Research On Cost Risk Analysis And Evaluation Of Onshore Wind Power Project

With the increasingly serious environmental pollution, new energy development will become a major trend in the development of social energy. Compared to the others, such as Thermal power and hydropower, the wind power technology’s advantages are obvious. Its construction will continue to develop rapidly in the next 20 years. However, the construction process of wind power project is affected by many complex factors. The risk identification is irregular and hard to distinguish, which are obstacles of the wind power project developing. In this paper, we are looking for the key cost risk of onshore wind power project, and put forward key risk control measures based on the actual project. The high dimensional data clustering and Monte Carlo simulation are used to demonstrate the scientific rationality of the method.When facing distributed dimension engineering data of the onshore wind power project, the use of the general clustering method will be limited. Based on the high dimensional data, the risk of project is divided into four stages:pre decision stage, constructing preparation stage, construction stage and operation maintenance stage. Taking the risk of each stage as a dimension in the 3D data model, this paper built and analyzed a three dimensional model of the onshore wind power project. This paper will do Monte Carlo simulation of unit project cost based on the onshore wind electric project, then using sensitivity analysis of the project cost to analyze the key cost of the onshore wind power project. The key risk of onshore wind power project will be extracted from the quantity of cost and the degree of cost. The paper takes 36 projects of the wind power project for example and uses 3D data model and Monte Carlo simulation technology to do risk clustering and simulation. Using the sensitivity analysis, this paper presents the corresponding stage control measures, then realizes the scientific and reasonable control of the risk. The cases demonstrate the scientificalness and rationality of the high dimensional clustering technique and Monte Carlo simulation, and provide scientific guidance for the practical engineering projects.After the assignment of nearly 50 risks identified by the stage of onshore wind power projects, the wind power engineering data clustering uses 3D cluster model to compare risk assignment among different projects. Then using Euclidean distance method to define the similarity degree of risk between two projects. The high dimensional data of the onshore wind power project can be divided into three dimensional, which can improve the accuracy of the key risk analysis by the method of dimension reduction, which is the innovation point of this paper.