A Survey on Investment Demand Assessment Models for Power Grid Infrastructure

Abstract

The investment demand forecast of the power grid is the further deepening of grid supply and demand management. Under the current reform of the power system in China, we are facing new challenges in analyzing and predicting the investment demand of the power grid. Firstly, the influencing factors of power grid investment demand from both internal and external aspects are analyzed in this paper. Then the selection principles of power grid investment demand factors by domestic and foreign scholars are summarized in this paper. Based on the above content, the forecast indicators and selection methods of domestic and foreign scholars on power grid investment demand are summarized in this paper. The common influencing factor mining algorithms are the grey correlation analysis, the core principal component analysis, and their combination methods. Moreover, the research status of domestic and foreign scholars in power grid investment demand, data mining technology, and forecasting technology is investigated. The current situation of power grid investment demand in China and the common forecasting methods are also elaborated. Finally, the key issues that need to be solved in future research on the investment demand of power grid infrastructure are summarized in this paper. This review will help power utilities find problems in power grid infrastructure investment demand and guide the future investment demand calculations of power utilities. INDEX TERMS Grid infrastructure, power grid investment, demand forecasting, indicator system, power utilities Power Demand Forecasting Methods Classic Forecasting Methods Traditional Forecasting Methods Modern Forecasting Methods Unit Consumption Method Power Elasticity Coefficient Method Per Capita Electricity Consumption Conversion Algorithm

A Learning-to-Rank-Based Investment Portfolio Optimization Framework for Smart Grid Planning

“…Each of the decision-making options is related to the other, which requires decisionmakers to analyze the overall situation (Zhao et al, 2021). Most of the existing investment portfolio optimization strategies are from the perspective of problem-oriented research, based on the known investment demands and investment capabilities of the enterprise, referring to the previous investment allocation, to allocate the investment scale and investment structure of each unit (Peng and Zhang, 2015;Li et al, 2021). The objective function and constraints are not considered enough comprehensive and systematic, and model boundary conditions and project priority ranking rely mainly on expert experience to make subjective decisions, lacking the recognition and consideration of objective factors (Guelpa et al, 2019;Liu et al, 2020;Ma et al, 2020).…”

Deep learning-based load forecasting considering data reshaping using MATLAB\Simulink

“…Several recent studies have comprehensively reviewed the state-of-the-art techniques used in load forecasting [3][4][5][6][7][8][9][10][11][12]. These techniques can be mainly classified into two groups: statistical and machine learning.…”

An Investment Efficiency Evaluation Model for Distribution Network With Distributed Renewable Energy Resources

“…Power grid investment is a crucial part of power grid operation and management, which has the characteristics of a high investment amount and a long payback period (Li et al, 2021). Under the intricate internal and external investment environment, investment efficiency becomes the key orientation and basic criterion for power grid investment decision-making.…”