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The pulp and paper sector is the fourth largest industrial sector in terms of energy use in the world. Of the numerous processes involved in paper-making by a paper machine, the dryer section is the process that consumes the largest amount of energy. A model-based method for estimating energy-saving potentials of the dryer section was put forward in the present study. It was done by four steps: establishing a mathematical model about energy consumption, determining the model parameters, benchmarking the drying performance to obtain the corresponding energy-saving measures, and estimating the energy-saving potentials by using the mathematical model. In a case study, a multi-cylinder dryer section was selected to illustrate the method. After a fundamental field test and observation, several operating problems that restricted the energy performance of paper drying were found. And then several reasonable energy-saving measures were suggested to the operators. Finally, applying the mathematic model, it was found that 0.32 ton of steam will be saved when producing 1 ton of paper in recommended operating conditions. With the designed capacity of 200,000 tons/year, the annual steam-savings will be 64,000 tons. Generally, the price of steam is 130–150 Chinese Yuan (about US$21–24) in China, and the annual economic benefits will be 8.32–9.60 million Chinese Yuan (about US$1.344–1.536 million). [ABSTRACT FROM AUTHOR]

Part of the dewatering in a paper machine takes place via vacuum suction boxes situated below the moving web. In addition to the removal of liquid water, considerable amounts of air are sucked through the paper. The air flow that accompanies dewatering is a crucial parameter for the electricity consumption of a vacuum system. The present study models this air flow, combining differential conservation equations with fiber characterization. Measured air flow rates for different vacuum levels, basis weights, and pulp types are compared to model predictions. More than 70% of the data agree within the range of experimental error. [ABSTRACT FROM PUBLISHER]

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

Electricity demand-response programmes, such as the incentive-based and price-based programmes, have been used by utilities to induce customers to reduce their electricity consumption during peak periods. This study investigates the production decisions of a two-stage production system under these programmes in a situation where peak periods arrive randomly in the manufacturing cycle. Analytical results show that under demand-response programmes, the manufacturer, who aims at minimising the total operational cost, usually selects a lower production rate during peak periods and a higher one during non-peak periods. Notably, the uncertainty of the peak periods also has a significant influence on the manufacturer's production plans under these programmes. This paper further investigates the efficiency of different demand-response programmes in reducing the inventory holding cost and electricity cost. The results indicate that participating in the demand-response programmes does not always result in a higher inventory holding cost, which goes against the manufacturer's intuition about these programmes. In addition, this paper evaluates the manufacturers' preference for these demand-response programmes by comparing the operational cost savings generated from participating in the two programmes. It turns out that both the demand-response signals and the inventory holding cost substantially influence the manufacturer's willingness to participate in the programmes. [ABSTRACT FROM AUTHOR]

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

Climate change has become increasingly important in recent years. It is the outcome of the burning of fossil fuels that increased the concentration of atmospheric carbon dioxide (CO 2 ), over the last century. Mitigating the impacts of climate change requires a better understanding and assessment of the countries' economic decisions on the amount of CO 2 emissions. This paper assesses the variability between the different countries in the trends of CO 2 emissions and electricity consumption from 1975 to 2014, while identifying clusters of countries of similar trends over time. The novel methodology applied in this paper enables us to assess long-debated issues in climate literature. The temporal dynamic effects of electricity consumption and economic growth on CO 2 emissions across countries are studied using functional data analysis (FDA) methods. The latter have proven to be useful tools for visualising similarities and differences in the non-linear trends of CO 2 emissions without forcing linear trends and stationary relationships which can be unrealistic and misleading. The results indicate the possibility of identifying changes in the trends of CO 2 emissions and electricity consumption for a wide range of heterogeneous countries over the study period. The findings also reveal that economic growth puts a strain on the environment, where many high-income countries are still away from attaining economic-energy sustainability. [ABSTRACT FROM AUTHOR]

Heatwaves are becoming more frequent and severe, intensifying cooling demand and reducing air conditioner efficiencies. This causes peaks in electricity demand that pose operational challenges to power grids. This paper provides methods to mitigate demand peaks and heat stress under heatwaves by jointly adjusting fan speeds and thermostat setpoints in buildings. The methods involve (1) learning baseline models to predict load and thermal comfort, (2) fitting perturbation models that relate fan speed and thermostat setpoint adjustments to perturbations in load and thermal comfort, and (3) optimizing peak load and thermal comfort. The methods are implementable in real buildings, providing fast, accurately predicted optimized solutions that flatten demand peaks and mitigate personal heat stress. This paper demonstrates the methodology through simulation-based case studies of a single building and a six-building neighbourhood. In case studies, the methods reduce peak load by 8–10% while maintaining occupants' thermal comfort within safe and comfortable ranges. Highlights This paper develops data-driven methods to reduce peak demand and mitigate heat stress during heatwaves. The methods are designed for straightforward implementation in the field. In case studies, the methods reduce peak demand by 8–10% while maintaining thermal comfort within safe and comfortable ranges. To achieve the same level of peak load reduction, jointly adjusting fan speed, rather than solely thermostat setpoint, improves thermal comfort by 5% in the test case. [ABSTRACT FROM AUTHOR]

Self-harvesting and consumption of electrical energy from a small-scale photovoltaic (PV) system became quite a beneficial option for households who seek for an economical, independent and environment-friendly power alternative. However, in practice, prosumers without battery storage systems face some energy flow management issues, when only part of the generated energy is consumed instantly in place, while the rest is exported to the distribution grid or simply lost. The paper examines profiles of some preselected prosumers with small-scale PV installations with the purpose to evaluate losses caused by the inability to consume instantly all generated energy and thus resulting in redundant energy exchange between prosumer's microgrid and distribution grid. The analysis reveals certain anomalies, when local PV system produces enough power to cover all the electricity demand of the household, but energy is still imported from the DSO grid at the same time. These simultaneous flows of import and export active energy were calculated and showed quite significant numbers. The paper digs into this problem through a quantitative evaluation of energy flows within one preselected microgrid using one-second resolution experimental data. The results presented here help us distinguish the main factors leading to inefficient distribution of generated power in households. [ABSTRACT FROM AUTHOR]

Reducing the power consumption of oil production wells is one of the important directions of oil industry development in the context of transition of many fields to the late stages of operation. At the same time, there is often a need to limit hydrocarbon production, which is often accomplished by shutting down wells completely. In this paper, the authors propose an approach to improve the energy efficiency of the electric centrifugal pump unit through the use of periodic operation mode, which allows to reduce the specific power consumption of the electric centrifugal pump unit. Also, this mode can be considered as a way to regulate the well flow rate. According to the results of calculations it was obtained that the reduction of specific power consumption of the unit when operating at nominal frequency can reach 5% relative to the continuous mode of operation. An assessment was made of the effect of reducing the frequency of the supply voltage in a periodic mode on the parameters of the operation of the installation and it was found that the well flow rate increased by 1.43% with a reduction in specific power consumption by 1.64%. [ABSTRACT FROM AUTHOR]

Since 2008, the South African economy has experienced several power cuts (unplanned or as part of a load-shedding schedule), presumably because of the inability of the electricity supply to cover the demand. This paper examines the impact of such a demand-supply mismatch on the country's economic growth within a production function framework. To do so, we use an Autoregressive Distributed Lag Model (ARDL) for the period 1985 to 2019. The paper finds that a positive mismatch (or surplus) of electricity (supply>demand) boosts economic growth in the long run. This finding provides evidence that supports the necessity of electricity supply expansion and the promotion of energy efficiency measures that both will create a mismatch (surplus) conducive to economic growth. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the 'start time' of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin. [ABSTRACT FROM AUTHOR]

This paper proposed and introduced an innovative concept of step-like server placement in data centers (DCs), replacing the traditional servers placed on the same vertical plane with step-like server placement. The studied simplified DC model was established based on an actual operating DC. The feasibility and reliability of the simplified model was validated by on-site experimental results. This paper is analyzed two scenarios: the effects of horizontal spacings (0, 0.01, 0.02, 0.03, and 0.04 m) between adjacent servers and with the effect of different supply air temperatures (SATs) (22.5, 23, 23.5, and 24°C) on the rack hotspot. The results show that step-like server placement can effectively enhance the thermal distribution, while the optimum thermal distribution is achieved with the spacing of 0.01 m in case 2. Under this circumstance, the rack hotspot reduced by 2.5°C, while case 2 is selected for further analysis in scenario 2. In scenario 2, the thermal environment in cases 6–8 are totally better than that in case 1 in terms of rack hotspot and temperature distribution. Although there appears another moderate heat accumulation at the top-side, the overall thermal distribution in the case with SAT of 24°C is still better than that in original case. The electricity consumptions of the CRACs can be saved by approximately 146 and 195 kWhr/day with the most optimal configuration. [ABSTRACT FROM AUTHOR]

Financial development (FD) is pioneered to solve the problem of vital capital supporting for carbon neutrality. Detecting the effect of FD on carbon emissions (CE) plays an important role in accurately planning FD, and guiding carbon neutrality in China. To clarify the influence of FD on CE and the underlying pathways concerning scale effect, structural effect, and technological effect, this paper use China’s 285 city-level data spanning 2003–2016 and fixed effect (FE) models and instrumental variable (IV) methods to test the effect of FD on carbon neutrality in China. The findings show that (1) the development of financial deepening, financial interrelation ratio, and financial intermediary all prompt carbon emissions at the national level. (2) Regional heterogeneity indicates that FD benefits carbon neutrality in mid-Western cities but fails in eastern cities. (3) FD increases carbon emissions by boosting electricity consumption but decreases carbon emissions by promoting structural optimization and technological progress. (4) Environmental regulations partially curb the carbon emissions effect of FD. This paper proposes some suggestions to reduce CE and gain carbon neutrality in China by greening FD, optimizing industrial structure, promoting technologi)cal innovation and strengthening environmental regulation. [ABSTRACT FROM AUTHOR]

At present, electricity demand and pollution are the main concerns all over the world. With the help of this paper, a small contribution to energy production and pollution in the environment can be reduced. Exhaust Fan Cum Micro Wind Turbine (EFCMWT) is used for power generation. In this paper, I study a novel approach of EFCMWT for power generation. It has been installed at a height of 14.11 m at the windows near the toilet in a high rise building at Kosi Hostel NIT Patna. The EFCMWT generates power at a height of 14.11 m in the building. With respect to wind speed, various parameters are analyzed, such as how much DC voltage is generated, how much DC current is generated and also it is analyzed that how much EFCMWT power is generated and tip speed ratio variation with respect to wind speed. On windows where EFCMWT is installed, place anemometer near EFCMWT and analyzed wind power. The power coefficient with respect to wind speed is also analyzed for EFCMWT. Maximum DC voltage is 16 V, maximum DC current is 0.076 A, tip speed ratio is 0.45 and maximum EFCMWT power is 1.216 W which is achieved at wind speed of 7 m/s. [ABSTRACT FROM AUTHOR]

Electricity is one of the essential requirements for the economic development of a country. In Ethiopia, more than half of the total population does not have access to electric power. Micro-hydropower (MHP) is one of the most feasible renewable energy options for providing reliable electricity to rural areas. However, in low-income countries such as Ethiopia, the high price of a purpose-made turbine to generate electricity from MHP resources is difficult. This paper presents the feasibility study of a pump as a turbine (PAT) for MHP applications with and without impeller blade modification. The analysis was conducted in terms of initial investment cost, the net present value, payback periods, and the cost of energy. Decreasing blade thickness, blade tip rounding, adjusting blade inlet angle, and blade grooving, then comparing the result with PAT without modification and cross-flow turbine are the modification techniques considered in this research. Based on the projected system lifetime, the equivalent cost of power for the PAT and cross-flow turbines are 0.065 and 0.100 $/kWh respectively. The cross-flow turbine's payback period is 6.04 years, which is significantly longer than the PATs payback period of 3.42 years. From the entire study, it can be concluded that the MHP coupled with PAT provides a lower payback period and cheaper power production. Whereas, impeller blade modification has no significant advantage from the economic point of view. Generally, the impeller modification cost needs to be less than or equal to 25% of the initial cost of the PAT to use the less cost advantage of the PAT. [ABSTRACT FROM AUTHOR]

Over the past years, the economies of the Gulf Cooperation Countries (GCC) countries, which hold a large of the world in oil and gas exports, have suffered from dramatic declines in oil prices. This has put pressure on their policymakers to seek relief from their heavy dependence on natural resources and diversify their exports. At the same time, the GCC countries have high electricity consumption due to their severe climatic conditions. This has prompted their policymakers to consider the inevitable shift to environmentally friendly resources for electricity generation. Foreign direct investment, facilitated by the financial systems of these countries, may play an important role in achieving the above objectives. Therefore, the objectives of our paper are to investigate the relationship between foreign direct investment, electricity consumption, and financial development in GCC countries using a novel GMM-Panel VAR approach. Data are observed from 1990 to 2019. We find bidirectional causality between foreign direct investment and financial development and two unidirectional paths: one from electricity consumption to foreign direct investment and from financial development to electricity consumption. In addition, we observe negative and significant responses of financial development to shocks in electricity consumption. Our results can have a double interpretation. First, they provide an overview of the interactions between the variables studied, whose evolution may be prognosticated with the analysis of shocks resulting from the methodology adopted in our paper. This is serviceable given the recurrent turbulence in the international financial markets and the incessant perturbations in the prices of energy commodities, visually examined especially in recent years. Second, our empirical findings are useful for policymakers willing to have earlier diagnoses for policies strengthening renewable energy use in these countries. Our results can be seen on the side of this objective. The quite developed financial system in these countries may play, indeed, an important role in encouraging the FDI installation in the field of renewable energy. [ABSTRACT FROM AUTHOR]

This paper combines the intelligent computer-assisted technology to carry out the architectural design and planning of the agricultural sightseeing area, and mainly plans the electricity consumption system of the agricultural sightseeing area to ensure that the agricultural sightseeing area can provide tourists with a good sightseeing experience at night. Moreover, this paper conducts an end-point analysis of the circuit system and analyzes multiple parameters, and combines the construction power demand of agricultural tourism areas to conduct power model analysis. The computer control system integrates various factor control methods, uses advanced control technology and control strategies, and fully considers the relationship between the control variables to realise the automation, intelligence and energy-saving regulation of the greenhouse environment of the agricultural sightseeing area. Finally, this paper takes the greenhouse system as an example to analyze the architectural planning. From the research point of view, it can be known that the architectural design method of agricultural sightseeing tourism area constructed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

This paper empirically examines electricity consumption–economic growth nexus in Uganda from 1981 to 2013. Autoregressive distributed lag-bounds testing and Granger causality tests are employed for analysis. The results confirm the existence of long-run relationship between electricity consumption and economic growth and vice versa. In addition, the Granger causality test results confirm conservation hypothesis in the short run and feedback hypothesis in the long run. Therefore, the paper recommends Ugandan authorities not only to develop energy policies geared toward promoting efficient energy use but also to expand electricity infrastructure to address increased electricity demand to support economic growth. [ABSTRACT FROM PUBLISHER]

This paper presents a simulation framework for evaluating building control strategies (BCSs), developed with VOLTTRON, an opensource platform that integrates data, devices, and systems for sensing and control applications, and is based on co-simulation interfaces. It realizes an integrated environment for both testing and deploying BCSs, thereby eliminating the need for having a dedicated implementation of BCS for testing. For the first time, this framework provides critical functionalities, including scalable communication management and time-drive simulation advance, i.e., advancing the simulation based on clock time. We applied this framework to evaluating two BCSs from ASHRAE Guideline 36-2008 and ASHRAE Standard 90.1-2019. The evaluation results show that those BCSs primarily benefit the heating operation by reducing gas usage but yield insignificant savings in electricity consumption. The results also emphasize the importance of tuning the parameters of the BCSs to achieve better performances. [ABSTRACT FROM AUTHOR]

In developed countries, people spend nearly 90% of their time in buildings or during transportation. Recent research studies demonstrated that occupant behaviors have a significant impact on building performance in relation to the indoor environment and energy use. This paper presents the ASHRAE Global Occupant Behavior Database which aims to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems. This database includes 34 field-measured occupant behavior datasets for both commercial and residential buildings, contributed by researchers from 15 countries and 39 institutions covering 10 different climate zones. It includes occupancy patterns, occupant behaviors, indoor and outdoor environment measurements. The database is open source, a public website was developed for the users to interactively explore, query, and download datasets. This paper focuses on a detailed data analysis to investigate patterns of nine occupant behavior types, examining impacted factors such as building type, country, and climate zone. EnergyPlus simulations have been implemented based on the occupancy profiles derived from this database, and results showed overall building electricity consumption can be reduced up to around 27% in Summer and around 10% in Winter. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric demand for clean energy for a panel of OECD countries. We construct a price index for clean energy based on information on the energy composition of electricity generation, and focus on the possibility of non-symmetric clean and dirty clean consumption due to rising and falling energy prices, and to positive income growth and negative income growth. Using an Autoregressive Distributed Lag (ARDL) model that encapsulates both long-run equilibrium among energy demands, energy prices and income, and short-run dynamics. We find evidence that clean energy demand adjusts more aggressively to a price decline than a price rise. In addition, we find that clean energy consumption is more responsive to negative income changes than to positive income changes. A climate policy implication is that policy should account for reversal effect of clean energy consumption during periods of economic downturn. [ABSTRACT FROM AUTHOR]

With increasing demand for electricity, wind turbines have gained significant attention from the public. Offshore wind power generation has emerged as a popular choice due to its potential to reduce power transmission losses and minimal impact on humans and organisms. However, it also presents challenges in detecting defects in Wind Turbine Rotor Blades (WTRB) and repairing them. To address this issue, this paper proposes an improved wavelet-based S-U-Net network to denoise WTRB images followed by a weakly supervised CNN method for removing background parts that could affect defective feature extraction. Defective features are then extracted using VGG16 network on a deep learning server platform, while an enhanced Particle Swarm Optimization (PSO) algorithm combined with K-means is used to classify defect features of WTRBs. Experimental results demonstrate that classification accuracy of unlabelled blade defect datasets improved significantly from 62.6% using only Kmeans clustering method up to 96.4% using our proposed algorithm approach. This study applies improved PSO and Kmeans algorithms towards offshore Wind Turbine Rotor Blade condition monitoring with precise detection test results enabling early-stage detection of Wind Turbine Rotor Blades defects leading to timely repairs. [ABSTRACT FROM AUTHOR]

For economic and ecological reasons, the interest in the energy demand of material-handling systems is rising. As a result, the operators of these systems increasingly pay attention to the energy demand and the costs resulting from it. The energy demand of automated warehouses, with multi-aisle automated storage and retrieval systems, is volatile with uncontrolled power-peaks. These power-peaks result in high energy and hardware costs. In this paper, the effect of a power-load management on the throughput of the material-handling systems is investigated. We assume that the peaks of energy consumption can be significantly reduced by delaying tasks, without having an impact on the throughput. The goal is to find out the interdependence between the electrical power-limits of the power-load management (mean power demand in a period and maximum power demand) and the throughput of the warehouse. The results show that, with a maximum power-limit and a mean power-limit, the peaks in energy consumption can be avoided with only a slight loss of throughput. Load management is an effective method to reduce the energy peaks of an automated warehouse, thereby lowering the costs of automated warehouses. [ABSTRACT FROM AUTHOR]

Although DC power is claimed to be more energy-efficient than AC power for EHD drying, experimental studies on this subject are still insufficient. This research compares the efficiency of DC versus AC (60 Hz frequency) power for EHD drying by using 1 × 1 and 2 × 2 multi-pin discharge electrodes. Energy indicators were determined from the measurements of electric power consumption at the corresponding drying rate of the wet tissue paper. The experiments revealed the key benefits of DC drying, such as enhanced drying rate, higher energy efficiency, and lower specific energy consumption. For the comparable drying flux of 0.2 g/(m2 s), the specific energy consumption of DC drying was in the range from 350 J/g (2 × 2) to 750 J/g (1 × 1) as compared with 2000 J/g (2 × 2) to 5000 J/g (1 × 1) for AC. The specific energy consumption of a 2 × 2 discharge electrode was consistently smaller than the 1 × 1 one, indicating the role of emitters spacing in the process efficiency. The specific energy consumption of both electrodes increased with the drying rate. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide an analysis of the determinants of economic efficiency and to assess the prerequisites for the energy security of smart cities. The main methods of the paper include an economic analysis of the infrastructure improvements that result in reducing the energy demand of the smart cities represented by the intelligent light-emitting diode (LED) street lighting system. Smart LED streetlights are getting increasingly popular in the world's major metropolises as one of the leading components of the "smart" city. We compare the efficiency of LED street lighting used in smart cities with a commonly used lighting system based on sodium lamps. Our results demonstrate that LED street lighting system can significantly reduce the energy demand of any modern city. Moreover, we show that smart grids might help distribution systems within smart cities to better integrate intermittent renewable energy sources such as wind and solar. The main research novelty of our study compared to previous studies from the literature is the estimation of net profit (NP), Net Discounted Savings (NDS), as well as the total savings (TS) using the example of an average European metropolis. Our findings show that there is a need for better management including strong networks of leaders to drive smart city policies and investments and to cover wider city areas with economically sustainable projects and plans. In addition, our findings yield that smart city projects should aim at finding solution for smart connected local energy storage systems to support more renewable energy sources on the power grids. Our results might be of a special interest for city planners, local government stakeholders, as well as urban policy makers dealing with planning and managing smart cities. [ABSTRACT FROM AUTHOR]

This paper studies the precise clock management technology for the power consumption information collection system which demands precise requirements of the electric power meter clock. The causes of the clock deviation of the collection system and the problems caused by the clock deviation are analysed first; Then the key technologies for achieving accurate time synchronization are explained; Next, the master station and terminal time synchronization algorithm is designed. Only two NLP synchronizations are required to obtain a high accuracy time deviation, and the accuracy of the algorithm is demonstrated through a test of an example. Finally, the electric power meter time adjustment scheme using a combination of broadcast time adjustment and ESAM point-to-point time adjustment is designed to achieve high accuracy and low error, which has high reliability and practicality. [ABSTRACT FROM AUTHOR]

Although nearly all households in Vietnam have connected to the national grid, electricity shortage in the residential sector is still an issue. One measure of residential electricity shortage is the micro-renewable energy system (micro-RES), but little is known about the drivers for household adoption of such system and whether the presence of a micro-RES can help to reduce household energy consumption. This paper examines the characteristics of households that have adopted a micro-RES and investigates whether the presence of micro-RES would result in a decrease in energy demand by using fixed-effects models. Analysis was carried out on the 2016 and 2018 Vietnam Household Living Standard Survey data sets. The results show that the micro-RES adopters are likely to have lower educational level, lower income or living conditions, and live in the mountainous regions. Micro-RES adopters are found to spend less on electricity and fuel than the non-adopters and this finding is consistent across households of different income groups. The results also suggest that the support schemes may be beneficial to households adopting micro-RES because they really need it to meet the increasing demand in electricity. [ABSTRACT FROM AUTHOR]

This study aims to scrutinize the determinants of household energy consumption needs in Kebbi State, Nigeria. The data for the study were sourced from household heads within the study area. The paper analyzes the determinants of household energy consumption using six energy consumption indicators (household expenditure on energy, electricity, LPG, kerosene, charcoal, and biomass). To analyze the data, the study used descriptive statistics and binary logistic regression (which has rarely been used in this kind of study) which broaden our understanding of social, economic, and environmental perspectives on energy usage. Our empirical strategy indicates that all the instruments used are appropriate based on Cronbach's alpha scale value of greater than 0.9. Education level was found to be a significant factor in energy expenditure by household, electricity, LPG, and kerosene usage, while negatively correlated with biomass usage. A binary logit regression model revealed that household head income, availability of different energy choices, reliability, and affordability are the major determinants of household energy consumption needs. Findings further show that low-income household heads which account for more than 60% of the respondents rely heavily on the traditional methods of biomass to meet their energy needs. The finding further revealed that 72.80% of the respondents confirmed that accessibility is one of the driving forces which determines their energy choice. Based on the findings, the study therefore, recommends the need to ensure the availability and affordability of safer forms of energy as well as invest more in making renewable energy available and affordable. [ABSTRACT FROM AUTHOR]

The change in the electricity demand pattern globally due to sudden extreme weather conditions or situations like COVID 19 pandemic has brought unanticipated challenges for the electric utilities and operators around the world. This work primarily deals with the issue of load forecasting during such type of high impact low frequency (HILF) events. In this paper, we propose a novel resilient short-term load forecasting model capable of producing good forecasting performance for normal as well as critical situations during the COVID 19 pandemic and will also be useful for load forecasting for other HILF situations like natural calamity effect on load demand of the power system. The proposed method uses a feed-forward neural network (FFNN) with an added training feature named resiliency factor to forecast load in both regular and special scenarios. The resiliency factor for any type of node in the distribution system is decided by the power utility using the historical data and declared in advance. The proposed model is tested using the smart metered data available from a real-life distribution grid of an academic cum residential campus. The model is giving satisfactory results for both normal as well as COVID scenario for the said network. [ABSTRACT FROM AUTHOR]

This paper explores the decentralized, centralized and bargaining models of a supply chain involving an electricity generator and a retailer. We found that the investment cost coefficient negatively impacts the supply chain profits but positively affects price decisions. In contrast, the probability of meeting the COVID-19 emergency positively impacts the supply chain profit and negatively affects price decisions. We contribute by designing a bargaining contract for energy supply chain firms to improve profits on low investment, thereby improving their financial health and competitive advantage. The results imply that financial managers can bridge the revenue gap in COVI9 19 period. [ABSTRACT FROM AUTHOR]

Floating photovoltaic (FPV) systems have generated much interest in the literature, due to the potential synergies with existing hydropower reservoirs. This is of particular relevance to Aotearoa New Zealand, where the national grid's heavy reliance on hydro resources has left the country prone to energy shortages during dry years. This paper investigates the potential implications of a 1-gigawatt peak fleet of distributed FPV systems on seven hydro schemes around the country. The results show that an FPV fleet in Aotearoa New Zealand could provide additional electricity generation when the controlled hydropower storage is low. A larger, wider rollout would allow for the reservoirs to replenish after the country's unique winter peak in electricity demand. [ABSTRACT FROM AUTHOR]

Owing to the wide distribution of rural areas, the construction of power facilities is not perfect, which poses a great challenge to the power supply stability of the rural power grid. As an important supplement, the microgrid (MG) plays a complementary role in improving the power supply quality. However, the addition of the MG increases the complexity, and how to balance the interests of the participants has become a substaintial problem. Based on the analysis of the power consumption characteristics of rural residential, agricultural and industrial areas, a game relationship model between MG operators and power users is established, and the overall optimal strategy is obtained in this paper. For consumers, it can effectively achieve the effect of 'peak shaving and valley filling' and reduce the cost of electricity. For MG operators, it can improve the consumption rate of renewable energy and increase revenue. [ABSTRACT FROM AUTHOR]

This paper examines the impact of tourist arrivals on environmental degradation, as well as highlights the implications of this interaction on environmental sustainability targets, while controlling for globalization, energy consumption and economic growth. The study adds to the existing literature by employing the quantile-on-quantile regression (QQR) to examine the nonlinear relationship between carbon emissions and the regressors over the period 1995-2018, using quarterly data. As a robustness check, the conventional quantile regression (QR) model is also applied to capture the nexus across quantiles (0.1-0.9). Furthermore, quantile causality is utilized to capture the causal interrelationships between CO2 and the regressors across quantiles (0.1-0.90). Overall, results show that globalization, tourist arrivals, economic growth and energy consumption increase environmental degradation at different quantiles. The quantile causality results also reveal evidence of causality in mean and variance from globalization, tourist arrivals, economic growth and energy consumption to CO2 emissions in majority of the quantiles. From a policy standpoint, it is recommended that policymakers in Thailand should come up with sound and effective environmental policies that would help the economy achieve sustainable environmental quality for both the immediate and future generations. [ABSTRACT FROM AUTHOR]

Demand for electricity, due to the fast growth in urbanization and industrialization, is on the rapid rise. Load shift is a basic method for demand side management (DSM) that can be used by the central controller in buildings and can lead to the maximum use of renewable energy sources, maximum economic benefits, and reduction of peak demand. This paper proposes an algorithm for shifting the flexible loads of four selected appliances with respect to boundary limits for each appliance. A standalone four-story building with different number of occupants is considered to evaluate this algorithm. The algorithm was trained on Richardson model to minimize two objectives including aggregated demand, and the scheduling discomfort. The proposed algorithm led to significant reduction in aggregated peak demand and thereby savings in standalone system investment. The results demonstrated a major reduction in peak demand from 37% to 44% for winter and summer seasons, respectively. [ABSTRACT FROM AUTHOR]

The increasing electric vehicles (EVs) and their integration with the electrical networks to achieve the charging demands has highly affected the operation of the power system. These effects are due to the increased and variable amount of electricity consumption caused by the irregular charging patterns of EVs. As the EV penetration level increases with such irregular charging patterns, the overall load profile in a network will increase resulting in load peaks. To overcome this, a well-planned charging-discharging algorithm is needed for the EV users and the power suppliers. In light of the above issues and requirements, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation-based EV charging (EVC) behavior models and charging-discharging power prediction models. The proposed methods analyze the traveling behavior of EVs, and their charging patterns to make EV a flexible load. This helps in understanding the variable electricity consumption and mitigating the peak loads and load variations. Further, to assess the operation of the proposed methods, the charging-discharging power prediction is carried out for a large EV fleet based on the historical data. The power predictions are carried out for 100 days and the convergence of the proposed algorithm is targeted to be kept below 0.5% to achieve optimal power prediction. The results showed the credibility of the proposed approach in identifying the daily average charging-discharging load curves. [ABSTRACT FROM AUTHOR]

This paper proposes the implementation of demand response (DR) programmes in large manufacturing facilities featuring distributed wind and solar energy. Manufacturing facilities are high consumers of electric power. For this reason, these facilities usually pay exorbitant utility bills, which could be as much as $10–20 million per year. A high consumption of electricity also means that upstream fossil-fuelled power plants must release thousands of metric tonnes of carbon annually during the generation of electricity. DR contracts offer a lower utility rate in return for a load reduction during contingent events (i.e. peak hours). This paper covers the modelling and implementation of an interruptible/curtailable DR programme participated by a manufacturer that possesses onsite renewable generation units. These complementary energy resources allow the manufacturer to meet the curtailment requirements without causing any major electricity shortage that adversely affects the normal production schedule. We developed a stochastic programming model to determine the capacity of the wind turbine and solar panels that maximise the DR programme savings. The optimal solutions are derived based on central composite design methodology. [ABSTRACT FROM PUBLISHER]

The purpose of this paper is to review, summarize, and critically assess the academic studies that have dealt with either the causal relationship between energy consumption and growth or the determinants of energy demand in South Africa from 2007 and outline recent forecasts for electricity demand. The results of this review aim to identify gaps in the existing research. From a policy point of view, the findings of this effort have the potential to inform the relevant stakeholders to make appropriate interventions to improve the status quo of the energy sector. The results have indicated that studies examining the causality direction between energy (electricity) consumption and economic growth have failed to reach a consensus. The main differences identified were the time periods examined, the econometric approaches, and the variables included in the estimations. Another potential reason for the results is the availability—or lack thereof—of data specific for the country. On the other side, the studies looking at the factor affecting energy (electricity) demand have agreed that economic growth or income or output are considered significant factors. The role of prices was debatable among different studies. This has become more apparent when reviewing the few forecasting efforts in the country that resulted in conflicting results. [ABSTRACT FROM PUBLISHER]

Ireland has a renewable heat sectoral target of 600,000 heat pumps by 2030 with currently just 44,000 installed. Such a high heat pump target will have a significant effect on electricity demand and on the management and operation of the electricity grid. Many studies rely on synthetic data to estimate the impact of the adoption of low carbon technologies. This paper explores residential electricity demand using an innovative dataset from a field trial of deeply retrofitted homes heated by air source heat pumps. We estimate the after diversity maximum demand during a period of extreme weather, comparing it to a typical winter day. We assess which statistical distributions best model the electricity demand per home heated by an air source heat pump. The after diversity max demand is 3.84 kW compared to 2.33 kW, while a Gamma distribution best models average coincident electricity demand for the homes. [ABSTRACT FROM AUTHOR]

Although the BBIN (Bangladesh-Bhutan-India-Nepal) sub-region shares identical socio-economic characteristics and secures considerable benefits from energy co-operation and power grid connectivity, it is still far behind to grasp its full potential. This paper reviews existing publications and papers from a viewpoint of resource distribution, energy supply, prospects, and policies related to power sector development and cross-border power trading with a detailed consideration of the spatial variation of the largest country, India. The member countries face similar energy problems and the challenge remains in overcoming growing energy demand by attaining emission reduction targets. Limited production but increasing use of imported fuel have also raised serious environmental and energy security concerns. The wide variation in the energy resource endowments among the countries, particularly with hydropower, coal, and natural gas presents both opportunity and challenge in sub-regional energy collaboration. [ABSTRACT FROM AUTHOR]

This paper analyses the nexus between renewable and non-renewable energy consumption, financial development, Information and Communication Technology (ICT) diffusion and economic growth, in MENA countries, over the period 1980–2018. We use the novel Cross-Section augmented Autoregressive Distributed Lag (CS-ARDL) estimation technique which accounts for cross-sectional dependence and cross-country heterogeneity issues. We find a positive impact of renewable and non-renewable energy on economic growth, but a negative effect of financial development on economic growth. We also find a positive and statistically significant influence of ICT on Gross Domestic Product (GDP). Renewable energy and ICT diffusion can be considered important determinants of improved economic activity, job creation and better environmental quality. Pairwise Dumitrescu-Hurlin panel causality tests were used to examine the causal relations among the variables. The findings of this study have considerable policy implications for the selected countries. [ABSTRACT FROM AUTHOR]

This paper presents a validation by simulation of feasibility analysis for the installation of the ground source heat pump systems that have been proposed the modeling study for use in Algeria. We design and calculate the energy needs of an example building with the COMSOL Multiphysics software and Geo TSOL Basic. The study addresses heating needs for the climate conditions of western Algeria, in one of North Africa's biggest cities. The houses energy needs are assumed to be covered by a ground source heat pump (GSHP) system coupled with solar panels. The GSHP consists of a vertical ground heat exchanger and water-to-water heat pumps and is analyzed using an in-house developed and validated code. Energy consumption was calculated and validated by a simulation of the building's properties. Results show that installation of the ground source heat pump system substantially reduces the building's energy consumption. Finally, an economic analysis of the proposed system for construction practices in Algeria was carried out. The main novelty of this article, being that the study was conducted to seek a solution to reduce the energy bill of a house located in a city of a country Natural Gas Producer. Algeria is one of the 10 largest producers of natural gas in the world. More than 80% of the energy bill of a house located in Algeria, is due to the consumption of gas (Heating, Hot Water and Cooking), the electricity is mainly used in summer for air conditioning. The use of renewable energy will cost more than the use of natural gas. It is for this main cause that renewable energies are struggling to develop in Algeria, the cost of gas is very low. The economic analysis shows that in countries like Algeria, where the cost of conventional energy is heavily subsidized, solar thermal is not competitive with natural gas. It is only by coupling the GSHP with solar collectors that solar can take off and attract households. As a result, the only renewable way to compete with natural gas is the combination of GSHP with solar collectors. The only solution adopted to reduce the energy bill in a house located in Algeria, is the solution presented in this article. This is the first technical–economic study of how a GSHP could contribute to the energy needs of buildings in the city of Tlemcen ALGERIA. [ABSTRACT FROM AUTHOR]

This paper examines the dependence structure of industrial electricity demand and financial indicators (the Korea Composite Stock Price Index [KOSPI], Korean Securities Dealers Automated Quotations [KOSDAQ], exchange rate, government bonds and exports) using the copula dynamic conditional correlation with symmetric and asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models to forecast volatility. We investigated symmetric and asymmetric GARCH types, such as the standard, exponential, Glosten–Jagannathan–Runkle and asymmetric power models to fit the marginal distribution. The two types of elliptical copula, Gaussian and Student's t-distributions, were also considered to investigate the tail dependence between financial and electricity time series. We analysed the monthly log returns for January 2002 to April 2020. The empirical results reveal that the best-fit models for the Akaike information criteria are the asymmetric GARCH models, specifically the exponential-GARCH (E-GARCH). Moreover, the asymmetric GARCH model is superior to the symmetric GARCH in terms of forecast volatility. Extreme tail dependence exists for the KOSDAQ and exports indicators with the electricity demand. The KOSPI, Korea's primary stock market, is the best-fit financial variable and presents the highest forecasting accuracy. [ABSTRACT FROM AUTHOR]

This paper frames itself in analyzing complex electricity load measurements to attribute the consumption behavior to a specific entity. Attribution may be seen as the first step in several grid-based activities, like energy management, privacy, and identification of illicit activities. This work proposes and tests a novel approach that utilizes consumption discords as the analysis carrier for attributing multiple load patterns to specific consumers. The discord-driven analysis is performed utilizing the synergism of the Matrix Profile with each of two wellestablished supervised learning classification methods: the K-Nearest-Neighbor and Support-Vector-Machine. The proposed approach is applied in attributing electricity consumption pattern behaviors to a set of academic institutions that are comprised of multiple academic units. Notably, multiple units within the same entity exhibit different consumption behavior, thus imposing a high challenge in attributing the unit's consumption behavior to its entity of origin when there is a variety of targeted entities. Obtained results demonstrate that the load discord-based MP-KNN and MPSVM combinations provide higher identification accuracy than the state-of-the-art method of performing supervised classification with a feed-forward artificial neural network of the full load patterns. The identification accuracy of the proposed method outperformed the ANN load classification by approximately 13%. [ABSTRACT FROM AUTHOR]

In this paper, the design of a Small-scale Medical Waste Incinerator (SMWI) equipped with an LPG-operated Porous Radiant Burner (PRBLPG) is presented as a solution for disposing of medical waste generated in remote areas. Based on simple mass and heat balance analysis, SMWI having primary and secondary chambers with a volume of 1 and 0.754 m3 is designed. The proposed SMWI is evaluated for its environmental impact by performing a Life Cycle Assessment (LCA) and compared with an SMWI equipped with an Electric Heater (SMWI–EH). The total primary energy required for the construction of SMWI is 48285.56 MJ. It is found that the damage caused by the operation of PRBLPG in SMWI is lesser when compared to that of an Electric Heater (EH). In SMWI–PRB, LPG consumption contributed to about 17488.27 kg CO2−eq in the global warming category, whereas in the case of SMWI–EH, electricity consumption contributed to about 243766.11 kg CO2−eq. The operation of SMWI–PRB showed a reduction of about 54% in the resource utilisation category in comparison with SMWI–EH. The results obtained from the LCA study indicated that PRBLPG is a better option as an auxiliary heating device in SMWI than EH due to its environmental superiority. [ABSTRACT FROM AUTHOR]

This paper intends to know the impact of information and communication technology (ICT) on the economic progress and renewable energy consumptions in two Asian economic giants namely India and China. To achieve the objectives, generalised method of moments (GMM) and pooled ordinary least square (OLS), fixed effect and random effect models have been applied using yearly panel data of individuals using internet as ICT, growth rate of gross domestic product (GDP) as economic progress, electric power consumption as energy consumptions and foreign direct investment (FDI). This study observes significantly positive impact of ICT on renewable energy consumptions while negative and insignificant impact on the economic progress in India and China. It is observed that countries are similar in renewable energy expansion, time spent on internet, electricity generation and policy support for renewable energy generation while they significantly differ in the number of internet subscribers, internet-based entrepreneurships and sources of renewable energy. This study proposes to apply ICT in all possible sectors to boost-up economic growth as well as to enhance the demand for renewable energy consumptions. [ABSTRACT FROM AUTHOR]

Energy modelling for the prediction of energy use in buildings, especially under novel energy management strategies, is of great importance. In buildings there are several flexible electrical loads which can be shifted in time such as thermostatically controllable loads. The main novelty of this paper is to apply an aggregation method to effectively characterize the electrical energy demand of air-conditioning (AC) systems in residential buildings under flexible operation during demand response and demand shaping programs. The method is based on clustering techniques to aggregate a large and diverse building stock of residential buildings to a smaller, representative ensemble of buildings. The methodology is tested against a detailed simulation model of building stocks in Houston, New York and Los Angeles. Results show good agreement between the energy demand predicted by the aggregated model and by the full model during normal operation (normalized mean absolute error, NMAE, below 10%), even with a small number of clusters (sample size of 1%). During flexible operation, the NMAE rises (around 20%) and a higher number of representative buildings become necessary (sample size at least 10%). Multiple cases for the input data series were considered, namely by varying the time resolution of the input data and the type of input data. These characteristics of the input time series data are shown to play a crucial role in the aggregation performance. The aggregated model showed lower NMAE compared to the original model when clustering is based on a hybrid signal resolved at 60-minute time intervals, which is a combination of the electricity demand profile and AC modulation level. [ABSTRACT FROM AUTHOR]

This paper presents a novel system of data-driven approaches for simulating the dynamics of electricity demand profiles. Demand profiles of individual dwellings are decomposed into deterministic (e.g. 'Trends' and 'Seasonal') and stochastic ('remainder') components using the STL (a Seasonal-Trend decomposition procedure based on Loess) approach. Stochastic components are modelled using a Hidden Markov Model (HMM) and combined with deterministic components to generate synthetic demand profiles. To simulate extreme (peak) demand, the synthetic profiles were post-processed using a Generalised Pareto (GP) distribution, and a percentile-based bias-correction scheme. All the techniques are systematically coupled into a hybrid system, referred to as 'STL_HMM_GP'. The STL_HMM_GP system is thoroughly accessed and validated by comparing a range of statistical characteristic of observed and simulated profiles for three case study communities. The potentials of the STL_HMM_GP system is demonstrated for simulating aggregated demand profiles, generated using an accessible small sample of observed individual demand profiles. [ABSTRACT FROM AUTHOR]

Motor-driven fan systems in air handling units (AHUs) account for 15.9% of the electricity consumption in commercial buildings. Currently electronically commutated motors (ECMs) have seen increasing applications to replace induction motors for energy savings. While efficiency models for motor-driven fan systems are essential for building energy management, there is no valid efficiency model for ECM fan systems. Therefore, this paper is to develop and validate an efficiency model for ECM fan systems, which can be applied to evaluate the motor input power at given fan airflow rate and head. First, an efficiency model is developed and calibrated using the manufacturer data of a 5.2 kW ECM fan. Innovatively a fan efficiency correction coefficient is introduced to consider the bias of affinity laws and the correlation between motor efficiency and motor shaft power is verified. Then the calibrated efficiency model is validated in an AHU with an array of four calibrated ECM fans. It was found that the proposed fan efficiency correction coefficient can improve the fan efficiency model accuracy by 20% at 20% rated motor speed. Moreover, the validation results show that the developed efficiency model can accurately evaluate the motor input power with an error of 0.096 kW or 1.8%. [ABSTRACT FROM AUTHOR]

A new bottom-up white-box building stock energy modelling approach is presented in this paper. The model is a physics simulation-based stock model that can be used to compare the base case building stock with technological variations for comparative assessments. The model accuracy is compared to known stock data for a variety of categories, including end-use, energy source and building type. The model predicts the energy use of a building stock in a Canadian region with good agreement across all categories, with the total energy consumption of the model within 1.5% of the real stock energy use. Our analysis shows that modelling a sufficiently large sample of stock is required to reduce the expected deviation for lesser-represented portions of the stock. A case study illustrates how the model can be applied to compare different heating system distributions, in order to assess their impact on greenhouse gas emissions and peak electricity demand. [ABSTRACT FROM AUTHOR]