Iranian Journal of Electrical and Electronic Engineering

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Unlike perfect competitive markets, in oligopoly electricity markets due to strategic producers and transmission constraints GenCos may increase their own profit through strategic biddings. This paper investigates the problem of developing optimal bidding strategies of GenCos considering participants’ market power and transmission constraints. The problem is modeled as a bi-level optimization that at the first level each GenCo maximizes its payoff through strategic bidding and at the second level, in order to consider transmission constraints a system dispatch is accomplished through an OPF problem. The AC power flow model is used for proposed OPF. Here it is assumed that each GenCo uses linear supply function model for its bidding and has information about initial bidding of other competitors. The impact of optimal biddings on market characteristics as well as GenCos’ payoffs are investigated and compared with perfect competitive markets where all the participants bid with their marginal costs. Furthermore, effects of exercising market power due to transmission constraints as well as different biddings of strategic generators on GenCos’ optimal bidding strategies are presented. Finally IEEE-30 bus test system is used for case study to demonstrate simulation results.

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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 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 presents a novel approach for evaluating impacts of price-sensitive loads on electricity price and market power. To accomplish this aim an analytical method along with agent-based computational economics are used. At first, Nash equilibrium is achieved by computational approach of Q-learning then based on the optimal bidding strategies of GenCos, which are figured out by Q-learning, ISO's social welfare maximization is restated considering demand side bidding. In this research, it was demonstrated that Locational Marginal Price (LMP) at each node of system can be decomposed into five components. The first constitutive part is a constant value for the respective bus, while the next two components are related to GenCos and the last two parts are associated to Load Serving Entities (LSEs). Market regulators can acquire valuable information from the proposed LMP decomposition. First, sensitivity of electricity price at each bus and Lerner index of GenCos to the bidding strategies and maximum pricesensitive demand of LSEs are revealed through weighting coefficients of the last two terms in the decomposed LMP. Moreover, the decomposition of LMP expresses contribution of LSEs to the electricity price. The simulation results on two test systems confirm the capability of the proposed approach.

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