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.

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.

This paper, presents a new approach for bidding strategy in spot Electricity Markets. A two-level optimization method is used for profit maximization of non-cooperative firms, while taking into account overall system constrains. In this approach, the Market equilibrium points are determined as Nash Equilibria. In order to capture the behavior of all Market participants and therefore, a much more competitive environment both the suppliers and consumers are considered as the players of the Market. To avoid local maxima solutions, Genetic Algorithm based optimization is incorporated. The proposed method has been applied to IEEE 9 bus system with satisfactory results.

In a deregulated electric power system in which a competitive Electricity Market can influence system reliability, system analysts are rapidly recognizing that they cannot ignore Market risks. This paper first proposes an analytical probabilistic model for the reliable evaluation of competitive Electricity Markets and then develops a methodology for incorporating the Market reliability problem into composite power system reliability studies. The Market reliability is evaluated using the Markov state space diagram. Since the Market is a continuously operated system, the concept of absorbing states is applied to it in order to evaluate reliability. The Market states are identified using Market performance indices and the transition rates are calculated using historical data. The key point in the proposed method is the concept that the reliability level of a restructured electric power system can be calculated using the availability of the composite power system and the reliability of the Electricity Market. To illustrate an interesting feature of the proposed methodology, two case studies are carried out over a test system.

Since the formation of the Iranian Electricity Market in 1382 (2003), power plants have been competing with each other on a daily basis in the ISO by registering their bid prices. In this competition, the winners are those power plants whose bid prices are lower than the Market clearing price for each hour in the next day, so the forecasting the next day Market prices is vital for energy producers. In this study, using a combination of K-means algorithm and support vector machine, a new model for predicting the next day Market settlement prices is proposed and the model has been used the hourly Electricity Market prices for 1395-1396 (2016-2017). According to the results, seven competitive clusters were identified for the Iranian Electricity Market. The average forecasting accuracy of the proposed model for each of these clusters for the years 1395 (2016) and 1396 (2017) was 96% and 94%, repectively.