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Coalitional Integration of Wind Turbines via Cooperative Energy Trading In Distributed Power System

Graduate #120
Discipline: Technology and Engineering
Subcategory: Electrical Engineering

Md F. Chowdhury - Tuskegee University
Co-Author(s): Mandoye Ndoye, Gregory V. Murphy, Tuskegee University, AL



The intermittent nature of many renewable energy resources gives the challenge to the coordination of modern electric power grids. The vision to sequestrate fossil fuel (e.g., coal, natural gas, etc.) power plants accelerates the occupation of fluctuating sources which introduces instability in power systems. For this purpose, a cooperative game frame is developed in this research and the coalitions among the traditional and distributed sources are formed to mitigate the system power fluctuations and to increase spinning reserve for operational flexibility by minimizing real power losses on distribution lines while maintaining system generation constraints, voltage constraints, and AC power flow constraints. Simulation results are obtained with the application of the coalition game concept in IEEE 39 bus test system. IEEE 39 bus system is a greatly reduced model of the power system in New England used for stability study and power market problems. Coalitional games are defined as the players enforce cooperative behaviour and are motivated to select an optimal strategy through interactions and agreement between players. The test system is decomposed into three separate geographical regions. Each of the control regions contains traditional sources, variable loads, and wind farms. Considering the uncertainty of geographical advantage of wind power, buses are selected randomly for wind turbine integration. A significant (8%) amount of real power loss has decreased, which also indicates same amount increment of spinning reserve of the system. Total load and wind for 24 hour period were 1592462.79 MW and 635321.75 MW consecutively, which indicates 39.9% intermittent wind penetration. The simulations were run for voltage set points range 〖 0.98≤A〗_ij^k≤1.02 with 3 steps and 5 steps. In this research, the algorithm identifies the boundary buses for the regions and no other information required between the areas. The algorithm was able to find optimum coalition for each time periods and all penetration conditions, while three different load and wind conditions were created in IEEE 39 test case for checking the validity of the algorithm. Overall, the simulation results look promising for pre-scheduling bus voltage set points of the generators to reduce the fluctuations in grid induced by variable load and renewables. This approach can be extended further by taking OLTCs, shunt compensators as control variables to maintain the steady voltage in larger power systems. References: Baeyens, Enrique, E. Y. Bitar, Pramod P. Khargonekar, and Kameshwar Poolla. ‘Wind energy aggregation: A coalitional game approach.’ In Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on, pp. 3000-3007. IEEE, 2011.
K. Baker, J. Guo, G. Hug, and X. Li, ‘Distributed MPC for efficient coordination of storage and renewable energy sources across control areas,’ IEEE Trans. Smart Grid., vol. 7, no. 2, pp. 992-1001, Mar. 2016.

Not Submitted

Funder Acknowledgement(s): Funder Acknowledgement: The authors would like to thank NSF and the department of energy under NSF award number EEC1041877 and the CURENT (Center for Ultra-Wide-Area Resilient Electric Energy Transmission Networks) industry partnership program for their support.

Faculty Advisor: Zhao Lu, zlu@tuskegee.edu

Role: The idea of integrating wind resources in each control areas in the test system, introducing variable 24 hour load and wind data, as well as writing the code of the designed algorithm.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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