Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the mai...Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.展开更多
In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the...In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.展开更多
In the past, decision model for energy system only provides usable tools to support decision making. It didn't probe the way to improve decision making process. The extended decision model for energy system, propo...In the past, decision model for energy system only provides usable tools to support decision making. It didn't probe the way to improve decision making process. The extended decision model for energy system, proposed in this paper, couples energy planning & evaluating system and energy expert system. It can play a part of an adviser and assistant in the decision making process of energy department.展开更多
Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy co...Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy costs,some issues still remain to be explored:when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors.For this purpose,the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity(PME)are studied with exploiting the large potential thermal elastic ability of heating,ventilation and air-conditioning(HVAC)units.Different from nanogrids,in terms of minimizing time-average costs,PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally.Such bilevel energy management problem is formulated as a stochastic form in a longterm horizon.Since there are uncertain system parameters,time-coupled queue constraints and the interplay of bilevel decision-making,it is challenging to solve the formulated problems.To this end,we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters.The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework.Then,theoretical analysis of the existence and uniqueness of Stackelberg equilibrium(SE)is developed based on the proposed game property.Following that,we devise an optimization algorithm to reach the SE with less information exchange.Numerical experiments validate the effectiveness of the proposed approach.展开更多
Based on the theory of marginal opportunity cost, one kind of green input-output table and models of power company are put forward in this paper. For an appliable purpose, analysis of integrated planning, cost analysi...Based on the theory of marginal opportunity cost, one kind of green input-output table and models of power company are put forward in this paper. For an appliable purpose, analysis of integrated planning, cost analysis, pricing of the power company are also given.展开更多
文摘Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.
基金Project(61272148) supported by the National Natural Science Foundation of ChinaProject(20120162110061) supported by the Doctoral Programs of Ministry of Education of China+1 种基金Project(CX2014B066) supported by the Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(2014zzts044) supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.
文摘In the past, decision model for energy system only provides usable tools to support decision making. It didn't probe the way to improve decision making process. The extended decision model for energy system, proposed in this paper, couples energy planning & evaluating system and energy expert system. It can play a part of an adviser and assistant in the decision making process of energy department.
基金Supported by the National Key Research and Development Program of China(2018YFB1702300)the National Natural Science Foundation of China(61731012)。
文摘Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy costs,some issues still remain to be explored:when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors.For this purpose,the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity(PME)are studied with exploiting the large potential thermal elastic ability of heating,ventilation and air-conditioning(HVAC)units.Different from nanogrids,in terms of minimizing time-average costs,PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally.Such bilevel energy management problem is formulated as a stochastic form in a longterm horizon.Since there are uncertain system parameters,time-coupled queue constraints and the interplay of bilevel decision-making,it is challenging to solve the formulated problems.To this end,we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters.The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework.Then,theoretical analysis of the existence and uniqueness of Stackelberg equilibrium(SE)is developed based on the proposed game property.Following that,we devise an optimization algorithm to reach the SE with less information exchange.Numerical experiments validate the effectiveness of the proposed approach.
文摘Based on the theory of marginal opportunity cost, one kind of green input-output table and models of power company are put forward in this paper. For an appliable purpose, analysis of integrated planning, cost analysis, pricing of the power company are also given.
