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.

In most of the developing countries, particularly in Asian countries, the initial step of electricity industry restructuring has begun by spot market design. In addition, electricity industry, all around the world, is approaching competitive markets. Meanwhile there are many unanswered questions including deregulation mechanism. In this new framework, producers are considered as private companies which are responsible for their decisions.This paper presents a regional model for the deregulated electricity market where producers trade the electricity with distribution utilities based on spot prices. A mathematical programming model is used to calculate an appropriate base for electricity market transactions. The model has been implemented while different power plants produce electricity and demand fluctuates permanently. The model minimizes total variable costs subject to generation and demand constrains. Isfahan electricity market is considered as empirical evidence. To run the model, General Algebraic Modeling System (GAMS) software has been used.

In a real electricity market, complete information of rivals’ behavior is not available to market participants. Therefore, they make their bidding strategies based on the historical information of the market clearing price. In this paper, a new market simulator is introduced for a joint energy and spinning reserve market, in which market participants’ learning process is modeled using Q-learning algorithm.The main feature of this simulator is simulating a real market, in which market participants make decisions based on incomplete information of the market. Using the proposed simulator, the clearing price for each submarket is computed considering the participants’ behavior, under different load levels and/or contingency conditions. The results show that Q-learning approach can modify the agent’s strategy under different market situations.

The Iranian electricity market is undergoing the first decade of restructuring. Effective competition in wholesale electricity market is a necessary feature of successful electricity industry restructuring. The paper examines the degree of competition in the Iranian electricity market during March to September 2009. The competitive benchmark analysis has been used to simulate producer's behavior as a price taker firms and compare the competitive market results with actual market outcomes. The competitive benchmark has been calculated through generation costs of producers. Moreover, the possibility of execution market power has been considered by structural index. The finding indicates that the Iranian electricity market has a considerable potential to exercise power market and there were significant departure from competitive behavior during 2009.

In this paper, scientific papers related to the participation of electricity retailers in the electricity market will be reviewed. In general, the planning of the participation of retailers in the electricity market can be divided into long-term, mid-term, and short-term planning. Various sources of energy purchasing for retailers include the electricity market, bilateral contracts, generating units and new energy sources, including wind turbines and photovoltaic systems. Also, electric vehicles, battery storage systems and hydrogen storage systems can be more flexible in energy management issues as energy storage systems. Also, the issue of determining the price of electricity sales to consumers is considered by electricity retailers. In addition, retailers' consumer participation in demand response programs as a virtual production unit has been explored to reduce consumer spending and increase retail profits. Also, in view of the fact that the relevant papers, the uncertainties of the retailers that typically model the prices of the electricity market and the consumption of their consumers, will be reviewed in a review of the sources. Finally, the risk modeling due to alternative uncertainties will be discussed. We will look into the details of the retailers' shareholding in the electricity market.

Policy makers need to be acquainted with market competition statue. it would ease the decision making and restructuring of market. The aim of this study is to measure the competition of the wholesale electricity market and then investigate the impact of market power on wholesale electricity price during 2013 to 2017. The Result shows that market share of P. U. s in the wholesale electricity market is not notably large and H. H. I. ’ s fall in the competitive range. Furthermore, generally the fluctuation of H. H. I. has been diminishing during studied period. Although lack of significant market power, the results show it has considerable impact on wholesale price index. We propose that an independent regulatory agency may be responsible for the supervision of this market to achieve more market efficiency.

This paper aims to determine the optimum price of electricity during restructuring process. We maximized social welfare function subject to market equilibrium, maximum production capacity of each group of power plants, maximum demand of each consumer type and the potential of electricity export and import. The model was run using 2007 monthly and annual data by means of GAMS optimization software. The calculated electricity shadow price for the year 2007 was 371.2 per KWh. To get more exact results we run the model for each month separately. The results show that the price of electricity in spring and summer is lower than fall and winter, for marginal cost of power provision in winter rises. This is due to substitution of gas by gasoil and other liquid fuels and also reduction of hydropower production. For both intervals the actual price has a significant deviation from optimum price in Iranian power market.