2018
DOI: 10.1002/tee.22804
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An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market

Abstract: The aim of this article is to present a method, which is called the ‘iterative collusive strategy (CS) search method’, to detect the CS in prisoner's dilemma game in which there is collusive equilibrium. We apply this method to an example of two‐player prisoner's dilemma game and a numerical duopoly example to show its effectiveness. To simulate the electricity market models, we use this method with a local optimization algorithm. Then, we employ a hybrid technique by applying an agent‐based model to the itera… Show more

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Cited by 9 publications
(2 citation statements)
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“…Recently, with the advancement of technology, widespread deployment of phasor measurement units (PMUs) and smart meters have enabled system operators and utilities to continuously monitor the grid to identify potential problems and improve the system's efficiency and reliability. A wealth of data measured by these devices has embraced the utilization of deep neural networks (DNNs) and other machine learning (ML) methods in various applications in power system data analytics such as load forecasting [16], [17], demand-side modeling [18], [19], wind and solar forecasting [20], [21], state estimation [22], [23], cyberpower anomaly detection [24], [25], security assessment [26], and PF calculations [27]. In particular, these methods are applied to power systems to achieve more accurate and reliable power flow calculation methods for more efficient power grid operations [27], [28], [29], [30], [31].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, with the advancement of technology, widespread deployment of phasor measurement units (PMUs) and smart meters have enabled system operators and utilities to continuously monitor the grid to identify potential problems and improve the system's efficiency and reliability. A wealth of data measured by these devices has embraced the utilization of deep neural networks (DNNs) and other machine learning (ML) methods in various applications in power system data analytics such as load forecasting [16], [17], demand-side modeling [18], [19], wind and solar forecasting [20], [21], state estimation [22], [23], cyberpower anomaly detection [24], [25], security assessment [26], and PF calculations [27]. In particular, these methods are applied to power systems to achieve more accurate and reliable power flow calculation methods for more efficient power grid operations [27], [28], [29], [30], [31].…”
Section: Introductionmentioning
confidence: 99%
“…Owing to oligopoly competition in electricity market, the oligopolistic game theory is widely used in electricity market analysis and has been broadly employed to examine strategic interactions among participants . Reference proposes a new practical integrated demand response program for smart energy hubs based on noncooperative game.…”
Section: Introductionmentioning
confidence: 99%