Non-convex power plant modelling in energy optimisation |
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| Institution: | 1. Process Vision Ltd, Melkonkatu 18, FIN-00210 Helsinki, Finland;2. University of Turku, Department of Information Technology, Lemminkäisenkatu 14 A, FIN-20520 Turku, Finland;1. Vilnius Gediminas Technical University, Sauletekio st 11, Vilnius, Lithuania;2. DIKU - Department of Computer Science, University of Copenhagen, Universitetsparken 1, DK-2100 Copenhagen, Denmark;1. Otto-von-Guericke-University Magdeburg, Faculty of Economics and Management, Management Science, P.O. Box 4120, 39016 Magdeburg, Germany;2. Department of Industrial & Systems Engineering, University of Florida, 303 West Hall, Gainesville, FL 32611-6595, USA;3. Department of Industrial Engineering, Bogaziçi University, 34342, Bebek-Istanbul, Turkey;4. Koç University, Department of Industrial Engineering, 34450 Sariyer-Istanbul, Turkey;5. University of Vienna, Department of Management Science, Bruenner Strasse 72, 1210 Vienna, Austria;1. Zaragoza Logistics Center, Spain;2. IESE Business School, Spain;3. Jiangsu Normal University, China;4. International Business College, Dongbei University of Finance and Economics, Dalian, China;1. Politecnico di Milano, Via Lambruschini 4, 20154 Milano, Italy;2. LEAP – Laboratorio Energia & Ambiente Piacenza, Via Nino Bixio 27/c, 29121 Piacenza, Italy |
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| Abstract: | The European electricity market has been deregulated recently. This means that energy companies must optimise power generation considering the rapidly fluctuating price on the spot market. Optimisation has also become more difficult. New production technologies, such as gas turbines (GT), combined heat and power generation (CHP), and combined steam and gas cycles (CSG) require non-convex models. Risk analysis through stochastic simulation requires solving a large number of models rapidly. These factors have created a need for more versatile and efficient decision-support tools for energy companies.We formulate the decision-problem of a power company as a large mixed integer programming (MIP) model. To make the model manageable we compose the model hierarchically from modular components. To speed up the optimisation procedure, we decompose the problem into hourly sub-problems, and develop a customised Branch-and-Bound algorithm for solving the sub-problems efficiently. We demonstrate the use of the model with a real-life application. |
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