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Showing 2 results for Mansoori
Load-Frequency Control: a GA based Bayesian Networks Multi-agent System
F. Daneshfar, H. Bevrani, F. Mansoori,
Volume 7, Issue 2 (June 2011)
Abstract
Bayesian Networks (BN) provides a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks but they have received little attention in the area of load-frequency control (LFC). In practice, LFC systems use proportional-integral controllers. However since these controllers are designed using a linear model, the nonlinearities of the system are not accounted for and they are incapable to gain good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem due to the distributed nature of a multi-area power system, is presented by using a BN multi-agent system. This method admits considerable flexibility in defining the control objective. Also BN provides a flexible means of representing and reasoning with probabilistic information. Efficient probabilistic inference algorithms in BN permit answering various probabilistic queries about the system. Moreover using multi-agent structure in the proposed model, realized parallel computation and leading to a high degree of scalability. To demonstrate the capability of the proposed control structure, we construct a BN on the basis of optimized data using genetic algorithm (GA) for LFC of a three-area power system with two scenarios.
Robust Operation Planning With Participation of Flexibility Resources Both on Generation and Demand Sides Under Uncertainty of Wind-based Generation Units
A. Mansoori, A. Sheikhi Fini, M. Parsa Moghaddam,
Volume 18, Issue 1 (March 2022)
Abstract
In recent years, the increasing of non-dispatchable resources has posed severe challenges to the operation planning of power systems. Since these resources are random in nature, the issue of flexibility to cover their uncertainty and variability has become an important research topic. Therefore, having flexible resources to cover changes in the generation of these resources during their operation can play an essential role in eliminating node imbalances, system reliability, providing the required flexible ramping capacity, and reducing system operating costs. Among flexibility resources, there are quick-act generation units such as gas units that can play an important role in covering net load changes. Also, on the demand side, the optimal design of demand response programs as responsive resources to price and incentive signals, by modifying the system load factor can prevent severe ramps at net load, especially during peak load hours, and as a result, increase system flexibility while decreasing operational cost of the power system. In this paper, unlike the existing literature, the effect of the mentioned flexibility resources (both on the generation side and the demand side) in day-ahead operation planning under high penetration of wind generation units has been studied on the IEEE RTS 24-bus test system. Also, for this scheduling, a mixed-integer, two-stage, and tri-level adaptive robust optimization have been used, which is solved by column-and-constraint generation decomposition-based algorithm to clear the energy and ramping capacity reserve jointly.

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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.