As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This pap...As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.展开更多
This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging...This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging at the charging station level,estimating its physical dispatchable capability,determining its economic dispatchable capability under economic incentives,modeling its participation in the grid,and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable capability.The results indicate that using economic dispatchable capability reduces charging prices by 9.7%compared to physical dispatchable capability and 9.3%compared to disorderly charging.Additionally,the peak-to-valley difference is reduced by 64.6%when applying economic dispatchable capability with 20%EV penetration and residential base load,compared to disorderly charging.展开更多
The online car-hailing industry, which provides the right of use, has a certain impact on the traditional automobile market, but there is no unified theory on whether it has a positive impact or a negative impact. Bas...The online car-hailing industry, which provides the right of use, has a certain impact on the traditional automobile market, but there is no unified theory on whether it has a positive impact or a negative impact. Based on 362 consumer questionnaire data, this study builds a structural equation model to discuss the driving factors of residents’ choice of online car-hailing and whether the development of online car-hailing will have a certain substitution impact on the sales of private cars. From the perspective of consumers’ purchase intention, the research results show that consumers’ price consciousness, convenience consciousness, environmental protection consciousness and possession tendency will affect their choice of travel mode, and the use of online car-hailing is positively correlated with consumers’ willingness to replace private car ownership with online car-hailing.展开更多
The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm...The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size,the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching.Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.展开更多
The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM...The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction,achieving 28%RMSE reduction on real-world datasets(CAISO,PJM),coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4%while ensuring stability constraints.The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility,validated through grid-scale case studies.Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems.These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms,emphasizing computational efficiency and interoperability with legacy infrastructure.展开更多
The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessi...The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.展开更多
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
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
In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integra...In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integrated with phase change material(PCM)thermal storage is proposed.First,a thermal energy management framework is constructed,combining PCM thermal storage with the alkaline electrolyzer(AE)waste heat recovery and the heat pump(HP),while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation.Next,a flue gas treatment method based on purification-separation-storage coordination is adopted,achieving spatiotemporal decoupling between waste incineration and flue gas treatment.Subsequently,a two-stage optimal dispatching strategy for the multi-energy system is developed:the first stage establishes a dayahead economic dispatch model with the objective of minimizing net system costs,while the second stage introduces model predictive control(MPC)to realize intraday rolling optimization.Finally,The optimal dispatching strategies under different scenarios are obtained using the Gurobi solver,followed by a comparative analysis of the optimized operational outcomes.Simulation results demonstrate that the proposed system optimizes the output and operational states of each unit,simultaneously reducing carbon trading costs while increasing electricity sales revenue.The proposed scheduling strategy demonstrates effective grid peak-shaving functionality,thereby simultaneously improving the system’s economic performance and operational flexibility while providing an innovative technical pathway for municipal solid waste(MSW)resource utilization and low-carbon transformation of energy systems.展开更多
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
聚焦在全国统一电力市场环境下,通过电力市场基本原理方法实现全网一体化电力平衡。首先,将全网一体化电力平衡基本原理与机组组合原理相结合,提出了一体化机组组合原理(integrated power balancing security constrained unit commitme...聚焦在全国统一电力市场环境下,通过电力市场基本原理方法实现全网一体化电力平衡。首先,将全网一体化电力平衡基本原理与机组组合原理相结合,提出了一体化机组组合原理(integrated power balancing security constrained unit commitment,IPB-SCUC),及其配套的成本效益计算方法、成本疏导机制和差价合约机制,系统构建出适应全国统一电力市场发展需要的全网一体化电力平衡市场模式,在不改变以平衡区为平衡主体的基本平衡模式下,实现了各地区平衡边界的广泛有序深度开放和全网平衡资源的市场化统一优化调用;其次,以所提全网一体化电力平衡市场模式为内核,系统提出了市场环境下调用全网资源解决通道受阻、电力保供、新能源消纳等电力平衡问题的通用方法;实现了对解决各类平衡问题经济性的量化计算,推动了一体化平衡电力流和价值流的融合统一;最后,基于实际生产运行数据的算例分析验证了所提理论的有效性和实用价值。展开更多
基金support of the National Key Research and Development Plan(Grant No.2021YFB3302501)the financial support of the National Science Foundation of China(Grant No.12161076)the financial support of the Fundamental Research Funds for the Central Universities(Grant No.DUT24LAB129).
文摘As carrier aircraft sortie frequency and flight deck operational density increase,autonomous dispatch trajectory planning for carrier-based vehicles demands efficient,safe,and kinematically feasible solutions.This paper presents an Iterative Safe Dispatch Corridor(iSDC)framework,addressing the suboptimality of the traditional SDC method caused by static corridor construction and redundant obstacle exploration.First,a Kinodynamic-Informed-Bidirectional Rapidly-exploring Random Tree Star(KIBRRT^(*))algorithm is proposed for the front-end coarse planning.By integrating bidirectional tree expansion,goal-biased elliptical sampling,and artificial potential field guidance,it reduces unnecessary exploration near concave obstacles and generates kinematically admissible paths.Secondly,the traditional SDC is implemented in an iterative manner,and the obtained trajectory in the current iteration is fed into the next iteration for corridor generation,thus progressively improving the quality of withincorridor constraints.For tractors,a reverse-motion penalty function is incorporated into the back-end optimizer to prioritize forward driving,aligning with mechanical constraints and human operational preferences.Numerical validations on the data of Gerald R.Ford-class carrier demonstrate that the KIBRRT^(*)reduces average computational time by 75%and expansion nodes by 25%compared to conventional RRT^(*)algorithms.Meanwhile,the iSDC framework yields more time-efficient trajectories for both carrier aircraft and tractors,with the dispatch time reduced by 31.3%and tractor reverse motion proportion decreased by 23.4%relative to traditional SDC.The presented framework offers a scalable solution for autonomous dispatch in confined and safety-critical environment,and an illustrative animation is available at bilibili.com/video/BV1tZ7Zz6Eyz.Moreover,the framework can be easily extended to three-dimension scenarios,and thus applicable for trajectory planning of aerial and underwater vehicles.
基金State Grid Henan Power Company Science and Technology Project‘Key Technology and Demonstration Application of Multi-Domain Electric Vehicle Aggregated Charging Load Dispatch’(5217L0240003).
文摘This paper introduces a method for modeling the entire aggregated electric vehicle(EV)charging process and analyzing its dispatchable capabilities.The methodology involves developing a model for aggregated EV charging at the charging station level,estimating its physical dispatchable capability,determining its economic dispatchable capability under economic incentives,modeling its participation in the grid,and investigating the effects of different scenarios and EV penetration on the aggregated load dispatch and dispatchable capability.The results indicate that using economic dispatchable capability reduces charging prices by 9.7%compared to physical dispatchable capability and 9.3%compared to disorderly charging.Additionally,the peak-to-valley difference is reduced by 64.6%when applying economic dispatchable capability with 20%EV penetration and residential base load,compared to disorderly charging.
文摘The online car-hailing industry, which provides the right of use, has a certain impact on the traditional automobile market, but there is no unified theory on whether it has a positive impact or a negative impact. Based on 362 consumer questionnaire data, this study builds a structural equation model to discuss the driving factors of residents’ choice of online car-hailing and whether the development of online car-hailing will have a certain substitution impact on the sales of private cars. From the perspective of consumers’ purchase intention, the research results show that consumers’ price consciousness, convenience consciousness, environmental protection consciousness and possession tendency will affect their choice of travel mode, and the use of online car-hailing is positively correlated with consumers’ willingness to replace private car ownership with online car-hailing.
基金supported by the National Natural Science Foundation of China(62103203)
文摘The economic dispatch problem(EDP) of microgrids operating in both grid-connected and isolated modes within an energy internet framework is addressed in this paper. The multi-agent leader-following consensus algorithm is employed to address the EDP of microgrids in grid-connected mode, while the push-pull algorithm with a fixed step size is introduced for the isolated mode. The proposed algorithm of isolated mode is proven to converge to the optimum when the interaction digraph of microgrids is strongly connected. A unified algorithmic framework is proposed to handle the two modes of operation of microgrids simultaneously, enabling our algorithm to achieve optimal power allocation and maintain the balance between power supply and demand in any mode and any mode switching. Due to the push-pull structure of the algorithm and the use of fixed step size,the proposed algorithm can better handle the case of unbalanced graphs, and the convergence speed is improved. It is documented that when the transmission topology is strongly connected and there is bi-directional communication between the energy router and its neighbors, the proposed algorithm in composite mode achieves economic dispatch even with arbitrary mode switching.Finally, we demonstrate the effectiveness and superiority of our algorithm through numerical simulations.
文摘The integration of deep learning into smart grid operations addresses critical challenges in dynamic load forecasting and optimal dispatch amid increasing renewable energy penetration.This study proposes a hybrid LSTM-Transformer architecture for multi-scale temporal-spatial load prediction,achieving 28%RMSE reduction on real-world datasets(CAISO,PJM),coupled with a deep reinforcement learning framework for multi-objective dispatch optimization that lowers operational costs by 12.4%while ensuring stability constraints.The synergy between adaptive forecasting models and scenario-based stochastic optimization demonstrates superior performance in handling renewable intermittency and demand volatility,validated through grid-scale case studies.Methodological innovations in federated feature extraction and carbon-aware scheduling further enhance scalability for distributed energy systems.These advancements provide actionable insights for grid operators transitioning to low-carbon paradigms,emphasizing computational efficiency and interoperability with legacy infrastructure.
基金funded by Guangxi Science and Technology Base and Talent Special Project,grant number GuiKeAD20159077Foundation of Guilin University of Technology,grant number GLUTQD2018001.
文摘The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power grids.This complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly desirable.In the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization problem.The proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs effectively.This study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the MAED.The PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence characteristics.Furthermore,the convergence efficiency of the PPA is significantly influenced by the parameter c.To address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational models.The computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch problems.The simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.
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.
文摘In order to address the synergistic optimization of energy efficiency improvement in the waste incineration power plant(WIPP)and renewable energy accommodation,an electricity-hydrogen-waste multi-energy system integrated with phase change material(PCM)thermal storage is proposed.First,a thermal energy management framework is constructed,combining PCM thermal storage with the alkaline electrolyzer(AE)waste heat recovery and the heat pump(HP),while establishing a PCM-driven waste drying system to enhance the efficiency of waste incineration power generation.Next,a flue gas treatment method based on purification-separation-storage coordination is adopted,achieving spatiotemporal decoupling between waste incineration and flue gas treatment.Subsequently,a two-stage optimal dispatching strategy for the multi-energy system is developed:the first stage establishes a dayahead economic dispatch model with the objective of minimizing net system costs,while the second stage introduces model predictive control(MPC)to realize intraday rolling optimization.Finally,The optimal dispatching strategies under different scenarios are obtained using the Gurobi solver,followed by a comparative analysis of the optimized operational outcomes.Simulation results demonstrate that the proposed system optimizes the output and operational states of each unit,simultaneously reducing carbon trading costs while increasing electricity sales revenue.The proposed scheduling strategy demonstrates effective grid peak-shaving functionality,thereby simultaneously improving the system’s economic performance and operational flexibility while providing an innovative technical pathway for municipal solid waste(MSW)resource utilization and low-carbon transformation of energy systems.
基金Supported by the National Natural Science Foundation of China(No.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
文摘聚焦在全国统一电力市场环境下,通过电力市场基本原理方法实现全网一体化电力平衡。首先,将全网一体化电力平衡基本原理与机组组合原理相结合,提出了一体化机组组合原理(integrated power balancing security constrained unit commitment,IPB-SCUC),及其配套的成本效益计算方法、成本疏导机制和差价合约机制,系统构建出适应全国统一电力市场发展需要的全网一体化电力平衡市场模式,在不改变以平衡区为平衡主体的基本平衡模式下,实现了各地区平衡边界的广泛有序深度开放和全网平衡资源的市场化统一优化调用;其次,以所提全网一体化电力平衡市场模式为内核,系统提出了市场环境下调用全网资源解决通道受阻、电力保供、新能源消纳等电力平衡问题的通用方法;实现了对解决各类平衡问题经济性的量化计算,推动了一体化平衡电力流和价值流的融合统一;最后,基于实际生产运行数据的算例分析验证了所提理论的有效性和实用价值。
