for further research are described. [ABSTRACT FROM AUTHOR]

The environmental impact of materials and energy is an important concern when cities and regions produce goods and services. To examine this requires an evaluation tool which can deal with the complex system of resources, energy inputs, products and wastes involved. This paper evaluates the urban material and energy flow efficiencies of 31 cities in China using a combination of the Data Envelopment Analysis model and Principal Component Analysis method. The results show that the urban flow system is composed of several input and output principal components, including energy, materials, urban capital investment, overall output, wastes, and others. In 2010, 16 cities were efficient as judged by the material and energy inputs and outputs. The remaining 15 cities were not efficient, primarily due to product shortfalls and waste excesses. Since 2000, half of the analyzed cities experienced a decline in the urban flow efficiencies, while only six cities showed a positive trend. The reasons include excess inputs of coal, coke, electricity, cement, investment, as well as output shortfalls in terms of overall output, wastes, services, agriculture and transport. We conclude that key priorities for improving urban systems with material and energy flow inefficiency are the use of urban renewable supplies, focus on sustainable endogenous development model, and improvement management of waste output, in order to increase efficiency and sustain economic growth. [ABSTRACT FROM AUTHOR]

A major part of the world fleet of more than 47,000 merchant ships operates under conditions that hamper energy efficiency and efforts to cut CO 2 emissions. Valid and reliable data sets on ships' energy consumption are often missing in shipping markets and within shipping organizations, leading to the non-implementation of cost-effective energy efficiency measures. Policy makers are aiming to remedy this, e.g., through the EU Monitoring, Verification and Reporting scheme. In this paper, current practices for energy consumption monitoring in ship operations are explored based on interviews with 55 professionals in 34 shipping organizations in Denmark. Best practices, which require several years to implement, are identified, as are common challenges in implementing such practices—related to data collection, incentives for data misreporting, data analysis problems, as well as feedback and communication problems between ship and shore. This study shows how the logic of good energy consumption monitoring practices conflict with common business practices in shipping companies – e.g., through short-term vessel charters and temporary ship organizations – which in turn can explain the slow adoption of energy efficiency measures in the industry. This study demonstrates a role for policy makers or other third parties in mandating or standardizing good energy consumption monitoring practices beyond the present requirements. [ABSTRACT FROM AUTHOR]

To improve the energy efficiency of hybrid electric city buses, a hierarchical predictive energy management strategy (HP-EMS) based on driver behavior and type is proposed in this paper. Within the model predictive control (MPC) framework, the k-Nearest Neighbor (kNN) method is applied to identify the driver type, and the deep neural network (DNN) is adopted to predict future speed based on the historical speed, driver type, and driver behavior. Combined with the city bus driving characteristics, the hierarchical strategy aims to reduce the frequent starts of the engine. The upper-level controller implements a rule-based strategy to limit the engine start-stop frequency. The lower-level controller uses dynamic programming (DP) to search for the best control strategy in the prediction horizon. Simulation results show that, compared with speed prediction without driver information, the new method can effectively improve the accuracy of future speed prediction, and RMSE between the prediction and measurement drops from 1.58 m/s to 1.45 m/s. The HP-EMS without driver information can reduce the number of engine starts by 30% while increase only 2% energy consumption compared with predictive energy management without hierarchical control. The paper also studies the benefits of considering driver behavior and type. The same HP-EMS controller is implemented with and without driver behavior and type. The one with the additional information reduces the energy consumption by 3.34% compared to the one without the information. [ABSTRACT FROM AUTHOR]

With the deepening of China's low-carbon energy transformation, microgrids are an essential means to promote the consumption of renewable energy. Therefore, improving the operational efficiency of microgrids is the key to promote the development of renewable energy. This paper establishes a three-layer Multi-Agent system model considering the energy storage system and power-heat load demand response based on the actual situation of China to solve the problem of microgrids energy management. In order to verify the effect of the energy storage system and demand response in microgrids, this paper designs three simulation cases, namely the underlying case, energy storage case and demand response case. In order to prove the efficiency of the proposed method, this paper applies the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) Multi-Agent system model can realize the collaborative optimization of 'source, grid, load, and storage.' (2) The introduction of the energy storage system and demand response in microgrids can stabilize the output of renewable energy units, promote renewable energy consumption and reduce the overall operating cost of microgrids. (3) The proposed particle swarm optimization can effectively reduce the cost and the number of iterations. • This study realizes a real-time energy management system of MG based on MAS. • This paper proves the effect of ESS and DR in MG. • This paper points out how to apply ESS and DR to MG operators. • This paper sets the stopping conditions to a chaotic process for efficiency. • Adaptive weight is added in this paper to reduce the number of iterations. [ABSTRACT FROM AUTHOR]

Accurate, detailed, and up-to-date information on energy costs is crucial for energy management in manufacturing companies. Yet, to what extent is such energy costs information actually available and used? This study reviews empirical information provided in papers published in research journals about the availability and use of energy costs information in manufacturing companies. The study aims to focus both on energy-intensive companies as well as non-energy-intensive companies, and also to distinguish between the practices of small and medium-size enterprises (SMEs) vs. large companies. The literature review covers 23 journals in the fields of business, accounting, energy, and engineering, leading to the final sample that includes 51 papers for the analysis. Most studies in this sample concern energy-intensive and large companies. The most striking result is that with only few exceptions, almost no studies provide a nuanced description of how measuring and allocating energy costs is being done. For example, almost no studies investigate specific cost allocation bases, the accuracy of cost allocations, or differentiation between first-stage allocation and second-stage allocation. Nevertheless, the overall impression is that many manufacturing companies resort to imprecise methods for measuring and allocating energy costs. They seem to lack much of the cost information necessary for energy management, such as information needed for improving energy efficiency, evaluating energy efficiency improvement investments, and holding managers accountable for energy efficiency. • There is limited empirical research on the availability of energy cost information. • Most papers in the sample concerned energy intensive and large companies. • Even those companies likely had insufficient cost information for energy management. • Large companies much more often report detailed and frequent measurement of energy use than SMEs. • Detailed descriptions of measuring and allocating energy costs were mostly lacking. [ABSTRACT FROM AUTHOR]

Energy management has been considered in the global agenda as a way to improve energy performance and greenhouse gas reduction in organizations. Industries account for a significant part of energy use worldwide and present opportunities for energy efficiency improvements. Within the industry, energy management is a complex task, regarding scenarios with variables related to the following perspectives: economics, contingency, technological change and behavioural. This paper aims at analyzing the influencing factors on energy management in industries from these perspectives. A survey with 40 variables was carried out with middle managers from different industrial sectors in Brazil. The variables were divided into three groups: drivers for investments in energy efficiency; organizational processes and actions in energy management; involvement of middle managers. Initially, an exploratory factor analysis technique was employed aiming at specifying the main factors influencing energy management. In the sequence , a confirmatory factor analysis was used to associate the variables to the main factors as well as to know how the factors relate to each other. The study showed a positive correlation among all the factors identified. Statistical tests suggested that the factors could not be explained separately. Hypotheses tests were applied to verify the influence of the factors among the groups surveyed. The final model comprised eight factors into the three groups: organizational (strategic , operational), involvement (motivation, support), drivers (production, economics , competitiveness, environment). The results and the main implications of the study are discussed in the paper. • The main factors on energy management presented a positive correlation among them. • Middle managers' involvement influenced strategy and operation on energy management. • Middle managers' involvement depended on the support and motivation from superiors. • Operational factor on energy management influenced drivers for energy efficiency. • Production process was a driver for energy efficiency influencing energy management. [ABSTRACT FROM AUTHOR]

The increasing global consumption of energy and its negative impacts on the environment has been an issue discussed both by academics and practitioners, including in the health sector. Hospitals are major consumers of energy, which leads to increased operating costs, decreased competitiveness, and negative environmental impacts. Energy management in hospitals must be understood as a way to contribute to environmental sustainability and as a way to improve competitiveness. Therefore, the objective of this paper is to propose guidelines for efficient and sustainable energy management in hospitals, developed based on the literature review, the challenges faced and the good practices of sustainable energy management in the hospitals studied, the cross analysis of the case studies, and the authors' expertise. As a result, the eight proposed guidelines are related to culture change, energy consumption management, energy management standard, purchase of renewable energy, green technologies, sustainable hospital buildings, social responsibility programs focused on energy use, and sustainable energy public policies. These guidelines contribute in an applied/managerial way to help managers of public and private hospitals overcome the challenges of energy management and contribute to UN's SDG7 due to its potential to improve energy efficiency and environmental performance, therefore, being a movement towards clean energy. This study also theoretically/scientifically contributes to the academic debate since the empirical findings found in the hospitals studied and the proposition of guidelines deepen and expand the key elements identified in the literature on efficient energy management in hospitals, providing the basis for developing new research on the subject. Energy management in hospitals is discussed from the perspective of sustainability. Challenges need to be overcome for sustainability benefits to be achieved. This work can assist public and private hospitals in developing sustainability. Guidelines for sustainable energy management in hospitals are proposed. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical novel approach to a marine infrastructure, which jointly uses an OWC (Oscillating Water Column) and hydrogen electrolysis as a clean source of primary conversion for energy management, capable to satisfy a continuous designed demand. A case study in the Port of Motril (Southern Spain) is proposed in order to analyze the performance of the power plant. The assessment covers the sale and management profits from the harvested energy from waves in the electricity market, and from the electrolysis of the oceanic water H 2 O plus dissolved salts μ into hydrogen H 2 , oxygen O 2 and salts μ components, also known as green hydrogen. The investment analysis of the project is conducted through the dynamic financial index Net Present Value (N P V), using Monte Carlo techniques to determine the repayment period. The results are consistent, showing that the plant could supply the annual electricity demand of 356 people with a short repayment period. The amount of energy depends on peak periods T p and wave heights H s , which means that other locations could be found so as to improve the performance of the plant in terms of productions of energy and demand supply. • Wave energy harvested through oscillating water column technology can reach an efficiency of 75%. • Harvested Energy is used for energy storage purposes through hydrogen electrolysis. • The civil infrastructure can effectively supply the demand according to the case study. • The recovery of investment take a short period due to positive environmental impacts. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents new design of pyro-processing unit in a cement factory. In this case decomposition reactions have been separated from other reactions which result in pure carbon dioxide production. Decomposition reactions take place in calciner without fuel consumption by utilizing a hot CO2 stream as heat carrier and exhaust gases have been employed to produce steam and power. As result of such novelties new process can significantly reduce 66% of CO2 emission and 2.3% of energy consumption compared to the existing process. Moreover proposed design demonstrated remarkable environmental advantages against some relevant studies and processes. As far as process economy is concerned, novel design can annually achieve 20.7 million USD gross incomes compared to the base case. [Copyright &y& Elsevier]

Abstract: China has now the largest annual outputs of galvanized steel and the most amount of galvanizing plants in the world. However, with obsolete technology, the hot dip galvanizing industry has been proved as one of the industries, which consume large amounts of raw materials and energy, produce numerous pollutants and causes serious contamination in China. Cleaner Production in the hot dip galvanizing industry is now the only way to achieve the sustainable development of hot dip galvanizing industry. The implementation of the Common Fund for Commodities (CFC) project: The Transfer of Technology and Promotion of demand: Batch hot dip galvanizing in China launched by International Zinc Association (IZA) is introduced in this paper, The Best Available Technologies of operation, energy and environmental management in the world are also introduced. With the implementation of the project, an assessment system of Cleaner Production and a training system have been developed and implementation of technical updating to the demonstration plant shows that the reduction of zinc loss, and water and fuel cost savings can be achieved. It is believed that the methodology of the project can be applied throughout China as the galvanizers see the cost benefits. [Copyright &y& Elsevier]

Generation of electricity comes with the emission of toxic gases into the atmosphere by the fossil fueled generators. Along with the promotion in the utilization of renewable energy sources (RES), it is also the duty of the power engineers to arrive at a compromised solution such that less emission of toxic gases occurs with economic generation of electricity. This paper proposes a balanced trade-off method for solving environment constrained economic dispatch (ECED) problems. A novel comparative analysis is performed among proposed ECED method with existing price-penalty-factor (PPF) and fractional programming (FP) methods for solving combined economic emission dispatch (CEED) problems on a 3-unit dynamic test system to sort out the method, which yields a better trade off solution between generation cost and pollutants emitted. An algorithm, following the hunting strategy of wolves, is improvised by incorporating strategies from population-based sine-cosine algorithm along with position updating methods of crows to form a robust hybrid algorithm, which was used as the optimization tool for the study. Involvement of RES diminished the generation cost to 5.5% for both economic dispatch and PPF based CEED, and 6.5% decrease in emission of pollutants was observed due to the same. The generation cost and amount of emitted pollutants, evaluated using proposed ECED approach, were much closer to the economic dispatch and emission dispatch values respectively compared to PPF based and FP based CEED solution. Furthermore, statistical analysis endorses the superiority of the proposed hybrid optimizer over other algorithms presented in the state-of-art literature. • Three different types of CEED methods are studied. • Subject test system is a 3-units RES integrated microgrid system. • Comparative analysis among the generation costs and pollutants emitted is performed. • A novel HMGWO algorithm was proposed as the optimization tool for the study. [ABSTRACT FROM AUTHOR]

To improve industrial sustainability performance in manufacturing, energy management and optimisation are key levers. This is particularly true for aluminium extrusions manufacturing —an energy-intensive production system with considerable environmental impacts. Many energy management and optimisation approaches have been studied to relieve such negative impact. However, the effectiveness of these approaches is compromised without the support of refined supply-side energy consumption information. Industrial internet of things provides opportunities to acquire refined energy consumption information in its data-rich environment but also poses a range of difficulties in implementation. The existing sensors cannot directly obtain the energy consumption at the granularity of a specific job. To acquire that refined energy consumption information, a supply-side energy modelling method based on existing industrial internet of things devices for energy-intensive production systems is proposed in this paper. First, the job-specified production event concept is proposed, and the layout of the data acquisition network is designed to obtain the event elements. Second, the mathematical models are developed to calculate the energy consumption of the production event in three process modes. Third, the energy consumption information of multiple manufacturing element dimensions can be derived from the mathematical models, and therefore, the energy consumption information on multiple dimensions is easily scaled. Finally, a case of refined energy cost accounting is studied to demonstrate the feasibility of the proposed models. [Display omitted] • Refined energy management is essential to support energy consumption optimisation. • Supply-side energy modelling method is developed for mixed production systems. • IIoT-based data acquisition network is designed for energy consumption information. • Production event correlates energy data with job, machine and process data. [ABSTRACT FROM AUTHOR]

The decreasing of Arctic sea ice provides opportunities for commercial shipping between Asia and Europe due to its shorter distance. However, the Arctic environment is vulnerable to the pollutants from shipping activities. Therefore, it is important to consider the impacts of shipping activities in the Northern Sea Route (NSR), especially with a view to the possibility of reducing emissions of short-lived pollutants. In this paper, modelling of a hybrid propulsion system is conducted and implemented in Matlab Simulink. A 3-level control strategy with efficiency of power sources taken into consideration, is applied to ensure stability of voltage and frequency, as well as reducing the pollutants through minimizing the fuel consumption. A target journey with specific loading condition, which is related to NSR shipping, has been conducted to test the performance of the hybrid propulsion system with the 3-level control strategy and diesel electric system, the simulation results of the hybrid propulsion system applied with the three-level control strategy in this research can offer up to 22.7% fuel savings compared with a DC diesel electric system over a typical operating cycle. This justifies the use of hybrid propulsion as a more environmentally friendly propulsion system for the future ice-capable ships in the NSR. Finally, a sensitivity analysis is conducted to find the main factors, which influence fuel consumption. • Novel application of hybrid propulsion to an ice-capable ship. • Advanced 3-level control strategy is developed for power sources working optimally. • Significant fuel consumption saving when compared with a diesel electric system. • Emission reduction avoiding further damage to sensitive Arctic environment. [ABSTRACT FROM AUTHOR]

The development of planetary gear hybrid powertrain (PGHT) in recent years has targeted at the fuel economy for hybrid electric vehicles. However, added complexity and flexibility brought by hybridized powertrain have lead to the increase of vibration and noise. In order to achieve a satisfying energy economy and ride comfort, this paper presents a torsional vibration-considered energy management strategy (TV-EMS) for PHEVs. Firstly, a torsional vibration model of PGHT is developed to observe the vibration behavior in different operating modes and help to develop the energy management strategy. Specifically, root mean square map of the powertrain vibration excited by engine ripple torque and angle-varying meshing stiffness of epicyclic gear are investigated by a lumped-parameter torsional vibration model, the maximum root mean square value of PGHT vibration in power-split mode is 35.8 m/s2. A multi-objective energy management strategy is then proposed to cope with the control objective of suppressing torsional vibration and reducing energy consumption. Finally, by means of simulation and hardware-in-the-loop test, it is concluded that the fuel consumption of TV-EMS increases slightly (by 2.5%) than that of ECMS, while the torsional vibration of PGHT is significantly reduced (by 25%), and the proportion of working points in resonance region is reduced from 3.8% to 1.0%. • Root mean square map of the powertrain vibration was derived from lumped-parameter model. • Fuel consumption of TV-EMS increased slightly (by 2.5%) than that of ECMS. • Torsional vibration level decreased significantly (by 25%) with TV-EMS. [ABSTRACT FROM AUTHOR]

The importance of implementing energy efficiency methodologies in industrial environments has increased considerably in the last decade given the high energy costs and environmental impact (e.g., greenhouse gas emissions). This paper proposes a methodology to improve the energy efficiency of an industrial machine, without sacrificing either production or quality, using an adaptive predictive controller based on dynamic energy models that manages peripheral devices to activate/deactivate them at the proper times. The proposed adaptive mechanism aggregates robustness to the control system in industrial environments, which experiment constantly changes related to equipment degradation and that affect their energy consumption profile over time. Thus, this novel adaptive mechanism automatically updates the energy model to minimize the error between prediction and real energy consumption, including new energy behavior resulting from machine degradation. This methodology has been validated via a testbed and its performance was compared with rule-based control, which is the most widely used control strategy in industry. The energy efficiency of both approaches was evaluated using performance indicators, which show the effectiveness of the proposed control approach, highlighting remarkable improvements in reducing both energy consumption (about 2%) and sudden power peaks (more than 11%). • Optimization-based control to manage peripheral equipment maximizing their efficiency. • A continuous machine energy model adaptation scheme to react to its degradation. • Experimental validation of control in a real industrial application via testbed. • Energy efficiency performance comparison between adaptive and rule-based control. • Results show that the proper management of peripheral equipment reduces energy costs. [ABSTRACT FROM AUTHOR]