Modeling and Optimization of Stand-Alone Hybrid Renewable Energy System for a Remote Community

سال انتشار: 1391
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,196

فایل این مقاله در 7 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

ICNMO01_262

تاریخ نمایه سازی: 19 اسفند 1391

چکیده مقاله:

The coordinating between generation periods of renewable resources and consumption periods is very complicated issue in stand-alone hybrid power systems. The optimal sizingmethod which can maximize the electricity match rate between demand and supplies is an important task. Most literatures sized hybrid renewable systems based on reliability and cost. They ignored the electricity match rate. In this paper, a triple multi-objective designof stand-alone hybrid power systems has been done by considering all match criteria. These criteria are Inequality Coefficient (IC), total cost throughout the useful life of the installation and Correlation Coefficient (CC). The optimization procedure minimizes IC and cost, and also maximizes CC, simultaneously. Six types of wind turbine (WT) and also six types of PV modules, with different output powers and costs are considered for thisoptimization procedure. For this task, the multi-objective particle swarm optimization algorithm (MOPSO) which is one of the multi-objective evolutionary algorithm (MOEA),have been used in order to find the best combination of components of hybrid power systems. As an example of application, a hybrid power system has been assumed. A set of possible solutions (Pareto set) for each configuration is obtained. The designers can selectthe best configuration among the Pareto set which fits their desire. The results achieved for this proposed method, demonstrate the practical utility of this procedure

نویسندگان

MohammadAli Yazdanpanah Jahromi

University of Sistan and Baluchestan

Said Farahat

University of Sistan and Baluchestan

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :