Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper pr...Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.展开更多
Due to the interaction and corrosion of the seawater,submarine pipelines are easy to be broken to spill oil.The special environment of subsea restricts the technical development of pipeline maintenance.Therefore,the s...Due to the interaction and corrosion of the seawater,submarine pipelines are easy to be broken to spill oil.The special environment of subsea restricts the technical development of pipeline maintenance.Therefore,the study on the oil spilling model of submarine pipeline is very important for predicting the movement and diffusion of spilled oil,so that oil spilling traces and relating strategies can be determined.This paper aims to establish an oil spilling model of a submarine pipeline,study the movement characteristics of spilled oil in seawater by numerical simulation,and determine the traces,diffusion range,time to sea surface,etc.Then,the maximum horizontal migration distance(MHMD)with corresponding time are analyzed under different oil densities,spilling speeds and seawater velocities.Results show that the MHMD decreases first and then increases while the time to achieve the MHMD increases along with increasing oil density.The MHMD increases while the time to achieve the MHMD decreases,along with increasing spilling speed.Both the MHMD and corresponding time increase along with increasing seawater velocity.Based on numerical results,a correlation of spilling distance and spilling time is proposed to give fast and accurate predictions.After the oil reaches sea surface,oil expansion and transport are simulated.Euler-Lagrange method is used in the simulation.Dynamic and non-dynamic factors are considered.Results show that wind velocity and water velocity are dominant in dynamic factors.When they are large,spilled oil moves very fast with variable directions in complex flow field.Nondynamic factors such as evaporation,emulsion and solution mainly reduce the volume of oil film.They almost do not affect the direction and displacement of spilled oil.Quick response should be made for large wind and water velocities when the placement of oil boom is given.With the correlation and simulation,emergency responses can be guided effectively to reduce the impact of submarine oil pollution.The computational results benefit pollution control and environmental protection in marine petroleum engineering.展开更多
The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllabl...The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.展开更多
基金supported by the Natural Science Foundation of China(Grants U2166211)Zhejiang Provincial Natural Science Foundation of China(Grants LY24E070006 and LMS25E070002).
文摘Driven by the global energy transition and the urgent“dual carbon”goals,regional integrated energy system(RIES)planning is undergoing a paradigm shift from carbon reduction to negative carbon emissions.This paper provides a comprehensive review of the theoretical frameworks and technical pathways for RIES planning from a carbon-centric perspective.A key contribution is the proposed Carbon-Energy-Economy(CEE)triple-dimensional governance framework,which endogenizes carbon factors into planning decisions through emission constraints,trading mechanisms,and capture technologies.We first analyze the fundamental characteristics of RIES and their critical role in achieving carbon neutrality,detailing advancements in multi-energy coupling models,energy router concepts,and standardized energy hub modeling.The paper further explores multi-energy flow analysis methods,and systematically compares the applicability and limitations of various planning algorithms,with emphasis on addressing uncertainties from renewable integration.Finally,we highlight the integration of artificial intelligence with traditional optimization methods,offering new pathways for intelligent,adaptive,and low-carbon RIES planning.This review underscores the transition towards data-physical fusion models,cooperative uncertainty optimization,multi-market planning,and innovative zero/negative-carbon technological routes.
基金This research was funded by National Natural Science Foundation of China(NSFC),grant number No.51576210.
文摘Due to the interaction and corrosion of the seawater,submarine pipelines are easy to be broken to spill oil.The special environment of subsea restricts the technical development of pipeline maintenance.Therefore,the study on the oil spilling model of submarine pipeline is very important for predicting the movement and diffusion of spilled oil,so that oil spilling traces and relating strategies can be determined.This paper aims to establish an oil spilling model of a submarine pipeline,study the movement characteristics of spilled oil in seawater by numerical simulation,and determine the traces,diffusion range,time to sea surface,etc.Then,the maximum horizontal migration distance(MHMD)with corresponding time are analyzed under different oil densities,spilling speeds and seawater velocities.Results show that the MHMD decreases first and then increases while the time to achieve the MHMD increases along with increasing oil density.The MHMD increases while the time to achieve the MHMD decreases,along with increasing spilling speed.Both the MHMD and corresponding time increase along with increasing seawater velocity.Based on numerical results,a correlation of spilling distance and spilling time is proposed to give fast and accurate predictions.After the oil reaches sea surface,oil expansion and transport are simulated.Euler-Lagrange method is used in the simulation.Dynamic and non-dynamic factors are considered.Results show that wind velocity and water velocity are dominant in dynamic factors.When they are large,spilled oil moves very fast with variable directions in complex flow field.Nondynamic factors such as evaporation,emulsion and solution mainly reduce the volume of oil film.They almost do not affect the direction and displacement of spilled oil.Quick response should be made for large wind and water velocities when the placement of oil boom is given.With the correlation and simulation,emergency responses can be guided effectively to reduce the impact of submarine oil pollution.The computational results benefit pollution control and environmental protection in marine petroleum engineering.
基金supported in part by Science and Technology Project of State Grid Corporation of China(No.5400-202319829A-4-1-KJ).
文摘The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.
