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Scheduling method and device for energy storage system of wind power plant integrated with prediction and decision

An energy storage system and a technology for forecasting and decision-making, applied in forecasting, neural learning methods, data processing applications, etc., can solve problems such as loss of effective decision-making basis, and achieve the effects of improving referenceability, avoiding modeling errors, and avoiding loss

Active Publication Date: 2019-11-05
SHANDONG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The inventor found that the separation of prediction and decision-making caused the process to lose many effective decision-making basis contained in the original data, and additionally introduced error interference caused by the prediction algorithm

Method used

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  • Scheduling method and device for energy storage system of wind power plant integrated with prediction and decision
  • Scheduling method and device for energy storage system of wind power plant integrated with prediction and decision
  • Scheduling method and device for energy storage system of wind power plant integrated with prediction and decision

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] The integrated wind farm energy storage system scheduling method of this embodiment includes:

[0038] Sample accumulation step: the wind farm status s t Input to the evaluation network, output the Q value of all actions in the action space A and determine the scheduling instruction a of the energy storage system by the ε-greedy strategy t , after the energy storage system executes the scheduling instruction, calculate the returned reward r t And observe the state of the wind farm in the next period s t+1 , will (s t ,a t ,r t ,s t+1 ) is stored in the buffer as a sample, and the above process is repeated until the number of samples in the buffer reaches a preset upper limit;

[0039] Among them, the evaluation network is a deep neural network, and the structure of the evaluation network in this example is as follows: image 3 shown;

[0040]Q value iterative step: Batch sampling of the stored samples, and then calculate the time difference deviation value of ea...

Embodiment 2

[0122] An integrated wind farm energy storage system dispatching device for forecasting and decision-making in this embodiment includes:

[0123] (1) Sample accumulation module, which is used for: the wind farm status s at the current moment t Input to the evaluation network, output the Q value of all actions in the action space A and determine the scheduling instruction a of the energy storage system by the ε-greedy action selection strategy t , after the energy storage system executes the scheduling instruction, calculate the returned reward r t And observe the state of the wind farm in the next period s t+1 , will (s t ,a t ,r t ,s t+1 ) is stored in the buffer as a sample, and the above process is repeated until the number of samples in the buffer reaches a preset upper limit;

[0124] (2) Q value iteration module, which is used to: batch sample the stored samples, then calculate the time difference deviation value of each sample through the evaluation network and th...

Embodiment 3

[0128] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps in the method for scheduling the wind farm energy storage system with integrated forecasting and decision-making as described in Embodiment 1 are implemented.

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Abstract

The invention provides a scheduling method and device for an energy storage system of a wind power plant integrated with prediction and decision. The method comprises the following steps of a sample accumulation step: inputting the state st of awind power plant to an evaluation network, outputting Q values of all actions in an action space A, and determining a dispatching instruction at of the energy storage system by an Epsilon-greedy strategy after the energy storage system executes the dispatch instruction, calculating the returned reward rt and observing the state of the wind power plant in the next period st + 1, storing (st, at, rt, st + 1) as a sample in a buffer, and repeating the above process until the number of samples in the buffer reaches the preset upper limit; a Q value iteration step; and a network training step; a learning finish juding step: if gains acquired by the wind power plant do not increase and fluctuate within a pre-set range, indicating the evaluation network converges with the current scheduling instruction being optimal; otherwise, repeating the above steps till the evaluation network converges and outputting the optimal scheduling instruction.

Description

technical field [0001] The disclosure belongs to the field of wind farm energy storage system optimization, and in particular relates to a wind farm energy storage system dispatching method and device integrating prediction and decision-making. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The output of wind turbines and the market price of electricity are directly related to the income of wind farms. The output of wind turbines is uncertain and unschedulable, and these characteristics make the income of wind farms fluctuate and uncontrollable. In addition, the electricity price released by the electricity market includes information on the balance of supply and demand in the market, transmission congestion, fuel costs, etc. Adjusting the electricity price is a means for the market to guide and restrict power generators to provide high...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q10/0631G06Q10/0637G06Q50/06G06N3/084G06N3/044
Inventor 杨明杨佳峻朱毅于一潇
Owner SHANDONG UNIV
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