scholarly journals The different characteristics of real options in contracts of wind power in Brazil

2021 ◽  
Vol 28 (4) ◽  
Author(s):  
Vanderson Aparecido Delapedra-Silva
Keyword(s):  
Real Options ◽  
Wind Power ◽  
Energy Price ◽  
Generation Efficiency ◽  
Short Term ◽  
The Real ◽  
Plant Expansion ◽  
New Energy ◽  
Expansion Capacity ◽  
Different Characteristics

Abstract: This paper investigates the existence of uncertainties in different wind power commercialization contracts in Brazil and their correlation with the Real Options associated with unmitigated risks in the Back up Energy and New Energy contracts. From a documentary review of existing contracts from 2009 to 2018, it was found that the Real Options on New Energy contracts are more susceptible to market uncertainties related to energy price in the short-term market. The Real Options associated with risks not mitigated in Back up Energy contracts are more linked to uncertainties regarding the power generation efficiency and the project plant expansion capacity in order to generate the anticipation of its supply.

scholarly journals Wind Power Prediction Based on Nonlinear Partial Least Square

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qian Wang ◽  
Yang Lei ◽  
Hui Cao
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Real Data ◽  
Partial Least Square ◽  
Least Square ◽  
Power Prediction ◽  
Short Term ◽  
The Real ◽  
Wind Power Prediction

Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical penetration of wind energy. The intermittent and the randomness of wind would affect the accuracy of prediction. According to the sequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction. The proposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the nonlinear partial least square is adopted to map the wind speed under the time series to wind power. The model carries the neural network as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of the original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the real data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation neural network, radial basis function neural network, support vector machine, and partial least square are performed on the real data and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the real power running curves also verify that the proposed method can predict the wind power in short-term effectively.

Hidden “holes” in the capital of banks and the supply of credit to the real sector of economy

2018 ◽  
pp. 49-68 ◽  
Author(s):  
M. E. Mamonov
Keyword(s):  
Credit Risk ◽  
The Other ◽  
Real Sector ◽  
Capital Management ◽  
Loan Loss ◽  
Short Term ◽  
The Real ◽  
Ex Post ◽  
The One

Our analysis documents that the existence of hidden “holes” in the capital of not yet failed banks - while creating intertemporal pressure on the actual level of capital - leads to changing of maturity of loans supplied rather than to contracting of their volume. Long-term loans decrease, whereas short-term loans rise - and, what is most remarkably, by approximately the same amounts. Standardly, the higher the maturity of loans the higher the credit risk and, thus, the more loan loss reserves (LLP) banks are forced to create, increasing the pressure on capital. Banks that already hide “holes” in the capital, but have not yet faced with license withdrawal, must possess strong incentives to shorten the maturity of supplied loans. On the one hand, it raises the turnovers of LLP and facilitates the flexibility of capital management; on the other hand, it allows increasing the speed of shifting of attracted deposits to loans to related parties in domestic or foreign jurisdictions. This enlarges the potential size of ex post revealed “hole” in the capital and, therefore, allows us to assume that not every loan might be viewed as a good for the economy: excessive short-term and insufficient long-term loans can produce the source for future losses.

Impact of crude oil price on the socio-political environment of global countries

2019 ◽  
Vol 118 (3) ◽  
pp. 110-122
Author(s):  
Johnson Clement Madathil ◽  
Velmurugan P. S
Keyword(s):  
Crude Oil ◽  
Descriptive Analysis ◽  
Oil Price ◽  
Policy Framework ◽  
Political Environment ◽  
Policy Reforms ◽  
Crude Oil Price ◽  
Short Term ◽  
Political Impact ◽  
New Energy

Crude oil is known to have an impact on people’s life of both producers and consumers of crude oil countries. A producer country’s socio-political impact will be different from a consumer country’s socio-political impact. This paper aims to show that crude oil price has a socio-political impact on global countries through descriptive analysis. The study found that there were similarities in the movement of crude oil price and change in GDP of both India and United States and further Russia and Venezuela have had crude oil impact on their respective GDP’s, which has made them take policy reforms. The paper identifies changes in the policy framework due to influence of crude oil price and eventual changes in existing socio-political environment. Taking oil producing countries such as Russia and Venezuela as examples, this paper suggests that policy reforms are the key to having a stable socio-political environment. Russia shows us that having a flexible monetary policy can keep the budget dependence on crude oil reduced in the short term. On the other hand, for oil consuming countries, having a stable supply and moving to new energy sources is the key to tackle the influence of crude oil price on the socio-political environment of global countries.

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