Electricity Consumption Research Articles

Building performance modelling approaches for a detached vertical green trellis: A case study in a tropical climate

Passive strategies involving greenery significantly increase energy performance in buildings and comfortable microclimate conditions. However, few studies model and simulate their effect on buildings’ energy performance. Thus, this work assesses modelling approaches for conducting building performance simulations where detached vertical green trellises (DVGT) are included. The DVGT characteristics are modelled by: (i) large solid component blocks and (ii) small opaque solid component blocks to form a grid. A building with glazed façades is evaluated through dynamic simulation under the tropical climate of Panama City, using DesignBuilder. Parametric analysis is performed to study the impact of the trellis configuration on the performance in reducing the annual cooling, lighting, and total electricity consumption. A cost-effective evaluation is also conducted based on the net present value for each trellis configuration. Results showed strong agreement with previous studies reporting significant cooling needs reduction while increasing lighting needs and promising return periods. This concludes that the correct optical and radiative properties of the vegetation layer that are wanted to be modelled in a detached vertical trellis are crucial.

Effectiveness of Cool and Green Roofs Inside and Outside Buildings in the Brazilian Context

Several studies have assessed the thermal performance of green and cool roofs. However, few have comprehensively addressed Brazilian buildings and climates, considering indoor and outdoor environments. Considering three Brazilian cities, this study aims to assess the performance of green and cool roofs compared with traditional fibre cement roofs in a typical multifamily residential building. Energy consumption, thermal comfort, and outside surface temperature were assessed using computer simulation. The results show that the cool roofs performed better in cities with warmer climates (e.g., Cfa and Aw), reducing electricity consumption by up to 24.8% compared with traditional roofs. Green roofs are better suited for colder climates (e.g., Cfb), with up to 28.2% energy savings. Green roofs provided the highest percentage of thermal comfort hours in all climates. Cool and green roofs provided hourly reductions in outside roof surface temperature of up to 16.5 °C and 28.4 °C, respectively, compared with the traditional roof. This work reinforces that the choice between these two roof types for each city depends on the parameter used for comparison. Based on the relevant information applied to Brazilian buildings and representative climates presented, this work provided recommendations for urban planning policies and building regulations in Brazil.

Electron-Efficient Co-Electrosynthesis of Formates from CO2 and Methanol Feedstocks.

The electrochemical conversion of CO2 into valuable chemicals using renewable electricity shows significant promise for achieving carbon neutrality and providing alternative energy storage solutions. However, its practical application still faces significant challenges, including high energy consumption, poor selectivity, and limited stability. Here, we propose a hybrid acid/alkali electrolyzer that couples the acidic CO2 reduction reaction (CO2RR) at the cathode with alkaline methanol oxidation reaction (MOR) at the anode. This dual electro-synthesis cell is implemented by developing Bi nanosheets as cathode catalysts and oxide-decorated Cu2Se nanoflowers as anode catalysts, enabling high-efficiency electron utilization for formate production with over 180 % coulombic efficiency and more than 90 % selectivity for both CO2RR and MOR conversion. The hybrid acid/alkali CO2RR-MOR cell also demonstrates long-term stability exceeding 90 hours of continuous operation, delivers a formate partial current density of 130 mA cm-2 at a voltage of only 2.1 V, and significantly reduces electricity consumption compared to the traditional CO2 electrolysis system. This study illuminates an innovative electron-efficiency and energy-saving techniques for CO2 electrolysis, as well as the development of highly efficient electrocatalysts.

Durable one-component polyurea icephobic coatings with energy-saving performance in electrical heating de-icing

Ice formation and accumulation on the surfaces of aircraft, wind turbines, and boat hulls might result in serious economic losses and catastrophic accidents. The development of a new generation of icephobic surfaces has emerged in recent years for anti-icing. However, insufficient durability of the existing icephobic surfaces remains a significant challenge to their applications. In this study, durable one-component polyurea icephobic coatings (OPICs) with a low ice adhesion strength (∼25.3 kPa) were prepared via a solvent-free method by combining the NCO-terminated prepolymer, latent curing agent, and silicone oil. The chemical structure, surface morphology, wettability, mechanical properties, icephobicity, and durability of the OPICs were studied. The optimized design of OPICs with synergistic icephobic mechanisms endowed them with excellent icephobicity and durability. The superior mechanical durability and chemical stability were revealed by the maintaining icephobicity of OPICs even after 3000 continuous Taber abrasion cycles, as well as 50 icing/de-icing cycles, 72 h of NaOH immersion, and 240 h of neutral salt spray test. The effect of OPICs on minimizing the electric power consumption was further investigated by icing wind tunnel. In comparison to the epoxy primer coated and Al alloy coated airfoil surface, airfoil surface coated by OPIC-30 was found to achieve about 43.7 % and 43.9 % energy savings in the keeping entire airfoil surface ice-free, respectively. The one-component polyurea icephobic coatings have great prospects for practical applications that require long-term anti-icing properties.

Development of a Smart Metering System for Efficient Energy Consumption

Smart energy concepts are based on the utilization of emerging integrated microprocessors and communication technologies for monitoring energy consumption. In the utilization of electricity, energy management is a core problem, especially for cost savings to the benefit of electricity customers. In modern electricity generation, transmission and consumption, energy management has occupied the center of attention for sustainable techno-economic motives. The present-day power sector has an immense role to play in the overall economic performance of the global community, thus, requiring the application of efficient and reliable metering systems for the measurement of energy consumption. The application of a smart metering system depends on the effectiveness of the embedded smart communication interface in the utility service provider system and the energy consumers’ metering devices based on energy supply reliability and consumption security. Therefore, this study focuses on the design and development of an efficient smart energy meter using the advancements provided by the Internet of Things (IoTs). The system was tested based on the collection of real-time data and subsequently analyzed for performance evaluation through a case study of household data collection.

An eco-friendly droplet-wet spinning technology for producing high-quality hemp/cotton blend yarn

In recent years, hemp textiles have gained attention as a sustainable alternative to cotton. However, the uneven fineness and length of hemp fibers, along with their stiffness, adversely affect spinnability and exacerbate yarn evenness and hairiness, particularly due to short fibers. This study introduces a modified sustainable droplet-wet spinning technology aimed at enhancing hemp yarn quality, with a primary focus on reducing yarn hairiness. Implemented on a ring spinning machine, the technology utilizes a syringe connected to an adjustable-speed pump, delivering water onto the front top roller. This innovative approach enhances hemp yarn in two ways: firstly, the cohesive force of water promotes the consolidation of short fibers on both sides of the fiber bundle towards the center, thereby condensing the spinning triangle. Consequently, the fibers on both sides of the fiber bundle are more readily drawn into the spinning triangle and twisted to form yarn; secondly, wet hemp fibers possess a lower modulus, rendering them softer and more deformable, facilitating their embedding into the yarn, thereby enhancing yarn tenacity and reducing yarn hairiness. The study systematically investigates the relationship between droplet-wet spinning parameters (water droplet rate, hemp fiber content of the roving, and yarn twist multiplier) and hemp yarn quality. Experimental results reveals that optimal droplet-wet spinning lead to a 22% increase in yarn tenacity and about 50% reduction in hairiness. A higher hemp fiber content in the roving contributes to better performance of droplet-wet spinning technology in enhancing yarn quality. Moreover, droplet-wet spinning enables the production of high-quality hemp yarn at lower twist multipliers, thereby reducing electricity consumption during yarn production. Subsequently, the performance of knitted fabrics made from conventional ring-spun yarn and droplet-wet spun yarn was compared and analyzed. The results indicate that fabric made from droplet-wet spun yarn exhibits an approximately 8% increase in bursting strength and a one-level improvement in pilling resistance. This research highlights the potential of droplet-wet spinning technology to enhance hemp yarn quality and promote sustainable textile production.

Inequalities in global residential cooling energy use to 2050

Intersecting socio-demographic transformations and warming climates portend increasing worldwide heat exposures and health sequelae. Cooling adaptation via air conditioning (AC) is effective, but energy-intensive and constrained by household-level differences in income and adaptive capacity. Using statistical models trained on a large multi-country household survey dataset (n = 673,215), we project AC adoption and energy use to mid-century at fine spatial resolution worldwide. Globally, the share of households with residential AC could grow from 27% to 41% (range of scenarios assessed: 33-48%), implying up to a doubling of residential cooling electricity consumption, from 1220 to 1940 (scenarios range: 1590-2377) terawatt-hours yr.−1, emitting between 590 and 1,365 million tons of carbon dioxide equivalent (MtCO2e). AC access and utilization will remain highly unequal within and across countries and income groups, with significant regressive impacts. Up to 4 billion people may lack air-conditioning in 2050. Our global gridded projections facilitate incorporation of AC’s vulnerability, health, and decarbonization effects into integrated assessments of climate change.

Enhanced inactivation of Aspergillus niger biofilms by the combination of UV-LEDs with chlorine-based disinfectants

The presence of pathogenic fungal biofilms in drinking water distribution systems poses significant challenges in maintaining the safety of drinking water. This research delved into the formation of Aspergillus niger (A. niger) biofilms and evaluated their susceptibility to inactivation using combinations of ultraviolet light emitting diodes (UV-LEDs) with chlorine-based disinfectants, including UV-LEDs/chlorine (Cl2), UV-LEDs/chlorine dioxide (ClO2), and UV-LEDs/chloramine (NH2Cl) at 265 nm, 280 nm and 265/280 nm. Results indicated that A. niger biofilms reached initial maturity within 24 h, with matured three-dimensional filamentous structures and conidiospores by 96 h. UV-LEDs combined with chlorine-based disinfectants enhanced A. niger biofilm inactivation compared to UV-LEDs alone and low-pressure UV combined with chlorine-based disinfectants. At an UV fluence of 400 mJ/cm2, log reductions of UV265, UV280, and UV265/280 combined with chlorine-based disinfectants were 2.95-fold, 3.20-fold, and 2.38-fold higher than that of UV265, UV280, and UV265/280, respectively. During the inactivation, A. niger biofilm cells experienced increased membrane permeability and intracellular reactive oxygen species levels, resulting in cellular apoptosis. Extracellular polymeric substances contributed to the higher resistance of biofilms. Regarding electrical energy consumption, the order was: UV-LEDs/ClO2 > UV-LEDs/NH2Cl > UV-LEDs/Cl2. These findings provide insights into the effective utilization of UV-LEDs for fungal biofilm disinfection.

Nonlinear Ensemble Deep Learning Model for Energy Consumption Prediction with Bayesian Optimization

Accurate prediction of electric energy consumption is crucial for efficient load dispatching, energy utilization, and grid operation. Traditional statistical and classical machine learning methods struggle with the nonlinear nature of energy consumption data, often leading to higher prediction errors. Additionally, deep learning models using a single approach face challenges such as convergence to local minima and poor generalization. This paper proposes a nonlinear ensemble deep learning model for residential energy consumption prediction, incorporating Bayesian optimization for hyperparameter tuning. The model combines Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and 1D Convolutional Neural Networks (1D-CNN), leveraging their powerful nonlinear feature learning capabilities. A k-means clustering approach is used to preprocess and reduce variability in the data, enhancing the ensemble model's performance. The ensemble model was tested on real energy consumption data from two districts in Addis Ababa, showing significant improvements in prediction accuracy with lower MAE, RMSE, and MAPE values compared to single models and un-clustered data. The integration of clustering and Bayesian optimization further enhanced model generalizability and minimized overfitting, demonstrating the effectiveness of a nonlinear approach in capturing complex energy consumption patterns.

Fabrication and building energy-saving performance evaluation of polyethylene glycol/polymethyl methacrylate/expanded graphite thermal enhanced shape-stable phase change material

Integrating Phase Change Material (PCM) with building envelope is a promising way to reduce building energy consumption. However, leakage and insufficient PCM loading rate for existing encapsulation strategies limit its further application. In this work, a safer and easier way to fabricate Shape-Stable Phase Change Material (SSPCM) is proposed, whose products combine the outstanding leakage-proof property and high PCM loading rate. With the method of in-situ polymerization, not only the Polyethylene Glycol (PEG) is packaged by Polymethyl Methacrylate (PMMA), but the Expanded Graphite (EG) also has a chance to be added as the property regulator. The prepared SSPCMs are proved to be shape-stable over melting temperature with a leakage rate under 1 %. The characterization test reveals that with EG mass fraction increase from 0 to 2.5 %, the thermal conductivity augments from 0.23 to 1.73 W/(m·K) while the phase change latent enthalpy changes from 85.11 kJ/kg to 116.10 kJ/kg. Notably, the EG is more than a thermal conductivity enhancer, it also plays an important role in the modification of comprehensive properties. To optimize the building energy-saving performance of SSPCMs, the factors affecting building cooling electricity consumption like deployment position and thermal properties are evaluated in detail. The simulation results show that the optimum strategy is employing SSPCM with 2.0 wt% EG in the middle of the envelope, the cooling electricity consumption can be reduced by 8.64 % compared with the benchmark. The physical mechanism of energy-saving is also discussed from multiple angles. From a statistical view, sensitivity and uncertainty analysis of the simulation results is completed, to identify the influence of input parameters and validate the reliability of the simulation results. Finally, an economic analysis is carried out, proving the economic feasibility for further application. This research provides valuable insights for SSPCM’s application and design of energy-efficient buildings.

Research on energy-saving renovation of old oceanarium based on energy consumption monitoring technology

This study takes an old oceanarium in Jiangsu Province, China, as a case study and monitors the energy consumption values related to electricity, water and gas consumption for the whole year before the renovation of the oceanarium. Based on the energy consumption monitoring data, an in-depth analysis of the energy consumption defects of the oceanarium before the renovation is conducted using energy audit technology. We combine the characteristics of the oceanarium itself according to: (1) the results of the building energy audit; (2) the results of the envelope calculation; and (3) the latest energy-saving retrofit technology. We developed a more scientific building energy-saving retrofit programme. Using this method, we can accurately monitor the energy consumption problems in old oceanarium buildings and find building energy consumption defects. In this study, we monitor the real-time energy consumption of nine old government office buildings in Jiangsu Province, China using energy monitoring technology. We analyse and study the monitoring results and finally target the scientific energy-saving retrofits. This method can be used to carry out targeted energy-saving building renovation in the future and put forward new methods and ideas for studying the energy consumption of existing public buildings.

LCA analysis for assessing environmenstal sustainability of new biobased chemicals by valorising citrus waste

The global shift towards using biomass for biofuels and chemicals is accelerating due to increasing environmental concerns and geopolitical strategies. This study investigates a biorefinery model using citrus-processing-waste, specifically citrus pulp, to produce high-value products for various industries, including cosmetics, pharmaceuticals, flavours, fragrances, and food packaging. In Italy, particularly Sicily region, citrus processing generates significant amounts of waste, often improperly disposed of, contributing to environmental problems. Researchers have demonstrated that citrus waste can yield commercially valuable compounds. This study specifically focuses on orange peel waste (OPW), which constitutes about half of the fruit's weight, aiming to extract pectin and limonene through a combined process. The extraction process was carried out on a laboratory scale, and its sustainability was evaluated using a life cycle assessment (LCA) with SimaPro 8.1 software and the Impact 2002 + method. The functional unit adopted for this study is 300 g of OPW, obtained after the pre-treatment phase, from which 0.14 g of limonene and 8.22 g of pectin were extracted. The LCA results revealed that pectin extraction has a significantly higher environmental impact compared to limonene extraction, primarily due to the use of ethanol as a solvent, followed by electricity consumption. To mitigate this impact, the LCA assessed alternative, more sustainable solvents, resulting in a 73.4% reduction in the environmental footprint of the pectin extraction process. These findings underscore the critical role of LCA, even at the laboratory scale, in identifying environmental hotspots and providing insights for improving and optimizing processes for potential industrial-scale applications.

Circuit categorization approach of office building energy consumption based on data features for energy-saving diagnosis

Energy-saving in office buildings is crucial. Research on data-driven diagnostics of building energy consumption is pivotal. The emergence and development of itemized electricity platforms have expanded the scope of diagnosis from total building energy consumption to individual electrical equipment. However current studies rarely address the characteristics of sub-circuits. This paper highlights the importance of recognizing the features of electrical equipment sub-circuits based on historical energy consumption data by examining various aspects of energy use in office buildings, including electrical consumption disaggregation, equipment operational strategies, sub-item consumption proportion, and sub-circuit composition. A circuit categorization approach in office buildings by features analysis of historical data is innovatively proposed. Operational conditions, time series stationarity, and correlations with factors affecting energy consumption are considered in historical data feature analysis. The method categorizes the electric circuits into 5 categories with typical features based on the ADF-KPSS co-test and Spearman correlation analysis. The rationality and validity of the circuit categorization method were verified with actual building data in case studies. On this basis, an interpretable energy consumption anomaly diagnosis method based on the categorization results is proposed. The circuit categorization approach explores how to further investigate the typical features of sub-circuits, and provides new directions for subsequent building operation and maintenance(O&M) research, including energy consumption diagnostics. Additionally, it offers decision support for building managers for O&M evaluation from an interpretable perspective.

Empirical analysis of electricity demand in Albania. the impact in ecosystem

Albania represents the case of a developing country with a volatile demand for power facing an unstable domestic supply, especially during the last decade. Understanding future patterns of electricity usage is crucial in various planning contexts, the most important of which would be security of supply. In this framework, acknowledging the future national demand for electricity is needed for electricity providers for them to plan the sufficient and security of electricity supply. The aim of this paper is electricity demand modelling and forecasting in Albania. The model developed for the determinants of electricity demand for Albania allows understanding of the patterns of consumption behaviour. The main demand drivers of the electric consumption considered here are macroeconomic and demographic factors. The current work empirically estimates that the main determinants of electricity consumption in Albania for the period 1990-2014 are GDP, population and remittances. Results coefficients are then used to forecast electricity consumption for the period 2015-2030. We believe that the main contribution of the proposed estimation forecasts would be for correctly informing policy makers regarding electricity demand patterns in Albania and in forming expectations on a vital sector such as electricity.

A novel multi-objective optimization scheme of electric turbo compressor system in hydrogen fuel cell for reducing energy consumption and axial thrust

Electric turbo compressor (ETC) can recover the exhaust energy and reduce the motor power consumption, which is the future development of the air management system for fuel cell. However, the large imbalances of axial thrust and energy between the compressor and turbine leads to low reliability and efficiency of ETC. In this study, a multi-objective optimization scheme was proposed to explore the lowest ETC axial thrust and electric power consumption. First, the coupled model including compressor, turbine and axial thrust sub-models were established in MATLAB. To verify the model, computational fluid dynamics(CFD) model were established in ANSYS CFX and experimental tests were carried out. The electric power differences between the MATLAB model and experiment is within 4.36 % and the axial thrust deviations between the MATLAB and CFD model is within 5.46 %.Then, eight geometric parameters of compressor and turbine rotors are selected as independent optimization variables. NSGA-ΙΙ multi-objective optimization algorithm was adopted to search for low axial thrust and power consumption solutions. Finally, the optimization results show that the efficiency of the compressor and turbine is increased by 6.39 % and 2.75 % at design point, respectively. The electric power consumed by the motor is reduced by 7.80 % and the axial thrust of the ETC is reduced by 18.83 %.

Insights into transition metal encapsulated inside carbon aerogel for accelerated electro-peroxone oxidation: Activity evaluation and reactive oxygen species generation

Transition metals have critical influences on the generation of reactive oxygen species in activating O3 and H2O2, as the binary oxidant sources in electro-peroxone (EP). To evaluate the catalytic activity of different metals and reveal the reactive oxygen species generation in heterogeneous EP, series of metals encapsulated inside carbon aerogel spheres (M@CS, M=Fe, Co, Ni, Zn) were fabricated for strengthening EP. Atrazine was used as model pollutant due to its low reactivity with O3, and the abatement trends followed Co@CS > Fe@CS > Ni@CS > Zn@CS. Co@CS exhibited the superior catalytic activity with maximum metal active site utilization (1.28 × 103 min-1 mol-1 per mole Co atom sites at pH 7) and minimum electric energy consumption (0.106 kWh/m3-log). The catalytic activity of M@CS was related to the adsorption energies for O3 and H2O2 (Fe@CS > Co@CS > Ni@CS > Zn@CS) and the graphitization degree (Co@CS > Fe@CS > Ni@CS > Zn@CS). Verified by theoretical calculation and in-situ Fourier transformed infrared, the metal effectively promoted the adsorption and decomposition of O3 and H2O2 into the surface oxygen intermediates, finally generating ·OH/·O2-/1O2. The carbon layer was conducive to the reduction of internal metal, ensuring catalyst durability. Electronic paramagnetic resonance and kinetic model revealed the occurrence, ratio and transient concentration of ·OH/·O2-/1O2 in M@CS/EP. M@CS/EP showed good performance for pollutant removal in coexistence of anions, humic acid and real water. This study provides guidance for selecting the metal–carbon catalyst in heterogeneous EP.

Comparative assessment of simple and detailed energy performance models for urban energy modelling based on digital twin and statistical typology database for the renovation of existing building stock

The renovation wave calls for an integrated, participatory, and neighbourhood to neighbourhood approach tailored to the local environments. This can be hindered by the lack of geometric and performance input data about the existing building stock. A parametric digital urban building energy modelling (UBEM) tool was developed for local municipalities to automate calculations for comprehensive renovation strategies utilizing public databases specifically at the neighbourhood level. This article compares the accuracy of two energy performance calculation models (seasonal heat balance method and hourly resistance–capacitance method) used within the tool to detailed energy simulation software and measured energy consumption data of existing group of residential buildings in a same neighbourhood. The accuracy for estimating the total primary energy demand was roughly below 10 % for both simplified models. The seasonal calculation method showed consistent overestimation of space heating, depending on building characteristics. Solar and internal heat gains significantly affect the accuracy of simplified heat balance calculations, particularly in well-insulated buildings, while the more detailed 5R1C lumped capacitance hourly method provides improved accuracy for space heating demand. The comparison of uniform (proportional) and project based window area distribution used in the calculation models showed only marginal difference. The study validated simple seasonal and 5R1C hourly calculation methods using data from an apartment building neighbourhood with 22 apartment buildings. Despite larger deviations in the case of individual buildings, the average deviation from measured heating demand was only around 3 %, the seasonal method slightly overestimating and the 5R1C hourly method slightly underestimating the measured values. The primary energy demand including typical values of domestic hot water and household electricity was overestimated by both simplified methods due to lower electricity and water consumption in reality although the difference was below 7 % for the entire district. This concludes that pooling the building envelope heat loss calculation on a district level improves accuracy on average allowing better assessment of comparative renovation strategies.

Forecasting of changes in electricity consumption due to EV diffusion in South Korea: Development of integrated model considering diffusion and macro-econometric model

Electrification is on the increase globally. The transportation sector is being electrified to reduce greenhouse gas emissions. South Korea aims to diffuse 8.3 million battery electric vehicles (BEVs) by 2040 to reduce emissions in the transportation sector. However, BEV diffusion policies have not been considered for BEVs' electricity consumption. Given that the rapid diffusion of BEVs significantly increases the electricity demand, forecasting BEVs' electricity consumption is necessary to inform electricity production plans. Furthermore, electricity is produced through various energy sources, and electricity prices and consumption are affected; therefore, forecasts that reflect the overall energy market are needed. This study presents a forecasting model for BEV demand and energy consumption by combining it with a macroeconometric model that reflects the overall energy market and socioeconomic impact using an innovation diffusion model. Incorporating electricity prices and renewable energy consumption derived from the macroeconometric model, annual BEV demand and electricity consumption are predicted. Moreover, BEV demand is more diffused and slower when forecasted using the Generalized Bass model with electricity prices and renewable energy consumption, compared to forecast without these factors, and the predictive power of BEVs is superior to that forecasted using the Bass Model alone.

Conditions for economic efficiency of latent heat thermal energy storage systems at nuclear power plants

In conditions of uneven schedules of electricity consumption and construction of power plants using renewable energy sources, nuclear power plants will participate in regulating the unevenness of daily energy consumption. At the same time, nuclear power plants have high economic feasibility of operating with a maximum installed capacity utilization factor due to significant capital investments at a relatively low fuel price. The use of nuclear power plants to cover uneven energy consumption will reduce the possibility of a return on investment, which will only be realized with greatly inflated capacity charges. At the same time, the annual operating costs will increase significantly. A possible decrease in the economic efficiency of a nuclear power plant is due to the fact that, at the request of the system operator of the energy system, the NPP will be to reduce electricity generation during off-peak hours or sell electricity on the market at significantly lower prices. In this regard, the problem of increasing economic efficiency of nuclear power plants under conditions of uneven daily energy consumption becomes particularly relevant. The solution to the problem could be the accumulation of excess energy and its subsequent use. Previously, the authors developed a method of additional redundancy of auxiliary needs of nuclear power plants based on the use of low-power steam turbines. In the present paper, schemes for increasing efficiency of using low-power steam turbines at nuclear power plants when regulating the load unevenness in the power system using thermal energy storage systems based on the latent heat thermal energy storage are developed. The design characteristics of the accumulator as part of a nuclear power plant are determined to ensure operation of the heat storage system throughout the entire period of energy consumption. In this research, the lithium nitrate was considered as a phase change material. The technical and system conditions under which the maximum economic effect is achieved have been determined. The boundary conditions under which a return on investment is achieved are shown.

Well-to-Wheel emissions of electric vehicles in Türkiye and emission mitigation by renewable penetration

Automotive technology is currently on the verge of transitioning to electric vehicles, and both the impact of these vehicles on electricity consumption and their environmental performance are the focus of significant interest. In this paper, the emission impacts based on the use of electric vehicles are analyzed using the Well-to-Wheel methodology. In order to evaluate electric vehicle emissions, emissions based on electricity generation and consumption are first determined. Emission intensities are found in gCO2e/kWh for electricity consumption and gCO2e/km for battery electric vehicles. Well-to-wheel emissions are found 172.2 and 89.1 gCO2e/km for conventional and electric vehicles, respectively. With the renewable penetration in electricity generation, the emission intensity will be lower, which means that the emissions of electric vehicles will be further reduced. Thus, a renewable energy supported charging station is simulated and it is shown that emission intensities can be reduced by 60 %. It is concluded that Türkiye should focus primarily on renewable energy sources and then nuclear energy in order to meet the increasing electricity demand and reduce emission intensities.