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To enable wind energy to surpass fossil fuels, the power-to-cost ratio of wind turbines must be competitive. Increasing installation capacities and wind turbine sizes indicates a strong trend toward clean energy. However, larger rotor diameters, reaching up to 170 m, introduce stability and aeroelasticity concerns and aerodynamic phenomena that cause noise disturbances. These issues hinder performance enhancement and social acceptance of wind turbines. A critical aerodynamic challenge is flow separation on the blade’s suction side, leading to a loss of lift and increased drag, ultimately stalling the blade and reducing turbine performance. Various active and passive flow control techniques have been studied to address these issues, with passive techniques offering the advantage of no external energy requirement. High-lift devices, such as leading-edge slats, are promising in improving aerodynamic performance by controlling flow separation. This study explores the geometric parameters of slats and their effects on wind turbine blades’ aerodynamic and acoustic performance. Using an adequate turbulence model at Re = 106 for angles of attack from 14° to 24°, 77 slat configurations were evaluated. Symmetric slats showed superior performance at high angles of attack, while slat chord length was inversely proportional to aerodynamic improvement. A hybrid method was employed to predict noise, revealing slat-induced modifications in eddy topology and increased low- and high-frequency noise. This study’s main contribution is correlating slat-induced aerodynamic improvements with their acoustic effects. The directivity reveals a 10–15 dB reduction induced by the slat at 1 kHz, while the slat induces higher noise at higher frequencies.

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.

The modern energy landscape is undergoing a significant transformation towards cleaner, decentralized energy sources. This change is driven by environmental and sustainability needs, causing traditional centralized electric grids, which rely heavily on fossil fuels, to be replaced by a diverse range of decentralized distributed energy resources. Virtual power plants (VPPs) have surfaced as a flexible solution in this transition. A VPP’s primary role is to optimize energy production, storage, and distribution by coordinating output from various connected sources. Relying on advanced communication and control systems, a VPP can balance supply and demand in real time, offer ancillary services, and support grid stability. However, aligning VPPs’ economic and operational practices with broader environmental goals and policies is a challenging yet crucial aspect. This article introduces a new VPP management and optimization algorithm designed for quick and intelligent decision-making, aiming for the lowest levelized cost of energy (LCOE), minimum grid technical losses, and greenhouse gas (GHG) emissions. The algorithm’s effectiveness is confirmed using the IEEE 33-bus grid with 10 different distributed power generators. Simulation results show the algorithm’s responsiveness to complex variables found in practical scenarios, finding the optimal combination of available energy resources. This minimizes the LCOE, technical losses, and GHG emissions in less than 0.08 s, achieving a total LCOE reduction of 16% from the baseline. This work contributes to the development of intelligent energy management systems, aiding the transition towards a more resilient and sustainable energy infrastructure.

Thermal comfort is very important for the well-being and safety of vehicle occupants, as discomfort can elevate stress, leading to distracted attention and slower reaction times. This creates a riskier driving environment. Addressing this, high-induction air diffusers emerge as a significant innovation, enhancing indoor environmental quality (IEQ) by efficiently mixing cool air from the heating ventilation and air conditioning (HVAC) system with the cabin’s ambient air. This process ensures uniform airflow, diminishes temperature discrepancies, prevents draft sensations, and boosts overall air quality by improving air circulation. In addition to enhancing thermal comfort in vehicles, the novel air diffuser also offers significant potential for personalized ventilation systems, allowing for individualized control over airflow and temperature, thereby catering to the specific comfort needs of each occupant. This study introduces a novel air diffuser that demonstrates a 48% improvement in air entrainment compared to traditional diffusers, verified through Ansys Fluent simulations and laser Doppler velocimetry (LDV) measurements. At a fresh airflow rate of 31.79 m3/h, the total air entrainment rate at 0.6 m for the standard air diffuser is 73.36 m3/h, while for the innovative air diffuser, it is 109.26 m3/h. This solution has the potential to increase the level of thermal comfort and air quality within vehicles, and also signals potential applications across various enclosed spaces, underscoring its importance in advancing automotive safety and environmental standards.

This article introduces a novel multiple-cycle generation system for efficient heat recovery at high and low temperatures. The system is modeled and optimized using the M2EP analysis method (mass, energy, exergy, and performance) and the particle swarm optimization algorithm. The multigeneration system produces electricity, cold, domestic hot water, and biogas by utilizing Kalina cycles, diffusion–absorption refrigeration machines, and high-performance heat exchangers by harnessing waste heat from cement kiln exhaust gases. The Kalina cycle is employed for electricity generation, wherein the H2O+NH3 mixture, heated by hot water, circulates through heat exchangers. Downstream of the Kalina cycle, the refrigeration machine generates cold by evaporating the strong solution of the H2O+NH3 mixture. Hydrogen circulates in the diffusion–absorption refrigerator (DAR) circuit, facilitating the exchange between the evaporator and the absorber. The domestic hot water and biogas production systems operate at lower temperatures (around 45 °C). The simulation results for the Kalina cycle indicate an electrical energy production of 2565.03 kW, with a release of usable energy (residual gases) estimated at 7368.20 kW and a thermal efficiency of 22.15%. Exergy destruction is highest at heat exchanger 1, accounting for 26% of the total. A coefficient of performance of 0.268 and an evaporator temperature of 10.57 °C were obtained for the DAR cycle. The absorber contributes the most to energy exchanges, comprising 37% of the entire circuit. Summarizing the potential for valorizing waste heat from cement kilns, this article lays the foundation for future research.

In the face of depleting fossil energy and the imperative of sustainable development, there is a compelling drive towards advancing renewable energies. In this context, sustainable and predictable alternatives, like marine energy, gain prominence. Marine energy presents a cleaner option devoid of the adverse effects associated with fossil fuels, playing a crucial role in environmental sustainability by safeguarding coastlines against erosion. This study focuses on Astara Port in the Caspian Sea, exploring the utilization of wave energy converters (WECs). The originality of this study’s research lies in exploring WECs’ dual role in energy generation and coastal protection. Using MIKE21 software simulations, the impact of number, location, arrangement, and orientation of WECs across various scenarios was investigated, including two WEC number scenarios (11 and 13), three structural placement scenarios (north, front, and south of the port), two structural arrangement scenarios (linear and staggered), two port layout scenarios (original layout and modified layout), and two orientation scenarios for the structures (facing north-east, which is the dominant wave direction, and facing southeast). The results show a remarkable decrease in the significant wave height behind WECs, notably with 13 staggered devices facing dominant waves (from northeast), reducing the significant wave height Hs by 23–25%. This setup also shows the highest wave height reduction, notably 36.26% during a storm event. However, linear WEC setup offers more extensive coastline protection, covering 47.88% of the model boundary during storms. Furthermore, the 11 staggered WECs facing southeast (SE) arrangement had the lowest sediment accumulation at 0.0358 m over one year, showing effective sedimentation mitigation potential. Conversely, the 13 linear WECs facing northeast (NE) had the highest accumulation at 0.1231 m. Finally, the proposed port design redirects high-velocity flow away from the port entrance and removes rotatory flow, reducing sediment accumulation near the harbor entrance.

Technical losses in electrical grids are inherent inefficiencies induced by the transmission and distribution of electricity, resulting in energy losses that can reach up to 40% of the generated energy. These losses pose significant challenges to grid operators regarding energy sustainability, reliability, and economic viability. Distributed Energy Resources (DERs) offer promising solutions to lower technical losses by decentralizing energy generation and consumption, reducing the need for long-distance transmission and optimizing grid operation. Hence, estimating the impact of DERs on grid technical losses becomes paramount for grid operators and planners. In response, this article proposes the application of regression modeling and nonlinear curve fitting algorithms to provide a more nuanced understanding and better characterize the intricate interplay between DER deployment and technical losses. Through a comprehensive case study based on more than 1080 computer simulations, we demonstrate the effectiveness of our proposed dynamic polynomial varying coefficient regression model in estimating the impact of DERs on technical losses within electrical grids. The proposed model offers a simple and effective methodology that allows grid operators to gain insights into the nonlinear dynamics of DER integration and make quicker and more informed decisions regarding grid management strategies, infrastructure investments, and policy interventions. Also, this research contributes to advancing the field of grid optimization by offering a simple equation that enhances our ability and haste to assess and mitigate technical losses in the context of an evolving energy landscape characterized by increasing DER adoption.

Escalating events such as extreme weather conditions, geopolitical incidents, acts of war, cyberattacks, and the intermittence of renewable energy resources pose substantial challenges to the functionality of global electric grids. Consequently, research on enhancing the resilience of electric grids has become increasingly crucial. Concurrently, the decentralization of electric grids, driven by a heightened integration of distributed energy resources (DERs) and the imperative for decarbonization, has brought about significant transformations in grid topologies. These changes can profoundly impact flexibility, operability, and reliability. However, there is a lack of research on the impact of DERs on the electric grid’s resilience, as well as a simple model to simulate the impact of any disturbance on the grid. Hence, to analyze the electric grid’s resilience, this study employs an extrapolation of Leontief’s input–output (IO) model, originally designed to study ripple effects in economic sectors. Nodes are treated as industries, and power transmission between nodes is considered as the relationship between industries. Our research compares operability changes in centralized, partially decentralized, and fully decentralized grids under identical fault conditions. Using grid inoperability as a key performance indicator (KPI), this study tests the three grid configurations under two fault scenarios. The results confirm the efficacy of decentralization in enhancing the resilience and security of electric grids.

Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building energy management has become critical with the increasing demand for sustainable buildings and energy-efficient systems. Simulation tools have become crucial in assessing the effectiveness of buildings and their energy systems, and they are widely used in building energy management. These simulation tools can be categorized into white-box and black-box models based on the level of detail and transparency of the model’s inputs and outputs. This review publication comprehensively analyzes the white-box, black-box, and web tool models for building energy simulation tools. We also examine the different simulation scales, ranging from single-family homes to districts and cities, and the various modelling approaches, such as steady-state, quasi-steady-state, and dynamic. This review aims to pinpoint the advantages and drawbacks of various simulation tools, offering guidance for upcoming research in the field of building energy management. We aim to help researchers, building designers, and engineers better understand the available simulation tools and make informed decisions when selecting and using them.

This paper introduces a novel techno-economic feasibility analysis of energy management utilizing the Homer software v3.14.5 environment for an independent hybrid microgrid. This study focuses on a school with twelve classes, classifying the electrical components of the total load into three priority profiles: green, orange, and red. The developed approach involves implementing demand management for the hybrid microgrid through Bayesian inference, emphasizing goal-directed decision making within embodied or active inference. The Bayesian inference employs three parameters as inputs: the total production of the hybrid system, the load demand, and the state of charge of batteries to determine the supply for charge consumption. By framing decision making and action selection as variational Bayesian inference, the approach transforms the problem from selecting an optimal action to making optimal inferences about control. The results have led to the creation of a Bayesian inference approach for the new demand management strategy, applicable to load profiles resembling those of commercial and service institutions. Furthermore, Bayesian inference management has successfully reduced the total unmet load on secondary and tertiary priority charges to 1.9%, thereby decreasing the net present cost, initial cost, and energy cost by 37.93%, 41.43%, and 36.71%, respectively. This significant cost reduction has enabled a substantial decrease in investments for the same total energy consumption.

Mining in Canada stands as one of the most energy-intensive sectors, playing a pivotal role as a significant provider of copper, nickel, and cobalt to the international market. Anticipated growth in the global population, coupled with the transition of several low-income economies to middle-income status, is poised to escalate the demand for essential raw materials. This surge in demand is expected to drive an increase in energy consumption across various stages of the Canadian mining industry, encompassing exploration, extraction, processing, and refining. Due to their geographical constraints, most Canadian mining operations rely heavily on fossil fuels such as diesel and heavy fuel. Considering the global shift towards decarbonization and the pursuit of net-zero emission targets, exploring avenues for adopting electrification solutions and integrating renewable energy technologies, particularly in sizable surface mines, is imperative. Within this context, our study delves into the challenges and prospects associated with infusing renewable energy technologies and embracing electrification alternatives within Canadian mining practices. This exploration encompasses a comprehensive review of pertinent literature comprising academic research, technical analyses, and data disseminated by international entities and experts. The findings underscore a prevalent trend wherein Canadian mining enterprises are prominently investing in robust electric truck fleets, particularly for heavy-duty operations. Additionally, incorporating renewable energy solutions is notably prevalent in remote sites with extended operational lifespans. However, an in-depth examination reveals that the most formidable hurdles encompass successfully integrating renewable energy sources and battery electric vehicles. Financial constraints, logistical intricacies, and the imperative to enhance research and development competencies emerge as pivotal challenges that demand strategic addressing.

This paper presents case studies for replacing diesel generators (DGs) that are used as the main and emergency power sources for alternating current (AC) auxiliary services in substations (SS) within the transmission network (voltage greater than or equal to 230 kV). The objective of this research is to present a solution that is more reliable, environmentally friendly, and financially viable than DGs. To achieve this, the proposed solutions incorporate Battery Energy Storage Systems (BESSs) with or without the integration of Photovoltaic (PV) Systems. These solutions were simulated using the HOMER PRO Version 3.14.5 software for the Messias SS /AL, and the results were analyzed and compared to the DG in terms of reliability, financial viability, and environmental impact. Based on the conducted analyses, the BESS solution with the PV system was found to be the most suitable for the main source. However, in the case of the emergency source, if one of the main sources is a DG/BESS, maintaining the emergency DG is the preferable option. If both main sources are independent, the BESS solution with the PV system is a suitable solution.

The global concern regarding the environmental repercussions of refrigerants has escalated due to their adverse effects. These substances deplete the ozone layer and intensify the greenhouse effect. International agreements such as the Montreal and Kyoto Protocols and COP21 have imposed restrictions on refrigerants with high global warming potential (GWP) to address these issues. This study aims to explore the feasibility, energy efficiency, and environmental impact of utilizing the HFO (hydrofluoric-olefin) refrigerant R1234ze as a substitute for HFCs (hydrofluoric-carbon) (R134a, R407C, and R410A) and HCFCs (R22) in air-cooled vapor compression refrigeration and air conditioning systems. To determine their effectiveness, we evaluate the energy performance of various refrigerant operating cycles across a wide range of ambient and evaporating temperatures. Additionally, we conduct environmental impact analyses based on the total equivalent warming impact (TEWI) parameter calculated for commercially available chillers that utilize the fluids mentioned above. Our findings indicate that vapor compression chilled water systems employing R1234ze exhibit the highest performance coefficient and the lowest annual TEWI.

Low load is one of the most challenging combustion stages for a fixed-speed diesel electric generator. Due to incomplete combustion during this phase, a significant proportion of contaminants form inside the cylinder. This can lead to numerous chemical and mechanical harms to the diesel engine, resulting in friction, efficiency reduction, increased fuel consumption, and prematurely ending the generator’s life. These phenomena are qualified as underperformance, possibly due to a misfire and/or a low-efficiency value (air fuel–fuel ratio). Therefore, detecting and preventing underperformance and reducing its extended operation is crucial. This paper deals with the performance and emission analysis of a multicylinder fixed-speed diesel engine driving an electric generator (300 kW) fueled with ultra-low sulfur diesel (≤15 mg/kg) to provide energy in an isolated Canadian community. The tests were carried out according to ISO 3046-1:2002 standard in a remote site to identify clues that can prevent prolonged operation in underperformance. Among the tests conducted, emissions such as sulfur (S), carbon dioxide (CO2), nitrogen oxide (NOx), and exhaust gas temperature are considered the best indices for detecting the underperformance of a fixed-speed diesel–electric generator under very-low and low load (0–30%) with the following registered values: 18 ppm for S, 4% for CO2, 150 ppm for NOx, and 210 °C for the temperature.

The energy transition, originating in the limitation of fossil resources and greenhouse gas (GHG) emission reduction, is the basis of many studies on renewable energies in different industrial applications. The diffusion absorption refrigeration machines are very promising insofar as they allow the use of renewable resources (solar, geothermal, waste gas, etc.). This technology is often considered an alternative to vapor compression systems in cooling and refrigeration applications. This paper aims to overview the thermal mechanisms related to modeling system energy sources and highlight the primary methodologies and techniques used. We study and analyze the technology’s current challenges and future directions and, finally, identify the gaps in the existing models to pave the way for future research. The paper also gives a classification of absorption refrigeration systems (ARS) to position and limit the scope of the study. The paper will help researchers who approach the various aspects to have a global synthetic analysis of the mechanisms characterizing the modeling of energy sources of absorption refrigeration machines.

The dwindling and scarcity of fossil energy sources is the basis of the energy transition, where renewable resources are increasingly valued. The purpose of the cogeneration system studied in this article is to recover the residual heat from the gases coming out of the chimneys of the cement kilns, to produce at the same time the electricity and the heat required for offices and residential houses of cement workers. Cement kilns are reputed to be energy-intensive, generating excessive heat losses. These heat losses are found mainly in the conduction–convective and radiative modes, representing about 26% of the overall heat input to the system. Nevertheless, the gases at the chimney outlet can still have temperatures between 250 and 350 °C, which presents a non-negligible potential for a cogeneration system. This study compares the thermal performance of different cogeneration plant configurations (KCA, KCB, and KCC systems) using the Kalina cycle to determine the best one. Several assumptions were made to reduce the complexity of the model. MATLAB and Excel software were used to solve the system of equations. After extensive analysis of the results, the KCA system showed the best performance, compared to the KCB and KCC systems, with a thermal efficiency of 22.15%, an exergy efficiency of 45.12%, and a net electrical capacity of 2565.03 kWe. Model sensitivity to concentration, temperature, and pressure variations also gave the KCA system the best-performing system. Evaluation of the excess heat flux removed from the process yields values of 7368.20 kW, 7421.86 kW, and 8094.15 kW for the KCA, KCB, and KCC systems. The results of this article serve as a decision support tool for installing the cogeneration system via the Kalina cycle in cement installations.

Following the international trend of using hydrogen as combustible in many industry branches, this paper investigates the impact of mixing methane gas with 23% hydrogen (G222) on condensing boilers’ operation. After modeling and testing several boilers with heat exchange surface different designs, the authors gathered enough information to introduce a new concept, namely High-Performance Condensing Boiler (HPCB). All the boilers that fit into this approach have the same operational parameters at nominal heat load, including the CO2 concentrations in flue gases. After testing a flattened pipes condensing boiler, a CO2 emission reduction coefficient of 1.1 was determined when converting from methane gas to G222 as combustible. Thus, by inserting into the national grid a G222 mixture, an important reduction in greenhouse gases can be achieved. For a 28 kW condensing boiler, the annual reduction in CO2 emissions averages 1.26 tons, value which was experimentally obtained and is consistent with the theoretical evaluation.

Icing is a severe problem faced by wind turbines operating in cold climates. It is affected by various fluctuating parameters. Due to ice accretion, a significant drop in the aerodynamic performance of the blades’ airfoils leads to productivity loss in wind turbines. When ice accretes on airfoils, it leads to a geometry deformation that seriously increases turbulence, particularly on the airfoil suction side at high angles of attack. Modeling and simulation are indispensable tools to estimate the effect of icing on the operation of wind turbines and gain a better understanding of the phenomenon. This paper presents a numerical study to assess the effect of surface roughness distribution, along with the effect of two turbulence models on estimating wind turbine airfoils’ aerodynamic performance losses in the presence of ice. Aerodynamic parameter estimation was performed using ANSYS FLUENT, while ice accretion was simulated using ANSYS FENSAP-ICE. The results using the adopted modeling approaches and the simulation tools were compared with another numerical study and validated against experimental data. The validation process demonstrated the model’s accuracy when considering roughness distribution via the beading model available in ANSYS FENSAP-ICE. The two turbulence models examined (Spalart–Allmaras and k-ω SST) gave comparable results except for the drag at high angles of attack. The k-ω SST model was more efficient in replicating turbulence at high angles of attack, leading to higher accuracy in aerodynamic loss estimation.

Wind turbine icing has been the subject of intensive research over the past two decades, primarily focusing on applying computational fluid dynamics (CFD) to 2D airfoil simulations for parametric analysis. As a result of blades’ airfoils deformation caused by icing, wind turbines experience a considerable decrease in aerodynamic performance resulting in a substantial loss of productivity. Due to the phenomenon’s complexity and high computational costs, a fully 3D simulation of the entire iced-up rotating turbine becomes infeasible, especially when dealing with several scenarios under various operating and weather conditions. The Quasi-3D steady-state simulation is a practical alternative method to assess power loss resulting from ice accretion on wind turbine blades. To some extent, this approach has been employed in several published studies showing a capability to estimate performance degradation throughout the generation of power curves for both clean and iced wind turbines. In this paper, applying the Quasi-3D simulation method on wind turbine icing was subject to a survey and in-depth analysis based on a comprehensive literature review. The review examines the results of the vast majority of recently published studies that have addressed this approach, summarizing the findings and bringing together research in this area to conclude with clear facts and details that enhance research on the estimation of wind turbine annual power production loss due to icing.

With stricter IMO regulations on CO2 taking effect in 2023 and ambitious goals to reduce carbon intensity by 2030, the maritime industry is scrambling to clean up its act. Conventional methods and equipment are now being reevaluated, upgraded or completely replaced. The difference between a short-term fix and a long-term sustainable option is how flexible vessels will be to use new energy sources or technology as they become viable. The review discusses the recent literature on renewable energy sources, technical and operational strategies for new and existing ships, technology maturity, and alternative fuels. It is found that the IMO’s targets can be met by combining two or three technologies, or via a radical technology shift which can provide innovative, high-efficiency solutions from an environmental and economic standpoint. It has also been noted that policies and enforcement are essential management instruments for mitigating the unfavourable environmental effects of marine transportation and directing the maritime industry toward sustainability on a regional, national, and international scale.

In this paper, we explore the improvement of the aerodynamic characteristics of wind turbine blades under stall conditions using passive flow control with slots. The National Renewable Energy Laboratory (NREL) Phase II rotor, for which detailed simulations and experimental data are available, served as a baseline for assessing the flow control system effects. The position and configuration of the slot used as a flow control system were determined using CFD analysis. The 3D-RANS equations are solved with ANSYS FLUENT using the k-ω SST turbulence closure model. The pressure coefficient for different wind speeds for the baseline configuration is compared to the available experimental data. The comparison shows that CFD results were better for the attached flow. The current work consists of a 3-D CFD modeling of a rotating blade equipped with different flow control systems: single-slot (S-S) and two-slots (T-S). The computation provides a better understanding of the influence of these flow control devices on the performance of wind turbine blades, the control of boundary layer separation, and the rotation effect. These control systems increase the power output by over 60% at high wind speeds with large separated boundary layer regions. For the configuration with the control system, the slot has shown its ability to delay the boundary layer separation. However, the improved aerodynamic performance has been proven for medium and high angles of attack where the flow is generally in the stall condition. The addition of the second slot changed the flow behavior, and an improvement was observed compared to the single slot configuration. The results are helpful for the design and development of a new generation of wind turbine blades.

The purpose of this research is to design an energy-efficient off-grid vending machine that has a refrigeration system powered by an environmentally friendly refrigerant. The paper presents details from the equipment’s design phase in terms of the resistance structure, photovoltaic panels area dimensioning, but also in terms of the necessary cooling load to be provided by the refrigeration system. The refrigerant chosen was R290 following a comparative analysis, which showed that it has an efficiency 6.6% higher than R134a. During the monitoring periods, the outdoors temperature varied between −4–28 °C, the relative humidity between 22–100% and yet only a slight indoor temperature variation of ±1.5 °C compared to the set value was recorded. The battery module managed to store enough energy from the photovoltaic panels to keep the vending machine functional through three consecutive days with adverse environmental conditions, as evidenced by the low variation of indoor temperature

This paper presents a literature review of recent research on introducing the Industry 4.0 approach to improving energy efficiency, especially in the wood industry. While researchers focus on processes, service, and customer concepts, the effect on energy consumption is less addressed in these studies and applications. This paper focuses on previous works that discuss how to apply Industry 4.0 concepts to energy issues, such as to achieve better efficiency and performance for the industry in general and the wood industry in particular. The complexity of the study requires a multistep development. First, we define each concept separately and the relationships between them. Second, we apply a search algorithm to find related articles with specific terms, and then use the PRISMA method to select the most important ones, eliminating duplicates and excluding articles that do not mention energy efficiency and Industry 4.0 in the manufacturing or wood industry. Third, we explain and categorize the results and consolidate the study with brief examples from other industries. Finally, we conclude the study by mentioning the limitations and perspectives.

The objective of this article is to review the methodologies used in the last 15 years to estimate the power loss in wind turbines due to their exposure to adverse meteorological conditions. Among the methods, the use of computational fluid dynamics (CFD) for the three-dimensional numerical simulation of wind turbines is highlighted, as well as the use of two-dimensional CFD simulation in conjunction with the blade element momentum theory (BEM). In addition, a brief review of other methodologies such as image analysis, deep learning, and forecasting models is also presented. This review constitutes a baseline for new investigations of the icing effects on wind turbines’ power outputs. Furthermore, it contributes to a continuous improvement in power-loss prediction and the better response of icing protection systems.

The energy efficiency of a renewable energy system is inextricably linked to the energy storage technologies used in conjunction with it. The most extensively utilized energy storage technology for all purposes is electrochemical storage batteries, which have grown more popular over time because of their extended life, high working voltage, and low self-discharge rate. However, these batteries cannot withstand the very low temperatures encountered in cold regions, even with these very promising technical characteristics. The cold northern temperatures affect the batteries’ electromotive force and thus decrease their storage capacity. In addition, they affect the conductivity of the electrolyte and the kinetics of electrochemical reactions, thus influencing the capacity and speed of electrons in the electrolyte. In this article, which is intended as a literature review, we first describe the technical characteristics of charge–discharge rate of different electrochemical storage techniques and their variations with temperature. Then, new approaches used to adapt these electrochemical storage techniques to cold climates are presented. We also conduct a comparative study between the different electrochemical storage techniques regarding their performance in the harsh climatic conditions of the Canadian North.

Diesel generators (DGs) are set to work as a backup during power outages or support the load in remote areas not connected to the national grid. These DGs are working at a constant speed to produce reliable AC power, while electrical energy demand fluctuates according to instantaneous needs. High electric loads occur only for a few hours a day in remote areas, resulting in oversizing DGs. During a low load operation, DGs face poor fuel efficiency and condensation of fuel residues on the walls of engine cylinders that increase friction and premature wear. One solution to increase combustion efficiency at low electric loads is to reduce diesel engine (DE) speed to its ideal regime according to the mechanical torque required by the electrical generator. Therefore, Variable Speed Diesel Generators (VSDGs) allow the operation of the diesel engine at an optimal speed according to the electrical load but require additional electrical equipment and control to maintain the power output to electrical standards. Variable speed technology has shown a significant reduction of up to 40% fuel consumption, resulting in low GHG emissions and operating costs compared to a conventional diesel generator. This technology also eliminates engine idle time during a low load regime to have a longer engine lifetime. The main objective of this survey paper is to present the state of the art of the VSDG technologies and compare their performance in terms of fuel savings, increased engine lifetime, and reduced greenhouse gases (GHG) emissions. Various concepts and the latest VSDG technologies have been evaluated in this paper based on their performance appraisal and degree of innovation.

In a context of energy abundance for industrial applications, industrial systems are exploited with minimal attention to their actual energy consumption requirements to meet the loads imposed on them. As a result, most of them are used at maximal capacity, regardless of the varying operational conditions. First, the paper studies pneumatic conveying systems and thoroughly reviews previously published work. Then, we overview simulations and operating data of the experimental parameters and their effects on the flow characteristics and transport efficiency. Finally, we summarize with a conclusion and some suggestions for further work. The primary goal of this study is to identify the parameters that influence the energy consumption of industrial dust collector systems. It is differentiated from previously published overviews by being concentrated on wood particles collection systems. The results will permit a better selection of an appropriate methodology or solution for reducing an industrial system’s power requirements and energy consumption through more precise control. The anticipated benefits are not only on power requirement and energy consumption but also in reducing greenhouse gas emissions. This aspect shows more impacts in regions that rely on electricity supplied by thermal power stations, especially those that use petrol or coal.

In this paper, a novel process is developed to cogenerate 4741 kg/h of methanol, 297.7 kW of electricity, and 35.73 ton/h of hot water, including a hydrogen purification system, an absorption–compression refrigeration cycle (ACRC), a regenerative Organic Rankine Cycle (ORC), and parabolic solar troughs. The heat produced in the methanol reactor is recovered in the ORC and ACRC. Parabolic solar troughs provide thermal power to the methanol distillation tower. Thermal efficiencies of the integrated structure and the liquid methanol production cycle are 78.14% and 60.91%, respectively. The process’s total exergy efficiency and irreversibility are 89.45% and 16.89 MW. The solar thermal collectors take the largest share of exergy destruction (34%), followed by heat exchangers (30%) and mixers (19%). Based on the sensitivity analysis, D17 (mixture of H2 and low-pressure fuel gas before separation) was the most influential stream affecting the performance of the process. With the temperature decline of stream D17 from −139 to −149 °C, the methanol production rate and the total thermal efficiency rose to 4741.2 kg/h and 61.02%, respectively. Moreover, the growth in the hydrogen content from 55% to 80% molar of the feed gas, the flow rate of liquid methanol, and the total exergy efficiency declined to 4487 kg/h and 86.05%.

Global economic growth, demographic explosion, digitization, increased mobility, and greater demand for heating and cooling due to climate change in different world areas are the main drivers for the surge in energy demand. The increase in energy demand is the basis of economic challenges for power companies alongside several socio-economic problems in communities, such as energy poverty, defined as the insufficient coverage of energy needs, especially in the residential sector. Two main strategies are considered to meet this increased demand. The first strategy focuses on new sustainable and eco-friendly modes of power generation, such as renewable energy resources and distributed energy resources. The second strategy is demand-side oriented rather than the supply side. Demand-side management, demand response (DR), and energy efficiency (EE) programs fall under this category. On the other hand, the decentralization and digitization of the energy sector convoyed by the emersion of new technologies such as blockchain, Internet of Things (IoT), and Artificial Intelligence (AI), opened the door to new solutions for the energy demand dilemma. Among these technologies, blockchain has proved itself as a decentralized trading platform between untrusted peers without the involvement of a trusted third party. This newly introduced Peer-to-Peer (P2P) trading model can be used to create a new demand load control model. In this article, the concept of an energy cap and trade demand-side management (DSM) model is introduced and simulated. The introduced DSM model is based on the concept of capping consumers’ monthly energy consumption and rewarding consumers who do not exceed this cap with energy tradeable credits that can be traded using blockchain-based Peer-to-Peer (P2P) energy trading. A model based on 200 households is used to simulate the proposed DSM model and prove that this model can be beneficial to both energy companies and consumers.

Rail transport, specifically diesel–electric trains, faces fundamental challenges in reducing fuel consumption to improve financial performance and reduce GHG emissions. One solution to improve energy efficiency is the electric brake regenerative technique. This technique was first applied on electric trains several years ago, but it is still considered to improve diesel–electric trains efficiency. Numerous parameters influence the detailed estimation of brake regenerative technique performance, which makes this process particularly difficult. This paper proposes a simplified energetic approach for a diesel–electric train with different storage systems to assess these performances. The feasibility and profitability of using a brake regenerative system depend on the quantity of energy that can be recuperated and stored during the train’s full and partial stop. Based on a simplified energetic calculation and cost estimation, we present a comprehensive and realistic calculation to evaluate ROI, net annual revenues, and GHG emission reduction. The feasibility of the solution is studied for different train journeys, and the most significant parameters affecting the impact of using this technique are identified. In addition, we study the influence of electric storage devices and low temperatures. The proposed method is validated using experimental results available in the literature showing that this technique resulted in annual energy savings of 3400 MWh for 34 trains, worth USD 425,000 in fuel savings.

When operating in cold climates, wind turbines are vulnerable to ice accretion. The main impact of icing on wind turbines is the power losses due to geometric deformation of the iced airfoils of the blades. Significant energy losses during the wind farm lifetime must be estimated and mitigated. Finding solutions for icing calls on several areas of knowledge. Modelling and simulation as an alternative to experimental tests are primary techniques used to account for ice accretion because of their low cost and effectiveness. Several studies have been conducted to replicate ice growth on wind turbine blades using Computational Fluid Dynamics (CFD) during the last decade. While inflight icing research is well developed and well documented, wind turbine icing is still in development and has its peculiarities. This paper surveys and discusses the models, approaches and methods used in ice accretion modelling in view of their application in wind energy while summarizing the recent research findings in Surface Roughness modelling and Droplets Trajectory modelling. An An additional section discusses research on the modelling of electro-thermal icing protection systems. This paper aims to guide researchers in wind engineering to the appropriate approaches, references and tools needed to conduct reliable icing modelling for wind turbines.

The installation of wind energy increased in the last twenty years, as its cost decreased, and it contributes to reducing GHG emissions. A race toward gigantism characterizes wind turbine development, primarily driven by offshore projects. The larger wind turbines are facing higher loads, and the imperatives of mass reduction make them more flexible. Size increase of wind turbines results in higher structural vibrations that reduce the lifetime of the components (blades, main shaft, bearings, generator, gearbox, etc.) and might lead to failure or destruction. This paper aims to present in detail the problems associated with wind turbine vibration and a thorough literature review of the different mitigation solutions. We explore the advantages, drawbacks, and challenges of the existing vibration control systems for wind turbines. These systems belong to six main categories, according to the physical principles used and how they operate to mitigate the vibrations. This paper offers a multi-criteria analysis of a vast number of systems in different phases of development, going from full-scale testing to prototype stage, experiments, research, and ideas.

This paper introduces a modular testbed to simulate AC/DC microgrids. The testbed is implemented in Matlab Simulink and is based on the energetic macroscopic representation (EMR) formalism. It is designed to be a tool to evaluate energy management strategies in AC/DC microgrids. The microgrid simulation model includes a photovoltaic generator, a fuel cell system, ultracapacitors, and batteries on the DC side. It includes voltage source converters (VSC) to couple the DC side with the AC side of the microgrid, which includes a variable AC load and a synchronous generator. Two case studies illustrate the use of the testbed. The model is implemented in Matlab Simulink and made openly available for the scientific community. Using this model, researchers can develop and evaluate energy management strategies in AC/DC microgrids.

Variable speed generators can improve overall genset performance by allowing the diesel engine to reduce its speed at lower loads. In this project, a variable speed diesel generator (VSDG) uses a rotating stator driven by a compensator motor. At lower loads, the stator turns in the opposite direction of the rotor, a process that can be used for purposes like maintaining a fixed relative speed between the two components of a generator. This allows the diesel engine to turn at a lower speed (same as the rotor) and to increase its efficiency. The present research addresses the control of the compensator motor driving the generator’s stator using a variable-frequency drive that adapts the speed to its optimal value according to the load. The performance of the proposed control strategy was tested using a Freescale microcontroller card programmed in C-code to determine the appropriate voltage for the variable-frequency drive. The control algorithm uses a real-time application implemented on an FDRM-KL25Z signal processor board. The control performance of a 2 kW asynchronous motor (LabVolt EMS 8503-00/208 V/3 ϕ/60(50) Hz) was demonstrated experimentally at different operating conditions.

Fundamentally, two main methodologies are used to reduce the electric energy bill in residential, commercial, and even industrial applications. The first method is to act on the supply side by integrating alternative means of power generation, such as renewable energy generators, having a relatively low levelized cost of energy. Whereas, the second methodology focuses on the management of the load to minimize the overall paid cost for energy. Thus, this article highlights the importance of demand side management by comparing it to the supply side management having, as criteria, the total achieved savings on the overall annual energy bill of a residential microgrid supplied by two power sources and equipped with an electric vehicle. The optimization takes into consideration the cost of kWh that is paid by the prosumer based on an economical model having as inputs the outcomes of the energy model. The adopted energy model integrates, on the demand side, an intelligent energy management system acting on secondary loads, and on the supply side, a photovoltaic (PV) system with and without battery energy storage system (BESS). The outcome of this work shows that, under the right circumstances, demand side management can be as valuable as supply side control.

Remote and isolated communities in Canada experience gaps in access to stable energy sources and must rely on diesel generators for heat and electricity. However, the cost and environmental impact resulting from the use of fossil fuels, especially in local energy production, heating, industrial processes and transportation are compelling reasons to support the development and deployment of renewable energy hybrid systems. This paper presents a computer model for economic analysis and risk assessment of a wind−diesel hybrid system with compressed air energy storage. The proposed model is developed from the point of view of the project investor and it includes technical, financial, risk and environmental analysis. Robustness is evaluated through sensitivity analysis. The model has been validated by comparing the results of a wind−diesel case study against those obtained using HOMER (National Renewable Energy Laboratory, Golden, CO, United States) and RETScreen (Natural Resources Canada, Government of Canada, Canada) software. The impact on economic performance of adding energy storage system in a wind−diesel hybrid system has been discussed. The obtained results demonstrate the feasibility of such hybrid system as a suitable power generator in terms of high net present value and internal rate of return, low cost of energy, as well as low risk assessment. In addition, the environmental impact is positive since less fuel is used.

A hybrid system combines two or more energy sources as an integrated unit to generate electricity. The nature of the sources associated varies between renewable and/or non-renewable energies. Such systems are becoming popular as stand-alone power systems to provide electricity, especially in off grid remote areas where diesel generators act as primary energy source. Wind−diesel systems are among the preferred solutions for new installations, as well as the upgrade of existing ones. However, efforts to address technical challenges towards energy transformation for sustainable development are multiple. The use of energy storage systems is a solution to reduce energy costs and environmental impacts. Indeed, efficient and distributed storage not only allows the electricity grid greater flexibility in the face of demand variations and greater robustness thanks to the decentralization of energy sources, it also offers a solution to increase the use of intermittent renewables in the energy mix. Among different technologies for electrical energy storage, compressed air energy storage is proven to achieve high wind energy penetration and optimal operation of diesel generators. This paper presents a computer model for performance evaluation of a wind−diesel hybrid system with compressed air energy storage. The model has been validated by comparing the results of a wind−diesel case study against those obtained using HOMER software (National Renewable Energy Laboratory, Golden, CO, United States). Different operation modes of the hybrid system are then explored. The impact of hybridization on time and frequency of operation for each power source, fuel consumption and energy dissipation has been determined. Recommendations are made on the choice of key parameters for system optimization.