Financial Decision Support System for Wind Energy _ Analysis of Mexican Projects and a Support Scheme Concept

Abstract

Energy consumption is constantly on the increase all over the world. Especially fast-growing economies in emerging countries contribute to this increase. Governments need to promote the expansion of renewable energies in these countries by providing adequate general conditions and suitable support schemes. We provide decision support for the assessment of wind energy projects and their financial conditions. Following design science research (DSR) principles, a discounted cash flow (DCF) model in combination with a Monte Carlo simulation (MCS) to consider project risks was created. On this basis, a decision support system (DSS) was implemented in MATLAB. The applicability of the DSS is evaluated in the course of an analysis of onshore wind projects in Mexico. Based on the analysis' results, a concept of a support scheme is designed to promote an expansion of onshore wind energy across Mexico.

Risk Evaluation Model of Wind Energy Investment Projects Using Modified Fuzzy Group Decision-making and Monte Carlo Simulation

“…Koukal et al (2017) used a triangular assumption for the probability distribution function of variables in their MCS model to propose the financial analysis of wind energy projects. They used the experts’ judgement to determine the three points of triangular assumption.…”

“…The triangular assumptions are determined based on the result of the FGDM model in the first risk analysis phase, as follows: Receive the estimated amount of each parameter of cash flow (most likely value) according to project information ( E ). Determine the risks that affect each parameter ( C p , C e , etc.) among the identified and analysed risks in the first phase according to the three expert opinions (involved in stage 1). Determine the factor ( R ) based on the result of the FGDM model for each parameter, as given by Equation (17), in which m is the number of risks that affect each parameter: Calculate the probability distribution of input variables by considering the maximum of 50 per cent variation (Koukal & Piel, 2017). For instance, the triangular assumption of C e is calculated by Equation (18): …”

“…Calculate the probability distribution of input variables by considering the maximum of 50 per cent variation (Koukal & Piel, 2017). For instance, the triangular assumption of C e is calculated by Equation (18): …”

“…MCS relies on large numbers of random sampling of input variables to evaluate the output variables with respect to the risks (Platon & Constantinescu, 2014). Koukal et al (2017) used a triangular assumption for the probability distribution function of variables in their MCS model to propose the financial analysis of wind energy projects. They used the experts' judgement to determine the three points of triangular assumption.…”