معرفی یک شبیه ساز عامل محور برای بازار برق

نویسندگان

1 موسسه آموزش عالی غیرانتفاعی غیر دولتی خراسان

2 دانشگاه فردوسی مشهد

چکیده

در یک بازار برق واقعی، اطلاعات کاملی از رفتار رقبا در اختیار شرکت کنندگان بازار قرار ندارد. بدین ترتیب شرکت کنندگان بازار، تصمیم گیری های خود را بر مبنای اطلاعات موجود از قیمت بازار در گذشته انجام می دهند. در این مقاله، یک شبیه ساز جدید برای بازارهای همزمان انرژی و ذخیره چرخان ارائه می گردد که در آن فرآیند کسب تجربه و یادگیری شرکت کنندگان بازار با استفاده از یک روش یادگیری تقویتی مدلسازی شده است. مهمترین خصیصه این شبیه ساز، شبیه سازی یک بازار واقعی است که در آن تصمیم سازیهای عاملان بازار در شرایط اطلاعات ناکامل انجام می گیرد. با استفاده از این شبیه ساز، قیمت تسویه بازار با توجه به رفتار قیمت دهی شرکت کنندگان بازار در سطوح مختلف بار و/یا وقوع حوادث در شبکه محاسبه می گردد. تحلیل نتایج نشان می دهد که روش بکارگرفته شده، قابلیت تطابق استراتژی قیمت دهی را با شرایط مختلف شبکه قدرت و بازار برق بخوبی دارا می باشد.

کلیدواژه‌ها


عنوان مقاله [English]

An Agent-based Electricity Market Simulator

نویسندگان [English]

  • Javid Khorasani 1
  • Habib Rajabi Mashhadi 2
1 Assistant Professor, Department of Electrical Engineering, Khorasan Institute of Higher Education, Mashhad, Iran
2 Ferdowsi University of Mashhad
چکیده [English]

In a real electricity market, complete information of rivals’ behavior is not available to market participants. Therefore, they make their bidding strategies based on the historical information of the market clearing price. In this paper, a new market simulator is introduced for a joint energy and spinning reserve market, in which market participants’ learning process is modeled using Q-learning algorithm. The main feature of this simulator is simulating a real market, in which market participants make decisions based on incomplete information of the market. Using the proposed simulator, the clearing price for each submarket is computed considering the participants’ behavior, under different load levels and/or contingency conditions. The results show that Q-learning approach can modify the agent’s strategy under different market situations.

کلیدواژه‌ها [English]

  • energy market
  • reserve market
  • market simulator
  • Q-learning algorithm
  • Bidding Strategy

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