Optimal self-scheduling of a dominant power company in electricity markets |
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| Affiliation: | 1. Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, PR China;2. Institute for Education and Information Sciences, IBW, University of Antwerp (UA), Antwerp B-2000, Belgium;3. KU Leuven, Department of Mathematics, Leuven B-3000, Belgium;4. National Science Library, Chinese Academy of Sciences, Beijing 100190, PR China;5. Kent Business School, University of Kent, Canterbury CT2 7PE, UK;1. School of Energy, Power and Mechanical Engineering, North China Electric Power University, Changping District, Beijing 102206, China;2. School of Control and Computer Engineering, North China Electric Power University, Changping District, Beijing 102206, China;1. Korea Research Institute for Human Settlements, 254 Simin-daero, Dongan-Gu, Anyang-Si, Gyeonggi-do 431-712, South Korea;2. GeoTrans Lab., Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA;1. Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain;2. Departamento de Ingeniería, Universidad Loyola Andalucía, Energía Solar 1. Edificio E, Campus Palmas Altas, 41014 Sevilla, Spain;1. Department of Physics, Acharya Institute of Technology, Bangalore-560107, India;2. C.N.R. Rao Centre for Advanced Materials, Tumkur University, Tumkur 572 103, India;3. Chattisgarh Swamy Vivekananda Technological University, Bhilai (CG)-493441, India;4. Research Center, Department of Science, East West Institute of Technology, Bangalore 560 091, India;5. Department of Chemistry, M.S. Ramaiah Institute of Technology, Bangalore 560 054, India;6. CSIR-National Aerospace Laboratories (CSIR), Bangalore 560 017, India |
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| Abstract: | This paper addresses the problem of the self-scheduling of a power company with a dominant role in both the production and retail sectors of an electricity market. An integrated 0/1 mixed integer linear programming (MILP) formulation is provided, which combines both thermal and hydro subsystems in a single portfolio for a dominant power company through a detailed modeling of the operating constraints of thermal units and hydroplants. Residual demand curves for energy and reserves are used to model the effect of the power company’s interactions with its competitors. Test results on a medium-scale real test system address the effect that the power company’s forward commitments and the market rules have on its daily self-scheduling and profits as well as on the resulting energy and reserve market clearing prices. |
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