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دسته بندی:
مدیریت انرژی - Energy Management
سال انتشار:
2020
عنوان انگلیسی مقاله:
Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
ترجمه فارسی عنوان مقاله:
تقویت قدرت مبتنی بر یادگیری تقویتی برای مدیریت انرژی سیستمهای ذخیره انرژی ترکیبی مستقل با توجه به عدم اطمینان
منبع:
Sciencedirect - Elsevier - Energy, 193 (2020) 116622. doi:10.1016/j.energy.2019.116622
نویسنده:
Bassey Etim Nyong-Bassey a, *, Damian Giaouris a, Charalampos Patsios a, Simira Papadopoulou b, c, Athanasios I. Papadopoulos b, SaraWalker a, Spyros Voutetakis b, Panos Seferlis d, Shady Gadoue
چکیده انگلیسی:
Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies
with complementary operating features aimed at enhancing the reliability of intermittent renewable
energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies
(EMS) introduces complexity. The latter has been previously addressed by the authors through a
systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting
for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation
and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at
negating load demand and RES stochastic variability. Each method has its own merits such as; reduced
computational complexity and improved accuracy depending on the probability density function of
uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive
control framework. The second employs a Kalman filter, whereas the third is based on a machine
learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In
validating the proposed methods against the DA PoPA, the proposed methods all performed better with
regards to violation of the energy storage operating constraints and plummeting carbon emission
footprint
Keywords: Hybrid energy storage systems | Energy management strategies | Model predictive control | Kalman filter | Reinforcement learning
قیمت: رایگان
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