Energy Performance Research Articles

View Factors Approach for Bifacial Photovoltaic Array Modeling: Bifacial Gain Sensitivity Analysis

Abstract Bifacial modules are highly valued in the global photovoltaic market since they are able to receive sunlight from both sides and can generate up to 10–30% additional energy compared to monofacial ones. They are integrated into various sectors, including building and notably Agrivoltaics. In this work, a coupled optical–thermal–electrical model was developed in matlab to simulate the energy performance and bifacial gain for a ground-raised bifacial system. The model allows optimization as a function of the photovoltaic (PV) system and bifacial module characteristics. A fixed, south-facing bifacial PV arrays composed of three rows assumed installed in Agadir, Morocco, is considered. The optical model is based on an analytical determination of view factors using the cross-string rule. These enable the determination of irradiances received by both sides of modules, which are subsequently used to evaluate energy yield of the system. Model validation was carried out based on various statistical metrics by comparing our results with simulation results generated by various softwares. The comparison shows a very good agreement, notably with 3dbifacialvf (R2 = 1, root-mean-square error (RMSE) = 0.03 W/m2 and R2 = 0.96, RMSE = 12.4 W/m2) for front and rear side irradiance, respectively. The DC energy generated by the system differs by less than 1% compared to pvsyst results. Sensitivity analysis revealed that all system parameters, particularly ground albedo, positively affect the bifacial gain. The bifacial gain increased from 10.6% to 20.1% as the ground albedo increased from 0.25 to 0.5.

Return-Temperature Reduction at District Heating Systems: Focus on End-User Sites

This review presents a comprehensive examination of recent advancements and findings related to return-temperature reduction in District Heating (DH) systems, with a focus on enhancing overall system efficiency at end-user sites. The review categorizes and clarifies various return-temperature reduction techniques, emphasizing aspects such as building energy performance, heat emitters, thermostatic radiator valves, and substation units. One shall note that return temperature is not a parameter that can be directly controlled within a DH system; instead, it is influenced indirectly by adjusting various system parameters throughout the design, commissioning, operation, and control phases. Key insights include the direct impact of heat demand on return temperatures; the pivotal role of indoor heating systems in optimizing thermal energy use in relation to heat demand; the significance of thermostatic radiator valves in regulating heat output and maintaining low return temperatures; the advantages of ventilation radiators and add-on fans in enhancing radiator efficiency; the necessity for effective substation operation to improve system cooling capacity; and the critical role of operational control strategies in achieving optimal system performance. These findings underscore the need for integrated approaches in DH system design and operation to achieve lower return temperatures and improve overall system efficiency.

An improved morphological filtering and feature enhancement method for rolling bearing fault diagnosis

Abstract The defects-induced periodic pulse is one of the important indices for the characterization of bearing failure. To solve the problem that the weak impact features caused by the early fault of the rolling bearing are easily to be interfered with by noise and strong background signal and are difficult to extract, an improved morphological filtering method combined with the Teager energy operator (TEO) is proposed to extract weak shock features. Firstly, according to the correlation between the periodic pulse induced by defects and the Morlet wavelet, the Morlet wavelet is used as the model to construct the structural elements. Then, capturing the Pearson correlation coefficient of the structural elements and the original signal and the signal is filtered by the variable scale morphological filter after threshold screening. Finally, the TEO is used as the post-enhancement link to suppress the noise in the signal after morphological processing and further highlight the fault characteristics. Simulation signals, experimental signals, and field signals verify the effectiveness and robustness of the proposed method.

Fault Feature Extraction of Rolling Bearing Based on Maximum Second-Order Cyclostationarity Blind Deconvolution and CEEMD-Teager

The operation of rolling bearings is directly related to the reliability of the whole rotating machinery. If the rolling bearing failure cannot be diagnosed and repaired in time, it will probably lead to equipment shutdown. The feature extraction process is an important part of bearing fault diagnosis. Some existing feature extraction methods are sensitive to noise and interference, and cannot fully explore and characterize useful information in nonlinear and non-stationary signals in complex scenes, and sometimes even lose important information contained in the data, resulting in low diagnostic accuracy. Therefore, a new method combining Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) was proposed for feature extraction of rolling bearing faults. Firstly, the cyclic frequency is set according to the failure frequency, and the original signal is filtered by CYCBD. Then, the filtered signal is decomposed into multiple Intrinsic Mode Function (IMF) by CEEMD, and the effective IMF components are selected by kurtosis criterion for reconstruction. Finally, the Teager energy operator is used to enhance the transient impact of the reconstructed signals. The results of simulation and comparison experiments show that the proposed method can extract bearing characteristics in different fault states more effectively, and the accuracy of network model diagnosis is improved compared with the traditional methods.

An architectural design framework to promote healthy indoor-outdoor connections in Arctic housing

This research proposes biophilic intermediate spaces as a promising architectural solution to improve indoor-outdoor connections, occupant well-being, and energy efficiency in Arctic housing. Basic examples of intermediate spaces in Arctic housing models include porches and vestibules. However, the architecture of these spaces has not yet been optimized to adress extreme climatic conditions and occupants’ needs. Therefore, this research develops an architectural design framework to optimize the architecture of intermediate spaces for Arctic housing to meet occupants’ well-being needs and improve housing energy efficiency. The research methodology combines an archetypal approach, exploratory case study analysis, and scoping literature review. The archetypal study examines the historical development of Canadian Arctic housing models in Nunavut, revealing the typological evolution, features, potentials, and deficiencies of intermediate spaces in the Arctic. The study identifies of the main design variables and performance indicators of intermediate spaces corresponding to healthy and positive indoor-outdoor connections, thermal and visual comfort, and energy efficiency. A conceptual model of an intermediate space is then simulated as an exploratory case study for a public Canadian Arctic residential building. The architectural design framework is established based on findings from the literature, archetypal studies of Arctic housing, and simulation results of an integrated model for an intermediate space and a public Arctic housing model in Nunavut. The proposed framework includes main architectural variables such as physical adjacency, material, orientation, space depth, and transparency ratio which impact thermal, lighting, and energy performance. This design framework can serve as a reference for creating policies and decision-making processes that integrate biophilic intermediate spaces with Arctic building practices, contributing to Canada's strategic plan for energy efficiency and vegetable production in the Arctic.

Energy consumption analysis and operating characteristics research on small time scale of variable refrigerant flow air conditioning systems in public buildings

In order to analyze the energy consumption and operation characteristics of the variable refrigerant flow (VRF) air-conditioning system on small time scale and put forward effective energy-saving suggestions, the VRF system operation experiments are carried out under different indoor set temperatures, different indoor unit opening quantities and different indoor set air velocities. This experiment studies the main influencing factors of energy consumption and operation of the VRF system, such as indoor temperature difference, indoor air velocity, outdoor environment temperature and so on. The results show that the rated power of indoor unit is linearly related to the energy consumption of the VRF system. The total active power changes greatly when the VRF starts and stops, and the outdoor ambient temperature changes. The indoor set air velocities have a great influence on the energy consumption and operation of the VRF system. The performance parameters increase first and then decrease with the increase of compressor frequency and load rate. When the load rate is 40 %∼60 %, the performance parameters can reach 3.2 ∼ 4.2.

Energy Efficiency and Sustainability in Food Retail Buildings: Introducing a Novel Assessment Framework

One of the primary global objectives is to decrease building energy consumption to promote energy efficiency and environmental sustainability. The large-scale food retail trade sector accounts for over 15% of total primary energy consumption in Europe, posing a significant challenge to the transition towards green energy. This study proposes a simple method for energy efficiency, environmental sustainability, and cost-saving assessment and improvement in large-scale food retail trade buildings. It aims to analyze the energy and environmental performance of building–plant systems, establishing an interactive network to assess intervention potential for the energy transition. The investigation focuses on the proper selection and analysis of the benefits of retrofit solution implementation, emphasizing potential energy savings in current and future climate change scenarios. Dynamic simulation with the Building Energy Model (BEM) was used to evaluate the impacts of building–plant system retrofit solutions, such as high thermal insulation, photovoltaic (PV) panels, Light Emitting Diode (LED) installation, waste heat recovery, and improvement in refrigeration units. The results show a reduction in annual energy consumption for the PV panel installation by up to 29% and lighting systems with high-quality LED to 60%. Additionally, CO2 emissions can be decreased by up to 41% by combining these two strategies.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Enhancing the Energy Performance of a Gas Turbine: Component of a High-Efficiency Cogeneration Plant

Cogeneration is widely recognized as one of the most efficient methods of electricity generation, with gas turbine-based systems playing a critical role in ensuring reliability, sustainability, and consistent power output. This paper presents an energy efficiency analysis of a 14 MW high-efficiency cogeneration unit, featuring a modernized gas turbine as its core component. Since gas turbines often operate under varying loads due to fluctuating demand, this study examines their performance at 100%, 75%, and 50% load levels. It is observed that the efficiency of the gas turbine declines as the load decreases, primarily due to losses resulting from deviations from the design flow conditions. A detailed energy balance, Sankey diagram, and a comparative analysis of performance metrics against the manufacturer’s guarantees are provided for each load scenario. The results indicate that net thermal efficiency decreases by 10.7% at 75% load and by 30.6% at 50% load compared to nominal performance at full load. The performance at full load closely aligns with the values guaranteed by the gas turbine supplier. The gross electrical power output is 1.33% higher than the guaranteed value, and the thermodynamic circuit’s efficiency is 0.49% higher under real conditions. This study represents the initial phase of transitioning the turbine to operate on a fuel blend of natural gas and up to 20% hydrogen, with the goal of reducing CO2 emissions. As a novel contribution, this paper provides a systematized method for calculating and monitoring the in-service performance of gas turbines. The mathematical model is implemented using the Mathcad Prime 8.0 software, which proves to be beneficial for both operators and researchers.

A novel life cycle assessment methodology for transitioning from nZEB to ZEB. Case-study

This study analyzes the CO2 equivalent emissions of a LEED-certified nearly Zero Energy Building (nZEB) using a simplified Life Cycle Assessment (LCA) methodology, aligned with EN 15978. Focusing on the building's energy performance across its life cycle, the study demonstrates that in 2022, 95 % of the energy used for heating came from renewable sources, which decreases to 86 % by 2050 due to milder winters. However, the need for cooling increases by 9 % over the same period due to hotter summers. By 2050 and 2080, if the EU transitions to renewable electricity, the operational Global Warming Potential (GWP) could approach zero. The study highlights that embodied emissions (69 %) outweigh operational emissions (31 %) in 2022, emphasizing the need to reduce embodied GWP through material reuse and recycling. Notably, concrete and aluminum were found to contribute the most to embodied emissions. The research also shows that nZEBs can exceed the EU's 2030 energy targets, with renewable energy contributing 67 % of the building's total consumption. As climate change favors nZEB performance, the operational emissions will trend towards zero, but embodied emissions will become increasingly significant. To achieve the EU's zero-emission goals, it is crucial to prioritize reducing embodied GWP in future nZEBs. This study underscores the importance of nZEBs in mitigating climate change impacts, offering a pathway toward sustainable construction and energy efficiency.

Uncertainty quantification: For an IAQ and energy performance assessment method for smart ventilation strategies

In new low-energy buildings or buildings after thermal refurbishment, the envelope high airtightness could have an impact on air renewal and could decrease the indoor air quality (IAQ). In this context smart-ventilation systems with variable airflows could play a role in providing better IAQ without compromising the energy performance.However, smart-ventilation strategies are quite recent, and their benefits need to be clearly quantified. This article, proposes to quantify the uncertainty of a recent multi-criteria performance assessment method, using global RBD-FAST sensitivity analysis. The impact of the pollutant emissions scenarios, model input parameters and ventilation strategies is assessed.Five ventilation systems were studied: two constant airflow, one humidity-controlled and two humidity/CO2 controlled, applied on a French low-energy house. 2500 simulations were performed to calculate 504 sensitivity indices across 12 input variables and 9 output performance indicators. The sensitivity analysis shows that occupant bio-effluent, formaldehyde and PM2.5 emissions rates are responsible for 11 %–87 % of the uncertainty for the IAQ performance indicators. The PM2.5 deposition velocity parameter is responsible for 50 % of the uncertainty on the PM2.5 indicator, which was an unknown impact. In addition, the benefits of humidity-controlled ventilation were highlighted regarding energy performance, with, in average, 20 % lower heat-loss compared to constant airflow ventilation. Moreover, smart-ventilation provides clear IAQ benefits without drastically increasing the energy demand. This work demonstrates the potential of the proposed evaluation method for ventilation performance assessment.

A dynamic migration route planning optimization strategy based on real-time energy state observation considering flexibility and energy efficiency of thermal power unit

New energy resources compromising intermittent and fluctuating natures have been integrated into the power grid on a large scale, and thermal power units are obliged to promote the depth of deep peak shaving and flexible response-ability to cope with this new scenario. The safety, flexibility, and high efficiency of coal-fired units are the ultimate goals of its transformation, and there is a mutual influence and mutual restriction relationship among these indicators. To exploit the flexibility of thermal power units, take into account efficiency and safety, and play a supporting role in the new power system, a control strategy that weighs various indicators needs to be developed. Based on heat flow model and dynamic state space model, and updated matrix in real time, the key process parameters affecting the flexibility of heat transfer can be fully characterized. By using the observed state and calculation, the load response index and the energy efficiency index are optimized under the boundary constraint. The results show that the multi-objective optimization methods can avoid over regulation, reduce the flue gas flow by 3.23%–3.59%, and reduce the fluctuation of state quantity under the premise of satisfying the load response rate, about 2.32%–2.52% away from the safety boundary of the design temperature parameter to achieve the flexibility of frequency modulation, high efficiency of energy transmission and operation safety.

Energy Performance Indicator of a Gold Mining Industry in Indonesia

Energy Performance Indicator of a Gold Mining Industry in Indonesia

An integrated approach for developing a durable and superhydrophobic anti-corrosion coating inspired by biomimetic starfish surface structures

Applying superhydrophobic coatings with excellent anti-corrosion, anti-fouling, self-cleaning, antibacterial, and wear-resistant properties is a promising approach to protect metal surfaces. This study presents a novel and cost-effective approach, inspired by the biomimetic structure of starfish, to develop a durable and superhydrophobic coating. Unlike traditional methods, we introduce a meticulously engineered tetrapodal ZnO@SiO2-fluorosilane composite that forms a unique hierarchical micro-nano core-shell structure, reducing surface energy and exceptional performance. The formulation combines perfluorooctyl triethoxysilane (FOTS)-modified SiO2, silicone polyester, and tetrapodal ZnO (T-ZnO) in thermoplastic polyurethane (TPU), resulting in a first-of-its-kind T-ZnO@SiO2-FOTS (ZSF) coating with superhydrophobic properties, demonstrated by a water contact angle of 160°, a sliding angle of 3°, and the lowest surface free energy of 14.50 mN/m, contributing to excellent anti-corrosion properties (corrosion rate of 1.44 × 10−6 mm/year) and a corrosion inhibition efficiency of 99.87 %. In addition, the coating exhibits remarkable mechanical durability, adhesion enhancement, and stability against harsh environmental agents, including salt spray, HCl, and NaOH. Our biomimetic approach leverages the starfish-inspired surface design and highly crosslinked interface to enhance resilience, making this coating more robust under real-world conditions. These results provide a promising pathway for the development of multifunctional coating materials suitable for various solid surfaces.

Stackelberg game analysis of the energy performance contracting considering altruistic preferences

The Stackelberg game analysis of an Energy Performance Contracting(EPC) is established to correctly analyze the impact of altruistic preference behaviors on the decision-makers, energy service companies(ESCOs) and energy-consuming units(EUs). Its motivation comes from the fact that, to achieve a win-win situation, EUs usually engage in altruistic behaviors, but the implementation results of the EPC are always contrary to their expectations. Using nonlinear optimization theory, analyzed the decisions of the Stackelberg models under different altruistic preference attributes, and then compared the impacts of altruistic preferences on decisions and profits. Results reveal: (1)compared to a situation lacking altruistic preference, when the leader, the EU, has an altruistic preference, the EU only needs to transfer a small part of its own interests to significantly increase the ESCO's profit and ultimately improve the overall EPC profit; (2) the altruistic preference of the ESCO functions akin to a "poison," engrossing the company in the satisfaction derived from the increased revenue of the energy-consuming entity and gradually eroding its own profits; and (3)the altruistic preference behavior of both parties is a kind of “benefit transfer behavior,” which is conducive to stable operation and long-term development of EPC industry. The research results can provide a theoretical reference to enhance the development of energy-saving service industry.

Bilevel low-carbon coordinated operation of integrated energy systems considering dynamic tiered carbon pricing methodology

The coordinated operation and management of energy and carbon emissions in an integrated energy system (IES) can effectively promote overall energy efficiency and reduce carbon emissions. However, allocating carbon emission responsibilities between transmission-level integrated energy system (TIES) and regional integrated energy system (RIES) is difficult. On the basis of carbon emission flow theory, a model for allocating carbon emission responsibility for IES is developed in this paper. Moreover, the Shapley value method is adopted to obtain the carbon emissions responsibility intervals at the RIES. A dynamic tiered carbon pricing methodology is proposed for the RIES on the basis of equitable carbon emission responsibility intervals. In addition, a bilevel coordinated operation model that imposes carbon price to more fully excavate the low-carbon benefits obtained from operating an IES is proposed. The upper-level model, namely, the TIES, explores the optimal low-carbon schedule for energy networks by imposing fixed carbon price on the energy production costs. The lower-level model, namely, the RIES, investigates the optimal low-carbon energy supply scheme of multienergy coupling equipment and imposes dynamic tiered carbon pricing to adjust the amount of electricity and natural gas consumed. After the whole bilevel model is solved iteratively, equilibrium is reached. Case studies verify the potential and efficacy of the proposed bilevel model, demonstrating superior effectiveness in reducing carbon emissions compared with existing methods.

The influence of the particle size and size synergistic effect of nano-copper on the tribological properties of lubricants

Friction and wear reduction are urgently sought to promote energy savings and performance of precision manufacturing equipment. The molecular dynamics (MD) simulation unveils the detailed lubrication mechanisms of the particle size effect of single Cu NPs and the size synergy effect of multiple Cu NPs. By setting various particle sizes and quantities, the tribological properties of the lubrication system under multiple complex conditions are compared. The stress, strain, wear volume, friction force, normal force, and temperature at the fluid-solid boundary are quantitatively calculated. The results indicate that the average values of friction force are maximally reduced approximately 83.65 %, the wear rate is maximally decreased 22.5 %, the max stress is maximally reduced 22.46 % and the peak temp is maximally reduced 8 % after the addition of Cu NPs. The tribological properties improves as particle size increases, but there is an optimal Cu NP size that yields the best tribological properties. In addition, the tribological properties of the multiple particles are superior to those of the single particle. The results indicate that small and uniform Cu NPs show superior tribological properties over dispersed sizes, small Cu NPs can contribute to improve the overall tribological properties of the system primarily comprises large Cu NPs.

Preparation of biomass fiber/polylactic acid foam using an oven-type free foaming method for energy savings and performance enhancement

Given the inherent challenges of low melt strength and slow crystallization rate associated with polylactic acid (PLA), this study introduces a sustainable and efficacious approach to enhance PLA crystallization utilizing biomass tomato peel pomace powder (TP) as a heterogeneous nucleating agent. ZnO-modified azodicarbonamide (ADC) was used as the chemical blowing agent. The blends were prepared with the aid of a torque rheometer, and TP/PLA composite foams were successfully prepared by the oven-type free foaming method. The empirical results demonstrate that the TP/PLA composite foam exhibits reduced processing requirements and superior comprehensive properties compared to pure PLA foam. Specifically, the composite containing 5 wt% TP achieved optimal foaming performance with 3 phr blowing agents and a foaming duration of 10 min. Furthermore, the annealing treatment markedly enhanced the compression modulus and strength of the foams. Under annealing conditions of 110 °C for 30 min, the compressive strength of the foam increased by 483.5 % relative to the untreated sample. The development of this composite foam signifies a novel direction for energy-efficient performance enhancement and holds significant potential for applications in building insulation and packaging industries.

Research on modeling method for digitizing distribution substation areas based on big data from smart meters

With the wide application of smart meters, real-time data collection in power grid operation can be carried out in real time, which provides big data support for the digital modeling of distribution station areas. At the same time, the digital transformation of the distribution area can be promoted through the digital modeling of the distribution area. In this situation, how to use the big data of smart meters to achieve the digital modeling of the distribution area needs to be further studied. Firstly, this paper briefly introduces the connotation of digitalization in the distribution station area. At the same time, aiming at the digitalization of the distribution station area, it analyzes the digitalization of station features, user features, new energy features, network parameters and operation features, and obtains the digitalization model. Finally, on the basis of the above, select an application scenario to introduce the application of digitalization modeling. The research results can provide a reference for the combination of big data application of smart meters and digitalization of the distribution area, and help the digital transformation of the distribution area.

Backstepping control of multilevel modified SVM inverter in variable speed DFIG-based dual-rotor wind power system

The backstepping control (BC) scheme has been successfully used in a variety of high-effectiveness industrial AC drives. This study presents the application of the BC technique based on multilevel modified space vector modulation (BC-MSVM) to get better the energy performance of a dual-rotor wind turbine system (DRWT). Two techniques are suggested to command the stator power of a doubly-fed induction generator (DFIG) driven by a DRWT. This work addresses the problems of the DRWT-based energy production system, such as stream fineness, power overshoot, and torque ripples. The use of a multilevel inverter in BC-MSVM led to an enhancement in the competence of the multilevel BC-MSVM technique and the system as a whole, and this is proven by the results performed using MATLAB software on a 1500 kW DFIG-DRWT. The use of a seven-level inverter led to a minimization in the rates of overshoot, ripples, steady-state error, and response time of active power by 26.66%, 53.84%, 2.09%, and 9.09%, respectively. Regarding the reactive power, the ratios were estimated at 18.12%, 34%, 25%, and 1.41%, respectively. These ratios prove that using a higher-level inverter significantly improves the voltage quality and characteristics of the DFIG-DRWT.