An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of e...An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme scenarios.First,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are obtained.Then,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather module.At the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are expanded.Then,the scenarios are reduced by clustering analysis.The normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by temperature.Finally,the optimal configuration of a multi-energy microgrid system is carried out.Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the system.The proposed method can accurately capture the complementary potential of energy sources.And the economy of the system configuration is improved by 14.56%.展开更多
Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and...Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.展开更多
With the increasing penetration of photovoltaics in distribution networks,the adaptability of distribution network under uncertainties needs to be considered in the planning of distribution systems.In this paper,the i...With the increasing penetration of photovoltaics in distribution networks,the adaptability of distribution network under uncertainties needs to be considered in the planning of distribution systems.In this paper,the interval arithmetic and affine arithmetic are applied to deal with uncertainties,and an affine arithmetic based bi-level multi-objective joint planning model is built,which can obtain the planning schemes with low constraint-violation risk,high reliability and strong adaptability.On this basis,a bi-level multi-objective solution methodology using affine arithmetic based non-dominated sorting genetic algorithm II is proposed,and the planning schemes that simultaneously meet economy and adaptability goals under uncertainties can be obtained.To further eliminate bad solutions and improve the solution qualities,an affine arithmetic based dominance relation weakening criterion and a deviation distance based modification method are proposed.A 24-bus test system and a 10 kV distribution system of China are used for case studies.Different uncertainty levels are compared,and a sensitivity analysis of key parameters is conducted to explore their impacts on the final planning schemes.The simulation results verify the advantages of the proposed affine arithmetic based planning method.展开更多
基金supported by National Key Research and Development Program of China(2019YFB1505400)Jilin Science and Technology Development Program(20160411003XH)Jilin Industrial Technology Research and Development Program(2019C058-8).
文摘An optimal configuration method of a multi-energy microgrid system based on the deep joint generation of sourceload-temperature scenarios is proposed to improve the multienergy complementation and the reliability of energy supply in extreme scenarios.First,based on the historical meteorological data,the typical meteorological clusters and extreme temperature types are obtained.Then,to reflect the uncertainty of energy consumption and renewable energy output in different weather types,a deep joint generation model using a radiation-electric load-temperature scenario based on a denoising variational autoencoder is established for each weather module.At the same time,to cover the potential high energy consumption scenarios with extreme temperatures,the extreme scenarios with fewer data samples are expanded.Then,the scenarios are reduced by clustering analysis.The normal days of different typical scenarios and extreme temperature scenarios are determined,and the cooling and heating loads are determined by temperature.Finally,the optimal configuration of a multi-energy microgrid system is carried out.Experiments show that the optimal configuration based on the extreme scenarios and typical scenarios can improve the power supply reliability of the system.The proposed method can accurately capture the complementary potential of energy sources.And the economy of the system configuration is improved by 14.56%.
基金supported by the National Natural Science Foundation of China(No.52077136)。
文摘Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.
基金supported in part by the National Natural Science Foundation of China(No.52077149)the State Grid Corporation of China Science and Technology Project(No.5400-202199280A-0-0-00).
文摘With the increasing penetration of photovoltaics in distribution networks,the adaptability of distribution network under uncertainties needs to be considered in the planning of distribution systems.In this paper,the interval arithmetic and affine arithmetic are applied to deal with uncertainties,and an affine arithmetic based bi-level multi-objective joint planning model is built,which can obtain the planning schemes with low constraint-violation risk,high reliability and strong adaptability.On this basis,a bi-level multi-objective solution methodology using affine arithmetic based non-dominated sorting genetic algorithm II is proposed,and the planning schemes that simultaneously meet economy and adaptability goals under uncertainties can be obtained.To further eliminate bad solutions and improve the solution qualities,an affine arithmetic based dominance relation weakening criterion and a deviation distance based modification method are proposed.A 24-bus test system and a 10 kV distribution system of China are used for case studies.Different uncertainty levels are compared,and a sensitivity analysis of key parameters is conducted to explore their impacts on the final planning schemes.The simulation results verify the advantages of the proposed affine arithmetic based planning method.
