Risk Analysis And Assessment Model On Investment In Renewable Energy Power

In recent years, the revolution of green and low-carbon energy has become a worldwide trend. Wind power, solar power, hydro power, biomass power and other renewable energy have national policy support. With this support, China’s renewable energy generation industry is developing rapidly. As an emerging industry, renewable energy generation is characterized by advantages of low-pollution, renewable and energy-conservation, and it has great development potential, which fits modern society’s development philosophy. More investment institutions start investing in renewable energy generation projects, however, it is accompanied by increasingly uncertainty. This paper applied modern risk management theory comprehensively and made a research on investment risk in renewable energy generation, including risk identification, risk control risk decision and risk evaluation.Firstly, this paper identified risk factors in renewable energy generation investment. This paper respectively summarized the risk factors of conditions and development process in renewable energy generation. On the basis of life cycle management theory, this paper identified risk factors in five stages, namely, decision-making, design, construction, operating and maintenance and recycle; reachability matrix is calculated based on Interpretative Structural Modeling method(ISM), then a risk hierarchy chart is formed by a level division of risks. The risks in early stage of a project’s life cycle would be passed to the later stage, thus investors should take risk management in the first stage of an investment, which would prevent risk from spreading and expanding.Secondly, this paper put forward a risk control model in a macro level. On the one hand, taking investment returns and investment risk into account, treating the biggest weighted effective of the returns and risk as target, setting investment share of various resources, energy condition, installed capacity growth and energy demand as constraints, a power.generation portfolio model of multi-zone and multi-energy types is established. On the other hand, comparing allocation position of the grid electricity in average scheduling mode with that in energy-saving power generation scheduling mode, this paper analyzed economic and social benefits of investing in renewable energy and coal-fired power. The results showed that the project will have a more secure benefit in energy-saving power generation scheduling mode, and investment should be increased under the condition that the power system could meet the renewable energy’s consumptive requirements.Moreover, this paper proposed a risk decision model from the investors’perspective. It built a cost prediction model based on genetic algorithm and BP neural network, through which prediction accuracy was improved. And it constructed an investment decision model of renewable energy projects based on option theory, in which options could reflect the value of project investment’s uncertainty, and could reflect the value of the project more comprehensively and made delay or expansion decisions in the course of project. Also, it constructed an investment decision model of gray clustering analysis, by which investors could identify the level of risk factors in renewable energy generation projects’investment; they could cluster the various projects, and then invest purposely.Finally, this paper proposed a risk evaluation model. Renewable energy power generation projects involved many risk factors, and part of which was difficult to quantify. Rough set could refine the association rules among the risk indicators according to the characteristics of data, and extracted risk indicators which were typical to make evaluation. The paper formed an operational risk evaluation indicator system by identifying the risk factors of renewable energy, and carried on indicators discretization, attribute reduction and striking attribute importance successively according to the rough set model. The simulation showed that the evaluation result of the rough set was consistent with the nation’s current strategy in wind power investment.