Shared energy storage helps lower user investment costs and enhances energy efficiency,which is considered a pivotal driver in accelerating the green transition of energy sectors.In view of the increasing demand for h...Shared energy storage helps lower user investment costs and enhances energy efficiency,which is considered a pivotal driver in accelerating the green transition of energy sectors.In view of the increasing demand for hydrogen,this paper proposes a bi-level optimization of configurations and scheduling for combined cooling,heating,and power(CCHP)microgrid systems considering shared hybrid electric-hydrogen energy storage service.The upper-level model addresses the capacity allocation problem of energy storage stations,while the lower-level model optimizes the operational strategies for the multi-microgrid system(MMS).To resolve the complexity of the coupled bi-level problem,Karush-Kuhn-Tucker(KKT)conditions and the Big-M method are applied to reformulate it into a solvable mixed-integer linear programming(MILP)model,compatible with CPLEX.The economic viability and rationality of the proposed approach are verified through comparisons of three cases.Numerical results show that the proposed approach reduces user annual costs by 20.15%compared to MMS without additional energy storage equipment and achieves 100%renewable absorption.For operators,it yields 5.71 M CNY annual profit with 3.02-year payback.Compared to MMS with electricity sharing,it further cuts user costs by 3.84%,boosts operator profit by 60.71%,and shortens payback by 15.88%.展开更多
This study develops a multivariate eco-hydrological risk-assessment framework based on the multivari-ate copula method in order to evaluate the occurrence of extreme eco-hydrological events for the Xiangxi River withi...This study develops a multivariate eco-hydrological risk-assessment framework based on the multivari-ate copula method in order to evaluate the occurrence of extreme eco-hydrological events for the Xiangxi River within the Three Gorges Reservoir （TGR） area in China. Parameter uncertainties in marginal distri-butions and dependence structure are quantified by a Markov chain Monte Carlo （MCMC） algorithm. Uncertainties in the joint return periods are evaluated based on the posterior distributions. The proba- bilistic features of bivariate and multivariate hydrological risk are also characterized. The results show that the obtained predictive intervals bracketed the observations well, especially for flood duration. The uncertainty for the joint return period in ＂AND＂ case increases with an increase in the return period for univariate flood variables. Furthermore, a low design discharge and high service time may lead to high bivariate hydrological risk with great uncertainty.展开更多
In this study,a Bayesian model averaging(BMA)-based ensemble modeling system is proposed to project future flood occurrences for the River Thames using downscaled high-resolution climate projections from the latest ge...In this study,a Bayesian model averaging(BMA)-based ensemble modeling system is proposed to project future flood occurrences for the River Thames using downscaled high-resolution climate projections from the latest general circulation models(GCMs)in the Coupled Model Intercomparison Project Phase 6(CMIP6).The BMA-based ensemble modeling system integrates multiple hydrological models into the BMA framework to enhance the accuracy of hydrological forecasting,which has shown good performance in validation with the NSE higher 0.91,KGE approaching 0.80,and correlation coefficient higher than 0.96.Daily projections of precipitation and temperature under all four shared socioeconomic pathways were obtained from three GCM models and were further employed to project future potential evaporation.The BMA-based ensemble modeling system was then used to forecast annual maximum flood rates and associated 3-day maximum flood volumes in the future.Our results show that the three GCM models exhibit considerable differences in terms of future flood projections,but all indicate a general increase in flood occurrence and magnitude under future climate change scenarios.The future daily flood events under different climate scenarios are likely to become more severe,as indicated by higher mean,maximum,and 90th quantile values of the AMAX flood series.Meanwhile,the corresponding 3-day flood volumes show varying patterns in terms of mean and extreme flood volumes under different scenarios,but we would have more chances to experience severe 3-day flood volumes in future.The results of our study can provide important information for flood risk management and adaptation planning in the River Thames basin.展开更多
文摘Shared energy storage helps lower user investment costs and enhances energy efficiency,which is considered a pivotal driver in accelerating the green transition of energy sectors.In view of the increasing demand for hydrogen,this paper proposes a bi-level optimization of configurations and scheduling for combined cooling,heating,and power(CCHP)microgrid systems considering shared hybrid electric-hydrogen energy storage service.The upper-level model addresses the capacity allocation problem of energy storage stations,while the lower-level model optimizes the operational strategies for the multi-microgrid system(MMS).To resolve the complexity of the coupled bi-level problem,Karush-Kuhn-Tucker(KKT)conditions and the Big-M method are applied to reformulate it into a solvable mixed-integer linear programming(MILP)model,compatible with CPLEX.The economic viability and rationality of the proposed approach are verified through comparisons of three cases.Numerical results show that the proposed approach reduces user annual costs by 20.15%compared to MMS without additional energy storage equipment and achieves 100%renewable absorption.For operators,it yields 5.71 M CNY annual profit with 3.02-year payback.Compared to MMS with electricity sharing,it further cuts user costs by 3.84%,boosts operator profit by 60.71%,and shortens payback by 15.88%.
基金This work was jointly funded by the National Natural Science Foundation of China （51520105013 and 51679087） and the National Key Research and Development Plan of China （2016YFC0502800）.
文摘This study develops a multivariate eco-hydrological risk-assessment framework based on the multivari-ate copula method in order to evaluate the occurrence of extreme eco-hydrological events for the Xiangxi River within the Three Gorges Reservoir （TGR） area in China. Parameter uncertainties in marginal distri-butions and dependence structure are quantified by a Markov chain Monte Carlo （MCMC） algorithm. Uncertainties in the joint return periods are evaluated based on the posterior distributions. The proba- bilistic features of bivariate and multivariate hydrological risk are also characterized. The results show that the obtained predictive intervals bracketed the observations well, especially for flood duration. The uncertainty for the joint return period in ＂AND＂ case increases with an increase in the return period for univariate flood variables. Furthermore, a low design discharge and high service time may lead to high bivariate hydrological risk with great uncertainty.
基金supported by the Royal Society International Exchanges Program(IESR2202075)
文摘In this study,a Bayesian model averaging(BMA)-based ensemble modeling system is proposed to project future flood occurrences for the River Thames using downscaled high-resolution climate projections from the latest general circulation models(GCMs)in the Coupled Model Intercomparison Project Phase 6(CMIP6).The BMA-based ensemble modeling system integrates multiple hydrological models into the BMA framework to enhance the accuracy of hydrological forecasting,which has shown good performance in validation with the NSE higher 0.91,KGE approaching 0.80,and correlation coefficient higher than 0.96.Daily projections of precipitation and temperature under all four shared socioeconomic pathways were obtained from three GCM models and were further employed to project future potential evaporation.The BMA-based ensemble modeling system was then used to forecast annual maximum flood rates and associated 3-day maximum flood volumes in the future.Our results show that the three GCM models exhibit considerable differences in terms of future flood projections,but all indicate a general increase in flood occurrence and magnitude under future climate change scenarios.The future daily flood events under different climate scenarios are likely to become more severe,as indicated by higher mean,maximum,and 90th quantile values of the AMAX flood series.Meanwhile,the corresponding 3-day flood volumes show varying patterns in terms of mean and extreme flood volumes under different scenarios,but we would have more chances to experience severe 3-day flood volumes in future.The results of our study can provide important information for flood risk management and adaptation planning in the River Thames basin.
