Smart grid has generated much attention recently due to its potential in bringing a revolutionary change in the production, distribution, and utilization of electric power. However, before a smart grid can become fully functional, it requires technological advancements in a number of interdisciplinary domains. Even though smart grid facilitates run-time optimal allocation of power via extensive instrumentation and information accessibility, the process of optimal power allocation becomes challenging due to the massively distributed generation facilities, loads, and due to the intermittency of generation. In this paper, a Market Based technique has been presented to solve the DC optimal power flow problem in a smart grid. The DC optimal power flow problem aims at determining the power generated at each station and voltage angles associated with each bus in the transmission system. The Market Based Resource Allocation is inspired from the concepts in economic market, where resources are allocated to activities through the process of competitive buying and selling. In the proposed technique, every bus in the system acts as a potential power buyer and/or seller. The proposed method derives its significance due to its ability in optimizing power flow in a grid in a distributed manner, i.e., from local interactions. This feature provides immense scalability and robustness to uncertainties. In addition, this paper presents the evaluation of the proposed Market Based technique via a number of simulated scenarios of power consumers and producers in a Smart Grid. The IEEE 30-bus system with six generation units is used to test the proposed method in optimizing the total generation cost, and the results are compared with that obtained from widely used Matpower software.
- Dynamic Systems and Control Division
A Distributed Market Based Solution for DC Optimal Power Flow Problem
HomChaudhuri, B, Kumar, M, & Devabhaktuni, V. "A Distributed Market Based Solution for DC Optimal Power Flow Problem." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 785-791. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8740
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