With the intensification of the energy crisis and the worsening greenhouse effect,the development of sustainable integrated energy systems(IES)has become a crucial direction for energy transition.In this context,this ...With the intensification of the energy crisis and the worsening greenhouse effect,the development of sustainable integrated energy systems(IES)has become a crucial direction for energy transition.In this context,this paper proposes a low-carbon economic dispatch strategy under the green hydrogen certificate trading(GHCT)and the ladder-type carbon emission trading(CET)mechanism,enabling the coordinated utilization of green and blue hydrogen.Specifically,a proton exchange membrane electrolyzer(PEME)model that accounts for dynamic efficiency characteristics,and a steam methane reforming(SMR)model incorporating waste heat recovery,are developed.Based on these models,a hydrogen production–storage–utilization framework is established to enable the coordinated deployment of green and blue hydrogen.Furthermore,the gas turbine(GT)unit are retrofitted using oxygenenriched combustion carbon capture(OCC)technology,wherein the oxygen produced by PEME is employed to create an oxygen-enriched combustion environment.This approach reduces energy waste and facilitates low-carbon power generation.In addition,the GHCT mechanism is integrated into the system alongside the ladder-type CET mechanism,and their complementary effects are investigated.A comprehensive optimization model is then formulated to simultaneously achieve carbon reduction and economic efficiency across the system.Case study results show that the proposed strategy reduces wind curtailment by 7.77%,carbon emissions by 65.98%,and total cost by 12.57%.This study offers theoretical reference for the low-carbon,economic,and efficient operation of future energy systems.展开更多
To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hyd...To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
To utilize exist SCADA(Supervisory Control and Data Acqui si tion)/EMS (Energy Management System) fully and economize capital, Henan Electric Power Dispatching and Communication Center in China established a set of H...To utilize exist SCADA(Supervisory Control and Data Acqui si tion)/EMS (Energy Management System) fully and economize capital, Henan Electric Power Dispatching and Communication Center in China established a set of Henan Dispatcher Training Simulator (HNDTS) base on its exist SCADA/EMS. In order to i ntegrated with exist SCADA/EMS, the integration method and technique are propose d. Graph data integration discussed with emphasis. After integration implemented , HNDTS can share all data with SCADA/EMS and dispatchers can be trained in same environment as real work situation, in the same time it can avoid amout of work of maintenance engineers. Both advantages and disadvantages of integration are analyzed. In the end of paper, the requirement for future DTS is put forward bas e on the experience of author.展开更多
Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy couplin...Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy coupling equipment,and renewable energy.An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost,carbon emission and enhance the power supply reliability.Firstly,the lowcarbon mathematical model of combined thermal and power unit,carbon capture system and power to gas unit(CCP)is established.Subsequently,we establish a low carbon multi-objective optimization model considering system operation cost,carbon emissions cost,integrated demand response,wind and photovoltaic curtailment,and load shedding costs.Furthermore,considering the intermittency of wind power generation and the flexibility of load demand,the low carbon economic dispatch problem is modeled as a Markov decision process.The twin delayed deep deterministic policy gradient(TD3)algorithm is used to solve the complex scheduling problem.The effectiveness of the proposed method is verified in the simulation case studies.Compared with TD3,SAC,A3C,DDPG and DQN algorithms,the operating cost is reduced by 8.6%,4.3%,6.1%and 8.0%.展开更多
Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbo...Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.展开更多
Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based o...Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper.The algorithm is based on external elite archive and Pareto dominance,and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm.Average entropy and cubic chaoticmapping initialization strategies are proposed to increase population diversity.In the proposed method,we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach.Unlike traditional models,this work takes the transmission losses as an optimization target and overcomes complex model constraints through a dynamic relaxation constraint approach.To solve the uncertainty caused by integrating wind and photovoltaic energy in power system scheduling,a multi-objective dynamic environment economical dispatch model is set up that takes the system spinning reserve and network highest losses into account.In this paper,the DE algorithm is improved to form the DGAGE algorithm for the objective optimization of the overall power system,The DE algorithm part of DGAGE is combined with the JAYA algorithm to form the system scheduling HDJ algorithm for multiple energy sources connected to the grid.The effectiveness of the proposed method is demonstrated using CEC2022 and CEC2005 test functions,showing robust optimization performance.Validation on a classical 10-unit system confirms the feasibility of the proposed algorithm in addressing power system scheduling issues.This approach provides a novel solution for dynamic power dispatch systems.展开更多
An integrated energy system includes multiple subsystems of electricity,heating,and gas.It is difficult to achieve dynamic dispatch of multi-energy flows due to challenges in establishing detailed mathematical models ...An integrated energy system includes multiple subsystems of electricity,heating,and gas.It is difficult to achieve dynamic dispatch of multi-energy flows due to challenges in establishing detailed mathematical models and the impacts of uncertain factors.Reinforcement learning has the ability to fit non-convex and non-linear problems.It avoids simplifying detailed mathematical models and complex,uncertain factors in the solution process,like traditional methods,which provides a new idea for achieving dynamic energy dispatch.However,existing research generally relies on soft constraints,such as reward function penalties,to obtain the dispatch strategy,which may violate the system’s operational safety.This paper ensures the security of dynamic energy dispatch strategies by adding two constraint mechanisms on reinforcement learning.First,boundary conditions of the action security domain are established through continuous interaction and feedback between the agent and the environment.It can effectively restrict infeasible actions.Second,the strategy is constrained by the truncation function in the trust domain,and strategy update is always kept within a controllable range to improve convergence and stability of the IES dynamic energy dispatch model.Finally,simulation results indicate that the proposed method can effectively constrain agent action selections and strategy updates during the dynamic energy dispatch process.This is of great significance to ensure the safe and stable operation of the system.展开更多
To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon...To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.展开更多
针对可再生能源和负荷的不确定性制约综合能源系统(integrated energy systems,IES)实现零碳目标的问题,提出了一种考虑不确定性的IES零碳容量规划和多时间尺度优化方法。以新疆某含电、热、冷负荷需求的IES为例,首先,利用多元联合分布...针对可再生能源和负荷的不确定性制约综合能源系统(integrated energy systems,IES)实现零碳目标的问题,提出了一种考虑不确定性的IES零碳容量规划和多时间尺度优化方法。以新疆某含电、热、冷负荷需求的IES为例,首先,利用多元联合分布并结合蒙特卡洛方法模拟可再生能源和负荷的不确定性及相关性。然后,提出利用氢储能电解水过程产生的碱性溶液吸收二氧化碳的IES全生命周期零碳容量规划方法。最后,构建了IES规划周期内的零碳排放调度框架,以协调IES在长时间规划周期和短时间调度周期的零碳优化。并基于该框架设计随机-滚动两阶段优化调度模型,以应对可再生能源和负荷的不确定性,最小化IES综合运行成本。仿真分析结果验证了所提方法在促进可再生能源消纳、减少一次能源消耗、实现零碳排放和经济运行方面的有效性。展开更多
为兼顾“双碳”目标与能源保供底线,提升能源系统源荷侧降碳能力,在有效挖掘系统降碳潜能的同时,缓解火电装机总量增长与风电消纳之间的矛盾,文章将绿氨生产、氨煤混燃以及多种灵活性资源结合构成绿氨耦合发电单元,同时考虑碳捕集等低...为兼顾“双碳”目标与能源保供底线,提升能源系统源荷侧降碳能力,在有效挖掘系统降碳潜能的同时,缓解火电装机总量增长与风电消纳之间的矛盾,文章将绿氨生产、氨煤混燃以及多种灵活性资源结合构成绿氨耦合发电单元,同时考虑碳捕集等低碳技术,并应用碳排放流理论,提出了考虑绿氨替代与多元阶梯碳势需求响应的综合能源虚拟电厂(integrated energy virtual power plant,IEVPP)低碳经济调度模型,以降低系统碳排放并提升系统经济性。首先,将绿氨替代作为解决风电供用能不匹配问题的手段,并将其与燃煤机组掺氨技术结合,分析其能量流动与低碳特性;其次,将电-碳-氢-氨多元耦合过程引入IEVPP,从而促进了风电消纳,提升了系统调节能力;最后,依托碳排放流理论,通过动态碳排放因子提出了多元阶梯碳势需求响应模型,从源荷两侧充分挖掘系统降碳潜能。算例仿真分析结果表明,所构建的考虑绿氨替代IEVPP联合多元阶梯碳势需求响应可有效降低系统碳排放量,提升系统经济性。展开更多
基金supported by National Natural Science Foundation of China(52477101)Natural Science Foundation of Jiangsu Province(BK20210932).
文摘With the intensification of the energy crisis and the worsening greenhouse effect,the development of sustainable integrated energy systems(IES)has become a crucial direction for energy transition.In this context,this paper proposes a low-carbon economic dispatch strategy under the green hydrogen certificate trading(GHCT)and the ladder-type carbon emission trading(CET)mechanism,enabling the coordinated utilization of green and blue hydrogen.Specifically,a proton exchange membrane electrolyzer(PEME)model that accounts for dynamic efficiency characteristics,and a steam methane reforming(SMR)model incorporating waste heat recovery,are developed.Based on these models,a hydrogen production–storage–utilization framework is established to enable the coordinated deployment of green and blue hydrogen.Furthermore,the gas turbine(GT)unit are retrofitted using oxygenenriched combustion carbon capture(OCC)technology,wherein the oxygen produced by PEME is employed to create an oxygen-enriched combustion environment.This approach reduces energy waste and facilitates low-carbon power generation.In addition,the GHCT mechanism is integrated into the system alongside the ladder-type CET mechanism,and their complementary effects are investigated.A comprehensive optimization model is then formulated to simultaneously achieve carbon reduction and economic efficiency across the system.Case study results show that the proposed strategy reduces wind curtailment by 7.77%,carbon emissions by 65.98%,and total cost by 12.57%.This study offers theoretical reference for the low-carbon,economic,and efficient operation of future energy systems.
文摘To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
文摘To utilize exist SCADA(Supervisory Control and Data Acqui si tion)/EMS (Energy Management System) fully and economize capital, Henan Electric Power Dispatching and Communication Center in China established a set of Henan Dispatcher Training Simulator (HNDTS) base on its exist SCADA/EMS. In order to i ntegrated with exist SCADA/EMS, the integration method and technique are propose d. Graph data integration discussed with emphasis. After integration implemented , HNDTS can share all data with SCADA/EMS and dispatchers can be trained in same environment as real work situation, in the same time it can avoid amout of work of maintenance engineers. Both advantages and disadvantages of integration are analyzed. In the end of paper, the requirement for future DTS is put forward bas e on the experience of author.
基金supported in part by the Scientific Research Fund of Liaoning Provincial Education Department under Grant LQGD2019005in part by the Doctoral Start-up Foundation of Liaoning Province under Grant 2020-BS-141.
文摘Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy coupling equipment,and renewable energy.An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost,carbon emission and enhance the power supply reliability.Firstly,the lowcarbon mathematical model of combined thermal and power unit,carbon capture system and power to gas unit(CCP)is established.Subsequently,we establish a low carbon multi-objective optimization model considering system operation cost,carbon emissions cost,integrated demand response,wind and photovoltaic curtailment,and load shedding costs.Furthermore,considering the intermittency of wind power generation and the flexibility of load demand,the low carbon economic dispatch problem is modeled as a Markov decision process.The twin delayed deep deterministic policy gradient(TD3)algorithm is used to solve the complex scheduling problem.The effectiveness of the proposed method is verified in the simulation case studies.Compared with TD3,SAC,A3C,DDPG and DQN algorithms,the operating cost is reduced by 8.6%,4.3%,6.1%and 8.0%.
基金supported by the State Grid Shandong Electric Power Company Economic and Technical Research Institute Project(SGSDJY00GPJS2100135).
文摘Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.
基金funded by the Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions,grant number 2023QN131National Innovation Training Program Project in China,grant number 202410451009.
文摘Considering the special features of dynamic environment economic dispatch of power systems with high dimensionality,strong coupling,nonlinearity,and non-convexity,a GA-DE multi-objective optimization algorithm based on dual-population pseudo-parallel genetic algorithm-differential evolution is proposed in this paper.The algorithm is based on external elite archive and Pareto dominance,and it adopts the cooperative co-evolution mechanism of differential evolution and genetic algorithm.Average entropy and cubic chaoticmapping initialization strategies are proposed to increase population diversity.In the proposed method,we analyze the distribution of neighboring solutions and apply a new Pareto solution set pruning approach.Unlike traditional models,this work takes the transmission losses as an optimization target and overcomes complex model constraints through a dynamic relaxation constraint approach.To solve the uncertainty caused by integrating wind and photovoltaic energy in power system scheduling,a multi-objective dynamic environment economical dispatch model is set up that takes the system spinning reserve and network highest losses into account.In this paper,the DE algorithm is improved to form the DGAGE algorithm for the objective optimization of the overall power system,The DE algorithm part of DGAGE is combined with the JAYA algorithm to form the system scheduling HDJ algorithm for multiple energy sources connected to the grid.The effectiveness of the proposed method is demonstrated using CEC2022 and CEC2005 test functions,showing robust optimization performance.Validation on a classical 10-unit system confirms the feasibility of the proposed algorithm in addressing power system scheduling issues.This approach provides a novel solution for dynamic power dispatch systems.
基金supported by National Natural Science Foundation of China(51977005).
文摘An integrated energy system includes multiple subsystems of electricity,heating,and gas.It is difficult to achieve dynamic dispatch of multi-energy flows due to challenges in establishing detailed mathematical models and the impacts of uncertain factors.Reinforcement learning has the ability to fit non-convex and non-linear problems.It avoids simplifying detailed mathematical models and complex,uncertain factors in the solution process,like traditional methods,which provides a new idea for achieving dynamic energy dispatch.However,existing research generally relies on soft constraints,such as reward function penalties,to obtain the dispatch strategy,which may violate the system’s operational safety.This paper ensures the security of dynamic energy dispatch strategies by adding two constraint mechanisms on reinforcement learning.First,boundary conditions of the action security domain are established through continuous interaction and feedback between the agent and the environment.It can effectively restrict infeasible actions.Second,the strategy is constrained by the truncation function in the trust domain,and strategy update is always kept within a controllable range to improve convergence and stability of the IES dynamic energy dispatch model.Finally,simulation results indicate that the proposed method can effectively constrain agent action selections and strategy updates during the dynamic energy dispatch process.This is of great significance to ensure the safe and stable operation of the system.
基金supported by the Key Project of Shanghai(Project Number A1-0224-25-002-02-040,Municipal Key Course—Heat Transfer)funded by the National Natural Science Foundation of China(Grant No.52077137).
文摘To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.
文摘针对可再生能源和负荷的不确定性制约综合能源系统(integrated energy systems,IES)实现零碳目标的问题,提出了一种考虑不确定性的IES零碳容量规划和多时间尺度优化方法。以新疆某含电、热、冷负荷需求的IES为例,首先,利用多元联合分布并结合蒙特卡洛方法模拟可再生能源和负荷的不确定性及相关性。然后,提出利用氢储能电解水过程产生的碱性溶液吸收二氧化碳的IES全生命周期零碳容量规划方法。最后,构建了IES规划周期内的零碳排放调度框架,以协调IES在长时间规划周期和短时间调度周期的零碳优化。并基于该框架设计随机-滚动两阶段优化调度模型,以应对可再生能源和负荷的不确定性,最小化IES综合运行成本。仿真分析结果验证了所提方法在促进可再生能源消纳、减少一次能源消耗、实现零碳排放和经济运行方面的有效性。
文摘为兼顾“双碳”目标与能源保供底线,提升能源系统源荷侧降碳能力,在有效挖掘系统降碳潜能的同时,缓解火电装机总量增长与风电消纳之间的矛盾,文章将绿氨生产、氨煤混燃以及多种灵活性资源结合构成绿氨耦合发电单元,同时考虑碳捕集等低碳技术,并应用碳排放流理论,提出了考虑绿氨替代与多元阶梯碳势需求响应的综合能源虚拟电厂(integrated energy virtual power plant,IEVPP)低碳经济调度模型,以降低系统碳排放并提升系统经济性。首先,将绿氨替代作为解决风电供用能不匹配问题的手段,并将其与燃煤机组掺氨技术结合,分析其能量流动与低碳特性;其次,将电-碳-氢-氨多元耦合过程引入IEVPP,从而促进了风电消纳,提升了系统调节能力;最后,依托碳排放流理论,通过动态碳排放因子提出了多元阶梯碳势需求响应模型,从源荷两侧充分挖掘系统降碳潜能。算例仿真分析结果表明,所构建的考虑绿氨替代IEVPP联合多元阶梯碳势需求响应可有效降低系统碳排放量,提升系统经济性。
