Intelligent electric vehicle charging and discharging decision-making method based on deep reinforcement learning

A smart electric vehicle and reinforcement learning technology, applied in neural learning methods, market forecasting, instruments, etc., can solve problems such as delay, difficult modeling, complex modeling, etc., to solve the problem of overestimation, overcome noise and inaccurate The effect of stabilizing and enhancing generalization ability

CN113627993APending Publication Date: 2021-11-09东北大学秦皇岛分校
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2021-11-09

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Abstract

The invention provides an intelligent electric vehicle charging and discharging decision-making method based on deep reinforcement learning, and relates to the technical field of electric vehicle charging and discharging. A machine learning algorithm based on data driving can be used for solving a complex optimization problem, priori knowledge of a system is not needed, dynamic learning of a historical operation state can be completed based on function iteration, and an optimal charging and discharging plan can be obtained based on experience accumulation and return analysis. From the perspective of a user, an MDP with an unknown transition probability is constructed and is used for describing a charging and discharging scheduling problem of an electric vehicle. The randomness of the electricity price and commuting behaviors are considered to describe an actual scene; a model-free method based on deep reinforcement learning is provided, and the method does not need any system model information to determine the optimal decision of the real-time decision problem; and the electricity price is iteratively predicted by using the single-step prediction LSTM network, and compared with a traditional time sequence prediction method (Arima), the prediction precision is higher.
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Description

technical field

[0001] The invention relates to the technical field of charging and discharging of electric vehicles, in particular to a decision-making method for charging and discharging of intelligent electric vehicles based on deep reinforcement learning. Background technique

[0002] With the improvement of residents' living standards and the intelligence level of electric vehicles, people will consider more factors when charging and discharging electric vehicles, such as the cumulative charging and discharging costs and user satisfaction. For many families with electric vehicles, if the electric vehicle is charged at the maximum power when it is parked, although the user satisfaction is relatively high, it will bring a large charging cost at the same time; Charging, although it can reduce charging costs, will reduce user satisfaction if the electric vehicle is not fully charged when it leaves.

[0003] Different from other controllable loads and energy storage devices...

Examples

Embodiment Construction

[0037] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0038] A smart electric vehicle charging and discharging decision-making method based on deep reinforcement learning, such as figure 1 Shown, specifically include the following steps:

[0039] Step 1: Collect electricity price data for the past 24 hours;

[0040] Step 2: Use the single-step prediction LSTM network to iteratively predict the electricity price in the next 24 hours, such as figure 2 shown;

[0041] Step 2.1: Expand the LSTM network into a 23-layer neural network, and use the same weight parameters for each layer;

[0042] Step 2.2: Let the input d of the first layer t-22 =p t-22 -p t-23 , where p t-22 and p t-23 Represent the electricity prices at t...