Multi-Criteria Decision-Making for Power Grid Construction Project Investment Ranking Based on the Prospect Theory Improved by Rewarding Good and Punishing Bad Linear Transformation

Using the improved prospect theory with the linear transformations of rewarding good and punishing bad(RGPBIT),a new investment ranking model for power grid construction projects(PGCPs)is proposed.Given the uncertainty of each index value under the market environment,fuzzy numbers are used to describe qualitative indicators and interval numbers are used to describe quantitative ones.Taking into account decision-maker’s subjective risk attitudes,a multi-criteria decision-making(MCDM)method based on improved prospect theory is proposed.First,the[−1,1]RGPBIT operator is proposed to normalize the original data,to obtain the best andworst schemes of PGCPs.Furthermore,the correlation coefficients between interval/fuzzy numbers and the best/worst schemes are defined and introduced to the prospect theory to improve its value function and loss function,and the positive and negative prospect value matrices of the project are obtained.Then,the optimization model with the maximum comprehensive prospect value is constructed,the optimal attribute weight is determined,and the PGCPs are ranked accordingly.Taking four PGCPs of the IEEERTS-79 node system as examples,an illustration of the feasibility and effectiveness of the proposed method is provided.

Default rules in investment decision-making:trait anxiety and decision-making styles

This study investigates the role of default options in the relationship between trait anxiety,and decision-making styles and financial decisions.One hundred and ninety-four participants were divided into three groups and subjected to three different conditions.Under each experimental condition,they had to decide whether to accept or reject investment proposals.In the first group,they had been enrolled in investment plans by default(opt-out condition),in the second group,they had not been automatically enrolled in these plans(opt-in condition),and in the third group they had to choose whether to enroll or not(control condition).The results showed that the investment decisions of anxious,avoidant,rational and dependent individuals could be facilitated by default options.In conclusion,using default options as a“nudge”can support specific groups of people to improve their financial decisions.

Application of PPC Model Based on RAGA in Real Estate Investment Decision-Making

According to the size of the projector function to evaluate the merits of the program, Projection Pursuit method is applied to real estate investment decision-making by using the real coding based on Accelerating Genetic Algorithm (RAGA) to optimize the Projection Pursuit Classification (PPC) process and a wide range of indicators value was projected linearly. The results are reasonable and verified with an example. At the same time, the subjective of the target weight can be avoided. It provides decision-makers with comprehensive information on all the indicators of new ideas and new

Sustainable Investment Forecasting of Power Grids Based on theDeep Restricted Boltzmann Machine Optimized by the Lion Algorithm

This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine(DRBM)optimized by the Lion algorithm(LA).Firstly,two factors including transmission and distribution price reform(TDPR)and 5G station construction were comprehensively incorporated into the consideration of influencing factors,and the fuzzy threshold method was used to screen out critical influencing factors.Then,the LA was used to optimize the parameters of the DRBM model to improve the model’s prediction accuracy,and the model was trained with the selected influencing factors and investment.Finally,the LA-DRBM model was used to predict the investment of a power grid enterprise,and the final prediction result was obtained by modifying the initial result with the modifying factors.The LA-DRBMmodel compensates for the deficiency of the singlemodel,and greatly improves the investment prediction accuracy of the power grid.In this study,a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model,and a comparison with the RBM,support vector machine(SVM),back propagation neural network(BPNN),and regression model was conducted to verify the superiority of the model.The conclusion indicates that the proposed model has a strong generalization ability and good robustness,is able to abstract the combination of low-level features into high-level features,and can improve the efficiency of the model’s calculations for investment prediction of power grid enterprises.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Extension Study on Electricity Market of Power Plant Investment Environment

When the global electricity industry has reorganized the structure and introduced competitive market mechanism world widely, China is no exception. Since 2002, “Electricity System Reform Program” was enacted. The mode of the State Power Corporation was divided. It means China's electricity market steps into the mode of Wholesale Competition. The meaning of the evaluation of power plant investment decision is to provide references for the market members when they made the decisions through the comprehensive evaluation of internal performance of Power Plant and the external investment environment, therefore further optimizing the allocation of funds, avoiding the risks, pursuing maximize of the profits.

Investment risk analysis of China's wind power industry based on pre-assessment matrix

Wind energy is a clean and sustainable energy,and wind power does not rely on fossil fuels.So there is no fuel price risk,and it,of course,does not include the environmental costs,such as carbon emissions.Because of these unique advantages,wind power has gradually become an important part of the strategy of sustainable development in China.Now with the growing voices on global greenhouse gas emission reduction,and as a clean and efficient energy, wind power has huge potential in combating climate change,energy security pressures and the needs for energy.Wind power in China began to develop from the 1980s.In the first 20 years,the speed of development was slow;but since 2004,it has had an extremely rapid growth.This paper,in order to study the development mechanism of China's wind power industry,investigated and analyzed the status quo of wind power industry in China,and then found that(1) the development trend of wind power industry in China appears exponential growth;(2)China's installed capacity of wind power is still smaller than that os some other countries;(3)new subsidy policies bring developing opportunities to wind power industry in China;(4)the sectors of wind power industry are in unbalanced growing;(5)the owners of proposed wind farms are too optimistic though the built wind farm had many problems.In addition,by using the methodology of Game Theory,this paper has also constructed the matrix of pre-assessing risks of China's wind power industry to further discuss the potential risk factors within China's wind power industry as risk factors of wind farm construction,risk factors of production of wind turbines,risk factors of parts and components manufacturing industry under risk indicators like R&D,patents,the domestic policy,the international policy,the quality of products and the market regulation,in order to provide a scientific assessment and self-assessment tool for investors or implementers, and also to promote the further development of the wind power industry.

Challenges in Power Production Investment in Vietnam

This paper presents the results of the survey to initially assess the current situation of power supply chain development in Vietnam. The research team used questionnaires to identify challenges and barriers in power production investment. Surveyors came from power generation units, power transmission and distribution units, service providers for power plants, consulting agency and research institutes. The results indicated that there is a difference among enterprises’ barriers and difficulties in the management, finance and investment environment. This difference is related to the business profiles of the enterprise, its segment of the electricity generation market, capacity and experience in the market.

The Realization of a Strong Trade Power and China’s Direct Investment in SCO Countries

China’s path to becoming a strong trade power can be divided into three levels:the micro level of promoting factor cultivation,the meso level of achieving industrial dominance,and the macro level of participating in the establishment of the world system.As a feature of globalization,factor flow is the foundation and key to achieve the above three-level goals.In the first stage of reform and opening-up,China complied with the globalization characteristics of factor flow and gathered a large number of capital factors.It is now the second stage of reform and opening-up;that is,the stage of export-oriented investment.International investment may help in the path to become a strong trade power,or it may become an obstacle.The maximization of benefits and evasion of disadvantages are influenced by the grasp of investment risks.Therefore,special attention should be paid to identifying potential risks and controlling risks.

Analysis on decision-making model of plan evaluation based on grey relation projection and combination weight algorithm

In military service joint operations, when there are more operational forces, more multifarious materials are consumed, the support is more complex and fuzzy, the deployment of personnel is more rapid, and the support provided by wartime military material support powers can be more effective. When the principles,requirements, influencing factors and goals of military material support forces are deployed in wartime, an evaluation indicator system is established. Thus, a new combined empowerment method based on an analytic hierarchy process（AHP） is developed to calculate the subjective weights, and the rough entropy method is used to calculate the objective weights. Combination weights can be obtained by calculating the weight preference coefficient error, which is determined by combining the cooperative game method and the minimum deviation into objectives. This approach can determine the grey relation projection coefficient and synthesize the measure scheme superiority to finally optimize the deployment plan using the grey relation projection decision-making method. The results show that the method is feasible and effective;it can provide a more scientific and practical decision-making basis for the military material support power deployment in wartime.

A group consensus model for evaluating real estate investment alternatives

Background:Individual decision-making largely influences the effectiveness of decisions and benefits of investments.Methods:In this article,a consensus model for group decision-making(GDM),based on the analytic hierarchy process(AHP),is developed to gather group ideas and analyze the real estate investment environment under multi-criteria problems.Twelve evaluation procedures of the developed model,which increase the convergence of the opinions of multiple experts,are proposed.Results:An empirical case about the real estate investment environment is applied to certify the feasibility of this developed model.Conclusions:the evaluation procedures have been fully observed with several rounds of discussions,and have manifested the experiences of experts.Besides,the evaluation results are in accordance with real-world situations,which demonstrates that our developed model is a feasible analysis tool for real estate investors to obtain better profits and lower risk.

Evaluation and Forecasting of Wind Energy Investment Risk along the Belt and Road Based on a Novel Hybrid Intelligent Model

The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road.In order to obtain the scientific and real-time forecasting result,this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM.Firstly,the factors influencing investment risk of wind energy along the Belt and Road are identified fromthree dimensions:endogenous risk,exogenous risk and process risk.Through the fuzzy threshold method,the final input index system is selected.Secondly,the risk evaluation method based on improved cloud model andGRA-TOPSIS is proposed.Thirdly,a modern intelligent model based on MBA-WLSSVMis designed.In modified bat algorithm(MBA),tent chaotic map is utilized to improve the basic bat algorithm,while weighted least squares support vector machine(WLSSVM)adopts wavelet kernel function to replace the traditional radial basis function to complete the model improvement.Finally,an example is given to verify the scientificity and accuracy of themodel,which is helpful for investors tomake fast and effective investment risk forecasting of wind energy along the Belt and Road.The example analysis proves that the proposedmodel can provide reference and basis for investment corpus to formulate the investment strategy in wind energy along the Belt and Road.

Fuzzy-logic Method for Global Quality Optimization Problem of the Programmed Action Investment

In order to analyze the planning of a transport linear infrastructure(railway or ordinary road),in order to optimize a relationship work-environment after-work,the study team(engineers,architects,economists,etc),realize a careful prearranged analysis about the characteristic of the site and the large area which are involved by the work project and,once one found all possible alternative solutions,he should compare them through the use of suitable technical,economical and environmental parameters,choosing that one which maximize the global utility of the public investment.In this paper we study a fuzzy-logic method in order to help the decision maker in the analysis of the programmed action public investment.

Optimization of Group Multiattribute Decision-Making Model in Commercial Space Investment

A group multiattribute decision-making model was proposed by implementing prospect theory,multi-attribute decision-making,group decision-making and entropy methods for the optimization in commercial space investment.First,the decision-making function was decided using prospect theory by the preference of each expert to reach the comprehensive prospect value based on different investment options;second,expert decision weights were reached according to entropy method;third,the expert group decision-making information was congregated according to the group decision-making congregation algorithm to reach the most optimized investment option;finally,an example was given to demonstrate the feasibility and effectiveness of the method.This model com-prehensively takes the advantages of many methods to congregate experts'experiences and avoid the subjective influences,thus providing a scientific decision-making approach for the commercial space investment.