A distribution network voltage regulation method based on deep reinforcement learning algorithm
A technology of reinforcement learning and voltage regulation, applied in neural learning methods, constraint-based CAD, electrical components, etc., can solve problems such as excessive state space, complex modeling, poor convergence, etc., to overcome poor convergence and high computational efficiency , the effect of improving application performance
Active Publication Date: 2022-03-08
STATE GRID BEIJING ELECTRIC POWER +1
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[0008] The purpose of the present invention is to solve the problems of complex uncertainty modeling and poor convergence in the prior art, and the difficulty in solving problems caused by too large state space, and to provide a distribution network voltage regulation method based on deep reinforcement learning algorithm
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[0086]In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.
[0087] Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art wit...
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The invention discloses a distribution network voltage regulation method based on a deep reinforcement learning algorithm. By understanding the factors affecting the voltage operation level of the distribution network, an energy storage system for system voltage regulation and other auxiliary services is connected at the end of the distribution network. It can effectively deal with the problem of system voltage operation level caused by the high intermittency of distributed renewable energy and the fluctuation of load demand. The invention models the operation of battery energy storage as a Markov decision-making process, considers its follow-up regulation ability, and approximates the optimal action value by embedding a Q deep neural network to solve the problem of excessive state space. The state eigenvector composed of the state of charge of energy storage, the predicted output of renewable energy and the load level is used as the input of the Q network, and the output is the optimal discretized charge and discharge action to increase the voltage operation level, and is trained through the playback strategy to obtain the optimal Energy storage control method with optimized voltage regulation strategy.
Description
【Technical field】 [0001] The invention belongs to the technical field of power system automation, and relates to a distribution network voltage regulation method based on a deep reinforcement learning algorithm. 【Background technique】 [0002] With the continuous improvement of the penetration rate of clean energy in the distribution network, its strong volatility and high uncertainty have an increasing impact on the safe and economic operation of the distribution network. When a large amount of renewable distributed generation (Renewable Distributed Generation, RDG) is connected to the distribution network, the fluctuation of its output will also have a negative impact on the voltage operation level of the distribution network, and even cause the voltage to exceed the limit. According to the non-decoupling characteristics of active power and reactive power in the distribution network, it can be known that controlling the balance of active power in the distribution network c...
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IPC IPC(8): H02J3/00H02J3/24H02J3/32G06F30/20G06N3/04G06N3/08G06F111/06G06F111/04
CPCH02J3/00H02J3/24H02J3/32G06F30/20G06N3/084H02J2203/20G06F2111/06G06F2111/04G06N3/045Y02E40/10Y02E40/70Y02E40/60Y02E70/30Y04S10/50
Inventor 史景坚周文涛张宁陈桥籍宁曹振博陈懿孟凡晨
Owner STATE GRID BEIJING ELECTRIC POWER