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نتیجه جستجو - محل اقامت در انرژی های تجدید پذیر

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Deep reinforcement learning and LSTM for optimal renewable energy accommodation in 5G internet of energy with bad data tolerant
یادگیری تقویتی عمیق و LSTM برای استفاده بهینه از انرژی تجدیدپذیر در اینترنت 5G انرژی با تحمل داده های بد-2020
With the high penetration of large scale distributed renewable energy generations, there is a serious curtailment of wind and solar energy in 5G internet of energy. A reasonable assessment of large scale renewable energy grid-connected capacities under random scenarios is critical to promote the efficient utilization of renewable energy and improve the stability of power systems. To assure the authenticity of the data collected by the terminals and describe data characteristics precisely are crucial problems in assessing the accommodation capability of renewable energy. To solve these problems, in this paper, we propose an L-DRL algorithm based on deep reinforcement learning (DRL) to maximize renewable energy accommodation in 5G internet of energy. LSTM as a bad data tolerant mechanism provides real state value for the solution of accommodation strategy, which ensures the accurate assessment of renewable energy accommodation capacity. DDPG is used to obtain optimal renewable energy accommodation strategies in different scenarios. In the numerical results, based on real meteorological data, we validate the performance of the proposed algorithm. Results show considering the energy storage system and demand response mechanism can improve the capacity of renewable energy accommodation in 5G internet of energy.
Keywords: 5G internet of energy | Renewable energy accommodation | Deep reinforcement learning | Demand response | LSTM
مقاله انگلیسی
2 Dynamic energy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach
استراتژی مدیریت و تبدیل انرژی پویا برای یک سیستم برق و گاز طبیعی یکپارچه با انرژی تجدید پذیر: رویکرد یادگیری تقویتی عمیق-2020
With the application of advanced information technology for the integration of electricity and natural gas systems, formulating an excellent energy conversion and management strategy has become an effective method to achieve established goals. Differing from previous works, this paper proposes a peak load shifting model to smooth the net load curve of an integrated electricity and natural gas system by coordinating the operations of the power-to-gas unit and generators. Moreover, the study aims to achieve multi-objective optimization while considering the economy of the system. A dynamic energy conversion and management strategy is proposed, which coordinates both the economic cost target and the peak load shifting target by adjusting an economic coefficient. To illustrate the complex energy conversion process, deep reinforcement learning is used to formulate the dynamic energy conversion and management problem as a discrete Markov decision process, and a deep deterministic policy gradient is adopted to solve the decision-making problem. By using the deep reinforcement learning method, the system operator can adaptively determine the conversion ratio of wind power, power-to-gas and gas turbine operations, and generator output through an online process, where the flexibility of wind power generation, wholesale gas price, and the uncertainties of energy demand are considered. Simulation results show that the proposed algorithm can increase the profit of the system operator, reduce wind power curtailment, and smooth the net load curves effectively in real time.
Keywords: Renewable energy accommodation | Dynamic energy conversion and management | Deep reinforcement learning
مقاله انگلیسی
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بازدید امروز: 4994 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 4994 :::::::: افراد آنلاین: 68