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Microgrid energy scheduling method based on double-Q-value network deep reinforcement learning

A technology of reinforcement learning and network depth, applied in the direction of AC network circuit, AC network load balancing, AC network with energy trade/energy transmission authority, etc.

Inactive Publication Date: 2020-12-22
STATE GRID ZHEJIANG ELECTRIC POWER COMPANY TAIZHOU POWER SUPPLY +1
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a micro-grid energy scheduling method based on double Q-value network deep reinforcement learning, which uses optimal strategies to schedule different types of energy storage devices, and solves the power imbalance caused by unexpected power changes problems, ensuring the economy of the microgrid

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  • Microgrid energy scheduling method based on double-Q-value network deep reinforcement learning
  • Microgrid energy scheduling method based on double-Q-value network deep reinforcement learning
  • Microgrid energy scheduling method based on double-Q-value network deep reinforcement learning

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Embodiment Construction

[0064] The technical solutions of the embodiments of the present invention are explained and described below, but the following embodiments are only preferred embodiments of the present invention, not all of them. Based on the examples in the implementation manners, other examples obtained by those skilled in the art without making creative efforts all belong to the protection scope of the present invention.

[0065] The present invention relates to an energy scheduling optimization method for a micro-grid system, which describes the energy scheduling control problem of a micro-grid as a Markov decision-making process under a reinforcement learning framework, and trains an intelligent body that continuously interacts with the environment on the forecast information of a day to obtain the optimal strategy.

[0066] The purpose of the present invention is to propose a method for controlling the charging and discharging of the energy storage system to achieve the effect of energy...

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Abstract

The invention discloses a microgrid energy scheduling method based on double-Q-value network deep reinforcement learning, and the method comprises the steps: taking one-day prediction information of amicrogrid as a training set for generating an optimal control strategy, and training an intelligent agent which is independent of a microgrid environment and takes an energy storage system as a control object; and achieving double optimization objectives of minimum operation cost of the microgrid and minimum power fluctuation of the public power grid by controlling charging and discharging actions of the energy storage system. The method does not depend on the construction of a specific micro-grid model, and the strategy is guided by the design of the reward function to achieve the purpose ofmicro-grid operation, so that the optimal strategy of global time can be obtained, and the power imbalance caused by uncertainty of new energy power generation and user load distribution can be effectively solved.

Description

technical field [0001] The invention relates to the technical field of electric power engineering, in particular to the field of micro-grid operation control and energy dispatching. Background technique [0002] Increasingly concerned environmental issues and flexible trading mechanisms have brought new challenges to the design and operation of power systems. As the main means to solve the energy crisis, the development of renewable energy has given birth to a microgrid composed of distributed energy, energy storage and loads. However, due to the natural intermittency and randomness of renewable energy such as photovoltaic or wind power generation, it is difficult to directly arrange production plans for them, which has an adverse impact on the balance of the power grid. Power imbalances caused by unexpected power changes can significantly reduce the economics of the microgrid by incurring the cost of expensive backup equipment or services. One of the effective measures to...

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Application Information

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IPC IPC(8): H02J3/00H02J3/24H02J3/28H02J3/32H02J3/38
CPCH02J3/00H02J3/008H02J3/24H02J3/28H02J3/32H02J3/381H02J2203/10H02J2203/20H02J2300/24H02J2300/28H02J2300/40Y02E70/30Y02E10/56
Inventor 高强毕文正朱逸芝张晶李建飞藏玉清陈迪雨董伟杨强
Owner STATE GRID ZHEJIANG ELECTRIC POWER COMPANY TAIZHOU POWER SUPPLY
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