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ردیف | عنوان | نوع |
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1 |
Multi-agent microgrid energy management based on deep learning forecaster
مدیریت انرژی میکروگیدر چند عامل مبتنی بر پیشگویی یادگیری عمیق-2019 This paper presents a multi-agent day-ahead microgrid energy management framework. The objective is
to minimize energy loss and operation cost of agents, including conventional distributed generators,
wind turbines, photovoltaics, demands, battery storage systems, and microgrids aggregator agent. To
forecast market prices, wind generation, solar generation, and load demand, a deep learning-based
approach is designed based on a combination of convolutional neural networks and gated recurrent
unit. Each agent utilizes the designed learning approach and its own historical data to forecast its
required parameters/data for scheduling purposes. To preserve the information privacy of agents, the
alternating direction method of multipliers (ADMM) is utilized to find the optimal operating point of
microgrid distributedly. To enhance the convergence performance of the distributed algorithm, an
accelerated ADMM is presented based on the concept of over-relaxation. In the proposed framework, the
agents do not need to share with other parties either their historical data for forecasting purposes or
commercially sensitive information for scheduling purposes. The proposed framework is tested on a
realistic test system. The forecast values obtained by the proposed forecasting method are compared
with several other methods and the accelerated distributed algorithm is compared with the standard
ADMM and analytical target cascading. Keywords: Microgrid energy management system | Short-term forecasting | Deep learning | Convolutional neural networks | Gated recurrent unit | Alternating direction method of multipliers |
مقاله انگلیسی |
2 |
Game-theoretical Energy Management for Energy Internet with Big Data-based Renewable Power Forecasting
مدیریت انرژی بازی تئوری برای اینترنت انرژی با پیش بینی قدرت قابل بازیافت مبتنی بر داده های بزرگ-2017 Energy internet, as a major trend in power system,
can provide an open framework for integrating equipments of
energy generation, transmission, storage and consumption, etc.,
so that global energy can be managed and controlled efficiently
by information and communication technologies. In this paper,
we focus on the coordinated management of renewable and
traditional energy, which is a typical issue on energy connections.
We consider a conventional power system consisting of the
utility company, the energy storage company, the microgrid, and
electricity users. Firstly, we formulate the energy management
problem as a three-stage Stackelberg game, and every player
in the electricity market aims to maximize its individual payoff
while guaranteeing the system reliability and satisfying users’
electricity demands. We employ the backward induction method
to solve the three-stage non-cooperative game problem, and
give the closed-form expressions of the optimal strategies for
each stage. Next, we study the big data-based power generation
forecasting techniques, and introduce a scheme of the wind power
forecasting, which can assist the microgrid to make strategies.
Furthermore, we prove the properties of the proposed energy
management algorithm including the existence and uniqueness
of Nash equilibrium and Stackelberg equilibrium. Simulation
results show that accurate prediction results of wind power is
conducive to better energy management.
Index Terms: energy internet | Stackelberg game | microgrid energy management | wind power forecasting. |
مقاله انگلیسی |