At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in p...At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.展开更多
To reduce peak electricity demand and hence reduce capacity costs due to added investment of generating additional power to meet short intervals of peak demand, can enhance energy efficiency. Where it is possible to a...To reduce peak electricity demand and hence reduce capacity costs due to added investment of generating additional power to meet short intervals of peak demand, can enhance energy efficiency. Where it is possible to adjust timing and the quantity of electricity consumption and at the same time achieve the same useful effect, the value of the energy service itself remains unchanged. Peak demand management is viewed as the balance between demand and generation of energy hence an important requirement for stabilized operation of power system. Therefore, the purpose of this study was to establish the correlation between peak electricity demand management strategies and energy efficiency among large steel manufacturing firms in Nairobi, Kenya. The strategies investigated were demand scheduling, Peak shrinking and Peak shaving. Demand scheduling involves shifting predetermined loads to low peak periods thereby flattening the demand curve. Peak shrinking on the other hand involves installation of energy efficient equipment thereby shifting the overall demand curve downwards. Peak shaving is the deployment of secondary generation on site to temporarily power some loads during peak hours thereby reducing demand during the peak periods of the plant. The specific objectives were to test the relationship between demand scheduling and energy efficiency among large steel manufacturing firms in Nairobi Region;to test the correlation between peak shrinking and energy efficiency among large steel manufacturing firms in Nairobi Region;and to test the association between peak shaving and energy efficiency among large steel manufacturing firms in Nairobi Region. The study adopted a descriptive research design to determine the relationship between each independent variable namely demand scheduling, peak shrinking, peak shaving and the dependent variable, the energy efficiency. The target population was large steel manufacturing firms in Nairobi Region, Kenya. The study used both primary and secondary data. The primary data was from structured questionnaires while secondary data was from historical electricity consumption data for the firms under study. The results revealed that both peak shrinking and peak shaving were statistically significant in influencing energy efficiency among the steel manufacturing firms in Nairobi Region, each with Pearson correlation coefficient of 0.903, thus a strong linear relationship between the investigated strategy and the dependent variable, energy efficiency. The obtained results are significant at probability value of 0.005 (p 0.05). The conclusion is that peak shrinking and peak shaving have an impact on energy efficiency in the population under study, and if properly implemented, may lead to efficient utilization of the available energy. The study further recommended that peak demand management practices need to be implemented efficiently as a way of improving the overall plant load factor and energy efficiency.展开更多
The effect of two nighttime ventilation strategies on cooling and heating energy use is investigated for a prototype office building in several northern America climates, using hourly building energy simulation softwa...The effect of two nighttime ventilation strategies on cooling and heating energy use is investigated for a prototype office building in several northern America climates, using hourly building energy simulation software （DOE2.1E）. The strategies include： scheduled-driven nighttime ventilation and a predictive method for nighttime ventilation. The maximum possible energy savings and peak demand reduction in each climate is analyzed as a function of ventilation rate, indoor-outdoor temperature difference, and building thermal mass. The results show that nighttime ventilation could save up to 32% cooling energy in an office building, while the total energy and peak demand savings for the fan and cooling is about 13% and 10%, respectively. Consequently, finding the optimal control parameters for the nighttime ventilation strategies is very important. The performance of the two strategies varies in different climates. The predictive nighttime ventilation worked better in weather conditions with fairly smooth transition from heating to cooling season.展开更多
This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand...This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.展开更多
Purpose The aim of this study was to examine the peak locomotor demands of match play and determine if these situations are replicated in training,and analyze their dynamics throughout the competitive microcycle in pr...Purpose The aim of this study was to examine the peak locomotor demands of match play and determine if these situations are replicated in training,and analyze their dynamics throughout the competitive microcycle in professional female soccer players based on their positions.Methods Measurements such as distance covered(DIS),high-speed running distance(HSRD),sprint distance(SPD),accelerating distance(ACCDIS),decelerating distance(DECDIS),and high metabolic load distance(HMLD)were registered during 1,3,5 and 10-min peak locomotor in both competitive matches(MD)and training sessions(ranked based on the number of days remaining until the next match,namely MD-4,MD-3,MD-2,and MD-1)within a competitive mesocycle.Results Central defenders were found to cover significantly less HMLD than full-backs and forwards,regardless of the time frame,as well as less HMLD than center midfielders in the 3,5 and 10-min time frames.Only in MD-3 did players exhibit a similar HMLD to MD,regardless of the analyzed time frame.Players covered significantly less HSRD and SPD in MD-2 and MD-1 compared to MD-3,and less HSRD in MD-4 compared to MD-3.Additionally,HSRD and SPD were significantly higher in MD-4 than in MD-1.There were no significant differences in HSRD or SPD relative to match play workload observed between positions within the same training session.Conclusion The microcycle showed a non-linear training load,with higher external loads in central sessions(e.g.,MD-3)and tapering strategies at the end of the microcycle in peak locomotor demands.展开更多
Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guara...Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment.This study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR events.According to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance.Meanwhile,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls.Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously.Compared with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.展开更多
In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on th...In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand management.In this paper,a novel deep neural network based onα^(2)-LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time resolution.Additionally,we employ AES-128 for station quantization and secure communication with users.Our proposed algorithm achieves a 9.2%reduction in both the Root Mean Square Error(RMSE)and the mean absolute error compared to LSTM,along with a 13.01%increase in demand accuracy.We present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed model.The prediction analysis has been conducted using Python programming.展开更多
The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumpt...The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore,identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.展开更多
While space cooling currently represents less than 1%of final energy use in the residential sector of the Euro-pean Union(EU-28),it was the fastest growing end-use during the 2000-15 period with a mean annual growth r...While space cooling currently represents less than 1%of final energy use in the residential sector of the Euro-pean Union(EU-28),it was the fastest growing end-use during the 2000-15 period with a mean annual growth rate of 6%per year.Currently,little is known about factors which have driven regional air-conditioning(AC)energy consumption over time,since the literature is limited to cross-sectional studies that lack differentiation between climatic and non-climatic influences.Future projections for the EU’s electricity sector may therefore neglect the potential implications of rapidly growing AC demand.We develop a novel decomposition framework,which breaks down residential space cooling energy consumption in EU-28 countries into the effect of different components from 2000 to 2015.Decomposition is extended to panel data models identifying specific drivers of space cooling’s climate-sensitive components.Finally,we explore scenarios of residential AC energy consumption up to 2050 and evaluate their impact on summer time peak loads.AC diffusion was found to be the key driver of space cooling energy consumption,but this effect was partly counterbalanced by efficiency gains.While weather influences AC equipment ownership rate in EU-28 households,personal income has a larger marginal effect.In baseline scenarios,AC diffusion saturates by 2050,while modestly increasing sectoral final energy use.Still,our range of scenario values for space cooling energy consumption in 2050 exceed the majority of those originat-ing from recently published projections.In a future renewables-driven electricity system,energy security risks may emerge from a scenario of fast AC up-take in new and renovated buildings,especially for colder European countries where modelled peak cooling electricity demand is shown to outgrow the projected expansion of solar capacity.These findings have important implications for the EU’s strategy to decarbonise energy supply.展开更多
文摘At present,a life-cycle assessment of energy storage systems(ESSs)is not widely available in the literature.Such an assessment is increasingly vital nowadays as ESS is recognized as one of the important equipment in power systems to reduce peak demands for deferring or avoiding augmentation in the network and power generation.As the battery cost is still very high at present,a comprehensive assessment is necessary to determine the optimum ESS capacity so that the maximum financial gain is achievable at the end of the batteries’lifespan.Therefore,an effective life-cycle assessment is proposed in this paper to show how the optimum ESS capacity can be determined such that the maximum net financial gain is achievable at the end of the batteries’lifespan when ESS is used to perform peak demand reductions for the customer or utility companies.The findings reveal the positive financial viability of ESS on the power grid,otherwise the projection of the financial viability is often seemingly poor due to the high battery cost with a short battery lifespan.An improved battery degradation model is used in this assessment,which can simulate the battery degradation accurately in a situation whereby the charging current,discharging current,and temperature of the batteries are intermittent on a site during peak demand reductions.This assessment is crucial to determine the maximum financial benefits brought by ESS.
文摘To reduce peak electricity demand and hence reduce capacity costs due to added investment of generating additional power to meet short intervals of peak demand, can enhance energy efficiency. Where it is possible to adjust timing and the quantity of electricity consumption and at the same time achieve the same useful effect, the value of the energy service itself remains unchanged. Peak demand management is viewed as the balance between demand and generation of energy hence an important requirement for stabilized operation of power system. Therefore, the purpose of this study was to establish the correlation between peak electricity demand management strategies and energy efficiency among large steel manufacturing firms in Nairobi, Kenya. The strategies investigated were demand scheduling, Peak shrinking and Peak shaving. Demand scheduling involves shifting predetermined loads to low peak periods thereby flattening the demand curve. Peak shrinking on the other hand involves installation of energy efficient equipment thereby shifting the overall demand curve downwards. Peak shaving is the deployment of secondary generation on site to temporarily power some loads during peak hours thereby reducing demand during the peak periods of the plant. The specific objectives were to test the relationship between demand scheduling and energy efficiency among large steel manufacturing firms in Nairobi Region;to test the correlation between peak shrinking and energy efficiency among large steel manufacturing firms in Nairobi Region;and to test the association between peak shaving and energy efficiency among large steel manufacturing firms in Nairobi Region. The study adopted a descriptive research design to determine the relationship between each independent variable namely demand scheduling, peak shrinking, peak shaving and the dependent variable, the energy efficiency. The target population was large steel manufacturing firms in Nairobi Region, Kenya. The study used both primary and secondary data. The primary data was from structured questionnaires while secondary data was from historical electricity consumption data for the firms under study. The results revealed that both peak shrinking and peak shaving were statistically significant in influencing energy efficiency among the steel manufacturing firms in Nairobi Region, each with Pearson correlation coefficient of 0.903, thus a strong linear relationship between the investigated strategy and the dependent variable, energy efficiency. The obtained results are significant at probability value of 0.005 (p 0.05). The conclusion is that peak shrinking and peak shaving have an impact on energy efficiency in the population under study, and if properly implemented, may lead to efficient utilization of the available energy. The study further recommended that peak demand management practices need to be implemented efficiently as a way of improving the overall plant load factor and energy efficiency.
文摘The effect of two nighttime ventilation strategies on cooling and heating energy use is investigated for a prototype office building in several northern America climates, using hourly building energy simulation software （DOE2.1E）. The strategies include： scheduled-driven nighttime ventilation and a predictive method for nighttime ventilation. The maximum possible energy savings and peak demand reduction in each climate is analyzed as a function of ventilation rate, indoor-outdoor temperature difference, and building thermal mass. The results show that nighttime ventilation could save up to 32% cooling energy in an office building, while the total energy and peak demand savings for the fan and cooling is about 13% and 10%, respectively. Consequently, finding the optimal control parameters for the nighttime ventilation strategies is very important. The performance of the two strategies varies in different climates. The predictive nighttime ventilation worked better in weather conditions with fairly smooth transition from heating to cooling season.
文摘This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.
基金supported by Proyecto:Red en valoración del Fitness femenino.Clave Orgánica:AL 96.
文摘Purpose The aim of this study was to examine the peak locomotor demands of match play and determine if these situations are replicated in training,and analyze their dynamics throughout the competitive microcycle in professional female soccer players based on their positions.Methods Measurements such as distance covered(DIS),high-speed running distance(HSRD),sprint distance(SPD),accelerating distance(ACCDIS),decelerating distance(DECDIS),and high metabolic load distance(HMLD)were registered during 1,3,5 and 10-min peak locomotor in both competitive matches(MD)and training sessions(ranked based on the number of days remaining until the next match,namely MD-4,MD-3,MD-2,and MD-1)within a competitive mesocycle.Results Central defenders were found to cover significantly less HMLD than full-backs and forwards,regardless of the time frame,as well as less HMLD than center midfielders in the 3,5 and 10-min time frames.Only in MD-3 did players exhibit a similar HMLD to MD,regardless of the analyzed time frame.Players covered significantly less HSRD and SPD in MD-2 and MD-1 compared to MD-3,and less HSRD in MD-4 compared to MD-3.Additionally,HSRD and SPD were significantly higher in MD-4 than in MD-1.There were no significant differences in HSRD or SPD relative to match play workload observed between positions within the same training session.Conclusion The microcycle showed a non-linear training load,with higher external loads in central sessions(e.g.,MD-3)and tapering strategies at the end of the microcycle in peak locomotor demands.
基金The authors gratefully acknowledge the support of this research by the National Natural Science Foundation of China(No.51908365,No.71772125)the Philosophical and Social Science Program of Guangdong Province(GD18YGL07).
文摘Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment.This study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR events.According to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance.Meanwhile,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls.Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously.Compared with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.
文摘In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand management.In this paper,a novel deep neural network based onα^(2)-LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time resolution.Additionally,we employ AES-128 for station quantization and secure communication with users.Our proposed algorithm achieves a 9.2%reduction in both the Root Mean Square Error(RMSE)and the mean absolute error compared to LSTM,along with a 13.01%increase in demand accuracy.We present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed model.The prediction analysis has been conducted using Python programming.
基金supported by the Department of Science and Technology(DST),New Delhi,India(No.DST/EE/2014127)
文摘The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore,identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.
基金funded by the Priestley International Centre for Climate(PICC),which is based in University of Leeds,UK.Pe-ter Taylor gratefully acknowledges support from UK Research and In-novation through the Centre for Research into Energy Demand Solu-tions,grant reference number EP/R035288/1Paul Brockway’s time was funded by the UK Research Councils,supported under EPSRC award EP/L024756/1part of the research programme of the UK Energy Research Centre(UKERC)and latterly under EPSRC Fellowship award EP/R024254/1.
文摘While space cooling currently represents less than 1%of final energy use in the residential sector of the Euro-pean Union(EU-28),it was the fastest growing end-use during the 2000-15 period with a mean annual growth rate of 6%per year.Currently,little is known about factors which have driven regional air-conditioning(AC)energy consumption over time,since the literature is limited to cross-sectional studies that lack differentiation between climatic and non-climatic influences.Future projections for the EU’s electricity sector may therefore neglect the potential implications of rapidly growing AC demand.We develop a novel decomposition framework,which breaks down residential space cooling energy consumption in EU-28 countries into the effect of different components from 2000 to 2015.Decomposition is extended to panel data models identifying specific drivers of space cooling’s climate-sensitive components.Finally,we explore scenarios of residential AC energy consumption up to 2050 and evaluate their impact on summer time peak loads.AC diffusion was found to be the key driver of space cooling energy consumption,but this effect was partly counterbalanced by efficiency gains.While weather influences AC equipment ownership rate in EU-28 households,personal income has a larger marginal effect.In baseline scenarios,AC diffusion saturates by 2050,while modestly increasing sectoral final energy use.Still,our range of scenario values for space cooling energy consumption in 2050 exceed the majority of those originat-ing from recently published projections.In a future renewables-driven electricity system,energy security risks may emerge from a scenario of fast AC up-take in new and renovated buildings,especially for colder European countries where modelled peak cooling electricity demand is shown to outgrow the projected expansion of solar capacity.These findings have important implications for the EU’s strategy to decarbonise energy supply.
