辐照随机模型在光伏项目投资决策中的应用

Marco Antonio Haikal-Leite, Carlos de Lamare Bastian-Pinto, André de Oliveira Dias, F. Pradelle, Sergio Luiz Pinto Castiñeiras-Filho, Luis Fernando Mendonça Frutuoso, Eloi Fernández y Femández
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引用次数: 0

摘要

光伏发电已经扩大了其在巴西的参与,并作为满足未来需求的一种有前途的替代方式展示了自己。投资决策的标准方法是所谓的现金流在一段时间内预测的净现值(NPV)和按风险调整率贴现(DCF)。尽管这种方法考虑了所分析项目的预期回报和风险,但它忽略了可能构成项目风险因素的几个不确定因素。其中之一是多年来太阳辐射的变化以及监管机构施加的处罚规则。在这篇论文中,对给定地点的太阳能资源进行了为期五年的月平均每日测量。确定了最适合这一时期辐照度变异性的统计分布,并计算了辐照度的月平均值和标准差。模拟一个光伏太阳能项目参加了巴西国家电力能源局(ANEEL)组织的拍卖,该拍卖具有自2014年至今举办的该发电源拍卖的典型特征,其能源将被送往受监管的合同环境。采用蒙特卡罗方法对模型进行辐照,基于确定的统计分布,计算财务收益低于规定值的风险,采用实物期权方法估计能源产出不足的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stochastic Modeling of Irradiation, Applied to Investment Decision in a Photovoltaic Project Subject to Regulatory Shortfall Penalty Rules
Photovoltaic energy generation has expanded its participation in Brazil and presents itself as a promising alternative as a way of meeting future demand. The standard approaches to investment decision taking are the so-called Net Present Value (NPV) of Cash Flows forecasted over a time horizon, and Discounted at a risk-adjusted rate (DCF). Although this approach considers both the expected return and the risk of the analyzed project, it overlooks several uncertainties involved that may constitute risk factors for the project. One of these is the variability of solar irradiation over the years as well as the penalty rules imposed by the regulatory agency. In this paper, monthly average daily measurements of the solar resource were taken at a given location over five years. The statistical distribution that best fit the variability of irradiance over this period is determined, and monthly averages and standard deviations of irradiation are calculated. A photovoltaic solar energy project is simulated as participating in an auction organized by the Brazilian National Agency of Electric Energy (ANEEL) with typical characteristics of auctions held since 2014 to the present date for this generation source, with energy destined to the Regulated Contracting Environment. Monte Carlo method is applied on the irradiation of the model, based on the determined statistical distribution, and the risk of the financial return falling below a specified value is calculated, using a Real Options approach to estimate the effect of energy output shortfall.
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