Multi-energy system energy scheduling method based on deep reinforcement learning

A technology of reinforcement learning and energy scheduling, applied in the field of smart grid, can solve problems such as difficulties in energy scheduling management of multi-energy systems

Active Publication Date: 2020-10-23
SHANGHAI JIAO TONG UNIV
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Compared with the pure electricity market, these factors bring great...

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  • Multi-energy system energy scheduling method based on deep reinforcement learning
  • Multi-energy system energy scheduling method based on deep reinforcement learning
  • Multi-energy system energy scheduling method based on deep reinforcement learning

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[0042] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings, so as to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.

[0043] Such as figure 1 As shown, it is a multi-energy trading market structure diagram; the main body is a three-layer structure, the first layer is the retail trading market, the second layer is the internal structure of producers and sellers, and the third layer is the local energy market. Producers and sellers can communicate with the retail energy market and trade in the local energy market.

[0044] A multi-energy market energy management scheme based on deep reinforcement learning proposed by the present invention includes the following steps:

[0045] Step 1: Construct a deep neural network ...

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Abstract

The invention discloses a multi-energy system energy scheduling method based on deep reinforcement learning, and relates to the field of intelligent power grids. The method comprises the following steps that: step 1, before each transaction is started, a producer and seller selects an effective transaction action according to the energy price of a retail energy market, own energy requirements, energy storage and the historical transaction average price of a local energy market in a current transaction period; 2, the producer and salesman obtains the actual transaction volume in the retail energy market and the local energy market according to the effective transaction action; 3, the producer and salesman calculates the income or overhead of the current transaction period according to the actual transaction volume; 4, the producer and seller updates a transaction strategy according to experience, and enters the next transaction period; and step 5, the above steps are repeated until a stable transaction strategy is obtained. According to the scheme, effective conversion between energy can be achieved, the energy utilization rate is increased, and long-term benefits of producers and marketers are increased.

Description

technical field [0001] The invention relates to the technical field of smart grids, in particular to an energy scheduling method for multi-energy systems based on deep reinforcement learning. Background technique [0002] In recent years, the concept of an energy hub has been proposed. The energy hub realizes the conversion of various energy sources (eg, natural gas, electricity) through different energy carriers to achieve more efficient use of energy. Due to the integration of energy hub and smart grid, a smart multi-energy system is formed. At the same time, the emergence of the role of producers and sellers has naturally led to the emergence of local energy markets. In addition to the retail energy market, it also provides producers and sellers with additional energy trading options. Therefore, the design of multi-energy scheduling strategies is crucial to improving energy utilization in multi-energy systems. [0003] There have been some related works on the multi-ene...

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Application Information

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IPC IPC(8): G06Q30/02G06Q40/04G06Q50/06G06N3/04G06N3/08
CPCG06Q30/0206G06Q40/04G06Q50/06G06N3/084G06N3/047Y04S10/50
Inventor 朱善迎王子馨于文彬杨博关新平
Owner SHANGHAI JIAO TONG UNIV
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