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Microgrid hybrid coordination control method based on reinforcement learning and multi-agent theory

A reinforcement learning and multi-agent technology, applied in electrical components, energy storage, circuit devices, etc., can solve problems such as general performance, loss of energy storage life, and single strategy, and achieve the goal of reducing unstable factors and stabilizing bus voltage Effect

Pending Publication Date: 2020-05-26
YANSHAN UNIV
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Problems solved by technology

However, most of the articles use a single energy storage solution. A single energy storage solution needs to switch charging and discharging continuously when the stable voltage fluctuates. Not only the performance is average, but also the life of the energy storage will be greatly reduced. The final dual energy storage control, but the dual energy storage control strategy is single

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Embodiment Construction

[0029] In order to make the technical solutions and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the drawings in the embodiments of the present invention:

[0030] Such as Figure 4 A hybrid coordinated control method of microgrid based on reinforcement learning and multi-agent theory is shown, including the following steps:

[0031] Step 1: In order to optimally control the bus voltage, the voltage layered control strategy divides the bus voltage into 6 detection levels: (—, 0.95U ref ], (0.95U ref , 0.96U ref ], (0.96U ref , 0.98U ref ], (0.98U ref , 1.02U ref ], (1.02U ref , 1.05U ref ], (1.05U ref , —], where U ref is the reference voltage.

[0032] Due to the randomness of renewable energy generation and load demand, the bus voltage will fluctuate to a certain extent. When the bus voltage jumps from a certain range to another ra...

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Abstract

The invention discloses a microgrid hybrid coordination control method based on reinforcement learning and a multi-agent theory. The method comprises the steps of designing a transition voltage layercontrol strategy based on a voltage layering mode, designing a double-energy-storage sub-role control strategy, and enabling two energy storage to work separately when an energy storage unit works ina voltage stabilization mode; when energy storage assistance is needed to continuously absorb power or supplement power, converting the two-energy-storage working mode into cooperative charging / discharging; constructing an action space and a state space based on Q-Learning; designing a reinforcement learning control framework based on multiple agents, specifically, designing a basic updating ruleof a state-action pair and selecting a corresponding value function; designing a basic action selection mechanism and a return value strategy, specifically, designing a selection strategy adopted by the system in an initial state and a return value in each state; and designing a reinforcement learning algorithm flow, specifically, designing a proper algorithm flow based on the strategy to realizea control strategy.

Description

technical field [0001] The invention relates to the field of smart grid control, in particular to a micro grid hybrid coordination control method based on reinforcement learning and multi-agent theory. Background technique [0002] With the rapid development of the economy, my country's energy consumption is increasing year by year, and the total consumption of non-renewable energy such as fossil energy is growing rapidly. my country's power supply still mainly comes from thermal power generation, but with the overexploitation of non-renewable energy such as fossil energy and the negative impact on the environment in the process of traditional power generation is becoming more and more serious, my country and the world are increasingly dependent on wind, light, water, etc. Research on renewable energy power generation has gradually been put on the agenda. The development and utilization of green and clean energy can not only make a certain contribution to environmental prote...

Claims

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Application Information

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IPC IPC(8): H02J3/00H02J3/38H02J3/28
CPCH02J3/00H02J3/381H02J3/28Y02E70/30
Inventor 窦春霞张立国
Owner YANSHAN UNIV
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