Volume 19, Issue 3 (September 2023)                   IJEEE 2023, 19(3): 115-129 | Back to browse issues page

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Ghanuni A, Sharifi R, Feshki Farahani H. An Information Gap Decision Theory Approach for Optimal Bidding of a Virtual Power Plant under System Uncertainties. IJEEE 2023; 19 (3) :115-129
URL: http://ijeee.iust.ac.ir/article-1-2779-en.html
An Information Gap Decision Theory Approach for Optimal Bidding of a Virtual Power Plant under System Uncertainties
A. Ghanuni , R. Sharifi , H. Feshki Farahani
Abstract:   (1258 Views)
Operation scheduling of a Virtual Power Plant (VPP) includes several challenges for the system according to the uncertain parameters, and security requirements, which intensify the need for more efficient models for energy scheduling and power trading strategies. Making suitable decisions under uncertainties, related to Renewable Energy Resources (RES), loads, and market prices impose extra considerations for the problem to make a clearer insight for the system operators to participate in local markets. This paper proposes a new risk-based hybrid stochastic model to investigate the effects of wind turbine power fluctuations on profit function, energy scheduling, and market participating strategies. Also, an incentivized Demand Response Program (DRP) is used, to enhance the system’s efficiency. The results of the study indicate that the proposed model based on Information Gap Decision Theory (IGDT) approach makes a clearer environment for the decision-maker to be aware of the effects of risk-taking or a risk-averse strategy on financial profits. The results show that a 30% of robustness and opportunity consideration would change the profit function from -12.5% up to 14.5%, respectively. A modified IEEE 33 bus test system is used to simulate a technical VPP considering the voltage stability and thermal capacity of line requirements.
Keywords: Virtual Power Plant, Uncertainty, Stochastic Programming, IGDT, Scheduling.
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Type of Study: Research Paper | Subject: Day-Ahead and Intra-Day Scheduling
Received: 2023/01/14 | Revised: 2023/11/25 | Accepted: 2023/09/10
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.