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
Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to foreca...Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.展开更多
This paper proposes a hybrid optimization to solve the scheduling of household power consumption for Step and Time-of-Use (TOU) tariff system. The target function is the cost of electricity, and the optimization objec...This paper proposes a hybrid optimization to solve the scheduling of household power consumption for Step and Time-of-Use (TOU) tariff system. The target function is the cost of electricity, and the optimization object is total instantaneous power within a billing period. The control variables are starting moments of each household appliance. The optimization procedure is divided into two stages. Firstly, the prerequisite for minimal cost is calculated through mathematical analysis and generalized function theory. Secondly, the solution is obtained by using a heuristic algorithm in which the result of the first stage is considered to reduce the searching space. And an evaluation methodology is deduced to evaluate the optimization. The computer simulation demonstrates that the proposed approach can reduce the cost of electricity evidently in the sense of probability. The approach shows great value for embedded applications.展开更多
Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To bui...Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.展开更多
The aim of this research is to study the optimal demand decision for the Taiwan Residents industries through CSO （cat swarm optimization） algorithm. The five formulations of optimal demand are developed to solve opt...The aim of this research is to study the optimal demand decision for the Taiwan Residents industries through CSO （cat swarm optimization） algorithm. The five formulations of optimal demand are developed to solve optimal contract capacity for TOU （time of use） customer. Results indicated that, the CSO algorithm is highly helpful to Taiwan Residents industries on the optimal demand decision. Also the CSO is superior to PSO （particle swarm optimization） in the fast convergence and better performance to find the global best solution in the same iterations.展开更多
While price schedules can help improve the economic efficiency of renewable energy-powered microgrids,timeof-use(TOU)pricing has been identified as an effective way for microgrid development,which is presently limited...While price schedules can help improve the economic efficiency of renewable energy-powered microgrids,timeof-use(TOU)pricing has been identified as an effective way for microgrid development,which is presently limited by its high costs.In this study,we propose an evolutionary game theoretic model to explore optimal TOU pricing for development of renewable energy-powered microgrids by applying a multi-agent system,that comprises a government agent,local utility company agent,and different types of consumer agents.In the proposed model,we design objective functions for the company and the consumers and obtain a Nash equilibrium using backward induction.Two pricing strategies,namely,the TOU seasonal pricing and TOU monthly pricing,are evaluated and compared with traditional fixed pricing.The numerical results demonstrate that TOU schedules have significant potential for development of renewable energy-powered microgrids and are recommended for an electric company to replace traditional fixed pricing.Additionally,TOU monthly pricing is more suitable than TOU seasonal pricing for microgrid development.展开更多
Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally inte...Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.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.
基金supported by Institute for Information&communications Technology Planning&Evaluation(IITP)funded by the Korea government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security)Research Cluster Project,R20143,by Zayed University Research Office.
文摘Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models.Especially,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid.However,the existing single machine learning or deep learning forecasting cannot easily avoid overfitting.Moreover,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on time-of-use.To overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on time-of-use.Experimental results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark models.To verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the method.Therefore,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.
文摘This paper proposes a hybrid optimization to solve the scheduling of household power consumption for Step and Time-of-Use (TOU) tariff system. The target function is the cost of electricity, and the optimization object is total instantaneous power within a billing period. The control variables are starting moments of each household appliance. The optimization procedure is divided into two stages. Firstly, the prerequisite for minimal cost is calculated through mathematical analysis and generalized function theory. Secondly, the solution is obtained by using a heuristic algorithm in which the result of the first stage is considered to reduce the searching space. And an evaluation methodology is deduced to evaluate the optimization. The computer simulation demonstrates that the proposed approach can reduce the cost of electricity evidently in the sense of probability. The approach shows great value for embedded applications.
文摘Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.
文摘The aim of this research is to study the optimal demand decision for the Taiwan Residents industries through CSO （cat swarm optimization） algorithm. The five formulations of optimal demand are developed to solve optimal contract capacity for TOU （time of use） customer. Results indicated that, the CSO algorithm is highly helpful to Taiwan Residents industries on the optimal demand decision. Also the CSO is superior to PSO （particle swarm optimization） in the fast convergence and better performance to find the global best solution in the same iterations.
基金supported by the National Natural Science Foundation of China(52277107,51977115)Shenzhen Science and Technology Innovation Program(WDZC20220808143010001).
文摘While price schedules can help improve the economic efficiency of renewable energy-powered microgrids,timeof-use(TOU)pricing has been identified as an effective way for microgrid development,which is presently limited by its high costs.In this study,we propose an evolutionary game theoretic model to explore optimal TOU pricing for development of renewable energy-powered microgrids by applying a multi-agent system,that comprises a government agent,local utility company agent,and different types of consumer agents.In the proposed model,we design objective functions for the company and the consumers and obtain a Nash equilibrium using backward induction.Two pricing strategies,namely,the TOU seasonal pricing and TOU monthly pricing,are evaluated and compared with traditional fixed pricing.The numerical results demonstrate that TOU schedules have significant potential for development of renewable energy-powered microgrids and are recommended for an electric company to replace traditional fixed pricing.Additionally,TOU monthly pricing is more suitable than TOU seasonal pricing for microgrid development.
基金King Saud University for funding this research through the Researchers Supporting Program Number(RSPD2024R704),King Saud University,Riyadh,Saudi Arabia.
文摘Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.
