可再生能源发电投资风险分析与评估模型
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摘要
近年来,能源的绿色、低碳变革已经在世界范围内成为一种发展趋势,风力发电、太阳能发电、水力发电、生物质能发电等可再生能源发电得到了国家政策的扶持,中国可再生能源发电产业处于迅速发展的阶段。作为一种新兴产业,可再生能源发电具有低污染、可再生、节能和减排的优势且开发潜力巨大,适合现代社会的发展理念。随着越来越多的投资机构开始投资建设可再生能源发电项目,但可再生能源发展的不确定性也日益凸显。本文综合运用现代风险管理理论,系统地对可再生能源发电项目投资风险进行识别、控制、决策与评价。
首先,对可再生能源发电投资的风险因素进行识别。分别归纳我国各类可再生能源发电发展的条件中以及发展进程中存在的风险因素；基于全寿命周期管理理论,识别可再生能源项目在决策、设计、施工、运营和维护以及报废回收五个阶段的风险因素；基于解释结构模型计算出风险指标的可达矩阵,对风险进行层次划分,最后形成风险的阶层结构图。项目生命周期前期的风险将向后期传递,投资者须在投资初期落实风险管理措施,避免风险的蔓延与扩大。
其次,从宏观层面提出风险控制模型。一方面,基于投资收益与投资风险的考虑,以投资收益与投资风险的加权效益最大化为目标,以各类资源的投资份额、资源条件、装机容量增长以及电力需求为约束,构建了多区域多能源类型的发电投资组合模型。另一方面,通过对比平均调度与节能发电调度模式下上网电量的分配状况,分析开发商投资可再生能源与燃煤发电的经济与社会效益水平。在节能调度模式下可再生能源发电项目的效益将更有保障,在电力系统能满足可再生能源消纳要求的情况下应该增加可再生能源项目的投资。
再次,从投资者的角度提出风险决策模型。构建了基于遗传算法与BP神经网络的造价预测模型,通过遗传算法对BP神经网络的优化提高预测精度；基于期权理论构建了可再生能源发电项目的投资决策模型,期权能够反映出项目投资不确定性的价值,更全面地反映项目的价值,并在项目进行过程中作出延迟、扩张等决策；构建了灰色聚类分析的投资决策模型,通过灰色聚类模型有助于投资者区分可再生能源发电项目投资中众多风险因素的风险程度,并将各类项目进行聚类并有针对性地实施投资方案。
最后,提出风险评价模型。可再生能源发电项目涉及的风险因素众多,且部分风险存在难以量化的问题。粗糙集则可以根据数据特征提炼风险指标间的关联规则,并析取出典型的、能够综合反映风险特性的风险指标进行评价。通过可再生能源风险因素识别形成具有操作性的风险评价的指标体系,并基于粗糙集模型依次对指标数据进行离散化、属性约简以及求取属性重要度。实例仿真结果显示粗糙集的评价结果与现阶段国家风电投资的战略趋势相对一致。
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.
首先,对可再生能源发电投资的风险因素进行识别。分别归纳我国各类可再生能源发电发展的条件中以及发展进程中存在的风险因素；基于全寿命周期管理理论,识别可再生能源项目在决策、设计、施工、运营和维护以及报废回收五个阶段的风险因素；基于解释结构模型计算出风险指标的可达矩阵,对风险进行层次划分,最后形成风险的阶层结构图。项目生命周期前期的风险将向后期传递,投资者须在投资初期落实风险管理措施,避免风险的蔓延与扩大。
其次,从宏观层面提出风险控制模型。一方面,基于投资收益与投资风险的考虑,以投资收益与投资风险的加权效益最大化为目标,以各类资源的投资份额、资源条件、装机容量增长以及电力需求为约束,构建了多区域多能源类型的发电投资组合模型。另一方面,通过对比平均调度与节能发电调度模式下上网电量的分配状况,分析开发商投资可再生能源与燃煤发电的经济与社会效益水平。在节能调度模式下可再生能源发电项目的效益将更有保障,在电力系统能满足可再生能源消纳要求的情况下应该增加可再生能源项目的投资。
再次,从投资者的角度提出风险决策模型。构建了基于遗传算法与BP神经网络的造价预测模型,通过遗传算法对BP神经网络的优化提高预测精度；基于期权理论构建了可再生能源发电项目的投资决策模型,期权能够反映出项目投资不确定性的价值,更全面地反映项目的价值,并在项目进行过程中作出延迟、扩张等决策；构建了灰色聚类分析的投资决策模型,通过灰色聚类模型有助于投资者区分可再生能源发电项目投资中众多风险因素的风险程度,并将各类项目进行聚类并有针对性地实施投资方案。
最后,提出风险评价模型。可再生能源发电项目涉及的风险因素众多,且部分风险存在难以量化的问题。粗糙集则可以根据数据特征提炼风险指标间的关联规则,并析取出典型的、能够综合反映风险特性的风险指标进行评价。通过可再生能源风险因素识别形成具有操作性的风险评价的指标体系,并基于粗糙集模型依次对指标数据进行离散化、属性约简以及求取属性重要度。实例仿真结果显示粗糙集的评价结果与现阶段国家风电投资的战略趋势相对一致。
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.
引文
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