CN-121710330-B - Capacity optimization method, system and device for multi-time scale hybrid energy storage system

CN121710330BCN 121710330 BCN121710330 BCN 121710330BCN-121710330-B

Abstract

The application relates to the technical field of energy storage and automatic control of an electric power system and discloses a capacity optimization method, a system and a device of a multi-time-scale hybrid energy storage system, wherein the method comprises the steps of establishing and solving an initial capacity optimization model based on a multi-time-scale decomposition result of new energy power fluctuation to obtain an initial capacity configuration parameter and a multi-time-scale division parameter; the method comprises the steps of controlling a hybrid energy storage system to participate in grid frequency modulation based on initial parameters, collecting running performance and equipment state data, generating performance feedback indexes according to the data, dynamically correcting adjustable parameters of a capacity optimization model based on the indexes, solving the model again, processing division parameters and capacity configuration parameters as related variables in the process to obtain updated parameters, applying the updated parameters to the system and returning to a frequency modulation running step to form closed loop optimization. The application realizes the collaborative dynamic optimization of the energy storage capacity and the division strategy, and improves the economical efficiency, the service life and the operation robustness of the system.

Inventors

MENG KEQILAO
LI DI
YANG WENHUAN
WANG HAIXIAO
WANG ZHE
Yan Qingkun

Assignees

内蒙古工业大学
华能乌拉特中旗新能源发电有限公司

Dates

Publication Date: 20260508
Application Date: 20260210

Claims (8)

1. A method of capacity optimization for a multi-time scale hybrid energy storage system, the method comprising: Step S1, based on a multi-time scale decomposition result of new energy power fluctuation, an initial capacity optimization model is established and solved to obtain initial capacity configuration parameters and initial multi-time scale division parameters of a hybrid energy storage system, wherein the hybrid energy storage system comprises a first type energy storage unit and a second type energy storage unit with complementary dynamic response characteristics; The method comprises the steps that S1, a first type of energy storage unit is flywheel energy storage or super capacitor energy storage, a second type of energy storage unit is compressed air energy storage, a flow battery or hydrogen energy storage, the multi-time-scale decomposition result comprises a high-frequency component, an intermediate-frequency component and a low-frequency component, the multi-time-scale division parameters are used for defining the power distribution proportion of the first type of energy storage unit and the second type of energy storage unit to different time-scale power components, and the solution result of the capacity optimization model is configured in such a way that the high-frequency component is mainly borne by the first type of energy storage unit, the low-frequency component is mainly borne by the second type of energy storage unit, and the intermediate-frequency component is cooperatively borne by the first type of energy storage unit and the second type of energy storage unit according to the multi-time-scale division parameters; step S2, controlling the hybrid energy storage system to participate in power grid frequency modulation based on the initial capacity configuration parameters and the initial multi-time-scale division parameters, and collecting performance data and equipment state data in the running process; Step S3, generating performance feedback indexes based on the performance data and the equipment state data, dynamically correcting adjustable parameters of the capacity optimization model according to the performance feedback indexes, and re-solving the corrected capacity optimization model, wherein in the re-solving process, the multi-time-scale labor division parameters and the capacity configuration parameters are treated as related variables, and updated capacity configuration parameters and updated multi-time-scale labor division parameters of the next operation period are obtained; the performance data at least comprises power grid frequency deviation, frequency change rate and frequency modulation power tracking error, the equipment state data at least comprises real-time SOC and rotating speed of flywheel energy storage and real-time tank pressure and temperature of compressed air energy storage, the performance feedback index at least comprises a frequency deviation integral value, an unsatisfied frequency modulation energy value and an equipment circulation loss index, and the adjustable parameters at least comprise weight coefficients of all sub objective functions in a capacity optimization model, boundary conditions of energy storage unit operation safety constraint and initial values or reference values of multi-time scale division parameters; The multi-time-scale division parameter and the capacity configuration parameter are used as related variables to be processed, and the method is realized in any one of the following modes, namely, the capacity configuration parameter and the multi-time-scale division parameter are used as decision variables together and are synchronously and optimally determined in the re-solving process; And S4, applying the updated capacity configuration parameters and the updated multi-time-scale division parameters to the hybrid energy storage system, and returning to execute the step S2 to form a closed-loop optimization flow.
2. The method of capacity optimization of a multi-time scale hybrid energy storage system according to claim 1, wherein in step S3, dynamically modifying the adjustable parameters of the capacity optimization model comprises adjusting at least one of the following parameters: The weight coefficient of each sub objective function related to frequency performance, investment cost and equipment life loss in the capacity optimization model; boundary conditions in the capacity optimization model for operational safety constraints of the first type of energy storage unit and/or the second type of energy storage unit; an initial value or reference value of the multi-time scale division parameter for the re-solution process.
3. The method of capacity optimization of a multi-time scale hybrid energy storage system according to claim 1, wherein the triggering execution of step S3 is controlled by any one of the following conditions: the time control parameter reaches the preset fixed period duration; Any one of the performance feedback indexes exceeds a corresponding early warning threshold; And receiving an update instruction from an external scheduling system.
4. The method of capacity optimization of a multi-time scale hybrid energy storage system of claim 1, further comprising, after step S3 and before step S4, an update verification step of: Based on the updated capacity configuration parameters and the updated multi-time-scale labor division parameters, short-term test operation is carried out on the hybrid energy storage system, and during the short-term test operation, operation performance indexes and equipment state indexes of the hybrid energy storage system are monitored and collected; If the running performance index and the equipment state index both meet preset admission criteria, confirming that the updated capacity configuration parameter and the updated multi-time-scale division parameter are applied to subsequent frequency modulation running, otherwise, triggering the capacity configuration parameter and the multi-time-scale division parameter which are returned to the previous running period to run, or carrying out amplitude limiting correction on the updated capacity configuration parameter and the updated multi-time-scale division parameter and then re-verifying; the admission criterion is a set of quantitative or qualitative criteria which are preset and used for judging whether the capacity configuration parameter and the multi-time-scale division parameter of the next operation period can be formally put into operation.
5. The method for optimizing capacity of a multi-time scale hybrid energy storage system according to claim 1, wherein in step S3, before dynamically modifying the adjustable parameters of the capacity optimization model according to the performance feedback index, the method further comprises a performance root cause analysis step: Performing fusion analysis on the performance data and the equipment state data to generate a feature vector representing the comprehensive state of the hybrid energy storage system, and performing matching analysis on the feature vector and a preset diagnosis rule base, wherein the diagnosis rule base defines the mapping relation between different abnormal state modes and potential root causes; Based on the matching analysis, outputting a diagnosis conclusion indicating a potential root cause causing the current frequency modulation performance deviation, wherein the type of the diagnosis conclusion comprises that the total frequency modulation capacity of the hybrid energy storage system is not configured sufficiently, the multi-time scale labor division ratio between the first type of energy storage unit and the second type of energy storage unit is not reasonable, or the operation load of any type of energy storage unit is overweight; and selecting and executing a directional correction strategy corresponding to the type of the diagnosis conclusion according to the diagnosis conclusion so as to correct the adjustable parameters of the capacity optimization model.
6. The method for optimizing capacity of a multi-time scale hybrid energy storage system according to claim 1, wherein in step S2, the step of controlling the hybrid energy storage system to participate in grid frequency modulation adopts a hierarchical coordination control strategy, includes: The upper layer coordinator generates a total frequency modulation power instruction and a power distribution instruction of each energy storage unit according to the power grid frequency deviation, the frequency change rate and the real-time state margin of the first type energy storage unit and the second type energy storage unit, and the lower layer executor carries out quick tracking control on the power distribution instruction, wherein when the upper layer coordinator judges that the real-time state margin of any type of energy storage unit is lower than a safety threshold, a power instruction reassigning logic is started, and partial power instructions which cannot be safely born by the energy storage units with the real-time state margin lower than the safety threshold are reassigned to another type of energy storage unit or standby resource.
7. A capacity optimization system for a multi-time scale hybrid energy storage system for implementing a method of capacity optimization for a multi-time scale hybrid energy storage system as defined in any one of claims 1-6, the system comprising: The initial configuration module is used for establishing and solving an initial capacity optimization model based on a multi-time-scale decomposition result of new energy power fluctuation to obtain initial capacity configuration parameters and initial multi-time-scale division parameters of the hybrid energy storage system, wherein the hybrid energy storage system comprises a first type energy storage unit and a second type energy storage unit with complementary dynamic response characteristics; The frequency modulation operation and data acquisition module is used for controlling the hybrid energy storage system to participate in power grid frequency modulation based on the initial capacity configuration parameter and the initial multi-time-scale division parameter, and acquiring performance data and equipment state data in the operation process; The closed loop feedback and reconfiguration module is used for generating a performance feedback index based on the performance data and the equipment state data, dynamically correcting the adjustable parameters of the capacity optimization model according to the performance feedback index, and re-solving the corrected capacity optimization model, wherein in the re-solving process, the multi-time-scale division parameters and the capacity configuration parameters are used as associated variables to be processed, and updated capacity configuration parameters and updated multi-time-scale division parameters of the next operation period are obtained; and the application and iteration module is used for applying the updated capacity configuration parameters and the updated multi-time-scale division parameters to the hybrid energy storage system and executing the frequency modulation operation and data acquisition module in a return mode to form a closed-loop optimization flow.
8. A capacity optimizing apparatus for a multi-time scale hybrid energy storage system, the apparatus comprising a memory and at least one processor, the memory having instructions stored therein; The at least one processor invokes the instructions in the memory to cause the apparatus to perform the method of capacity optimization of the multi-time scale hybrid energy storage system of any one of claims 1-6.

Description

Capacity optimization method, system and device for multi-time scale hybrid energy storage system Technical Field The application relates to the technical field of energy storage and automatic control of power systems, in particular to a capacity optimization method, a system and a device of a multi-time-scale hybrid energy storage system. Background With the continuous improvement of permeability of new energy sources represented by wind power and photovoltaic in an electric power system, the randomness and fluctuation of the output of the new energy sources bring serious challenges to the stability of the frequency of a power grid. In order to stabilize new energy power fluctuation and support grid frequency, the hybrid energy storage system utilizes the adjustment advantages of the hybrid energy storage system on different time scales by integrating energy storage units (such as power type energy storage such as flywheel, super capacitor and energy type energy storage such as compressed air energy storage and battery) with complementary dynamic response characteristics, and becomes an important technical direction for improving the flexibility and stability of the grid. Technological development in the field mainly goes through the stages of single energy storage configuration, multi-type energy storage combination, fixed capacity planning, preliminary scene adaptive design, open-loop control, multi-time scale coordination control and the like. The early research mainly focuses on capacity optimization of single type energy storage, and is difficult to cope with wide frequency domain fluctuation demand, and then researchers begin to explore a hybrid system containing power type and energy type energy storage, and fluctuation power is distributed according to frequency bands through a multi-time scale decomposition technology, so that the division cooperation of different energy storage media on fast and slow frequency bands is primarily realized. However, most of the existing methods still use a "one-time design, static operation" mode, i.e. after the energy storage capacity and fixed division strategy are determined based on historical or typical data in the planning phase, no adjustments are made during long-term operation. The mode has obvious limitations that firstly, the static design based on a limited scene is difficult to adapt to the continuous dynamic change of new energy output and power grid frequency modulation requirement, the capacity allocation and the actual requirement are possibly caused to be disjointed, investment waste or adjustment capability is insufficient, secondly, a fixed division strategy cannot be adaptively adjusted according to the actual running state (such as the state of charge and the equipment health) of an energy storage unit, the long-term overload running of part of the energy storage unit is easy to cause acceleration loss, or the division proportion imbalance influences the overall adjustment performance, and furthermore, a closed-loop mechanism for feeding real-time running data back to a capacity and strategy optimization link is lacked, the system cannot continuously learn and self-perfect in running, and the economy and reliability of long-term running and the robustness for coping with extreme disturbance are limited. Therefore, how to construct a hybrid energy storage system capable of dynamically optimizing self capacity configuration and multi-time scale division strategy according to actual operation performance becomes a key problem to be broken through in the current technical development. Disclosure of Invention In order to solve the technical problems, the application provides a capacity optimization method, a system and a device of a multi-time scale hybrid energy storage system, which are used for improving the accuracy of resource allocation and the adaptability of the system to a dynamic environment. In a first aspect, the present application provides a method for capacity optimization of a multi-time scale hybrid energy storage system, the method comprising: Step S1, based on a multi-time scale decomposition result of new energy power fluctuation, an initial capacity optimization model is established and solved to obtain initial capacity configuration parameters and initial multi-time scale division parameters of a hybrid energy storage system, wherein the hybrid energy storage system comprises a first type energy storage unit and a second type energy storage unit with complementary dynamic response characteristics; step S2, controlling the hybrid energy storage system to participate in power grid frequency modulation based on the initial capacity configuration parameters and the initial multi-time-scale division parameters, and collecting performance data and equipment state data in the running process; Step S3, generating performance feedback indexes based on the performance data and the equipment state data, dynamically correcting adjustable