To address the issue of resource co-allocation with constraints to budget and deadline in grid environments, a novel co-allocation model based on virtual resource agent was proposed. The model optimized resources depl...To address the issue of resource co-allocation with constraints to budget and deadline in grid environments, a novel co-allocation model based on virtual resource agent was proposed. The model optimized resources deployment and price scheme through a three-side co-allocation mechanism, and applied queuing system to model the work of grid resources for providing quantitative deadline guarantees for grid applications. The validity and solutions of the model were presented theoretically. Extensive simulations were conducted to examine the effectiveness and the performance of the model by comparing with other co-allocation policies in terms of deadline violation rate, resource benefit and resource utilization. Experimental results show that compared with the three typical co-allocation policies, the proposed model can reduce the deadline violation rate to about 3.5% for the grid applications with constraints to budget and deadline. Also, the system benefits can be increased by about 30% compared with the those widely-used co-allocation policies.展开更多
This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a...This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.展开更多
With the huge rise of energy demand,the power system in the current era is moving to a new standard with increased access to renewable energy sources(RESs)integrated with distribution generation(DG)network.The RESs ne...With the huge rise of energy demand,the power system in the current era is moving to a new standard with increased access to renewable energy sources(RESs)integrated with distribution generation(DG)network.The RESs necessitate interfaces for controlling the power generation.The multilevel inverter(MLI)can be exploited for RESs in two diverse modes,namely,the power generation mode(stand-alone mode),and compensator mode(statcom).Few works have been carried out in optimization of controller gains with the load variations of the single type such as reactive load variation in different cases.Nevertheless,this load type may be unbalanced hence,to overcome such issues.So,a sophisticated optimization algorithm is important.This paper aims to introduce a control design via an optimization assisted PI controller for a 7-level inverter.In the present technique,the gains of the PI controller are adjusted dynamically by the adopted hybrid scheme,grey optimizer with dragon levy update(GD-LU),based on the operating conditions of the system.Here,the gains are adjusted such that the error between the reference signal and fault signal should be minimal.Thus,better dynamic performance could be attained by the present optimized PI controller.The proposed algorithm is the combined version of grey wolf optimization(GWO)and dragonfly algorithm(DA).Finally,the performance of the proposed work is compared and validated over other state-of-the-art models concerning error measures.展开更多
基金Project(60673165) supported by the National Natural Science Foundation of China
文摘To address the issue of resource co-allocation with constraints to budget and deadline in grid environments, a novel co-allocation model based on virtual resource agent was proposed. The model optimized resources deployment and price scheme through a three-side co-allocation mechanism, and applied queuing system to model the work of grid resources for providing quantitative deadline guarantees for grid applications. The validity and solutions of the model were presented theoretically. Extensive simulations were conducted to examine the effectiveness and the performance of the model by comparing with other co-allocation policies in terms of deadline violation rate, resource benefit and resource utilization. Experimental results show that compared with the three typical co-allocation policies, the proposed model can reduce the deadline violation rate to about 3.5% for the grid applications with constraints to budget and deadline. Also, the system benefits can be increased by about 30% compared with the those widely-used co-allocation policies.
文摘This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.
文摘With the huge rise of energy demand,the power system in the current era is moving to a new standard with increased access to renewable energy sources(RESs)integrated with distribution generation(DG)network.The RESs necessitate interfaces for controlling the power generation.The multilevel inverter(MLI)can be exploited for RESs in two diverse modes,namely,the power generation mode(stand-alone mode),and compensator mode(statcom).Few works have been carried out in optimization of controller gains with the load variations of the single type such as reactive load variation in different cases.Nevertheless,this load type may be unbalanced hence,to overcome such issues.So,a sophisticated optimization algorithm is important.This paper aims to introduce a control design via an optimization assisted PI controller for a 7-level inverter.In the present technique,the gains of the PI controller are adjusted dynamically by the adopted hybrid scheme,grey optimizer with dragon levy update(GD-LU),based on the operating conditions of the system.Here,the gains are adjusted such that the error between the reference signal and fault signal should be minimal.Thus,better dynamic performance could be attained by the present optimized PI controller.The proposed algorithm is the combined version of grey wolf optimization(GWO)and dragonfly algorithm(DA).Finally,the performance of the proposed work is compared and validated over other state-of-the-art models concerning error measures.
