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Micro-grid optimization operation strategy generation method, system and device and storage medium

A technology for optimizing operation and operating cost, applied in the field of power system, it can solve the problems of complex scheduling content, relying on accurate modeling, complex process, etc., to achieve the effect of short optimization time, fast optimization convergence speed, and low complexity.

Active Publication Date: 2021-11-26
CHINA ELECTRIC POWER RES INST
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] However, the existing technology has complex scheduling content. In addition, the objective function needs to be transformed into a mixed integer quadratic programming, which requires mathematical convex optimization requirements. At the same time, the process of formula derivation is also complicated. At the same time, intraday scheduling also depends on new energy sources and Accurate modeling of loads

Method used

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  • Micro-grid optimization operation strategy generation method, system and device and storage medium
  • Micro-grid optimization operation strategy generation method, system and device and storage medium

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Embodiment 1

[0056] The micro-grid optimization operation strategy generation method described in the present invention includes:

[0057] 1) Establish a neural network model under the deep deterministic policy gradient algorithm;

[0058] refer to figure 2 , the five-tuple in the deep deterministic policy gradient algorithm is (S, A, L, r, γ), where S is the state space, A is the action space, and L is the state transition probability, that is, the agent is in the current The state st executes the probability of transferring the action at to the next state st+1, r is the reward function, and γ is the discount factor, which is the attenuation coefficient for future returns.

[0059] 2) With the lowest total operating cost of the microgrid and the largest degree of new energy consumption as the optimization goal, based on the classification experience playback mechanism, the neural network model is optimized by using the deep deterministic strategy gradient algorithm;

[0060] In the opt...

Embodiment 2

[0090] refer to Image 6 , the micro-grid optimization operation strategy generating system of the present invention includes:

[0091] Establish module 1, which is used to establish the neural network model under the deep deterministic policy gradient algorithm;

[0092] Optimization module 2 is used to optimize the neural network model with the lowest total operating cost of the microgrid and the largest degree of new energy consumption, based on the classification experience playback mechanism, and the deep deterministic strategy gradient algorithm.

[0093] Acquisition module 4; used to obtain the state space S of the deep deterministic strategy gradient algorithm for the microgrid, the state space S of the deep deterministic strategy gradient algorithm includes wind power generation output, user load, time-based electricity price, lithium battery charge, etc. Power status and time period;

[0094] The generation module 3 is used to input the state space S of the deep de...

Embodiment 3

[0096] A computer device, comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program, the micro-grid optimization operation strategy generation method is implemented The step, wherein, the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc.; the processor, the network interface, and the memory are connected to each other through an internal bus, and the internal The bus can be an industry standard architecture bus, a peripheral component interconnection standard bus, an extended industry standard architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, and the like. The memory is used to store programs, specifically, the programs may include program codes, and the program codes include computer operation instructions. Storage, which can inclu...

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Abstract

The invention discloses a micro-grid optimization operation strategy generation method, system and device and a storage medium. The method comprises the following steps: establishing a neural network model under a depth deterministic strategy gradient algorithm; optimizing the neural network model by adopting a depth deterministic strategy gradient algorithm based on a classification experience playback mechanism by taking the lowest total operation cost of the micro-grid and the maximum new energy consumption degree as optimization objectives; and generating a microgrid optimized operation strategy using the optimized neural network model. According to the method, the system and the device and the storage medium, the calculation complexity is low, and the method, the system, the equipment and the storage medium do not depend on accurate modeling of new energy output and load.

Description

technical field [0001] The invention belongs to the field of power systems, and relates to a microgrid optimization operation strategy generation method, system, equipment and storage medium. Background technique [0002] The key to realizing the optimal operation of the microgrid is to deal with the challenges brought by the uncertainty of distributed renewable energy output and load to the operation of the microgrid. Real-time optimization strategy generation for output and load. At present, research on microgrid optimization and operation is mainly to build scheduling models through stochastic optimization, robust optimization, or model predictive control. Stochastic optimization uses the probability distribution of random variables to describe uncertainty, and there will be certain errors, while using scene generation to describe the research object. Uncertainty will make it difficult to solve as the scale of the problem becomes larger. Robust optimization is mainly ai...

Claims

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Application Information

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IPC IPC(8): G06F30/25G06F30/27G06Q10/04G06Q10/06G06Q50/06G06N3/00G06N3/04G06N3/08G06F111/04G06F111/08
CPCG06F30/25G06F30/27G06Q10/04G06Q10/0631G06Q50/06G06N3/006G06N3/04G06N3/08G06F2111/04G06F2111/08Y02E40/70Y04S10/50
Inventor 王继业蒲天骄周翔陈盛王新迎
Owner CHINA ELECTRIC POWER RES INST
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