Iranian Journal of Electrical and Electronic Engineering

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In this paper capacity withholding in an oligopolistic electricity market that all Generation Companies (GenCos) bid in a Cournot model is analyzed and the capacity withheld index, the capacity distortion index and the price distortion index are obtained and formulated. Then a new index, Distortion-Withheld Index (DWI), is proposed in order to measure the potential ability of market for capacity withholding. In these indices the impact of demand elasticity on capacity withholding is considered and it is shown that demand elasticity plays an important role for capacity withholding and market power mitigation. Due to the significant role of forward contracts for market power mitigation and risk hedging in power markets, the impacts of these contracts on capacity withholding are considered. The effects of GenCos’ strategic forward contracts on capacity withholding are also discussed. Moreover, the relationship between capacity withholding of GenCos and market price distortion is acquired. A two-settlement market including a forward market and a spot market is used to describe GenCos’ strategic forward contracting and spot market competition.

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This paper presents a method to improve the accuracy of DC Optimal Power Flow problem, based on evaluating some nodal shares of transmission losses, and illustrates its efficiency through comparing with the conventional DCOPF solution, as well as the full AC one. This method provides three main advantages, confirming its efficiency: 1- It results in such generation levels, line flows, and nodal voltage angles that are more accurate than the conventional DCOPF solution. 2- Like the previous DCOPF problem, the new method is derived from a non-iterative DC power flow algorithm, and thus its solution requires no long run time. 3- Its formulation is simple and easy to understand. Moreover, it can simply be realized in the form of Lagrange representation, makes it possible to be considered as some constraints in the body of any bi-level optimization problem, with its internal level including the OPF problem satisfaction.

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This paper proposes a method for determining the price bidding strategies of market participants consisting of Generation Companies (GENCOs) and Distribution Companies (DISCOs) in a day-ahead electricity market, while taking into consideration the load forecast uncertainty and demand response programs. The proposed algorithm tries to find a Pareto optimal point for a risk neutral participant in the market. Because of the complexity of the problem a stochastic method is used. In the proposed method, two approaches are used simultaneously. First approach is Fuzzy Genetic Algorithm for finding the best bidding strategies of market players, and another one is Mont-Carlo Method that models the uncertainty of load in price determining algorithm. It is demonstrated that with considering transmission flow constraints in the problem, load uncertainty can considerably influences the profits of companies and so using the second part of the proposed algorithm will be useful in such situation. It is also illustrated when there are no transmission flow constraints, the effect of load uncertainty can be modeled without using a stochastic model. The algorithm is finally tested on an 8 bus system.

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Congestion in the transmission lines is one of the technical problems that appear particularly in the deregulated environment. The voltage stability issue gets more important because of heavy loading in this environment. The main factor causing instability is the inability of the power system to meet the demand for reactive power. This paper presents a new approach for alleviation congestion relieving cost by feeding required reactive power of system in addition to re-dispatching active power of generators and load shedding. Furthermore with considering different static load models in congestion management problem with both thermal and voltage instability criteria, tries to the evaluated congestion management cost become more real, accurate and acceptable. The voltage stability is a dynamic phenomenon but often static tools are used for investigating the stability conditions, so this work offers new method that considers two snapshots after contingency to consider voltage stability phenomena more accurate. This algorithm uses different preventive and corrective actions to improve unsuitable voltage stability margin after contingency. The proposed method is tested on IEEE 24-bus Reliability test system, the simulation results shows the effectiveness of the method.

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Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. This paper presents a probabilistic neural network (PNN) and new feature selection technique for fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-fault three phase currents at one end of line. In the proposed method three classifiers corresponding with three phases are used which fed by normalized particular features as wavelet energy ratio (WER) and ground index (GI). The PNNs are trained to provide faulted phase selection in different ten fault types. Finally, logic outputs of classifiers and GI identify the fault type. The feasibility of the proposed algorithm is tested on transmission line using PSCAD/EMTDC software. Variation of operating conditions in train cases is limited, but it is wide for test cases. Also, quantity of the test data sets is larger than the train data sets. The results indicate that the proposed technique is high speed, accurate and robust for a wide variation in operating conditions and noisy environments.

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This paper proposes an index for nodal market power detection in power market under locational marginal pricing (LMP). This index is an ex-ante technique to detect the market power. More precisely, this criterion detects the potential of exercising market power regardless of detecting the actual market power. Also it is obvious that pricing and market clearing method affect the potential of exercising market power. Different potential of market power exists in different pricing methods. This index has been analyzed under LMP method which seems to be a desirable environment to exercise market power. In LMP method by load growth, in some determined load levels which is called Critical Load Levels (CLLs), locational marginal prices have step change. This step change in locational marginal prices causes step change in revenue and benefit of Gencos. So it is significant to detect the behavior of Gencos in the CLLs. The proposed criterion has been tested on constant system load and CLLs of system.

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This paper develops a method for nodal pricing and market clearing mechanism considering reliability of the system. The effects of components reliability on electricity price, market participants’ profit and system social welfare is considered. This paper considers reliability both for evaluation of market participant’s optimality as well as for fair pricing and market clearing mechanism. To achieve fair pricing, nodal price has been obtained through a two stage optimization problem and to achieve fair market clearing mechanism, comprehensive criteria has been introduced for optimality evaluation of market participant. Social welfare of the system and system efficiency are increased under proposed modified nodal pricing method.

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Locational Marginal Pricing (LMP) is a method for energy pricing in deregulated power systems. Loss and congestion cause different prices for energy at load or generation buses. In this pricing method there is a different between payments of customers and revenue of generators which is called Merchandizing Surplus (MS). Independent System Operator (ISO) receives MS and generally renders it to Transmission Company (Transco). It is rational that the MS be allocated among power market participants fairly instead of granting whole MS to Transco. In this paper a novel method is proposed to allocate MS among market participant according to their role in the congestion of system. In the presented method by decomposing LMP and identifying congestion part of LMP, the part of generators’ revenue and customers’ payments which caused by congestion are calculated. Then MS is allocated among market participants as the payment of customers to be equal to revenue of generators. The proposed method has been tested on five bus test system. Results indicate the effectiveness of the proposed method to allocate MS between power market participants.

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This paper addresses the possibility of capacity withholding by energy producers, who seek to increase the market price and their own profits. The energy market is simulated as an iterative game, where each state game corresponds to an hourly energy auction with uniform pricing mechanism. The producers are modeled as agents that interact with their environment through reinforcement learning (RL) algorithm. Each producer submits step-wise offer curves, which include the quantity-price pairs, to independent system operator (ISO) under incomplete information. An experimental change is employed in the producer's profit maximization model that causes the iterative algorithm converge to a withholding bidding value. The producer can withhold the energy of his own generating unit in a continuous range of its available capacity. The RL relation is developed to prevent from becoming invalid in certain situations. The results on a small test system demonstrate the emergence of the capacity withholding by the producers and its effect on the market price.

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Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses only half cycle of pre-fault and post fault current samples at relay location to feed the classifier. A lot of forward and backward fault simulations under different system conditions upon a transmission line with a fixed series capacitor are carried out using PSCAD/EMTDC software. The applicability of decision tree (DT), probabilistic neural network (PNN) and support vector machine (SVM) are investigated using simulated data under different system conditions. The performance comparison of the classifiers indicates that the SVM is a best suitable classifier for fault direction discriminating. The backward faults can be accurately distinguished from forward faults even under current inversion without require to detect of the current inversion condition.

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