Battery energy storage systems(BESSs)serve a crucial role in balancing energy fluctuations and reducing carbon emissions in net-zero power systems.However,the efficiency and cost performance have remained significant ...Battery energy storage systems(BESSs)serve a crucial role in balancing energy fluctuations and reducing carbon emissions in net-zero power systems.However,the efficiency and cost performance have remained significant challenges,which hinders the widespread adoption and development of BESSs.To address these challenges,this paper proposes a real-time energy management scheme that considers the involvement of prosumers to support net-zero power systems.The scheme is based on two shared energy storage models,referred to as energy storage sale model and power line lease model.The energy storage sale model balances real-time power deviations by energy interaction with the goal of minimizing system costs while generating revenue for shared energy storage providers(ESPs).Additionally,power line lease model supports peer-to-peer(P2P)power trading among prosumers through the power lines laid by ESPs to connect each prosumer.This model allows ESP to earn profits from the use of power lines while balancing power deviations and better consuming renewable energy.Experimental results validate the effectiveness of the proposed scheme,ensuring stable power supply for net-zero power systems and providing benefits for both the ESP and prosumers.展开更多
An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neu...An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas.展开更多
基金supported in part by the National Key Research and Development Program of China(No.2018YFA0702200)the National Natural Science Foundation of China(No.52377079)the Liaoning Revitalization Talents Program(No.XLYC2007181)。
文摘Battery energy storage systems(BESSs)serve a crucial role in balancing energy fluctuations and reducing carbon emissions in net-zero power systems.However,the efficiency and cost performance have remained significant challenges,which hinders the widespread adoption and development of BESSs.To address these challenges,this paper proposes a real-time energy management scheme that considers the involvement of prosumers to support net-zero power systems.The scheme is based on two shared energy storage models,referred to as energy storage sale model and power line lease model.The energy storage sale model balances real-time power deviations by energy interaction with the goal of minimizing system costs while generating revenue for shared energy storage providers(ESPs).Additionally,power line lease model supports peer-to-peer(P2P)power trading among prosumers through the power lines laid by ESPs to connect each prosumer.This model allows ESP to earn profits from the use of power lines while balancing power deviations and better consuming renewable energy.Experimental results validate the effectiveness of the proposed scheme,ensuring stable power supply for net-zero power systems and providing benefits for both the ESP and prosumers.
基金Project(51066002/E060701) supported by the National Natural Science Foundation of ChinaProject(U0937604) supported by the NSFC-Yunnan Joint Fund of China
文摘An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed.This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter,Elman neural network and least squares support vector machines.Then,according to the prediction,the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation,and prevent unfavorable byproduct gas emission and equipment trip as well.The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution,which provides a remarkable guidance for reasonable scheduling of byproduct gas.
