Multi-objective optimal design of hybrid renewable energy systems using preference-inspired coevolutionary approach

Solar Energy

Volumn 118, Issue , 2015, Pages 96-106

  Shi, Zhichao ^a   Wang, Rui ^a   Zhang, Tao ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Hybrid renewable energy systems; Optimization; Preference inspired coevolutionary algorithm

Indexed keywords

ALGORITHMS; DECISION MAKING; FOSSIL FUEL DEPOSITS; HYBRID SYSTEMS; OPTIMIZATION; PHOTOVOLTAIC CELLS; WIND TURBINES;

ANNUALIZED COST OF SYSTEM; CO-EVOLUTIONARY ALGORITHM; CO-EVOLUTIONARY APPROACH; HYBRID RENEWABLE ENERGY SYSTEM (HRES); HYBRID RENEWABLE ENERGY SYSTEMS; LOSS OF POWER SUPPLY PROBABILITY; NONDOMINATED SOLUTIONS; STAND-ALONE HYBRID SYSTEM;

RENEWABLE ENERGY RESOURCES;

ALGORITHM; DESIGN; MULTIOBJECTIVE PROGRAMMING; OPTIMIZATION; PHOTOVOLTAIC SYSTEM; RENEWABLE RESOURCE; WIND TURBINE;

PICEA;

EID: 84930624412     PISSN: 0038092X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.solener.2015.03.052     Document Type: Article

References (25)

1. Abedi S., Alimardani A., Gharehpetian G.B., Riahy G.H., Hosseinian S.H. A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems. Renew. Sustain. Energy Rev. 2012, 16(3):1577-1587.
2. Deb K. Multi-objective Optimization using Evolutionary Algorithms 2001, John Wiley & Sons, England.
3. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6(2):182-197.
4. Dufo L.R., Bernal A.J.L., Contreras J. Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage. Renew. Energy 2007, 32(7):1102-1126.
5. Dufo L.R., Bernal A.J.L. Multi-objective design of PV-wind-diesel-hydrogen-battery systems. Renew. Energy 2008, 33(12):2559-2572.
6. Ekren O., Ekren B.Y. Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing. Appl. Energy 2010, 87(2):592-598.
7. Esen H., Inalli M., Esen M. Technoeconomic appraisal of a ground source heat pump system for a heating season in eastern Turkey. Energy Convers. Manage. 2006, 47(9-10):1281-1297.
8. Esen H., Inalli M., Esen M. A techno-economic comparison of ground-coupled and air-coupled heat pump system for space cooling. Build. Environ. 2007, 42(5):1955-1965.
9. Esen M., Yuksel T. Experimental evaluation of using various renewable energy sources for heating a greenhouse. Energy Build. 2013, 65:340-351.
10. Fonseca C.M., Fleming P.J. Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE Trans. Syst., Man Cybern., Part A: Syst. Hum. 1998, 28(1):26-37.
11. Giannakoudis G., Papadopoulos A.I., Seferlis P., Voutetakis S. Optimum design and operation under uncertainty of power systems using renewable energy sources and hydrogen storage. Int. J. Hydrogen Energy 2010, 35(3):872-891.
12. Katsigiannis Y.A., Georgilakis P.S., Karapidakis E.S. Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables. Renew. Power Gen., IET 2010, 4(5):404-419.
13. Koutroulis E., Kolokotsa D., Potirakis A., Kalaitzakis K. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. Sol. Energy 2006, 80(9):1072-1088.
14. Purshouse R.C., Jalbă C., Fleming P.J. Preference-driven co-evolutionary algorithms show promise for many-objective optimisation. Evol. Multi-Criterion Opt. 2011, 136-150.
15. Sharafi M., ELMekkawy T.Y. Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. Renew. Energy 2014, 68:67-79.
16. Shi, Z.C., Wang, R., Zhang, T., 2014. PICEA-g using an enhanced fitness assignment method. In: 2014 IEEE Symposium on computational intelligence in Multi-Criteria Decision-Making (MCDM), USA, pp. 72-77.
17. Srinivas N., Deb K. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 1994, 2(3):221-248.
18. Trivedi, M., 2007. Multi-objective Generation Scheduling with Hybrid Energy Resources. PhD Dissertation, Department of Electrical Engineering, Clemson University, USA.
19. Wang R., Purshouse R.C., Fleming P.J. Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Trans. Evol. Comput. 2013, 17(4):474-494.
20. Wang, R., Purshouse, R.C., Fleming, P.J., 2014. Preference-inspired co-evolutionary algorithms using weights for many objective optimization. Eur. J. Oper. Res. (in press). doi:10.1016/j.ejor.2014.05.019.
21. Wang, R., Zhang, T., 2014. Multi-objective optimal design of hybrid renewable energy systems using MOEA/D. In: International Conference on Renewable Energy Research and Applications, IEEE, USA, pp. 161-167.
22. Yang H.X., Lu L., Zhou W. A novel optimization sizing model for hybrid solar-wind power generation system. Sol. Energy 2007, 81(1):76-84.
23. Yang H.X., Zhou W., Lu L., Fang Z.H. Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using genetic algorithm. Sol. Energy 2008, 82(4):354-367.
24. Zhang Q.F., Li H. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 2007, 11(6):712-731.
25. Zitzler, E., Laumanns, M., Thiele, L., 2002. SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Proc. Evol. Methods Des. Opt. Contr. Appl. Ind. Probl. EUROGEN, vol. 3242, no. 103, pp. 95-100.