Transmission loss allocation using combined game theory and artificial neural network |
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| Affiliation: | 1. Power System Simulation Lab, Jadavpur University, Kolkata 700 032, India;2. Jadavpur University, Kolkata 700 032, India;1. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China;2. Atmospheric and Environmental Research, Lexington, MA 02421, USA;3. Key Laboratory of Planetary Sciences, Chinese Academy of Sciences, Shanghai 200030, China;4. Graduate University of Chinese Academy of Sciences, 100049 Beijing, China;1. Department of Physics of the Earth, Astronomy and Astrophysics I (Geophysics and Meteorology), Complutense University of Madrid (UCM), 28040 Madrid, Spain;2. Telecommunications/ICT Development Laboratory (T/ICT4D), Abdus Salam International Center for Theoretical Physics (ICTP), 34014 Trieste, Italy;3. Interdisciplinary Mathematics Institute (IMI) UCM, 28040 Madrid, Spain;4. Departament of Physics, Federal University Oye-Ekiti State, Nigeria;1. Department of Energy, Politecnico di Milano, via Ponzio 34/3, 20133 Milan, Italy;2. Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, Ecole Centrale Paris and Supelec, Paris, 92295 Chatenay-Malabry Cedex, France;3. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK;1. Electrical Engineering Department, Kalyani Government Engineering College, Kalyani, Nadia 741235, India;2. Electrical Engineering Department, Jadavpur University, Kolkata 700032, India;1. Royal Observatory of Belgium, Brussels, Belgium;2. CNES, Toulouse, France |
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| Abstract: | An artificial neural network based method is suggested for allocation of transmission loss in a deregulated power system involving bilateral contract based power transactions between power suppliers and the distribution companies. The proposed method allocates transmission losses to operating transactions on the basis of Shapley value game theoretic approach. For this, bilateral transactions for power system are simulated and corresponding losses are obtained through load flow analysis. Shapley value approach is used to calculate the loss allocations. A large number of such simulation cases are generated and a large data pool stores these possible bilateral transactions and the corresponding loss allocations following Shapley value approach. For allocating losses to the transactions of an operating system environment the required neural network is developed online. A simple filtering technique is used to extract the suitable training data that are close enough to the actual operating condition from the generated data pool and neural network is trained online. The performance of such a network when tested on standard power networks has been found to be very encouraging. |
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