(NB: This site is made for beginners who would like to do research on optimization of HRES)
The worldwide rapid depletion of conventional energy sources such as coal and natural gas has made it an urgency to search for alternative energy resources to meet the present energy demand. Alternative energy resources like solar and wind have attracted energy sectors due to their advantages over conventional energy sources such as a decrease in external energy dependence and carbon emissions. However, a common drawback of solar and wind energy is their unpredictable nature and dependence on weather and climatic conditions. A hybrid renewable energy system (HRES), integrating different energy resources in a proper combination, can overcome the problems caused by the uncertainties of solar and wind. HRESs are becoming increasingly popular both in theory and engineering due to their higher reliability and lower cost. (当今世界，常规能源如煤、石油、天然气等的快速消耗，迫使我们加紧寻找可替代能源以满足现在的能源需求。可替代能源如太阳能和风能相比于常规能源具有减少对外部能源依赖和碳排放的优势，从而引起了能源等相关部门的关注。然而，太阳能、风能的一个普遍的缺点是它们的不可预测性和对天气与气候条件的依赖性。混合可再生能源系统通过合理地组合利用不同形式的能源资源，能够克服太阳能、风能的不确定性带来的问题。由于较高的可靠性和较低的成本，混合可再生能源系统在理论和工程上的研究越来越广泛。）
The sizing of renewable energy systems mainly focused on solar, wind and other renewable energy, conventional energy sources and storage devices are also included. Generally, the optimization objectives of the systems are the minimization of cost and carbon emissions, the maximization of reliability. Nowadays there are many studies over the planning of renewable energy systems, the approaches include traditional mathematical methods and advanced evolutionary algorithm such as GA and PSO. （可再生能源系统规划主要涉及的可再生能源包括风能、太阳能，还包括常规能源和储能装置等。系统的优化目标一般是最小化成本、最大化可靠性、最小化排放等。目前关于可再生能源系统规划的研究已经相当广泛，其规划设计方法既有传统的数学规划方法，又有先进的遗传算法、粒子群算法等进化方法。）
Our latest study:
Multi-objective optimal design of hybrid renewable energy systems using preference-inspired coevolutionary approach. (Zhichao Shi, Rui Wang, Tao Zhang, 2015. Solar Energy, 118: 96-106)
Please contact nkshizc@163.com (Z. Shi) or ruiwang@gmail.com (R. Wang) for more details
Survey Papers：
1. Optimum design of hybrid renewable energy systems: Overview of different approaches. (Erdinc O, Uzunoglu M, 2012. Renewable and Sustainable Energy Reviews 16 (3), 1412-1425.)
2. Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review. (Fadaee M, Radzi M A M, 2012. Renewable and Sustainable Energy Reviews 16 (5), 3364-3369.)
3. Multi-objective planning of distributed energy resources: A review of the state-of-the-art. (Alarcon-Rodriguez A, Ault G, Galloway S, 2010. Renewable and Sustainable Energy Reviews 14 (5), 1353-1366.)
4. Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems. (Zhou W, Lou C, Li Z, Lu L, Yang HX, 2010. Applied Energy 87 (2), 380-389.)
5. A current and future state of art development of hybrid energy system using wind and PV-solar: A review. (Nema P, Nema R K, Rangnekar S, 2009. Renewable and Sustainable Energy Reviews 13 (8), 2096-2103.)
6. Optimal distributed renewable generation planning: A review of different approaches. (Tan WS, Hassan MY, Majid MS, Rahman HA, 2013. Renewable and Sustainable Energy Reviews 18: 626-645.)
Some representative studies
1. Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms. (Koutroulis E, Kolokotsa D, Potirakis A, Kalaitzakis K, 2006. Solar Energy, 80: 1072-88.)
2. Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage. (Dufo-López R, Bernal-Agustín JL, Contreras J, 2006. Renewable Energy.)
3. Optimal placement of hybrid PV-wind systems using genetic algorithm. (Masoum MAS, Seyed M, Badejani M, Kalantar M, 2010. IEEE Conferences.)
4. Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in southeast of Iran, 2009. (Moghaddas-Tafreshi SM, Hakimi SM, 2009. Renewable Energy, 34: 1855-1862.)
5. Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages. (Kashefi K A, Riahy GH, Kouhsari SHM, 2009. Renewable Energy, 34: 2380-2390.)
6. Optimum design and operation under uncertainty of power systems using renewable energy sources and hydrogen storage. (Garyfallos G, Athanasios IP, Panos S, Spyros V, 2010. Hydrog Energy, 35(3): 872-889.)
7. Optimal sizing of small isolated hybrid power systems using Tabu search. (Katsigiannis YA, Georgilakis PS,2008. Optoelectron Adv Mater, 10(5): 1241-1245.)
8. Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables. (Katsigiannis YA, Georgilakis PS, Karapidakis ES, 2010. Renewable Power Generation, IET, 4 (5): 404-419.)
9. Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm. (Yang HX, Zhou W, Lu L, Fang ZH, 2008. Solar Energy, 82 (4): 354-367.)
10. Multi-objective design of PV-wind-diesel-hydrogen-battery systems. (Dufo-López R, Bernal-Agustín JL, 2008. Renew Energy, 33(12): 2559-2572.)
11. A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems. (Abedi S, Alimardani A, Gharehpetian GB, Riahy GH, Hosseinian SH, 2012. Renew Sustain Energy Rev, 16(3): 1577-1587.)
12. Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach. (Sharafi M, ELMekkawy TY, 2014. Renewable Energy, 68: 67-79.)
13. Multi-objective optimal design of hybrid renewable energy systems using MOEA/D. (Wang R, Zhang T, 2014. ICRERA 2014.)
14. 基于NSGA-Ⅱ的风光互补独立供电系统多目标优化. (徐大明,康龙云,曹秉刚, 2006. 太阳能学报, 27(6): 593-598.)
15. 孤岛型混合可再生能源发电系统的优化设计. (马艺玮,杨苹,吴捷,周少雄, 2012. 华南理工大学学报（自然科学版）, 40(11): 113-120.)
16. 风光互补独立供电系统的多目标优化设计. (宋洪磊,吴俊勇,冀鲁豫,高立志,刘印磊,黄鹏洲, 2011. 电工技术学报, 26(7): 104-111.)
17. 基于混沌多目标遗传算法的微网系统容量优化. (王瑞琪,李珂,张承慧, 2011. 电力系统保护与控制, 39(22): 16-22.)
Additional papers
1. Optimization of a wind/PV hybrid power generation system. (Eke R, Kara O, Ulgen K, 2005. Int J Green Energy, 2(1): 57-63.)
2. Stochastic optimization for power system configuration with renewable energy in remote areas. (Ludwig K, Bo Z, Grisselle C, Zhixin M, 2012. Annu Operat Res, 195: 1-22.)
3. Optimum utilization of renewable energy sources in a remote area. (Akella AK, Sharma MP, Saini RP, 2007. Renew Sustain Energy Rev, 11(5): 894-908.)
4. Modeling and control of hydrogen and energy flows in a network of green hydrogen refueling stations powered by mixed renewable energy systems. (Hanane D, Ahmed O, Roberto S, 2012. Hydrog Energy, 37(6):5360-5371.)
5. Planning of community-scale renewable energy management systems in a mixed stochastic and fuzzy environment. (Cai YP, Huang GH, Tana Q, Yang ZF, 2009. Renew Energy, 34(7): 1833-1847.)
6. Energy cost analysis of a solar-hydrogen hybrid energy system for stand-alone applications. (Jeremy L, Marcelo GS, Abdellatif M, Philippe C, 2008. Hydrog Energy, 33(12): 2871-2879.)