Hydro-power is one of the most important ways of providing energy in peak hours. Restructuring in the Electricity industry has created rivalry among the country's Electricity suppliers. In order to increase the profitability of investment and better utilization of resources, estimating the future price of Electricity is of particular importance to producers. Artificial Neural Networks (ANNs), as one of the most important methods of artificial intelligence, have many uses in predicting and predicting phenomena. Recently, in order to improve the performance of the model of artificial intelligence models, their combination with optimization models has become widespread. The purpose of this study was to compare the performance of ANN, PSO-ANN and GA-ANN models in predicting the dispersed and sinusoidal data of peak daily Electricity prices in Iran. The results show that the use of PSO-ANN and GA-ANN models in this case study has no superiority to the ANN model and has not improved the performance and forecast of the Electricity market data.

Following the rise of the fuels that power plants consume in the deregulated market, an increase in the Electricity prices would be expected. This article tries to study the effects of power plants’ fuel prices adjustment on the amount of increase in Electricity prices in the market. The components of the deregulated power market were modeled in the systems dynamics method with the aim of finding a reasonable answer to the question of this research. The model encompasses three parts, namely demand, price and production run through powersim software. The change in the prices of fuels delivered to power plants is considered as the policy variable of the model. The results of running the model illustrated that it is anticipated that the price of Electricity will reach 409 rials in a kilowatt per hour by the end of the period providing that the power plants’ fuel prices are not adjusted. If the prices of fuels delivered to power plants are adjusted, the power market prices will reach 556 and 585 rials in a kilowatt per hour by the end of the period, assuming that the price growth may be by 5 or 8 percent, respectively. In the final part of the paper, the effect of economic growth increase as well as that of value added taxation on power market is analyzed in the framework of a suggestion model. Therefore, in such a case, assuming a 6 and 8 percent price growth, the prices of power market will reach 611 and 641 rials in a kilowatt per hour by the end of the period.

In this work, two models are proposed for Electricity prices as energy commodity prices which in addition to mean-reverting properties have jumps and spikes, due to non-storability of Electricity. The models are simulated using an Euler scheme, and then the Monte-Carlo method is used to estimate the expectation of the discounted cash-flow under historical probability, which is considered as the option price. A so called random variable simulation and a control variate method are then used to decrease, the discretization error and the Monte-Carlo error, respectively. As the option prices satisfy PDE's associated with the models, by solving these PDE's, numerically, we can find the option prices by a second method, thereby being able to make comparisons.

We develop an agent-based model to study the effects of the Electricity market reform on the Electricity prices, the power plants’ technology mix as well as their capacity utilization and profits. The reform’ s main objective is to open the Electricity market to more competition and increase the efficiency. The new wholesale Electricity market will operate based on the one-day-ahead auctions organized by the independent system operator. We set up two scenarios in which the market clearing mechanism changes from the pay-as-bid to market clearing price (MCP) and the fuel subsidy to power plants will be removed. The effects of the two scenarios will be analyzed on the market prices, plant revenues, market shares, and power generating units’ capacities. The simulation results show that the Electricity prices are higher during the peak load mainly due to the entry of the higher cost plants during those periods. Electricity prices are lower under the MCP scenario and higher under the fuel subsidy removal scenario, leading to lower and higher revenues for the power plants, respectively. The results also indicate that the market shares and the capacity utilization of the more efficient plants will increase under both scenarios.

Iran Energy Exchange started to work on 03/09/2013. Electricity trading is carried out within the framework of forward contracts in this exchange. This paper examines the impact of trading in the Electricity forward market on the volatility of Electricity spot market. Using daily data of Electricity market prices between 03/21/2011 to 06/21/2015, we employed GARCH technique to investigate the impact of forward trading on the volatility of the spot market in Iran. Our two main findings are as follows: (1) forward trading has led to increased volatility in Iran Electricity spot market, (2) there is an increase in sensitivity to new information while sensitivity to historical information decreases after introduction of the Electricity forward contracts.

Autoregressive integrated moving average (ARIMA) has been one of the widely used linear models in time series forecasting during the past three decades. Recent studies revealed the superiority of Artificial Neural Network (ANN) over traditional linear models in forecasting. But neither ARIMA nor ANNs can be adequate in modeling and forecasting time series since the first model cannot deal with nonlinear relationships and the latter one is not able to handle both linear and nonlinear patterns simultaneously. Hence by combining ARIMA with ANN and designing the hybrid model, data relationship can be modeled more accurately. In this research, a hybrid of ARIMA and ANN models is designed and its prediction performance is compared with those of competing models. Forecasting performance is examined using common criteria such as MSE, RMSE and MAD. Also the significance of any difference between these measures is tested through application of Granger and Newbold statistic. Forecasting results for world wheat price data indicates that combined model significantly improves accuracy achieved by separate models.

One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these methods is investigated in predicting the time-series of monthly prices of copper during early 1987 till late 2014. This study shows that the mean of about thousand runs using the Stochastic Differential Equations (SDE) method for 33 out of range cases gives better forecasting results for copper price time-series in comparison to traditional linear or non-linear functional forms (such as ARIMA and TGARCH) to model the price movement.