Development of a genetic algorithm for maximizing wind power integration rate into the electric grid

In this paper, a new method was proposed with the objective of maximizing the rate of wind power integration into the electric grid. This method was based on the optimization of the parameters of the turbine governors (TGs) by means of a genetic algorithm. The tuning of TGs' parameters was formulated relying on an objective function aiming at reaching the maximum wind power penetration rate. The dynamic grid modeling consisted of synchronous machines, regulators, and wind turbines. The IEEE 14-bus modified test system was adopted to test the grid using the Power System Analysis Toolbox. The simulation results revealed that, with the optimized TGs' parameters, the rate of wind power integration improved considerably.In this paper, a new method was proposed with the objective of maximizing the rate of wind power integration into the electric grid. This method was based on the optimization of the parameters of the turbine governors (TGs) by means of a genetic algorithm. The tuning of TGs' parameters was formulated relying on an objective function aiming at reaching the maximum wind power penetration rate. The dynamic grid modeling consisted of synchronous machines, regulators, and wind turbines. The IEEE 14-bus modified test system was adopted to test the grid using the Power System Analysis Toolbox. The simulation results revealed that, with the optimized TGs' parameters, the rate of wind power integration improved considerably.

[2]  John K. Kaldellis,et al.  Maximum wind potential exploitation in autonomous electrical networks on the basis of stochastic analysis , 2008 .

[3]  Ezequiel A. Di Paolo,et al.  An efficient genetic algorithm with uniform crossover for air traffic control , 2009, Comput. Oper. Res..

[4]  Henrik Lund,et al.  Renewable energy strategies for sustainable development , 2007 .

[5]  Radomil Matousek.  Genetic Algorithm and Advanced Tournament Selection Concept , 2008, NICSO.

[6]  B. Shabani,et al.  Multi-objective sizing optimisation of a solar-thermal system integrated with a solar-hydrogen combined heat and power system, using genetic algorithm , 2018 .

[7]  Soulaymen Kammoun,et al.  Modelling and analysis of transient state during improved coupling procedure with the grid for DFIG based wind turbine generator , 2017 .

[8]  Annette Evans,et al.  Assessment of sustainability indicators for renewable energy technologies , 2009 .

[9]  Sam Kwong,et al.  Genetic algorithms: concepts and applications [in engineering design] , 1996, IEEE Trans. Ind. Electron..

[10]  Saptarshi Das,et al.  Global solar irradiation prediction using a multi-gene genetic programming approach , 2013, ArXiv.

[11]  J. Lopes,et al.  Barriers (and Solutions...) to Very High Wind Penetration in Power Systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[12]  Weerakorn Ongsakul,et al.  Development of PSO based control algorithms for maximizing wind energy penetration , 2011, 2011 IEEE Power and Energy Society General Meeting.

[13]  Jonathan Shek,et al.  Optimised operation of an off-grid hybrid wind-diesel-battery system using genetic algorithm , 2016 .

[14]  David E. Goldberg,et al.  Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise , 1996, Evolutionary Computation.

[15]  Mohammad Nazri Mohd. Jaafar,et al.  Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction , 2017 .

[16]  Tomasz Prauzner,et al.  Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks , 2017 .

[17]  Somyot Kaitwanidvilai,et al.  Coordinated SVC and AVR for robust voltage control in a hybrid wind-diesel system , 2010 .

[18]  Tao Yu,et al.  Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine , 2017 .

[19]  A. Filios,et al.  A new computational algorithm for the calculation of maximum wind energy penetration in autonomous electrical generation systems , 2009 .

[20]  S. C. Tripathy,et al.  Digital speed governor for steam turbine , 1994 .

[21]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[22]  H. Hadj Abdallah,et al.  A Study of the Problem of Load Flow in a Network Involving a Renewable Source of Energy , 2014 .

[23]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[24]  Nursel Öztürk,et al.  A genetic algorithm for minimizing energy consumption in warehouses , 2016 .