Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sara...Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.展开更多
3GPP LTE(Long Term Evolution)以其较高的带宽可以为用户提供更丰富的多媒体业务。但是,每个LTE运营商最关心的还是想通过有效的运维成本(OPEX.Operational Expenditure)来取得较高的利润。因此,在LTE中控制运维成本将成为一个非常具...3GPP LTE(Long Term Evolution)以其较高的带宽可以为用户提供更丰富的多媒体业务。但是,每个LTE运营商最关心的还是想通过有效的运维成本(OPEX.Operational Expenditure)来取得较高的利润。因此,在LTE中控制运维成本将成为一个非常具有挑战性的问题。本文详细介绍3GPP中为LTE所提出的一种新运维策略,即自组织网络(SON,Self-Organizing Networks)。LTE运营商通过SON机制可以明显降低OPEX,从而进一步提升LTE的竞争优势。展开更多
Smart electric motorcycle-sharing systems based on the digital platform are one of the public transportations that we use in daily lives when the sharing economy is considered.This transportation provides convenience ...Smart electric motorcycle-sharing systems based on the digital platform are one of the public transportations that we use in daily lives when the sharing economy is considered.This transportation provides convenience for users with low-cost systems while it also promotes an environmental conservation.Normally,users rent the vehicle to travel from the origin station to another station near their destination with a one-way trip in which the demand of renting and returning at each station is different.This leads to unbalanced vehicle rental systems.To avoid the full or empty inventory,the electric motorcycle-sharing rebalancing with the fleet optimization is employed to deliver the user experience and increase rental opportunities.In this paper,the authors propose a fleet optimization to manage the appropriate number of vehicles in each station by considering the cost of moving tasks and the rental opportunity to increase business return.Although the increasing number of service stations results in a large action space,the proposed routing algorithm is able filter the size of the action space to enable computing tasks.In this paper,a Deep Reinforcement Learning(DRL)creates the decisionmaking function to decide the appropriate action for fleet allocation from the last state of the number of vehicles at each station in the real environment at Suranaree University of Technology(SUT),Thailand.The obtained results indicate that the proposed concept can reduce the Operating Expenditure(OPEX).展开更多
基金supported by Swinburne University of Technology Sarawak Campus and Birmingham City University.
文摘Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.
文摘Smart electric motorcycle-sharing systems based on the digital platform are one of the public transportations that we use in daily lives when the sharing economy is considered.This transportation provides convenience for users with low-cost systems while it also promotes an environmental conservation.Normally,users rent the vehicle to travel from the origin station to another station near their destination with a one-way trip in which the demand of renting and returning at each station is different.This leads to unbalanced vehicle rental systems.To avoid the full or empty inventory,the electric motorcycle-sharing rebalancing with the fleet optimization is employed to deliver the user experience and increase rental opportunities.In this paper,the authors propose a fleet optimization to manage the appropriate number of vehicles in each station by considering the cost of moving tasks and the rental opportunity to increase business return.Although the increasing number of service stations results in a large action space,the proposed routing algorithm is able filter the size of the action space to enable computing tasks.In this paper,a Deep Reinforcement Learning(DRL)creates the decisionmaking function to decide the appropriate action for fleet allocation from the last state of the number of vehicles at each station in the real environment at Suranaree University of Technology(SUT),Thailand.The obtained results indicate that the proposed concept can reduce the Operating Expenditure(OPEX).
