Extreme Scenarios Based Data-adaptive Probability Uncertainty Set for Distributionally Robust Transmission Expansion Planning

Increasing penetration of renewable energy into power systems is the development trend of future energy systems.One of the main challenges is to plan the expansion scheme of transmission systems to accommodate uncertainties of wind power.In this letter,we propose a novel extreme scenarios(ESs)based data-adaptive probability uncertainty set for the transmission expansion planning problem.First,available historical data are utilized to identify data-adaptive ESs through the convex hull technology,and the probability uncertainty set with respect to the obtained ESs is then established,from which we draw the final expansion decision based on the worst-case distribution.The proposed distributionally robust transmission expansion planning(DRTEP)model can guarantee optimality of expected cost under the worst-case distribution,while ensuring feasibility of all possible wind power generation.Simulation studies are carried out on a modified IEEE RTS 24-bus system to verify the effectiveness of the proposed DRTEP model.

Distributionally robust optimization configuration method for island microgrid considering extreme scenarios

The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.

A Day-ahead Economic Dispatch Method Considering Extreme Scenarios Based on Wind Power Uncertainty

As the intermittency of wind power is a growing concern in the day-ahead economic dispatch,this paper proposes a day-ahead economic dispatch method considering extreme scenarios of wind power by using an uncertainty set.The uncertainty set inspired by robust optimization is used to describe wind power intermittency in this paper.Four extreme scenarios based on the uncertainty set are formulated to represent the worst cases of wind power fluctuation.An economic dispatch method considering the costs of both load shedding and wind curtailment is proposed.The economic dispatch model can be easily solved by a quadratic programming method owing to the introduction of four extreme scenarios and the uncertainty set of wind power.Simulation is done using the IEEE 30-bus system and the results verify the effectiveness of the proposed method.

Extreme scenario extraction of a grid with large scale wind power integration by combined entropy-weighted clustering method

Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning,making the power system operation scenario highly diversified and variable.In conventional power system planning,some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation.While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power.In this paper,we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning.Specifically,based on an annual time-series data of wind power and load,a combined weighted clustering method is used to pick the typical scenarios of power grid operation,and the edge operation points far from the clustering center are extracted as the extreme scenarios.The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process,to further improve the efficiency and rationality of the extreme-scenario extraction.A set of case studies was used to verify the performance of the method,providing an intuitive understanding of the extreme scenario variety under wind power integration.

Analysis of an Extreme Scenario in the Vacuum Vessel in KTX

The Vacuum Vessel （VV） system is a vital component of Keda Torus for experiment （KTX）. Various accidental scenarios might occur on the VV. In this report, an extreme scenario is assumed and studied： plasma accidental termination during the fiat-top stage. Numerical simulations based on finite element are performed as the major tool for aualyses. The detailed distributions of eddy and the reaction forces on VV are extracted, and the total eddy current and the maximum reaction force due to electromagnetic load are figured out. In addition, according to the results, the VV can be approximately regarded as a centrally symmetric structure, even though its ports distribution is asymmetric.

Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard.Using the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in 2050.The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment.The distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes.The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk class.The LPAR increased with the intensity of extreme precipitation.The LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)”scenario and the highest in the“regional rivalry(SSP3)”scenario.In summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability.The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.