scholarly journals Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea

2021 ◽  
Vol 13 (19) ◽  
pp. 10613
Author(s):  
Jungmin An ◽  
Dong-Kwan Kim ◽  
Jinyeong Lee ◽  
Sung-Kwan Joo
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Renewable Energy ◽  
Least Squares ◽  
Sustainable Energy ◽  
Clean Energy ◽  
Optimal Investment ◽  
Investment Timing ◽  
Least Squares Monte Carlo ◽  
The Impact

Solar power for clean energy is an important asset that will drive the future of sustainable energy generation. As interest in sustainable energy increases with Korea’s renewable energy expansion plan, a strategy for photovoltaic investment (PV) is important from an investor’s point of view. Previous research primarily focused on assessing and analyzing the impact of the volatility but paid little attention to the modeling decision-making project to obtain the optimal investment timing. This paper utilizes a Least Squares Monte Carlo-based method for determining the timing of PV plant investment. The proposed PV decision-making method is designed to simulate the total PV generation revenue period with all uncertain PV price factors handled before determining the optimal investment time. The numerical studies with nine different scenarios considering system marginal price (SMP) and renewable energy certificate (REC) spot market price in Korea demonstrated how to determine the optimal investment time for different PV capacities. Therefore, the proposed method can be used as a decision-making tool to provide PV investors with information on the best time to invest in the renewable energy market.

scholarly journals Decision Making Under Uncertainty: Applying the Least-Squares Monte Carlo Method in Surfactant-Flooding Implementation

SPE Journal ◽  
2013 ◽  
Vol 18 (04) ◽  
pp. 721-735 ◽  
Author(s):  
A.. Alkhatib ◽  
M.. Babaei ◽  
P.R.. R. King
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Least Squares ◽  
Optimal Policy ◽  
Evaluation Method ◽  
Production Costs ◽  
Surfactant Flooding ◽  
Heterogeneous Model ◽  
Least Squares Monte Carlo ◽  
Reservoir Models

Summary This study introduces a decision-making evaluation method for flexibility in surfactant flooding. The method aims to capture the effects of uncertainty in the time series for both technical and economic parameters and produce a near-optimal policy with respect to these uncertainties as they vary with time. The evaluation method used was the least-squares Monte Carlo (LSM) method, which is best-suited for evaluating flexibility in project implementation. The decision analyzed was that of finding the best time to start surfactant flooding during the lifetime of a field under uncertainty. The study was conducted on two reservoir models: a 3D homogeneous model and a 2D heterogeneous model. The technical uncertainties considered were the residual oil saturation (ROS) to the surfactant flood, surfactant adsorption, and reservoir heterogeneity, and the main economic uncertain parameters considered were oil price, surfactant cost, and water-injection and -production costs. The results show that the LSM method provides a decision-making tool that was able to capture the value of flexibility in surfactant-flooding implementation and provides some insight into the effect of uncertainty on decision making, which can help mitigate adverse circumstances should they arise or capture the upside potential if circumstances prove beneficial. The results found that the optimal policy obtained was reliable and that heterogeneity and different well-placement patterns affect the value of flexibility and optimal policy for different reservoir models. Furthermore, possible extensions to enhance the LSM method were discussed.

Renewable energy selection based on a new entropy and dissimilarity measure on an interval-valued neutrosophic set

2021 ◽  
pp. 1-18
Author(s):  
ShuoYan Chou ◽  
Truong ThiThuy Duong ◽  
Nguyen Xuan Thao
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Membership Function ◽  
Fossil Fuels ◽  
Multiple Criteria Decision Making ◽  
Clean Energy ◽  
Dissimilarity Measure ◽  
Neutrosophic Set ◽  
Trade Offs ◽  
And Performance

Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed.

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