A reinforced learning-based urban rail transit ground type super-capacitor energy storage system energy management method

A technology of energy storage system and urban rail transit, which is applied in the field of energy management of urban rail transit ground-type supercapacitor energy storage system. It can solve problems such as difficult to accurately model, achieve online optimization of voltage stabilization effect, and improve learning efficiency.

Active Publication Date: 2018-04-10
北京交通大学长三角研究院
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide an energy management method for ground-based supercapacitor energy storage systems of urban rail transit based on reinforcement learning, which can be used for complex and time-varying urban rail traction power supply networks and difficult to model accurately. The co

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  • A reinforced learning-based urban rail transit ground type super-capacitor energy storage system energy management method
  • A reinforced learning-based urban rail transit ground type super-capacitor energy storage system energy management method
  • A reinforced learning-based urban rail transit ground type super-capacitor energy storage system energy management method

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[0042] This patent proposes an energy management strategy for ground-based supercapacitor energy storage systems in urban rail transit based on reinforcement learning, which consists of two parts: a strategy network initialization module and an online learning module, such as Figure 5 shown. Among them, the strategy network initialization part makes full use of the known lines and vehicle information in urban rail transit, the pre-compiled train operation diagram, and the actual collected historical vehicle data to establish a multi-vehicle operation scenario model; the multi-vehicle operation scenario model, no-load The voltage prediction model, DC power flow calculation algorithm and approximate dynamic programming algorithm are combined to solve the optimal control problem of the energy storage system offline, and the strategy network is obtained as the initial value of the online learning module. Due to the fact that there is a certain deviation between the simulation mod...

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Abstract

The invention relates to a reinforced learning-based urban rail transit ground type super-capacitor energy storage system energy management method. The method comprises two parts which are policy network initialization and online learning. The policy network initialization is to build a multi-vehicle operation scene model by using known line and vehicle information, a pre-compiled train working diagram and practically collected historical vehicle data in urban rail transit, and combine the multi-vehicle operation scene model, a no-load voltage prediction model, a direct current supply load flow calculation algorithm and an approximate dynamic programming algorithm to obtain a policy network which serves as an initial value of an online learning module; the online learning module employs amodel-free reinforced learning algorithm and performs charging and discharging threshold value online adjustment through a super-capacitor intelligent agent trial and error method. The method can perform online learning on super-capacitor energy storage system control policies in urban rail traction power networks, thereby optimizing an energy-saving effect and a voltage stabilizing effect.

Description

technical field [0001] The invention relates to rail transit control and energy-saving technology, in particular to an energy management method for an urban rail transit ground-type supercapacitor energy storage system based on reinforcement learning. Background technique [0002] In the traction power supply system of urban rail transit, the traction substation usually adopts 24-pulse diode rectification to convert 10kV / 35kVAC AC power into 750V / 1500V DC power to provide traction energy for line trains. Due to the unidirectionality of diode rectification, when the train brakes, the braking energy is transmitted to the traction network. If there is no traction train nearby to absorb it, the voltage of the traction network will rise rapidly, causing the start-up of the braking resistor and the occurrence of regenerative failure. In order to fully recover the regenerative energy of trains, reduce regenerative failures and voltage fluctuations in the traction network, supercapa...

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Application Information

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IPC IPC(8): H02J3/32H02J7/34
CPCH02J3/32H02J7/345
Inventor 诸斐琴杨中平林飞杨志鸿信月
Owner 北京交通大学长三角研究院
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