Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours ...Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.展开更多
An operating schedule of the parallel electric arc furnaces(EAFs)considering both productivity and energy related criteria is investigated.A mathematical model is established to minimize the total completion time and ...An operating schedule of the parallel electric arc furnaces(EAFs)considering both productivity and energy related criteria is investigated.A mathematical model is established to minimize the total completion time and the total electricity cost.This problem is proved to be an NP-hard problem,and an effective solution algorithm,longest processing time-genetic(LPT-gene)algorithm,is proposed.The impacts of varied processing energy consumption and electricity price on the optimal schedules are analyzed.The integrated influence of the different weight values and the variation between the peak price and the trough price on the optimal solution is studied.Computational experiments illustrate that considering the energy consumption costs in production has little influence on makespan;the computational performance of the proposed longest processing time-genetic algorithm is better than the genetic algorithm(GA)in the issue to be studied;considerable reductions in the energy consumption costs can be achieved by avoiding producing during high-energy price periods and reducing the machining energy consumption difference.The results can be a guidance for managers to improve productivity and to save energy costs under the time-of-use tariffs.展开更多
1) The paper examines the relationship between electricity demand and climate/non-climate related factors using statistical regression analysis. 2) It focuses on the environmental, demographic, policy (energy pricing)...1) The paper examines the relationship between electricity demand and climate/non-climate related factors using statistical regression analysis. 2) It focuses on the environmental, demographic, policy (energy pricing) and technological factors as the main factors affecting the consumption pattern in Jordan. 3) The paper also presents the variations occurred in the electricity demand over the period 1994-2008. The variations that are observed during the period of study are: Shifting of the peak load occurrence from evening to morning period, Modification in the annual daily load curve especially in winter season, Variation in relationship between space temperature and demand especially in winter, and dramatic increase in electric generation after year 2003. The shift in peak load from evening to morning period is mainly due to technological factor as a result of wide use of the air conditions in houses, services and government offices for cooling in summer instead of ordinary air fans. The variations in consumption pattern between 2000 and 2007 are mainly associated with economic, social and demographic factors. The high demand at lower space temperature is governed by introducing new appliances for heating in winter as a result of low electricity pricing comparing with gasoline price. The dramatic increase in electric generation after 2003 is probably due to demographic factors as a result of high growth of population after the Gulf war II. 4) The correlation between the daily maximum loads in morning and evening periods with the differential temperature (ΔT) above 20?C threshold in summer and below 15?C threshold in winter, shows pronounced changes in 2007 compared with year 2000. The regression tests show that a decrease of 1?C below 15?C threshold in winter 1) increases the morning demand by only 2 MW/?C in 2000 and 16.7 MW/?C in 2007, 2) decreases the evening peak by ?2.6 MW/1?C in 2000 and increases the evening peak by 22.9 MW/1?C in 2007. Results show that the demographic, technological, environmental and national energy pricing factors play a vital rule in consumption pattern in Jordan. Moreover, the paper reveals that planners and decision makers should be careful when applying new tariff in the developing countries such as Jordan.展开更多
基金Key R&D Program of Tianjin,China(No.20YFYSGX00060).
文摘Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.
基金The National Natural Science Foundation of China(No.71271054,71571042,71501046)the Fundamental Research Funds for the Central Universities(No.2242015S32023)the Scientific Research Innovation Project for College Graduates in Jiangsu Province(No.CXZZ12_0133)
文摘An operating schedule of the parallel electric arc furnaces(EAFs)considering both productivity and energy related criteria is investigated.A mathematical model is established to minimize the total completion time and the total electricity cost.This problem is proved to be an NP-hard problem,and an effective solution algorithm,longest processing time-genetic(LPT-gene)algorithm,is proposed.The impacts of varied processing energy consumption and electricity price on the optimal schedules are analyzed.The integrated influence of the different weight values and the variation between the peak price and the trough price on the optimal solution is studied.Computational experiments illustrate that considering the energy consumption costs in production has little influence on makespan;the computational performance of the proposed longest processing time-genetic algorithm is better than the genetic algorithm(GA)in the issue to be studied;considerable reductions in the energy consumption costs can be achieved by avoiding producing during high-energy price periods and reducing the machining energy consumption difference.The results can be a guidance for managers to improve productivity and to save energy costs under the time-of-use tariffs.
文摘1) The paper examines the relationship between electricity demand and climate/non-climate related factors using statistical regression analysis. 2) It focuses on the environmental, demographic, policy (energy pricing) and technological factors as the main factors affecting the consumption pattern in Jordan. 3) The paper also presents the variations occurred in the electricity demand over the period 1994-2008. The variations that are observed during the period of study are: Shifting of the peak load occurrence from evening to morning period, Modification in the annual daily load curve especially in winter season, Variation in relationship between space temperature and demand especially in winter, and dramatic increase in electric generation after year 2003. The shift in peak load from evening to morning period is mainly due to technological factor as a result of wide use of the air conditions in houses, services and government offices for cooling in summer instead of ordinary air fans. The variations in consumption pattern between 2000 and 2007 are mainly associated with economic, social and demographic factors. The high demand at lower space temperature is governed by introducing new appliances for heating in winter as a result of low electricity pricing comparing with gasoline price. The dramatic increase in electric generation after 2003 is probably due to demographic factors as a result of high growth of population after the Gulf war II. 4) The correlation between the daily maximum loads in morning and evening periods with the differential temperature (ΔT) above 20?C threshold in summer and below 15?C threshold in winter, shows pronounced changes in 2007 compared with year 2000. The regression tests show that a decrease of 1?C below 15?C threshold in winter 1) increases the morning demand by only 2 MW/?C in 2000 and 16.7 MW/?C in 2007, 2) decreases the evening peak by ?2.6 MW/1?C in 2000 and increases the evening peak by 22.9 MW/1?C in 2007. Results show that the demographic, technological, environmental and national energy pricing factors play a vital rule in consumption pattern in Jordan. Moreover, the paper reveals that planners and decision makers should be careful when applying new tariff in the developing countries such as Jordan.
