A Novel Integrated Energy System Decision Model Based on Minimax Regret Criterion

التفاصيل البيبلوغرافية
العنوان: A Novel Integrated Energy System Decision Model Based on Minimax Regret Criterion
المؤلفون: Zhuofei Yu, Jian Luo, Sun Houtao, Di Wu
المصدر: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2).
بيانات النشر: IEEE, 2018.
سنة النشر: 2018
مصطلحات موضوعية: Mathematical optimization, Computer science, business.industry, 020209 energy, Photovoltaic system, Distributed power, Regret, 02 engineering and technology, Renewable energy, Energy management system, Electric power system, 0202 electrical engineering, electronic engineering, information engineering, Grid energy storage, business, Decision model
الوصف: With the development of integrated energy system, amount of electrical equipment are synchronized to the power grid, making the operating conditions of distribution network rather complicated, so as to the requirement of power system operation and management being improving. However, the refined management of electrical energy has always been a difficult problem, because electricity are not able to be stored on a large scale, not to mention some renewable energy cannot be predicted accurately. In order to reduce the stochastic effect of new energy generation, such as photovoltaic, this paper introduces the concept of minimax regret criterion to model an integrated energy grid energy management system to improve the efficiency and economic benefit. Meanwhile, both operating conditions and maintenance requirements are taking into consideration so as to make the total energy cost minimum and the local distributed power utilization maximum. The time- domain rolling method is applied to solve this model. At last, a study case is used to verify the proposed method.
DOI: 10.1109/ei2.2018.8582021
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::41abc46e23a3d039e44efce8b10d7123
https://doi.org/10.1109/ei2.2018.8582021
رقم الانضمام: edsair.doi...........41abc46e23a3d039e44efce8b10d7123
قاعدة البيانات: OpenAIRE
الوصف
DOI:10.1109/ei2.2018.8582021