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With 2.89 % of global carbon emissions caused by human activity, the maritime industry faces an imminent challenge in curbing its carbon footprint despite regulatory initiatives. As the shipping sector expands, the industry faces a projected increase in carbon emissions. Recognizing the key role of seafarers in emission reduction, this article introduces a comprehensive training framework designed to enhance awareness, knowledge, understanding, and skills for implementing energy-efficient ship operations. This study utilizes structural equation modeling to assess the effectiveness of seafarers' training on energy-efficient operation of ships (EEOS) by surveying 144 seafarers across 42 shipping companies worldwide and using structured questionnaires. This study found significant positive correlations between implementing the Ship Energy Efficiency Management Plan (SEEMP) and the training programs initiated by the International Maritime Organization (IMO) and shipping companies. The results of this study indicate that traditional institutional and specialized training programs on EEOS are relatively ineffective for seafarers in implementing SEEMP onboard ships. Furthermore, the study argues that computer and simulator-based training facilitates knowledge, understanding, and proficiency of SEEMP among seafarers more effectively than the onboard training provided by the ship's master and the chief engineer. The proposed training framework emphasizes the importance of initial training using the IMO E-Learning course and IMO “Train the Trainer” programs, followed by shipping companies' in-house training by classification societies, company project teams, and simulator-based training by service providers. The study proposes a strategic training framework that encompasses in-house training conducted by shipping companies in collaboration with partners, simulator-based training provided by specialized training providers, and ongoing onboard training facilitated by the vessel's master and chief engineer, with integration of computer-based training (CBT). This strategic approach intends to improve seafarers' competence in energy-efficient ship operations to meet predetermined carbon intensity targets, which aligns with the broader goal of leading the maritime industry toward a future of net-zero emissions.

Additive manufacturing (AM) is one of the technologies of Industry 4.0 that has been contributing to the development of different manufacturing industries. The integration of sustainability concepts into additive manufacturing has been gaining attention among researchers. This integration is essential in the development of AM technologies and can be a significant asset in terms of decision making for organizations. This work aims to present a concise literature review on the integration of decision making, especially multi-criteria decision making, and sustainability into the AM environment. The literature on this topic currently possesses a total of fifteen documents, which were analyzed in this work. Some developments on this topic have been achieved in domains such as material selection, process selection and challenges, and drivers’ analysis of sustainable AM. This review shows that even though there has been an effort in recent years to integrate sustainability into additive manufacturing, there is still a long road to the development of this topic for the future, and so some recommendations for future research paths are presented.

Additive manufacturing (AM) is an emerging area with the potential to modify present business models in the near future. In contrast with conventional manufacturing (CM), AM allows the development of a product from a smaller amount of raw material, while allowing an improvement in properties in terms of weight and functionality. Its production flexibility and creativity in terms of materials have enabled not only the industry to use this technology, but also has been used in healthcare (e.g., in the production of human tissue) and by the final consumer. Despite the invaluable opportunities that this technology could provide, the uncertainties concerning its future developments and impacts on business models remain. New business models in AM will convey the need to: specialize the workforce in the design of new parts produced locally or remotely; regulation in the use and sharing of intellectual property rights by partner companies or between users; regulate the possibility of reverse engineering of highly customized products; etc. The present research proposes a conceptual maturity model to support the phases of evolution of AM in the industry, in supply chains, and in terms of open business models.

The industrial progress made throughout these years has led to great results in terms of producing fast and with good quality. However, the impacts related to that production, whether these are environmental, economic, or social have been, at times, neglected. The manufacturing sector, as one of the most polluting sector, felt the urge to adapt to this industrial progress and find ways to produce with improved sustainability goals without compromising the quality of the final product and the production time. Industry easily understood the benefits of this greener approach, and, with this, new sustainable technologies started to emerge. Additive Manufacturing (AM) is one of those technologies that provide alternative sustainable paths to traditional manufacturing. In order to generalize the benefits of AM production in terms of sustainability, when compared to traditional processes, further investigations must be conducted. In this sense, the proposed work has the intention of finding the environmental impacts associated with a particular AM technique for the fabrication of metal parts, Wire Arc Additive Manufacturing (WAAM). A practical work based on the production of three different complexity metal parts considering an additive (WAAM) and a subtractive (Computer Numerical Control (CNC) Milling) manufacturing process is developed. To quantify the environmental impacts of both processes, the author resorts to the Life Cycle Assessment (LCA) methodology. The assessment is conducted in the SimaPro 9.2 software, accordingly to ISO 14044:2006 standard. The results allow a comparison between both types of manufacturing and enable the suggestion of measures to decrease the environmental footprint of WAAM. It was found that WAAM approach leads to a material saving ranging between 40% and 70% and an environmental impact reduction in the range of 12%–47%, compared to the subtractive approach for fabricating the 3 geometries considered in this study. The conclusions obtained are specific to this particular application and, once more, it is acknowledged that in order to reach a global understanding relative to this technology's environmental implications, extra research still needs to be made.

Faced with globalization, high competition, and the demands of a market in constant dynamism, companies strive to adopt measures for increasing their productivity, among which Lean Manufacturing stands out. Although this set of strategies allows optimizing the production by reducing waste, the literature review showed that, in several organizations, the implementation of Lean does not reflect positive impacts on productivity. It is frequently related to the superficial nature of the approach: the tools and methods are applied, but the repercussions on the workers are commonly neglected. In response, companies seek to implement Risk Management policies to assess injury risk factors for operators during task execution. This study highlights the importance of integrating Lean Manufacturing and Ergonomics principles into organizations to increase productivity and improve working conditions simultaneously. Therefore, by identifying improvement opportunities using the VSM tool, this work aims to implement an innovative and systematic intervention model, which enables the integrated application of Single-Minute Exchange of Dies (SMED) and ergonomic analysis in a metallurgical factory. To this end, the innovative Ergonomic SMED (ESMED) Model is proposed, comprising six steps, which, in this study, focus on the setup operations of a molding machine and by including Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), Job Strain Index (JSI), Key Indicator Methods (KIM), and Shoaf's Model methods. Based on the results obtained, it is possible to evidence the usefulness and effectiveness of the proposed model in this scenario, emphasizing the 55% reduction in setup time and the extreme attenuation of the level of Work-Related Musculoskeletal Disorders (WMSDs) risk in workers.

Currently, the search for alternative sources of energy is not only due to the scarcity of non-renewable sources, since these still have an availability capable of meeting actual consumption needs, but also due to the negative environmental impacts that its consumption presents. Thus, the use of biomass as a renewable and sustainable energy source is increasingly presented as an alternative that must be taken into account. Torrefaction is a conversion process that aims to improve the properties of biomass through its thermal decomposition at temperatures between 220 and 320 °C. Torrefaction can be defined by several variables, which have an impact on the final quality of the torrefied biomass. Therefore, there is an increase in the number of studies involving this topic, in order to improve the production of biomass and its use as a renewable energy source, in addition to reducing the costs of this process. In this work, a protocol was developed for a laboratory test procedure to produce low-cost torrefied biomass samples using equipment that can present a cost reduction of around 90%. The samples were analyzed to prove the viability of the developed protocol. The results obtained agree with the current literature, also confirming the improvement of the biomass properties. This work can serve as a platform for the development of other technologies, such as gasification for the production of hydrogen from torrefied biomass.

In the domain of Marine Education and Training (MET), simulators have been utilized for the purpose of training seafarers in the norms for avoiding collisions or for developing the skill of ship manoeuvrability, and even the operation of machinery in the engine room, as well as for conducting research on the subject matter of ship structure, specialized vessel operation, working principle of equipment, and shipboard safety training. These tools are even more important when facing disruptive events such as the COVID-19 pandemic. In MET institutions, full-mission bridge and engine room simulators have been utilized for teaching seafarers for more than a decade. A Systematic Literature Review (SLR) was conducted to identify immersive and non-immersive simulator applications produced over the previous ten years to improve seafarers’ experiential teaching and learning, in the maritime domain. We retrieved 27 articles using the four stages of PRISMA paradigm: Identification, Screening, Eligibility, and Inclusion. The selected papers were read and analyzed according to the training type, the area of training, and the technologies used. The utilization of immersive and non-immersive simulators in the context of the MET domain has been identified and mapped. A few research studies (9 out of 27) compared immersive and non-immersive simulator-based training with conventional training. The quality and efficacy of immersive and non-immersive simulator training at MET institutions have been studied. A model from the learner’s perspective is essential and recommended for future research to assess efficiency and efficacy.

Background: The present work is related to a project developed in a company dealing with the security and communication systems, fire alarms, and electronic displays sector, located in the district of Aveiro. This work aims to improve the logistics flow of material reception in the warehouse of a company by using case study research methodology. Methods: Initially, there were too many queues at reception and high throughput times. The approach used to solve these problems was balancing the workstations, with the help of industrial simulation software (Arena®) and changing the existing layout, making the queues residual in order to reduce the throughput times. Results: Considerable savings were achieved with this approach in terms of operational costs. A reduction of the throughput time by 28% is reached and a reduced number of workstations is achieved, as well as a greater capacity to receive more material without the need for further investment. Conclusions: The changes in the layout allowed improvements at the ergonomic level and greater organization (5 S’s) at the workstations, thus having a positive impact on the employees’ working conditions.

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.

Considering the advantages associated with the port sector concerning the potential of offshore wind energy, several international ports are aiming to achieve a solid integration in offshore wind energy clusters. This article performs an analysis of several seaports and offshore wind energy clusters in Europe to assess the feasibility of implementing an offshore wind energy cluster in the Port of Aveiro, Portugal. A deep literature review and a case study methodology were performed. Based on the analysis of the offshore energy market, and of the value chain of the company case study (ASM Offshore), it was concluded that the Portuguese market in this sector is not competitive, compared with other international markets. Nevertheless, the development of an offshore wind energy cluster could increase the included companie’s prospects, as well as dispute the growth of other companies associated with this sector. This research is relevant, because there is a lack of research that links the importance of seaports in offshore wind energy projects, in particular with a cluster structure, and because it contributes to knowledge for the development of the Aveiro region and Portugal, based on the exploitation of the renewable energy market and to the increase of related activities.

The biomass industry is growing due to the current search for greener and more sustainable alternatives to fossil energy sources. However, this industry, due to its singularity, presents several challenges and disadvantages related to the transportation of raw materials, with the large volumes that are usually involved. This project aimed to address this internal logistics situation in torrefied biomass pellets production with two different biomass storage parks, located in Portugal. The main park receives raw material coming directly from the source and stores it in large amounts as a backup and strategic storage park. The second park, with smaller dimensions, precedes the production unit and must be stocked daily. Therefore, a fleet of transport units with self-unloading cranes is required to help to unload the biomass at the main park and transport the raw material from this park to the one preceding the production unit. Thus, the main goal was to determine the dimensions of the fleet used in internal transportation operations to minimize the idle time of the transport units using a methodology already in use in the mining and quarrying industry. This methodology was analyzed and adapted to the situation presented here. The implementation of this study allows the elimination of unnecessary costs in an industry where the profit margins are low.

Additive Manufacturing (AM) is viewed as a disruptive technology with the potential to replace many manufacturing processes. With its rapid proliferation in recent years, it is crucial to understand how to measure this technology’s social impacts. There are many studies available in the existing literature regarding the economic and environmental impacts of AM technology. However, research regarding the social impacts of this technology is still scarce. To this end, this research has developed a framework based on Social Life Cycle Assessment (SLCA) methodology to measure AM products’ social impacts throughout their life cycle stages. The proposed framework includes an underlying mathematical model that was developed to allow for the aggregation of indicator scores. Additionally, cut-off scores were determined to differentiate between positive and negative social impacts. This framework can be applied to case studies, which provides a final score that quantifies the social impacts of an AM product throughout all its life cycle stages. It also provides scores for each stakeholder category and life cycle stage, which facilitates the identification of hotspots that require attention from organizations.

The world population is growing at a very high rate, which also entails a massive increase in energy consumption, and also, therefore, in its production, which is gradually and steadily increasing. Energy and the environment are essential to achieving sustainable development, and constitute a fundamental part of human activity. If we consider energy efficiency as the use of an appliance, process or installation for which we try to produce more energy, but with less energy consumption than the average for these appliances, processes or installations, then achieving a higher energy efficiency is imperative. Energy efficiency is a cornerstone policy on the road to stopping climate change and to achieving sustainable societies, along with the development of renewable energy and an environmentally friendly transport policy. In this Special Issue, 11 selected and peer-reviewed articles have been contributed, on a wide range of topics under the umbrella of sustainable energy systems. The published articles encompass distinct areas of interest. One area addresses distributed generation, which addresses such topics as the optimal planning of distributed generation, protection of blind areas in distribution networks, multi-objective optimization in distributed generation, energy management of virtual power plants in distributed generation, and the impact of demand-response programs on a home microgrid, as well as concentrating solar power into a highly renewable, penetrated power system. The second section of the Special Issue addresses a wide range of topics, from parametric studies of 2 MW gas engines or data centers, to combustion characteristics of a non-premixed oxy-flame, to new techniques of PV Tracking, to applications of nanofluids in the thermal performance enhancement of parabolic trough solar collectors.

In recent years, there has been an increase in the adoption of quality tools by companies. As such, there has been a commitment to innovation by the organizations to obtain competitive advantages by the development of new products and technologies focused on the creation of economic value but also on delivering sustainability. This study aims to develop an application model of the inventive resolution theory in conjunction with the Eco-Compass ecological innovation tool, in order to allow solutions to be obtained systematically, and to present a performance increase of certain environmental parameters, promoting thus sustainable innovation. The case study research methodology is used to frame the research. The company under study is Nokia enterprise, located in Portugal, which offers a set of services related to telecommunications infrastructures. The unit of analysis is the department of transformation and continuous improvement, and the study illustrated the application of combined use of theory of inventive problem solving (TRIZ) and Eco-compass to develop innovative solutions systematically. The results show that it is possible to achieve innovation according to a certain level of established sustainable environmental parameters, while at the same time solving the identified inventive problem.

In order to reach better results for pulse width modulation (PWM)-based methods, the reference waveforms known as control laws have to be achieved with good accuracy. In this paper, three control laws are created by considering the harmonic components of modular multilevel converter (MMC) state variables to suppress the circulating currents under nonlinear load variation. The first control law consists of only the harmonic components of the MMC’s output currents and voltages. Then, the second-order harmonic of circulating currents is also involved with both upper and lower arm currents in order to attain the second control law. Since circulating current suppression is the main aim of this work, the third control law is formed by measuring all harmonic components of circulating currents which impact on the arm currents as well. By making a comparison between the switching signals generated by the three proposed control laws, it is verified that the second-order harmonic of circulating currents can increase the switching losses. In addition, the existence of all circulating current harmonics causes distributed switching patterns, which is not suitable for the switches’ lifetime. Each upper and lower arm has changeable capacitors, named “equivalent submodule (SM) capacitors” in this paper. To further assess these capacitors, eliminating the harmonic components of circulating currents provides fluctuations with smaller magnitudes, as well as a smaller average value for the equivalent capacitors. Moreover, the second-order harmonic has a dominant role that leads to values higher than 3 F for equivalent capacitors. In comparison with the first and second control laws, the use of the third control-law-based method will result in very small circulating currents, since it is trying to control and eliminate all harmonic components of the circulating currents. This result leads to very small magnitudes for both the upper and lower arm currents, noticeably decreasing the total MMC losses. All simulation results are verified using MATLAB software in the SIMULINK environment.

Transformers are one of the more expensive pieces of equipment found in a distribution network. The transformer’s role has not changed over the last decades. With simple construction and at the same time mechanically robust, they offer long term service that on average can reach half a century. Today, with the ongoing trend to supply a growing number of non-linear loads along with the notion of distributed generation (DG), a new challenge has arisen in terms of transformer sustainability, with one of the possible consequences being accelerated ageing. In this paper we carefully review the existing studies in the literature of the effect of loads and other key factors on oil-transformer ageing. The state-of-the-art is reviewed, each factor is analysed in detail, and in the end a smart transformer protection method is sought in order to monitor and protect it from upcoming challenges.

The paper discusses Home Area Networks (HAN) communication technologies for smart home and domestic application integration. The work is initiated by identifying the application areas that can benefit from this integration. A broad and inclusive home communication interface is analysed utilizing as a key piece a Gateway based on machine-to-machine (M2M) communications that interacts with the surrounding environment. Then, the main wireless networks are thoroughly assessed, and later, their suitability to the requirements of HAN considering the application area is analysed. Finally, a qualitative analysis is portrayed.

In recent years, reducing energy consumption has been relentlessly pursued by researchers and policy makers with the purpose of achieving a more sustainable future. The demand for data storage in data centers has been steadily increasing, leading to an increase in size and therefore to consume more energy. Consequently, the reduction of the energy consumption of data center rooms is required and it is with this perspective that this paper is proposed. By using Computational Fluid Dynamics (CFD), it is possible to model a three-dimensional model of the heat transfer and air flow in data centers, which allows forecasting the air speed and temperature range under diverse conditions of operation. In this paper, a CFD study of the thermal performance and airflow in a real data center processing room with 208 racks under different thermal loads and airflow velocities is proposed. The physical-mathematical model relies on the equations of mass, momentum and energy conservation. The fluid in this study is air and it is modeled as an ideal gas with constant properties. The model of the effect of turbulence is made by employing a k–ε standard model. The results indicate that it is possible to reduce the thermal load of the server racks by improving the thermal performance and airflow of the data center room, without affecting the correct operation of the server racks located in the sensible regions of the room.

The energy management of virtual power plants faces some fundamental challenges that make it complicated compared to conventional power plants, such as uncertainty in production, consumption, energy price, and availability of network components. Continuous monitoring and scaling of network gain status, using smart grids provides valuable instantaneous information about network conditions such as production, consumption, power lines, and network availability. Therefore, by creating a bidirectional communication between the energy management system and the grid users such as producers or energy applicants, it will afford a suitable platform to develop more efficient vector of the virtual power plant. The paper is treated with optimal sizing of DG units and the price of their electricity sales to achieve security issues and other technical considerations in the system. The ultimate goal in this study to determine the active demand power required to increase system loading capability and to withstand disturbances. The effect of different types of DG units in simulations is considered and then the efficiency of each equipment such as converters, wind turbines, electrolyzers, etc., is achieved to minimize the total operation cost and losses, improve voltage profiles, and address other security issues and reliability. The simulations are done in three cases and compared with HOMER software to validate the ability of proposed model.

The desire to increase energy efficiency and reliability of power grids, along with the need for reducing carbon emissions has led to increasing the utilization of Home Micro-grids (H-MGs). In this context, the issue of economic emission dispatch is worthy of consideration, with a view to controlling generation costs and reducing environmental pollution. This paper presents a multi-objective energy management system, with a structure based on demand response (DR) and dynamic pricing (DP). The proposed energy management system (EMS), in addition to decreasing the market clearing price (MCP) and increasing producer profits, has focused on reducing the level of generation units emissions, as well as enhancing utilization of renewable energy units through the DR programs. As a consequence of the nonlinear and discrete nature of the H-MGs, metaheuristic algorithms are applied to find the best possible solution. Moreover, due to the presence of generation units, the Taguchi orthogonal array testing (TOAT) method has been utilized to investigate the uncertainty regarding generation units. In the problem being considered, each H-MG interacts with each other and can negotiate based on their own strategies (reduction of cost or pollution). The obtained results indicate the efficiency of the proposed algorithm, a decrease in emissions and an increase in the profit achieved by each H-MG, by 37% and 10%, respectively.

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty in electricity price while managing the battery storage. The forecasted value for demand and PV unit are predicted by a seasonal autoregressive integrated moving average model (SARIMA)—capable of accurately characterizing both seasonality effects and tail fatness. The optimal operation of the battery is determined by resolving a linear optimization program in which the objective function comprises the conditional value at risk (CVaR). Using CVaR ensures that the demand is fully supplied while minimizing the risk and operational cost. The cost function is the difference between power sold and bought subject to the charging and discharging rates for the battery and defining upper and lower bounds for the level of battery charge. The simulation results confirm that the risk consideration has a significant effect on the optimized management of a storage energy battery in a photovoltaic grid-connected microgrid.

This article presents an innovative switched-capacitor based nine-level inverter employing single DC input for renewable and sustainable energy applications. The proposed configuration generates a step-up bipolar output voltage without end-side H-bridge, and the employed capacitors are charged in a self-balancing form. Applying low-voltage rated switches is another merit of the proposed inverter, which leads to extensive reduction in total standing voltage. Thereby, switching losses as well as inverter cost are reduced proportionally. Furthermore, the comparative analysis against other state-of-the-art inverters depicts that the number of required power electronic devices and implementation cost is reduced in the proposed structure. The working principle of the proposed circuit along with its efficiency calculations and thermal modeling are elaborated in detail. In the end, simulations and experimental tests are conducted to validate the flawless performance of the proposed nine-level topology in power systems.

The principal aim of this study is to devise a combined market operator and a distribution network operator structure for multiple home-microgrids (MH-MGs) connected to an upstream grid. Here, there are three distinct types of players with opposite intentions that can participate as a consumer and/or prosumer (as a buyer or seller) in the market. All players that are price makers can compete with each other to obtain much more possible profitability while consumers aim to minimize the market-clearing price. For modeling the interactions among partakers and implementing this comprehensive structure, a multi-objective function problem is solved by using a static, non-cooperative game theory. The propounded structure is a hierarchical bi-level controller, and its accomplishment in the optimal control of MH-MGs with distributed energy resources has been evaluated. The outcome of this algorithm provides the best and most suitable power allocation among different players in the market while satisfying each player’s goals. Furthermore, the amount of profit gained by each player is ascertained. Simulation results demonstrate 169% increase in the total payoff compared to the imperialist competition algorithm. This percentage proves the effectiveness, extensibility and flexibility of the presented approach in encouraging participants to join the market and boost their profits.

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

The amount of energy generated from a photovoltaic installation depends mainly on two factors—the temperature and solar irradiance. Numerous maximum power point tracking (MPPT) techniques have been developed for photovoltaic systems. The challenge is what method to employ in order to obtain optimum operating points (voltage and current) automatically at the maximum photovoltaic output power in most conditions. This paper is focused on the structural analysis of mathematical models of PV cells with growing levels of complexity. The main objective is to simulate and compare the characteristic current-voltage (I-V) and power-voltage (P-V) curves of equivalent circuits of the ideal PV cell model and, with one and with two diodes, that is, equivalent circuits with five and seven parameters. The contribution of each parameter is analyzed in the particular context of a given model and then generalized through comparison to a more complex model. In this study the numerical simulation of the models is used intensively and extensively. The approach utilized to model the equivalent circuits permits an adequate simulation of the photovoltaic array systems by considering the compromise between the complexity and accuracy. By utilizing the Newton⁻Raphson method the studied models are then employed through the use of Matlab/Simulink. Finally, this study concludes with an analysis and comparison of the evolution of maximum power observed in the models.

This study suggests a model to include a solar power system or photovoltaic system (PV) in the control of frequency by taking into account a percentage of the PV power production for back-up reserve. This is done by investigating two scenarios: PV contribution in (1) initial primary frequency control and (2) entire primary frequency control. As explained in section three, 10% power of the PV modules which receive more than 400 w/m2 irradiation is allocated for the power reserve. The power generation of photovoltaic systems depends largely on weather conditions which makes their output power associated with some degree of uncertainty. For this reason, in this paper, a PV system is considered along with conventional hydro and thermal units and they are modeled in MATLAB/Simulink (version 9.3, MathWorks, Natick, MA, USA) with the purpose of exploring the behavior of the intended method. In the next phase, for further studies, this system is extended to multi-area power systems including gas turbines. The results of the simulation demonstrated that the photovoltaic involvement in the control of frequency can successfully amend the frequency of the overall network. Not only it can decrease the overshoot and undershoot of the frequency response, it has the ability to improve the settling time as well, which helps the system reach the steady state easily and in shorter time. Specifically, the overshoot has reached nearly zero in both one area and two area systems and undershoot has declined up to 60% and 50% in the one area and two-area system, respectively. Considering settling time, while it had a negligible improvement in the one area system, it showed a remarkable enhancement in the two-area system, which improved from about 25 s to 6 s by using the proposed method.

The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.

Recently the circular economy has increasingly received attention worldwide due to the recognition that the security of the supply of resources and environmental sustainability are crucial for the prosperity of all the countries and businesses. G20 countries are stimulating the development of frameworks that enhance the circular economy and generally more sustainable production and consumption modes. In this context, this paper aims to suggest an index to assess the sustainability and the circularity of manufacturing companies. With this tenet, a Sustainable Circular Index (SCI) is proposed based on a five-phase framework. This index could support managers in assessing their level of sustainability and circularity and in implementing some practices that could improve the performances of their companies regarding these two topics. This index represents an important benchmarking tool for manufacturing companies to assess their sustainable and circular behavior and represents a guideline for managers.

This paper presents a home area network (HAN)-based domestic load energy consumption monitoring prototype device as part of an advanced metering system (AMS). This device can be placed on individual loads or configured to measure several loads as a whole. The wireless communication infrastructure is supported on IEEE 805.12.04 radios that run a ZigBee stack. Data acquisition concerning load energy transit is processed in real time and the main electrical parameters are then transmitted through a RF link to a wireless terminal unit, which works as a data logger and as a human-machine interface. Voltage and current sensing are implemented using Hall effect principle-based transducers, while C code is developed on two 16/32-bit microcontroller units (MCUs). The main features and design options are then thoroughly discussed. The main contribution of this paper is that the proposed metering system measures the reactive energy component through the Hilbert transform for low cost measuring device systems.