As a key component of an integrated energy system(IES),energy storage can effectively alleviate the problem of the times between energy production and consumption.Exploiting the benefits of energy storage can improve ...As a key component of an integrated energy system(IES),energy storage can effectively alleviate the problem of the times between energy production and consumption.Exploiting the benefits of energy storage can improve the competitiveness of multi-energy systems.This paper proposes a method for day-ahead operation optimization of a building-level integrated energy system(BIES)considering additional potential benefits of energy storage.Based on the characteristics of peak-shaving and valley-filling of energy storage,and further consideration of the changes in the system’s load and real-time electricity price,a model of additional potential benefits of energy storage is developed.Aiming at the lowest total operating cost,a bi-level optimal operational model for day-ahead operation of BIES is developed.A case analysis of different dispatch strategies verifies that the addition of the proposed battery scheduling strategy improves economic operation.The results demonstrate that the model can exploit energy storage’s potential,further optimize the power output of BIES and reduce the economic cost.展开更多
Building operations are a significant source of urban carbon dioxide(CO_(2))emissions.However,the specific amounts and spatiotemporal distribution of these emissions remain unclear,complicating targeted emission reduc...Building operations are a significant source of urban carbon dioxide(CO_(2))emissions.However,the specific amounts and spatiotemporal distribution of these emissions remain unclear,complicating targeted emission reduction goals.This study introduces a building-level CO_(2) emissions estimation method and applies it to the Pearl River Delta Urban Agglomeration(PRDUA).By integrating the Designer’s Simulation Toolkit(DeST)for electricity consumption modeling with an energy decomposition approach for natural gas(NG)and liquefied petroleum gas(LPG)usage,we calculated CO_(2) emissions for each building using specific carbon emission factors.The methodology was validated in terms of the electricity consumption intensity per square meter and the monthly electricity consumption of individual buildings.In 2021,the annual hourly emission peak in the PRDUA was 26.1 thousand tons,with a low of 606.2 t.Commercial buildings have the highest monthly CO_(2) emission intensity per unit area(MCEIA)among all building types,ranging from 3.7 kgCO_(2)/(m^(2)·mo)in February to 6.9 kgCO_(2)/(m^(2)·mo)in July.The total annual CO_(2) emissions from buildings in the PRDUA were 82.14 million tons,with the top four cities accounting for 75.6%of the emissions;the remaining five cities contributed only 24.4%,highlighting a significant imbalance.Residential and commercial buildings were responsible for 76% of total emissions,emphasizing the disparity in contributions among different building categories.By mapping the spatiotemporal distribution of emissions,we identified the critical areas for targeted carbon reduction.The proposed method provides a robust framework for supporting sustainable urban energy management and guiding effective carbon mitigation strategies.展开更多
Long-term flood risk adaptation and decision making are complex because the future is full of deep uncertainties.Flexibility and robustness can be used to deal with future uncertainty.This study developed an integrate...Long-term flood risk adaptation and decision making are complex because the future is full of deep uncertainties.Flexibility and robustness can be used to deal with future uncertainty.This study developed an integrated modeling framework that extends previous studies to the spatial domain to assess the future flood risks and the cost and benefit of three adaptation measures for four types of buildings in Shanghai.Real options analysis(ROA)and dynamic adaptive policy pathways(DAPP)were integrated to develop a dynamic adaptation pathway and identify robust adaptation options.The results show that:(1)Sea level rise and land subsidence will significantly exacerbate the flood risks in Shanghai;(2)Among the three flood control measures,wet-floodproofing has the best economic performance in terms of both the net present value and the benefit/cost ratio,followed by dry-floodproofing,and elevation;(3)Dryfloodproofing can be used at the beginning of the future period(2030–2100),and it can be replaced by wet-floodproofing in 2035–2042;the elevation measure also shows good performance at the beginning of implementation,but its performance will decline after 2041–2045;(4)The combined strategy of dry-and wet-floodproofing in 2044–2046and a hybrid strategy combining the three measures should be the optimal solution for reducing the flood risks in 2047–2051.The methodology developed in this study can provide insights for coastal cities to formulate cost-effective and feasible adaptation strategies in a deeply uncertain future.展开更多
基金Supported by National Nature Science Foundation of China under Grant 51907024.
文摘As a key component of an integrated energy system(IES),energy storage can effectively alleviate the problem of the times between energy production and consumption.Exploiting the benefits of energy storage can improve the competitiveness of multi-energy systems.This paper proposes a method for day-ahead operation optimization of a building-level integrated energy system(BIES)considering additional potential benefits of energy storage.Based on the characteristics of peak-shaving and valley-filling of energy storage,and further consideration of the changes in the system’s load and real-time electricity price,a model of additional potential benefits of energy storage is developed.Aiming at the lowest total operating cost,a bi-level optimal operational model for day-ahead operation of BIES is developed.A case analysis of different dispatch strategies verifies that the addition of the proposed battery scheduling strategy improves economic operation.The results demonstrate that the model can exploit energy storage’s potential,further optimize the power output of BIES and reduce the economic cost.
基金the support of this research by National Key Research and Development Program of China(Grant No.2023YFC3804804)National Science Fund for Distinguished Young Scholars(Grant No.42225107)the National Natural Science Foundation of China(Grant Nos.42171409,42171410,and 42471513).
文摘Building operations are a significant source of urban carbon dioxide(CO_(2))emissions.However,the specific amounts and spatiotemporal distribution of these emissions remain unclear,complicating targeted emission reduction goals.This study introduces a building-level CO_(2) emissions estimation method and applies it to the Pearl River Delta Urban Agglomeration(PRDUA).By integrating the Designer’s Simulation Toolkit(DeST)for electricity consumption modeling with an energy decomposition approach for natural gas(NG)and liquefied petroleum gas(LPG)usage,we calculated CO_(2) emissions for each building using specific carbon emission factors.The methodology was validated in terms of the electricity consumption intensity per square meter and the monthly electricity consumption of individual buildings.In 2021,the annual hourly emission peak in the PRDUA was 26.1 thousand tons,with a low of 606.2 t.Commercial buildings have the highest monthly CO_(2) emission intensity per unit area(MCEIA)among all building types,ranging from 3.7 kgCO_(2)/(m^(2)·mo)in February to 6.9 kgCO_(2)/(m^(2)·mo)in July.The total annual CO_(2) emissions from buildings in the PRDUA were 82.14 million tons,with the top four cities accounting for 75.6%of the emissions;the remaining five cities contributed only 24.4%,highlighting a significant imbalance.Residential and commercial buildings were responsible for 76% of total emissions,emphasizing the disparity in contributions among different building categories.By mapping the spatiotemporal distribution of emissions,we identified the critical areas for targeted carbon reduction.The proposed method provides a robust framework for supporting sustainable urban energy management and guiding effective carbon mitigation strategies.
基金funded by the National Key Research and Development Program of China (Grant No. 2018YFC1508803)the National Social Science Foundation of China (Grant No. 18ZDA105)+1 种基金the National Natural Science Foundation of China (Grant No. 41971199, 42171080, 42001182)the Shanghai Science and Technology Support Program (Grant No. 19DZ1201505)
文摘Long-term flood risk adaptation and decision making are complex because the future is full of deep uncertainties.Flexibility and robustness can be used to deal with future uncertainty.This study developed an integrated modeling framework that extends previous studies to the spatial domain to assess the future flood risks and the cost and benefit of three adaptation measures for four types of buildings in Shanghai.Real options analysis(ROA)and dynamic adaptive policy pathways(DAPP)were integrated to develop a dynamic adaptation pathway and identify robust adaptation options.The results show that:(1)Sea level rise and land subsidence will significantly exacerbate the flood risks in Shanghai;(2)Among the three flood control measures,wet-floodproofing has the best economic performance in terms of both the net present value and the benefit/cost ratio,followed by dry-floodproofing,and elevation;(3)Dryfloodproofing can be used at the beginning of the future period(2030–2100),and it can be replaced by wet-floodproofing in 2035–2042;the elevation measure also shows good performance at the beginning of implementation,but its performance will decline after 2041–2045;(4)The combined strategy of dry-and wet-floodproofing in 2044–2046and a hybrid strategy combining the three measures should be the optimal solution for reducing the flood risks in 2047–2051.The methodology developed in this study can provide insights for coastal cities to formulate cost-effective and feasible adaptation strategies in a deeply uncertain future.
