A review of uncertain factors and analytic methods in long-term energy system optimization models

A larger number of uncertain factors in energy systems influence their evolution.Owing to the complexity of energy system modeling,incorporating uncertainty analysis to energy system modeling is essential for future energy system planning and resource allocation.This study focusses on long-term energy system optimization model.The important uncertain parameters in the model are analyzed and divided into policy,economic,and technical factors.This study specifically addresses the challenges related to carbon emission reduction and energy transition.It involves collecting and organizing relevant research on uncertainty analysis of long-term energy systems.Various energy system uncertainty modeling methods and their applications from the literature are summarized in this review.Finally,important uncertainty factors and uncertainty modeling methods for long-term energy system modeling are discussed,and future research directions are proposed.

A Study of the Effect of Uncertain Factors in Far From the Madding Crowed

Far from the Madding Crowd is one of the most eminent works of British writer Thomas Hardy. In the novel, Hardy set amounts of uncertain factors such as death, love, and coincidence to push the development of the novel, figures' destiny and characteristics.

RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method.

TWO-DIMENSIONAL STOCHASTIC AIRFOIL OPTIMIZATION DESIGN METHOD BASED ON NEURAL NETWORKS

To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.

Classification and evaluation of uncertain influence factors for farm machinery service

Uncertainty extremely interferes with the execution of farm machinery operation.Treating uncertainties is especially important for machinery cooperatives providing social service since they face more uncertain influence factors(UIFs)than family farms.Under social service circumstance,uncertainties may arise from participants and environments.Classification and evaluation of UIFs were studied in this research.According to the production system,32 UIFs are defined and classified into six categories,which include supply,demand,interactivity,nature,society and others.Uncertainty composite index(UCI)is defined to evaluate the importance of UIFs,which is the square root of the product of occurrence frequency(OF)and impact degree(ID)calculated from the well-designed questionnaire responded by farm machinery operators.UCI is divided into five ranks based on normalization distribution test to illustrate the level of importance.Results from questionnaire showed that natural UIFs have an extreme impact on farm operation,UIFs of the demand and the supply have a serious influence on farm operation,UIFs of interactivity cannot be ignored,and social UIFs have a weak impact on farm operations.This study discovered the uncertainty problems under the specific circumstance of farm machinery service,which may provide a theoretical basis and potential methods for risk management of machinery cooperatives.

Optimal scheduling strategy for virtual power plants based on credibility theory

The virtual power plant(VPP)is a new and efficient solution to manage the integration of distributed energy resources(DERs)into the power system.Considering the unpredictable output of stochastic DERs,conventional scheduling strategies always set plenty of reserve aside in order to guarantee the reliability of operation,which is too conservative to gain more benefits.Thus,it is significant to research the scheduling strategies of VPPs,which can coordinate the risks and benefits of VPP operation.This paper presents a fuzzy chance-constrained scheduling model which utilizes fuzzy variables to describe uncertain features of distributed generators(DGs).Based on credibility theory,the concept of the confidence level is introduced to quantify the feasibility of the conditions,which reflects the risk tolerance of VPP operation.By transforming the fuzzy chance constraints into their equivalent forms,traditional optimization algorithms can be used to solve the optimal scheduling problem.An IEEE 6-node system is employed to prove the feasibility of the proposed scheduling model.Case studies demonstrate that the fuzzy chance strategy is superior to conservative scheduling strategies in realizing the right balance between risks and benefits.