The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanis...The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanisms lack sufficient incentives for emission reductions,and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling.To address these issues,this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration,hydrogen utilization,and the Secretary Bird Optimization Algorithm(SBOA).Key innovations include:(1)A dynamic reward-penalty carbon trading mechanism with coefficients(μ=0.2,λ=0.15),which reduces carbon trading costs by 47.2%(from$694.06 to$366.32)compared to traditional tiered models,incentivizing voluntary emission reductions.(2)The integration of P2G-CCS coupling,which lowers natural gas consumption by 41.9%(from$4117.20 to$2389.23)and enhances CO_(2) recycling efficiency,addressing the limitations of standalone P2G or CCS technologies.(3)TheSBOA algorithm,which outperforms traditionalmethods(e.g.,PSO,GWO)in convergence speed and global search capability,avoiding local optima and achieving 24.39%faster convergence on CEC2005 benchmark functions.(4)A four-energy PIES framework incorporating electricity,heat,gas,and hydrogen,where hydrogen fuel cells and CHP systems improve demand response flexibility,reducing gas-related emissions by 42.1%and generating$13.14 in demand response revenue.Case studies across five scenarios demonstrate the strategy’s effectiveness:total operational costs decrease by 14.7%(from$7354.64 to$6272.59),carbon emissions drop by 49.9%(from 5294.94 to 2653.39kg),andrenewable energyutilizationincreases by24.39%(from4.82%to8.17%).These results affirmthemodel’s ability to reconcile economic and environmental goals,providing a scalable approach for low-carbon transitions in industrial parks.展开更多
Building a low-carbon park is crucial for achieving the carbon neutrality goals.However,most research on low-carbon economic planning methods for park-level integrated energy systems(PIES)has focused on multi-energy f...Building a low-carbon park is crucial for achieving the carbon neutrality goals.However,most research on low-carbon economic planning methods for park-level integrated energy systems(PIES)has focused on multi-energy flow interactions,neglecting the“carbon perspective”and the impact of the dynamic coupling characteristics between multi-energy flows and carbon emission flow(CEF)on carbon reduction and planning schemes.Therefore,this paper proposes a two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub(CESH).Firstly,this paper establishes a CESH model for PIES to describe the synergistic relationship between CEF and multi-energy flows from input,conversion,storage,to output.Secondly,a PIES two-layer low-carbon economic planning model with CESH is proposed.The upper model determines the optimal device types and capacities during the planning cycle.The lower model employs the CESH model to promote carbon energy friendly interactions,optimize the daily operation scheme of PIES.The iterative process between the two layers,initiated by a genetic algorithm(GA),ensures the speed and ac-curacy.Finally,case studies show that,compared to planning methods without the CESH model,the proposed method is effective in reducing carbon emissions and total costs during the planning cycle.From a dual“carbon-energy”perspective,it enhances investment effectiveness and carbon reduction sensitivity by deeply exploring the energy conservation and carbon reduction potential of PIES.展开更多
精确的环境感知是实现自主代客泊车(automated valet parking,AVP)功能的基础,传统的AVP系统主要依赖于单车的感知,但随着场端智能技术的不断发展,车端与场端之间协同交互成为自主代客泊车落地的必然趋势。本文提出了一种基于V2X车场协...精确的环境感知是实现自主代客泊车(automated valet parking,AVP)功能的基础,传统的AVP系统主要依赖于单车的感知,但随着场端智能技术的不断发展,车端与场端之间协同交互成为自主代客泊车落地的必然趋势。本文提出了一种基于V2X车场协同的地下停车场全域感知方法,该方法将地下停车场的全域感知问题转化为大规模图模型的构建与优化问题。通过输入场端激光雷达、摄像头的传感器信息以及智能网联车的感知数据,以车辆位姿为节点,建立多种边约束关系。为了提高感知精度,本文提出了一种融合车道级地图信息的大规模图模型方法,通过将停放车辆作为半静态信息约束,并结合车道级地图信息构建横向约束,在求解过程中引入滑动窗口以减小图模型的规模,最终以地图形式输出感知结果供车端使用。通过仿真实验和在占地面积为2 500 m^(2)以上的地下停车场场景中进行实地实验,结果表明,该方法显著提升了在复杂停车场环境下的感知能力,实现了地下停车场的全域感知。展开更多
针对“双碳”目标下园区综合能源系统(Park-level integrated energy system,PIES)与充电站(Charging station,CS)协同调度中利益协调不足、碳配额机制应用不充分的问题,提出一种双层优化模型,弥补现有研究对CS独立运营经济诉求的忽视,...针对“双碳”目标下园区综合能源系统(Park-level integrated energy system,PIES)与充电站(Charging station,CS)协同调度中利益协调不足、碳配额机制应用不充分的问题,提出一种双层优化模型,弥补现有研究对CS独立运营经济诉求的忽视,并挖掘电动汽车碳配额交易在多主体场景下的潜力。上层以PIES运行成本最小化为目标,结合可再生能源出力与负荷供需关系设计灵活定价机制;下层以CS收益最大化为目标,构建电动汽车(Electric vehicle,EV)碳配额核算与交易模型,通过出售多余配额提升收益。模型中引入序列运算理论处理可再生能源与负荷不确定性,将机会约束规划转化为混合整数线性规划问题,并利用CPLEX求解。仿真结果显示,灵活定价机制与碳配额交易协同作用下,园区运行成本降低6.92%,充电站收益提高76.49%,验证了EV碳配额交易在平衡多主体利益、提升系统经济性与环境效益中的有效性。展开更多
基金funded by State Grid Beijing Electric Power Company Technology Project,grant number 520210230004.
文摘The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanisms lack sufficient incentives for emission reductions,and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling.To address these issues,this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration,hydrogen utilization,and the Secretary Bird Optimization Algorithm(SBOA).Key innovations include:(1)A dynamic reward-penalty carbon trading mechanism with coefficients(μ=0.2,λ=0.15),which reduces carbon trading costs by 47.2%(from$694.06 to$366.32)compared to traditional tiered models,incentivizing voluntary emission reductions.(2)The integration of P2G-CCS coupling,which lowers natural gas consumption by 41.9%(from$4117.20 to$2389.23)and enhances CO_(2) recycling efficiency,addressing the limitations of standalone P2G or CCS technologies.(3)TheSBOA algorithm,which outperforms traditionalmethods(e.g.,PSO,GWO)in convergence speed and global search capability,avoiding local optima and achieving 24.39%faster convergence on CEC2005 benchmark functions.(4)A four-energy PIES framework incorporating electricity,heat,gas,and hydrogen,where hydrogen fuel cells and CHP systems improve demand response flexibility,reducing gas-related emissions by 42.1%and generating$13.14 in demand response revenue.Case studies across five scenarios demonstrate the strategy’s effectiveness:total operational costs decrease by 14.7%(from$7354.64 to$6272.59),carbon emissions drop by 49.9%(from 5294.94 to 2653.39kg),andrenewable energyutilizationincreases by24.39%(from4.82%to8.17%).These results affirmthemodel’s ability to reconcile economic and environmental goals,providing a scalable approach for low-carbon transitions in industrial parks.
基金financially funded by the National Natural Science Foundation of China(Grant/Award Numbers:52177107 and 52222704).
文摘Building a low-carbon park is crucial for achieving the carbon neutrality goals.However,most research on low-carbon economic planning methods for park-level integrated energy systems(PIES)has focused on multi-energy flow interactions,neglecting the“carbon perspective”and the impact of the dynamic coupling characteristics between multi-energy flows and carbon emission flow(CEF)on carbon reduction and planning schemes.Therefore,this paper proposes a two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub(CESH).Firstly,this paper establishes a CESH model for PIES to describe the synergistic relationship between CEF and multi-energy flows from input,conversion,storage,to output.Secondly,a PIES two-layer low-carbon economic planning model with CESH is proposed.The upper model determines the optimal device types and capacities during the planning cycle.The lower model employs the CESH model to promote carbon energy friendly interactions,optimize the daily operation scheme of PIES.The iterative process between the two layers,initiated by a genetic algorithm(GA),ensures the speed and ac-curacy.Finally,case studies show that,compared to planning methods without the CESH model,the proposed method is effective in reducing carbon emissions and total costs during the planning cycle.From a dual“carbon-energy”perspective,it enhances investment effectiveness and carbon reduction sensitivity by deeply exploring the energy conservation and carbon reduction potential of PIES.
文摘精确的环境感知是实现自主代客泊车(automated valet parking,AVP)功能的基础,传统的AVP系统主要依赖于单车的感知,但随着场端智能技术的不断发展,车端与场端之间协同交互成为自主代客泊车落地的必然趋势。本文提出了一种基于V2X车场协同的地下停车场全域感知方法,该方法将地下停车场的全域感知问题转化为大规模图模型的构建与优化问题。通过输入场端激光雷达、摄像头的传感器信息以及智能网联车的感知数据,以车辆位姿为节点,建立多种边约束关系。为了提高感知精度,本文提出了一种融合车道级地图信息的大规模图模型方法,通过将停放车辆作为半静态信息约束,并结合车道级地图信息构建横向约束,在求解过程中引入滑动窗口以减小图模型的规模,最终以地图形式输出感知结果供车端使用。通过仿真实验和在占地面积为2 500 m^(2)以上的地下停车场场景中进行实地实验,结果表明,该方法显著提升了在复杂停车场环境下的感知能力,实现了地下停车场的全域感知。
