Optimisation algorithmique et modèles aléatoires d'un système électrique de cogénération : application au système électrique au Liban. (Algorithmic optimization and random models of a cogeneration system : application to the libanese electric system)

Les systemes de cogeneration (SC) sont largement definis par la production simultanee ou coincidente de la production combinee de chaleur et d'electricite. L’idee de la cogeneration revet une importance particuliere puisqu’elle est un outil de reduction des emissions a effet de serre. Comme les systemes electriques ont ete developpes selon les carburants et leur utilisation energetique, de meme, les SC ont ete developpes afin d'utiliser l'energie possible du carburant pour produire de l’electricite et de la chaleur. La decentralisation de la production electrique est desormais un evenement existant. La favorisation maximale de l’electricite d’origine renouvelable ou des systemes de
cogeneration, a abouti a cette decentralisation formant une partie de la production electrique.
Cette these est appliquee au cas du systeme electrique libanais. Elle sert a evaluer la puissance optimale de cogeneration qui doit etre installee par le secteur public ou le secteur prive, ainsi que la mise en evidence des impacts economiques et environnementaux dus a l’integration des SC et des energies renouvelables dans le reseau. Dans ce travail de these, nous nous sommes interesses a l’integration des systemes de cogeneration dans un reseau electrique. Nous avons travaille sur deux themes principaux et les avons appliques au cas du reseau electrique libanais. Le premier theme principal est l’innovation d’une strategie de prise de decision qui sert a trouver une puissance de cogeneration respectant l’economie et l’environnement. Le second theme principal est l’optimisation et le controle du reseau electrique en fonction des energies renouvelables (ER) et des SC integres. Les deux themes cites sont ensuite appliques au cas du reseau electrique libanais pour montrer les avantages de l’integration des SC et des ER dans ce reseau.

[1]  Ming-Tong Tsay,et al.  Interactive best-compromise approach for operation dispatch of cogeneration systems , 2001 .

[2]  Theodor J. Stewart,et al.  Interactive multiobjective optimization with NIMBUS for decision making under uncertainty , 2014, OR Spectr..

[3]  Witold Pedrycz,et al.  Analytic Hierarchy Process (AHP) in Group Decision Making and its Optimization With an Allocation of Information Granularity , 2011, IEEE Transactions on Fuzzy Systems.

[4]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[5]  Sven Werner,et al.  Profitability of sparse district heating , 2008 .

[6]  John W. Sawyer,et al.  Sawyer's gas turbine engineering handbook , 1972 .

[7]  Ming-Tong Tsay,et al.  Operation strategy of cogeneration systems under environmental constraints , 2000, PowerCon 2000. 2000 International Conference on Power System Technology. Proceedings (Cat. No.00EX409).

[8]  Hassan A. Hamdan Stochastic-based production costing model. (c2010) , 2010 .

[9]  H. Sugihara,et al.  A cooperation with customer-side cogeneration systems for power flow congestion relief and its environmental impact , 2006, 2006 IEEE Power Engineering Society General Meeting.

[10]  Enrico Carpaneto,et al.  Cogeneration planning under uncertainty. Part II: Decision theory-based assessment of planning alternatives , 2011 .

[11]  Ming-Tong Tsay,et al.  Application of evolutionary programming to optimal operational strategy cogeneration system under time-of-use rates , 2000 .

[12]  Henrik Lund,et al.  Modelling of energy systems with a high percentage of CHP and wind power , 2003 .

[13]  T. Saaty,et al.  Negative priorities in the analytic hierarchy process , 2003 .

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Piyuan Lin,et al.  Broiler Growth Performance Analysis: From Correlation Analysis, Multiple Linear Regression, to Neural Network , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[16]  Brian Vad Mathiesen,et al.  A review of computer tools for analysing the integration of renewable energy into various energy systems , 2010 .

[17]  Kalyanmoy Deb,et al.  An interactive evolutionary multi-objective optimization and decision making procedure , 2010, Appl. Soft Comput..

[18]  Tao Guo,et al.  An algorithm for combined heat and power economic dispatch , 1996 .

[19]  T. Saaty,et al.  Ranking by Eigenvector Versus Other Methods in the Analytic Hierarchy Process , 1998 .

[20]  Sri Lanka,et al.  A hybrid tool to combine multi-objective optimization and multi-criterion decision making in designing standalone hybrid energy systems , 2013 .

[21]  Guillaume Sandou,et al.  SHORT TERM OPTIMIZATION OF COGENERATION SYSTEMS CONSIDERING HEAT AND ELECTRICITY DEMANDS , 2005 .

[22]  Alex Ferguson,et al.  Fuel cell modelling for building cogeneration applications , 2004 .

[23]  R. Chedid,et al.  A simulation model for reliability-based appraisal of an energy policy: The case of Lebanon , 2012 .

[24]  Thomas L. Saaty,et al.  A new macroeconomic forecasting and policy evaluation method using the analytic hierarchy process , 1987 .

[25]  Ruzhu Wang,et al.  Energy optimization model for a CCHP system with available gas turbines , 2005 .

[26]  Thomas L. Saaty,et al.  How to handle dependence with the analytic hierarchy process , 1987 .

[27]  Otto Rentz,et al.  Model-based analysis of effects from large-scale wind power production , 2007 .

[28]  Christos A. Frangopoulos,et al.  Effect of reliability considerations on the optimal synthesis, design and operation of a cogeneration system , 2004 .

[29]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[30]  Hong-Zhong Huang,et al.  Fuzzy multi-objective optimization decision-making of reliability of series system , 1997 .

[31]  Taher Niknam,et al.  Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index , 2012 .

[32]  Kaisa Miettinen,et al.  Synchronous approach in interactive multiobjective optimization , 2006, Eur. J. Oper. Res..

[33]  V. I. Ugursal,et al.  Residential cogeneration systems: Review of the current technology , 2006 .

[34]  Gevork B. Gharehpetian,et al.  Optimization of distributed generation capacities in buildings under uncertainty in load demand , 2013 .

[35]  K.M. Nor,et al.  Optimal Sizing for a Gas-Fired Grid-Connected Cogeneration System Planning , 2009, IEEE Transactions on Energy Conversion.

[36]  S. Rahimi,et al.  Industrial implementation of economic dispatch for co-generation systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[37]  David H. Scott Advanced power generation from fuel cells - implications for coal , 1993 .

[38]  Christos S. Ioakimidis,et al.  On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools , 2011 .

[39]  V. Ismet Ugursal,et al.  Modeling of internal combustion engine based cogeneration systems for residential applications , 2007 .

[40]  Evaristo Chalbaud Biscaia,et al.  Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors , 2003, Comput. Chem. Eng..

[41]  Ming-Tong Tsay,et al.  Applying the multi-objective approach for operation strategy of cogeneration systems under environmental constraints , 2003 .

[42]  Andras Dan,et al.  Simulation results of cogeneration units as system reserve power source using multiagent modeling , 2011, 2011 16th International Conference on Intelligent System Applications to Power Systems.

[43]  Siaw Kiang Chou,et al.  Performance study of a microturbine system for cogeneration application , 2004 .

[44]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[45]  Neil Petchers Combined Heating, Cooling & Power Handbook: Technologies & Applications: An Integrated Approach to Energy Resource Optimization , 2002 .

[46]  José L. Bernal-Agustín,et al.  Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage , 2011 .

[47]  Fabio Freschi,et al.  Economic and environmental analysis of a trigeneration system for food-industry: A case study , 2013 .

[48]  Kaisa Miettinen,et al.  Interactive multiobjective optimization system WWW-NIMBUS on the Internet , 2000, Comput. Oper. Res..

[49]  S. Azarm,et al.  Multi-objective robust optimization using a sensitivity region concept , 2005 .

[50]  Georgia Badelt,et al.  The way to restructure the Lebanese electric power sector: a challenge for the transitional management , 2000 .

[51]  Farouk Fardoun,et al.  Energy status in Lebanon and electricity generation reform plan based on cost and pollution optimization , 2013 .

[52]  M. Strubegger,et al.  User's Guide for MESSAGE III , 1995 .

[53]  Farouk Fardoun,et al.  Multi-variable optimization for future electricity-plan scenarios in Lebanon , 2013 .

[54]  Sunanda Sinha,et al.  Review of software tools for hybrid renewable energy systems , 2014 .

[55]  S. Ashok,et al.  Optimal operation of industrial cogeneration for load management , 2003 .

[56]  Thomas L. Saaty,et al.  Introduction to a modeling of social decision processes , 1983 .

[57]  P. A. Pilavachi Mini- and micro-gas turbines for combined heat and power , 2002 .

[58]  Rainer Dudek,et al.  Multi-objective decision support system in numerical reliability optimization of modern electronic packaging , 2009 .

[59]  J. Rosen The future role of renewable energy sources in European electricity supply: A model-based analysis for the EU-15 , 2008 .

[60]  Fabricio I. Salgado,et al.  Short-term operation planning on cogeneration systems : A survey , 2008 .

[61]  Ryohei Yokoyama,et al.  Optimal unit sizing of cogeneration systems in consideration of uncertain energy demands as continuous random variables , 2002 .