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Power grid pricing method based on reinforcement learning

A technology that enhances learning and power grids, applied in data processing applications, marketing, instruments, etc., and can solve problems such as simultaneous consideration and incomplete grid pricing.

Pending Publication Date: 2019-11-08
ZHOUSHAN ELECTRIC POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER +2
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Problems solved by technology

The existing methods consider the optimization scheme of power grid pricing from three perspectives: reliability, supply-demand balance, and real-time pricing, but they do not consider the above three aspects at the same time, and grid pricing is not comprehensive.

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  • Power grid pricing method based on reinforcement learning
  • Power grid pricing method based on reinforcement learning
  • Power grid pricing method based on reinforcement learning

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Embodiment Construction

[0058] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0059] Such as figure 1 Shown, the present invention comprises the following steps:

[0060] Step S1: Define the state space and behavior space in the reinforcement learning model;

[0061] Step S2: Define the specific calculation method of the reward function in the reinforcement learning model;

[0062] Step S3: Initialize the Q value table (that is, the value table of the state-behavior pair) and the E-value table (that is, the value table that records the state-behavior pair utility trace), and set the initial electricity price;

[0063] Step S4: The intelligent agent observes the current state at the initial moment, and selects the corresponding behavior through the ∈-greedy strategy to adjust the initial electricity price;

[0064] Step S5: Feedback the behavior obtained in step S4 to the grid electricity sales market enviro...

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Abstract

The invention discloses a power grid pricing method based on reinforcement learning, and relates to a power grid pricing method. Existing power grid pricing is not comprehensive. The method comprisesthe following steps: initializing a Q value table and an E value table, and setting an initial electricity price; obtaining a state space and a behavior space of the reinforcement learning model, andadjusting the initial electricity price; feeding back the electricity price adjustment behavior to the power grid electricity selling market environment, and waiting for a time interval T to obtain environment information; calculating a return function of the current time by using the environmental feedback information and the power price information proposed in the previous state to obtain an instant return R, and updating an E value table and a Q value table; selecting a new behavior by using the updated Q value table, and adjusting the electricity price;, after each time T, circulating thesteps S4-S7 until the Q value table converges to a set range, and obtaining the optimal power grid pricing. According to the technical scheme, real-time pricing of the power grid is realized accordingto factors of supply and demand balance, power grid reliability and real-time pricing.

Description

technical field [0001] The invention relates to a grid pricing method, in particular to a grid pricing method based on reinforcement learning. Background technique [0002] Restricted by factors such as the environment and reserves, the proportion of fossil energy in future power grids will gradually decrease. At the same time, new energy (variable renewable energy) represented by wind energy and solar energy has a lot of room for growth and will gradually replace traditional energy. Due to the characteristics of intermittent, fluctuating and decentralized renewable energy, when it is integrated into the traditional power grid, it will increase the impact on the stability of the power grid. [0003] Electricity price has always been the focus of electricity market construction. Building a unified, open, competitive and orderly electricity market will be conducive to the sound and rapid development of the electric power industry. It is necessary to continuously deepen the re...

Claims

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Application Information

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IPC IPC(8): G06Q50/06G06Q30/02
CPCG06Q30/0283G06Q50/06Y04S50/14
Inventor 李懑君莫益军陈浩朱鲁敏罗腾范舟永余凡韩叶林
Owner ZHOUSHAN ELECTRIC POWER SUPPLY COMPANY OF STATE GRID ZHEJIANG ELECTRIC POWER
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