CN-122026453-A - Cooperative working method of particle lithium battery double-energy-storage system

CN122026453ACN 122026453 ACN122026453 ACN 122026453ACN-122026453-A

Abstract

The invention provides a cooperative working method of a particle lithium battery double-energy-storage system, which belongs to the technical field of double-energy-storage systems, and aims to build a double-layer optimization structure with maximum system operation efficiency as an upper-layer target and maximum load tracking precision as a lower-layer target through a double-energy-storage game optimization model to carry out high-precision power distribution calculation, monitor the change of power grid load demand in real time, carry out intelligent power distribution optimization and cooperative control parameter adjustment through a multi-layer cooperative optimization model, monitor the charge state of the lithium battery energy-storage system, dynamically adjust the output power distribution proportion of the double-energy-storage system according to different state intervals, build an energy management strategy of the double-energy-storage system, optimally adjust charge and discharge scheduling according to power grid load prediction data and residual capacity of the energy-storage system, and solve the technical problem of low overall response precision of the system caused by unreasonable power distribution during cooperative working of the double-energy-storage system.

Inventors

XIE JINCHAO
XU MEI
ZHANG CHENXI
WANG ZEZHONG
ZHU CHANG
WEI FEI
BAI DINGRONG

Assignees

鄂尔多斯实验室
清华大学

Dates

Publication Date: 20260512
Application Date: 20251210

Claims (10)

1. The cooperative working method of the particle lithium battery double-energy-storage system is characterized by comprising the steps of collecting real-time power output data of the particle energy-storage system and real-time voltage and current data of the lithium battery energy-storage system, respectively obtaining a power value of the particle energy-storage system and a charge and discharge state parameter of the lithium battery energy-storage system through a power sensor and a voltage and current sensor, and establishing a double-energy-storage system operation data collection matrix; establishing a dual energy storage system cooperative control model, setting a particle energy storage system as a main energy storage unit, setting a lithium energy storage system as an auxiliary regulation unit, adopting a dual energy storage ladder cooperative algorithm to calculate a power distribution strategy, setting a particle energy storage system power output threshold range, carrying out power distribution calculation through the dual energy storage game optimization model, monitoring the change of the load demand of a power grid in real time, calculating a power gap value and starting the lithium energy storage system to supplement output through the dual energy storage ladder cooperative algorithm when the load demand of the power grid exceeds the current output power of the particle energy storage system, simultaneously calling a multi-level cooperative optimization model to carry out power distribution optimization, increasing the discharge power of the lithium energy storage system through the multi-level cooperative optimization model when the power value of the particle energy storage system is lower than the set threshold range, adjusting the particle flow rate parameter, enabling the total output power to be kept within the power demand power range of the power grid, monitoring the state of charge of the lithium energy storage system, reducing the output power of the lithium energy storage system and improving the output power of the particle energy storage system to the maximum rated power when the state value of the state belongs to the set range, recalculating the dual energy storage ladder cooperative optimization model, calculating the dual energy storage ladder cooperative algorithm parameter again through the multi-level cooperative optimization model, establishing the dual energy storage system energy storage management strategy according to the load prediction data of the power grid load and the residual capacity of the energy storage system, and the charge and discharge time schedule of the particle energy storage system and the lithium battery energy storage system is optimally regulated by combining the calculation result of the double-energy storage game optimization model, so that the dynamic balance of the cooperative working state of the double-energy storage system is realized.
2. The cooperative working method of the particle lithium battery double energy storage system according to claim 1, wherein the double energy storage game optimization model is a double energy storage system power distribution optimization model established by adopting a game theory principle, an upper model maximizes system operation efficiency to be an objective function, a lower model maximizes load tracking precision to be an objective function, and the two objective functions are mutually constrained and optimized through a power distribution coupling term.
3. The cooperative working method of the particle lithium battery double energy storage system according to claim 2, wherein the double energy storage ladder cooperative algorithm is a cooperative control algorithm for establishing a multi-level power adjustment strategy according to different response characteristics of the particle energy storage system and the lithium battery energy storage system, the double energy storage ladder cooperative algorithm divides a power adjustment process into three layers of a basic power layer, a buffer power layer and a peak power layer, the basic power layer is stably output by the particle energy storage system, the buffer power layer is jointly responsible for power fluctuation adjustment by the double energy storage system, the peak power layer is mainly responsible for quick response adjustment by the lithium battery energy storage system, and seamless connection and cooperative working are realized between the three layers through a ladder switching mechanism.
4. The method for collaborative operation of a dual energy storage system of a particle lithium battery according to claim 3, wherein the charge and discharge state parameters of the energy storage system of the lithium battery refer to a plurality of technical parameters reflecting the operation state of the energy storage system of the lithium battery, including a charge and discharge current value, a charge and discharge voltage value, a battery temperature value, an internal resistance value and a power factor value, and the charge and discharge state parameters of the energy storage system of the lithium battery are used for monitoring the operation state and performance of the energy storage system of the lithium battery in real time.
5. The method according to claim 4, wherein the particle flow rate parameter is an adjustment parameter for controlling the flow rate of solid particles in the particle energy storage system in the heat storage container, and the power output characteristic of the particle energy storage system is controlled by adjusting the particle flow rate parameter.
6. The method for collaborative operation of a dual energy storage system for a particle lithium battery according to claim 5, wherein the upper objective function of the dual energy storage game optimization model is used to maximize the operating efficiency of the dual energy storage system, the input includes the power value of the particle energy storage system, the charge and discharge state parameters of the lithium battery energy storage system, the power demand of the power grid, the power loss coefficient and the system response time, and the output is the power distribution ratio with optimal efficiency.
7. The collaborative work method of a particle lithium battery double energy storage system according to claim 6, wherein a lower objective function of the double energy storage game optimization model is used for maximizing load tracking precision, inputs comprise power required by a power grid, actual output power of the double energy storage system, load prediction errors and power response time, outputs are power adjustment strategies with optimal precision, and an upper objective function and a lower objective function form a coupling relation through a product term of power distribution proportion and load tracking precision.
8. The collaborative work method of a particle lithium battery dual energy storage system according to claim 7, wherein the multi-level collaborative optimization model is a neural network optimization model established based on a dual energy storage stepwise collaborative algorithm and is used for realizing intelligent optimization of power distribution and collaborative control of the dual energy storage system, and the multi-level collaborative optimization model is used for mapping three power hierarchies of the dual energy storage stepwise collaborative algorithm into hierarchical feature representation of the neural network based on a specific implementation mode of the dual energy storage stepwise collaborative algorithm.
9. The collaborative work method of the particle lithium battery double energy storage system according to claim 8, wherein the specific structure of the multi-level collaborative optimization model is a deep network structure combining a multi-level perceptron framework with an attention mechanism, the deep network structure comprises an input layer, 3 hidden layers and an output layer, the input layer receives a particle energy storage system power value, a lithium battery energy storage system charge and discharge state parameter and a power grid load demand power as characteristic inputs, a first hidden layer establishes a basic level characteristic to be used for extracting an operation characteristic of a single energy storage system, a second hidden layer establishes a collaborative level characteristic to be used for extracting an interaction characteristic between the double energy storage systems, a third hidden layer establishes an optimization level characteristic to be used for generating an optimal power distribution strategy, and the output layer generates a control parameter of the double energy storage stepped collaborative algorithm.
10. The collaborative work method of a particle lithium battery dual energy storage system according to claim 9, wherein the multi-level collaborative optimization model dynamically adjusts a sparse mode of network connection according to the importance of the power values of the particle energy storage system by adopting a sparse connection optimization strategy, activates a full connection mode when the importance value of the power values of the particle energy storage system belongs to an interval, activates a partial connection mode when the importance value belongs to the interval, and activates the sparse connection mode when the importance value belongs to the interval.

Description

Cooperative working method of particle lithium battery double-energy-storage system Technical Field The invention belongs to the technical field of double energy storage systems, and particularly relates to a cooperative working method of a particle lithium battery double energy storage system. Background In the field of energy storage systems, a traditional single energy storage technology such as a lithium battery energy storage system has a quick response characteristic, can realize power adjustment in millisecond time, is widely applied to scenes such as grid frequency modulation, electric power peak clipping and valley filling, renewable energy grid connection and the like, and a particle energy storage system is used as an emerging heat storage and energy storage technology, realizes large-capacity long-time energy storage through gravity flow and heat exchange of solid particle materials, and is mainly applied to the fields such as industrial waste heat recovery, solar thermal power generation, regional heat supply and the like. However, in the current cooperative application of the dual energy storage systems, because the response speed of the particle energy storage system is slow and the power density of the lithium battery energy storage system is limited, the two energy storage technologies lack an effective power distribution strategy and a real-time coordination mechanism during cooperative work, so that the systems cannot fully exert the respective technical advantages, and the problem of larger power tracking deviation occurs when the load of a power grid changes rapidly. In the prior art, a simple power superposition or time sequence switching control mode is often adopted by the double energy storage systems, an accurate power distribution algorithm based on characteristic differences of each energy storage system is lacked, and when power grid load demands are suddenly changed, the system cannot quickly and accurately adjust the power output proportion of the double energy storage units, so that larger deviation exists between actual output power and load demands, that is, the technical problem that the overall response accuracy of the system is low due to unreasonable power distribution when the double energy storage systems work cooperatively in the prior art exists. Disclosure of Invention In view of the above, the invention provides a cooperative working method of a particle lithium battery dual energy storage system, which can solve the technical problem of low overall response accuracy of the system caused by unreasonable power distribution when the dual energy storage systems cooperate in the prior art. The invention provides a cooperative working method of a particle lithium battery double-energy-storage system, which is used for acquiring real-time power output data of the particle energy-storage system and real-time voltage and current data of the lithium battery energy-storage system, respectively acquiring a power value of the particle energy-storage system and a charge and discharge state parameter of the lithium battery energy-storage system through a power sensor and a voltage and current sensor, and establishing a double-energy-storage system operation data acquisition matrix; establishing a dual energy storage system cooperative control model, setting a particle energy storage system as a main energy storage unit, setting a lithium energy storage system as an auxiliary regulation unit, adopting a dual energy storage ladder cooperative algorithm to calculate a power distribution strategy, setting a particle energy storage system power output threshold range, carrying out power distribution calculation through the dual energy storage game optimization model, monitoring the change of the load demand of a power grid in real time, calculating a power gap value and starting the lithium energy storage system to supplement output through the dual energy storage ladder cooperative algorithm when the load demand of the power grid exceeds the current output power of the particle energy storage system, simultaneously calling a multi-level cooperative optimization model to carry out power distribution optimization, increasing the discharge power of the lithium energy storage system through the multi-level cooperative optimization model when the power value of the particle energy storage system is lower than the set threshold range, adjusting the particle flow rate parameter, enabling the total output power to be kept within the power demand power range of the power grid, monitoring the state of charge of the lithium energy storage system, reducing the output power of the lithium energy storage system and improving the output power of the particle energy storage system to the maximum rated power when the state value of the state belongs to the set range, recalculating the dual energy storage ladder cooperative optimization model, calculating the dual energy storage ladder cooperative a