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
随着分布式新能源和电动汽车等虚拟储能的广泛接入,电力系统的不确定性和低惯量特性变得更加显著,系统的频率运行风险增加,并且主动配电网内部的供需关系呈现出更加灵活多样的特征,导致其与输电网之间的功率交互关系也更加复杂。为此,...随着分布式新能源和电动汽车等虚拟储能的广泛接入,电力系统的不确定性和低惯量特性变得更加显著,系统的频率运行风险增加,并且主动配电网内部的供需关系呈现出更加灵活多样的特征,导致其与输电网之间的功率交互关系也更加复杂。为此,构建了一种基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法,以充分挖掘输配系统中分布式资源的支撑能力及其碳减排效益。首先,建立了与输配协同系统匹配的动态频率安全约束和基于双向调节模型的主动配电网灵活性支撑机制,提出了输配系统间的电-碳-绿证协同交互框架。为应对新能源出力的不确定性,将两侧子系统中涉及不确定变量的约束建模为联合机会约束。采用交替方向乘子法(alternating direction of multipliers algorithm,ADMM)进行输配系统的分布式协同。仿真结果表明,所提模型能够有效挖掘配电网中分布式资源的支撑潜力,灵活体现主动配电网的向外支撑能力或被支撑需求。与传统方法相比,系统动态频率安全性和输配协同调节灵活性显著提升,总运行成本较输电网单独调度模型降低13.42%,新能源减载量较忽略多市场耦合模型减少16.76%,系统运行经济性和低碳性明显改善。此外,所提出的求解方法比基于传统的样本平均近似方法减少约98%的计算时间,能够实现输配系统的快速分布式协同优化。展开更多
Demand response(DR)is considered to be an effective way to bring significant economic benefit to the commercial campus integrated energy system(CCIES)due to the large amount of flexible cooling and electric vehicle(EV...Demand response(DR)is considered to be an effective way to bring significant economic benefit to the commercial campus integrated energy system(CCIES)due to the large amount of flexible cooling and electric vehicle(EV)charging loads.To maximize DR’s benefits,this paper proposes an integrated DR framework that includes direct load control for cooling loads and time-of-use for EV charging station load in the CCIES.Moreover,multiple uncertainties threaten the secure and economic operation of the CCIES.To deal with these challenges,this paper establishes an interval optimization(IO)based economic dispatch(ED)model,considering the uncertain parameters,including ambient temperature,DR parameters,pipeline parameters,and maximum available PV power output.To improve the solution efficiency,the nonlinear constraints are linearized by applying multi-layer perceptron and affine arithmetic.The order relation and the possibility degrees of intervals are used to transform the interval ED model into a bi-level optimization model.The extreme value theorem of linear interval functions is used to obtain the analytical expressions of the optimal solutions of inner-level models,and the ED model is finally transformed into a solvable mix-integer linear programming model.Test results from actual CCIES demonstrate that the DR can improve the economy and reduce the uncertain fluctuation range of both the objective function and state variables.The ED result can maintain an economical and secure operation under multiple uncertain fluctuations.展开更多
This paper proposes a decentralized robust two-stage dispatch framework for multi-area integrated electric-gas systems (M-IEGSs), with the consideration of Weymouth and linepack equations of tie-pipelines. The overall...This paper proposes a decentralized robust two-stage dispatch framework for multi-area integrated electric-gas systems (M-IEGSs), with the consideration of Weymouth and linepack equations of tie-pipelines. The overall methodology includes the equivalent conversion for the robust two-stage program and the decentralized optimization for the equivalent form. To obtain a tractable and equivalent counterpart for the robust two-stage program, a quadruple-loop procedure based on the column-and-constraint generation (C&CG) and the penalty convex-concave procedure (P-CCP) algorithms is derived, resulting in a series of mixed integer second-order cone programs (MISOCPs). Then, an improved I-ADMM is proposed to realize the decentralized optimization for MISOCPs. Moreover, three acceleration methods are devised to reduce the computation burden. Simulation results validate the effectiveness of the proposed methodology and corresponding acceleration measures.展开更多
An integrated GPS and GIS based vehicle dispatching system was presented. The system uses GIS technology for the development of digital mine map database and GPS for vehicle positioning. The system consists of five mo...An integrated GPS and GIS based vehicle dispatching system was presented. The system uses GIS technology for the development of digital mine map database and GPS for vehicle positioning. The system consists of five modules: position module incorporated GPS and dead reckoning (DR); a map database structure for displaying and guidance purposes; a routing module based on the map database is able to give out the best route for the vehicles; map matching and route guidance module put the vehicle position to its exact location on the road despite of errors in positioning and map data; and the client-server module allows client exchange information between driver and control centre. The system can be operated in client-server level in which users can request routing and guidance with devices such as hand phone and PDA by communicating their current positions to the server or runs in autonomous mode when users cannot reach the server.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.
文摘随着分布式新能源和电动汽车等虚拟储能的广泛接入,电力系统的不确定性和低惯量特性变得更加显著,系统的频率运行风险增加,并且主动配电网内部的供需关系呈现出更加灵活多样的特征,导致其与输电网之间的功率交互关系也更加复杂。为此,构建了一种基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法,以充分挖掘输配系统中分布式资源的支撑能力及其碳减排效益。首先,建立了与输配协同系统匹配的动态频率安全约束和基于双向调节模型的主动配电网灵活性支撑机制,提出了输配系统间的电-碳-绿证协同交互框架。为应对新能源出力的不确定性,将两侧子系统中涉及不确定变量的约束建模为联合机会约束。采用交替方向乘子法(alternating direction of multipliers algorithm,ADMM)进行输配系统的分布式协同。仿真结果表明,所提模型能够有效挖掘配电网中分布式资源的支撑潜力,灵活体现主动配电网的向外支撑能力或被支撑需求。与传统方法相比,系统动态频率安全性和输配协同调节灵活性显著提升,总运行成本较输电网单独调度模型降低13.42%,新能源减载量较忽略多市场耦合模型减少16.76%,系统运行经济性和低碳性明显改善。此外,所提出的求解方法比基于传统的样本平均近似方法减少约98%的计算时间,能够实现输配系统的快速分布式协同优化。
基金supported by the National Natural Science Foundation of China(51977080)Natural Science Foundation of Guangdong Province(2022A1515010332,2023A1515240075)。
文摘Demand response(DR)is considered to be an effective way to bring significant economic benefit to the commercial campus integrated energy system(CCIES)due to the large amount of flexible cooling and electric vehicle(EV)charging loads.To maximize DR’s benefits,this paper proposes an integrated DR framework that includes direct load control for cooling loads and time-of-use for EV charging station load in the CCIES.Moreover,multiple uncertainties threaten the secure and economic operation of the CCIES.To deal with these challenges,this paper establishes an interval optimization(IO)based economic dispatch(ED)model,considering the uncertain parameters,including ambient temperature,DR parameters,pipeline parameters,and maximum available PV power output.To improve the solution efficiency,the nonlinear constraints are linearized by applying multi-layer perceptron and affine arithmetic.The order relation and the possibility degrees of intervals are used to transform the interval ED model into a bi-level optimization model.The extreme value theorem of linear interval functions is used to obtain the analytical expressions of the optimal solutions of inner-level models,and the ED model is finally transformed into a solvable mix-integer linear programming model.Test results from actual CCIES demonstrate that the DR can improve the economy and reduce the uncertain fluctuation range of both the objective function and state variables.The ED result can maintain an economical and secure operation under multiple uncertain fluctuations.
文摘This paper proposes a decentralized robust two-stage dispatch framework for multi-area integrated electric-gas systems (M-IEGSs), with the consideration of Weymouth and linepack equations of tie-pipelines. The overall methodology includes the equivalent conversion for the robust two-stage program and the decentralized optimization for the equivalent form. To obtain a tractable and equivalent counterpart for the robust two-stage program, a quadruple-loop procedure based on the column-and-constraint generation (C&CG) and the penalty convex-concave procedure (P-CCP) algorithms is derived, resulting in a series of mixed integer second-order cone programs (MISOCPs). Then, an improved I-ADMM is proposed to realize the decentralized optimization for MISOCPs. Moreover, three acceleration methods are devised to reduce the computation burden. Simulation results validate the effectiveness of the proposed methodology and corresponding acceleration measures.
基金Project (202183380) supported by the Research Programof the Educational Depart ment of Liaoning Province
文摘An integrated GPS and GIS based vehicle dispatching system was presented. The system uses GIS technology for the development of digital mine map database and GPS for vehicle positioning. The system consists of five modules: position module incorporated GPS and dead reckoning (DR); a map database structure for displaying and guidance purposes; a routing module based on the map database is able to give out the best route for the vehicles; map matching and route guidance module put the vehicle position to its exact location on the road despite of errors in positioning and map data; and the client-server module allows client exchange information between driver and control centre. The system can be operated in client-server level in which users can request routing and guidance with devices such as hand phone and PDA by communicating their current positions to the server or runs in autonomous mode when users cannot reach the server.
