Due to the stochastic nature of wind energy, allocating an appropriate INVESTMENT incentive for wind GENERATION technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the INVESTMENTs towards locations with more GENERATION potential. In CPM, a part of INVESTMENT cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to DYNAMIC forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system DYNAMICs (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind GENERATION can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

Attention has been paid to the Demand Response (DR) programs in recent years to improve the power system operation. Demand Response affects market clearing price, which is the main motivation for INVESTMENT, and thus the trend of INVESTMENT on GENERATION expansion will be affected. So far, the demand has been considered inelastic for modeling the INVESTMENT on GENERATION expansion. However, increasing the deployment of smart grid has waived this assumption.In this paper, we have used stochastic DYNAMIC programming for modeling the GENERATION expansion problem. We have considered both the renewable resources and demand response in modeling. In the proposed model, the uncertainty in GENERATION and the accuracy of the price responsive demand performance are considered. In this regard, the market clearing price is determined by interaction between price and the price responsive demand. Some long-term indices are proposed and using the simulation results, they are evaluated. The achieved results, quantitatively confirm that the demand response results in decreasing the price and the outages and therefore improving the reliability.

Without capacity mechanisms, GENERATION INVESTMENT in the deregulated power system tends towards boom and bust cycles. Due to lack of significant demand response to electricity price, the cycles may jeopardize supply adequacy or even leads to a black-out in the bust periods. In this paper, encouragement of INVESTMENT in distributed GENERATIONs, which is designed by the regulatory body is proposed to reduce the GENERATION INVESTMENT cycles. The traditional long-term model of GENERATION INVESTMENT DYNAMIC is modified to model and consider the regulatory policies. Moreover, an optimization model of regulatory policies is also proposed in order to preserve the supply adequacy in power system. Particle swarm optimization algorithm is employed to solve the optimization problem. The proposed model is implemented in MATLAB software. The simulation results indicate the efficiency of the proposed model in improving the boom and bust cycles and preserving supply adequacy.

The purpose of this paper is to provide a new approach to incorporating uncertainty into assessing the profitability of INVESTMENT projects. In the real world, the capital budgeting problem is accompanied by uncertainty and risk associated dealing with imprecise data. The major contribution of this research is the development of a novel approach to evaluating the profitability of an INVESTMENT project in uncertainty condition. At first, we presented a new discount method that can be used by investors when they wants to be able to make an INVESTMENT decision. That is, we developed a new method to evaluate the profitability of INVESTMENT projects by or-dered fuzzy net present value (OFNPV). In addition, ordered fuzzy numbers (OFN) are used to describe the DYNAMICs of changes of the defined INVESTMENT parameters in the assumed time horizon. By using ordered fuzzy numbers, we develop an effective tool for assessing the profitability of INVESTMENT projects. This assessment tool not only enables decision-makers to decide under uncertainty conditions whether or not a given INVESTMENT project should be carried out or rejected, but also facilitates selecting the most effective project, e.g. a project with the most expected probability of success.

Due to the lack of measurements in many regions, wave characteristics are estimated using different methods. Wave climate hindcasting/forecasting is mostly conducted by numerical models or empirical methods. Until now, different empirical methods have been developed for wave hindcasting. However, with the development of high speed processors, several sophisticated numerical models have been developed for wave prediction. These models are mostly phase-averaged spectral wave models developed in three GENERATIONs. In the last two decades, third GENERATION wave models have been used widely in academic and practical projects. In this regard, Port and Maritime Organization has produced his own model, PMO DYNAMIC. This model has been developed as a part of first three phases of Monitoring and Modeling of Study of Iranian Coasts project. PMO DYNAMIC package is a software available for engineering purposes. It has several modules that have been developed for different objectives. Wave model is the module which is used for the GENERATION and transformation of wind waves in coastal areas. In this paper, in order to test the PMO DYNAMIC model capabilities, it has been applied for the prediction of wave parameters in Bushehr Bay and the results have been compared with MIKE21 SW model and measured data.

INVESTMENT in the agricultural sector has a crucial role in the economic growth of the country. The variability of INVESTMENT expenses on the one hand, and the extent and potential capacities of the agricultural sector on the other hand, increases the necessity of investigating the factors affecting the improvement of INVESTMENT in this sector. Developing a DYNAMIC model is an appropriate approach for analyzing complex feedback systems to improve INVESTMENT in the agricultural sector of the country. In the present study, library research and interviews with experts were used to identify the factors affecting the improvement of INVESTMENT in the agricultural sector in the country to develop a DYNAMIC model. The participants of the present study include 20 experts in the natural resources management organization of the country and several university expert professors in the field of agriculture finance were interviewed. Based on the opinion of experts the key variables were identified and cause-and-effect and mathematical relationships between these variables were evaluated in the form of two diagrams of cause and effect and the flow state diagram in the DYNAMICs system approach during the years 2011 to 2025 were simulated in the Vensim software. Then different scenarios were examined aimed to increase and improve the INVESTMENT in the agricultural sector of the country. The findings show that by implementing policies to improve INVESTMENT in the agricultural sector through increasing exports and implementing land protection laws and regulations, INVESTMENT in the agricultural sector of the country can be improved.

The general objective of this study is to develop an appropriate model for analyzing the effects of direct govermental payments, income and wealth on agricultural INVESTMENT. An improved DYNAMIC model was built on the basis of the cost of adjustment theory to estimate the impacts of direct govermental payment on agricultural INVESTMENT for western Canada and northern tier U.S. The results indicate that direct govermental payments have positive and significant impacts on agricultural INVESTMENT in most regions.Furthermore, the results indicate that the impacts of direct govermental payment are much larger than the impacts of farmers' wealth and income. Based on the results, adjustment rate of capital for U.S. agriculture is much larger than Canada. The results from the estimated elasticities, indicate that there is a slight increase from short-run to long-run for the U.S.and Canadian agriculture.

There are different ways of system analysis. System thinking is a novel and very effective method in this field. Actually, system DYNAMIC approach offers a DYNAMIC model of a process in the real world and systematically analyzes related factors. In other words, it indicates the influence of changes on each effective factor on the entire system in each moment. In this paper, primarily, a DYNAMIC model is proposed by investigating different effective factors on future price of assets which include known income and the way of arbitrage opportunities with non-correct prices are simulated. Thus, scenarios are offered to illustrate the changes of different effective factors on system and arbitrage opportunities made as a result to these changes.