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Showing 3 results for Parsa Moghaddam

H. Abdi, M. Parsa Moghaddam, M. H. Javidi,
Volume 1, Issue 3 (July 2005)

Restructuring of power system has faced this industry with numerous uncertainties. As a result, transmission expansion planning (TEP) like many other problems has become a very challenging problem in such systems. Due to these changes, various approaches have been proposed for TEP in the new environment. In this paper a new algorithm for TEP is presented. The method is based on probabilistic locational marginal price (LMP) considering electrical loss, transmission tariffs, and transmission congestion costs. It also considers the load curtailment cost in LMP calculations. Furthermore, to emphasize on competence of competition ability of the system, the final plan(s) is (are) selected based on minimization of average of total congestion cost for transmission system.
Sh. Yousefi, M. Parsa Moghaddam, V. Johari Majd,
Volume 7, Issue 3 (September 2011)

In this paper, an agent-based structure of the electricity retail market is presented based on which day-ahead (DA) energy procurement for customers is modeled. Here, we focus on operation of only one Retail Energy Provider (REP) agent who purchases energy from DA pool-based wholesale market and offers DA real time tariffs to a group of its customers. As a model of customer response to the offered real time prices, an hourly acceptance function is proposed in order to represent the hourly changes in the customer’s effective demand according to the prices. Here, Q-learning (QL) approach is applied in day-ahead real time pricing for the customers enabling the REP agent to discover which price yields the most benefit through a trial-and-error search. Numerical studies are presented based on New England day-ahead market data which include comparing the results of RTP based on QL approach with that of genetic-based pricing.
A. Mansoori, A. Sheikhi Fini, M. Parsa Moghaddam,
Volume 18, Issue 1 (March 2022)

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.