Operation regulation method and system based on unified description of multi-element energy storage feasible region

Abstract

The application relates to an operation regulation method and system based on a unified description of a multi-element energy storage feasible region, which comprises the following steps: acquiring operation parameters of each energy storage unit, constructing a single feasible region based on a physical model and operation constraint conditions by using a numerical sampling method; performing linear aggregation in the power dimension to construct a joint feasible region representing the boundary of the overall regulation capability of the system; dividing the function roles according to the net power instruction frequency band components and the energy storage characteristics to obtain a preliminary power distribution scheme; formulating a multi-level priority rule in combination with the function roles and the margin matching condition to perform power shortage redistribution to obtain basic distribution power; constructing a dynamic state variable balancing mechanism based on real-time state variables to calculate a dynamic priority factor correction weight to obtain pre-distribution power; and checking the single feasible region boundary constraint of the pre-distribution power to output the final instruction execution regulation. The method realizes accurate description and fine coordination control of the real operation boundary of a multi-element energy storage system.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system operation and control technology, specifically relating to an operation and control method and system based on a unified characterization of the feasible domain of multi-element energy storage. Background Technology

[0002] With the accelerating construction of new power systems based on new energy sources, energy storage technology is showing a diversified and heterogeneous development trend. The synergistic cooperation of different types of energy storage systems has become crucial to meeting the grid's multi-timescale and multi-power-level regulation needs. In order to fully leverage the complementary advantages of diverse energy storage systems, reasonable and precise operation and regulation of their charging and discharging power is fundamental to ensuring grid power balance, improving the absorption rate of new energy sources, and enhancing power supply reliability.

[0003] However, the precise regulation of multi-element energy storage systems faces severe physical constraints and control strategy challenges, posing significant difficulties for long-term stable operation and high-precision power point tracking. Existing technologies struggle to accurately characterize the true dynamic regulation boundaries of multi-element heterogeneous energy storage systems and lack operational regulation strategies that balance power point tracking accuracy with state health. Therefore, the accuracy, operational stability, and long-term regulation margin of multi-element energy storage system regulation need improvement. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides an operation control method and system based on a unified characterization of the feasible domain of multi-element energy storage. The technical problem to be solved by this invention is achieved through the following technical solution: This invention provides an operation and control method based on a unified characterization of the feasible domain of multi-element energy storage, comprising the following steps: Step 1: Obtain the operating parameters of each energy storage unit, establish a single feasible domain based on the physical model and operating constraints of each energy storage unit and perform geometric characterization, and linearly aggregate the single feasible domains of each energy storage unit in the power dimension to construct a joint feasible domain of the multi-element energy storage system that characterizes the boundary of the overall charging and discharging regulation capability of the system. Step 2: Based on the different frequency band components in the system net power command, and combined with the response speed and continuous charging and discharging characteristics of each energy storage unit, the functional roles of each energy storage unit are divided to obtain a preliminary power allocation scheme. Step 3: Combining the functional roles and the margin weighting coefficients of the joint feasible region, formulate multi-level progressive power allocation priority rules, and redistribute the power deficit of the preliminary power allocation scheme to obtain the basic allocated power. Step 4: Based on the real-time state variables of the energy storage unit, construct a dynamic state variable balancing mechanism, calculate the deviation-driven dynamic priority factor, and dynamically correct the weight of the basic allocated power to obtain the pre-allocated power. Step 5: Verify whether the pre-allocated power meets the individual feasible domain of each energy storage unit. If it does, output the final power command for each energy storage unit. If it does not meet, truncate according to the boundary of the individual feasible domain and redistribute the power deficit, and execute the operation and control of the multi-energy storage system.

[0005] In one embodiment of the present invention, step 1 involves obtaining the operating parameters of each energy storage unit, establishing a single feasible region based on the physical model and operating constraints of each energy storage unit, and performing geometric characterization, including: Step 1.1: Obtain the operating parameters of each energy storage unit and establish the individual feasible region based on the physical model and operating constraints of each energy storage unit; wherein, for a single energy storage state-power plane, its individual feasible region at time t is... Represented as:

[0006] in, For the first State variables for energy storage; This refers to the charging and discharging power. This is the upper limit for the safe operation of state variables. This is the lower bound for the safe operation of state variables; The lower boundary of power related to the state, The upper boundary of power is state-dependent; Step 1.2: The range of state variables of the energy storage unit is uniformly discretized into multiple sampling points; Step 1.3: For each state sampling point, numerical solutions are performed based on the physical model and operating constraints of the corresponding energy storage unit to obtain the upper limit of the maximum discharge power and the lower limit of the maximum charging power for each state. Step 1.4: Substitute the discrete power boundary sequence into the trapezoidal integral formula to calculate the feasible region area. :

[0007] in, The number of sampling points. The sampling step size, State variables The range of values ​​for , For the first Power adjustment range at each sampling point; Step 1.5, sequence the upper boundary points and lower boundary point sequence Connecting the ends together forms a closed polygon, thus obtaining the geometric representation of the feasible region of the single entity.

[0008] In one embodiment of the present invention, the combined feasible domain of the multi-element energy storage system described in step 1 for:

[0009] in, The total charging and discharging power of the energy storage system; For the first The feasible domain of a single energy storage system under the current state is defined by its upper boundary being the maximum discharge capacity of the system and its lower boundary being the maximum charging capacity of the system.

[0010] In one embodiment of the present invention, step 2 includes: Step 2.1: Extract the intraday trend baseload component from the system net power command and allocate it to long-term energy storage; the intraday trend baseload component of the long-term energy storage is:

[0011] in, This refers to the intraday trend base load component in the net power instruction; Step 2.2: Extract the power change of the system net power command in adjacent time periods as the slope compensation component, and allocate it to the fast response energy storage; the slope compensation component undertaken by the fast response energy storage is:

[0012] in, This is the net power instruction for the current time period. This refers to the net power command from the previous time period; Step 2.3: Subtract the intraday trend base load component and the slope compensation component from the system net power command, and allocate the remaining residual component as a fluctuation smoothing component to the medium-time energy storage; the fluctuation smoothing component of the medium-time energy storage is: .

[0013] In one embodiment of the present invention, step 3 includes: Step 3.1: Project the preliminary power allocation scheme onto the joint feasible domain of the multi-element energy storage system and verify whether it meets the individual feasible domain constraints and power balance constraints of the energy storage unit. Step 3.2, calculate the margin weighting coefficient; wherein, the margin weighting coefficient The remaining usable energy, rated capacity, and economic efficiency weights of a single energy storage unit are all considered.

[0014] in, For the first The remaining usable energy in the form of energy storage; For the first i Rated capacity of energy storage devices; Weighted by economic efficiency; for discharge demand: , For the first State of charge of energy storage For the first The state of charge limit for energy storage systems; regarding charging requirements: , For the first Upper limit of the state of charge of energy storage devices; Step 3.3: Based on the multi-level progressive power allocation priority rule, the power deficit exceeding the boundary of a single feasible region is distributed among all other energy storage units with adjustment margins according to their respective margin weighting coefficients to obtain the basic allocated power; wherein, the power deficit allocation formula is:

[0015] in, This is due to a power deficit. For the initial allocation of power, This represents the actual output power. The upper and lower bounds of the feasible region. For the first Type of energy storage.

[0016] In one embodiment of the present invention, the multi-level progressive power allocation priority rule includes: First priority criterion: Based on the functional roles of various energy storage units, allocate the control power to the energy storage units in the corresponding frequency bands to initially determine the power allocation range of each energy storage unit; Second priority criterion: Verify whether the power allocation range exceeds the feasible domain boundary of a single energy storage unit; redistribute the excess power deficit to other energy storage units with adjustment margins according to the margin weighting coefficient ratio. The third priority criterion: Provided that the first and second priority criteria are met, select energy storage units with higher overall efficiency to undertake the power tasks. (The overall efficiency is...) Defined as:

[0017] in, For the first i The charging / discharging efficiency of energy storage devices For the first i Self-discharge loss rate of energy storage devices.

[0018] In one embodiment of the present invention, step 4 includes: Step 4.1: Define the midpoint of the safe operating range of the State of Charge (SOC) of each energy storage unit as the reference center value, and calculate the individual SOC deviation of each energy storage unit and the system average SOC deviation. Step 4.2: Calculate the dynamic priority base factor so that energy storage units with individual SOC deviations greater than the system average SOC deviation receive a priority factor greater than 1, and energy storage units with individual SOC deviations less than the system average SOC deviation receive a priority factor less than 1, in order to achieve the objective function of SOC balance control. Step 4.3: Modify the dynamic priority base factor based on the charging and discharging direction of the current time period. In the discharging scenario, increase the discharge priority of energy storage with a state of charge (SOC) higher than the reference center value. In the charging scenario, increase the charging priority of energy storage with a SOC lower than the reference center value. Step 4.4: The modified dynamic priority factor is superimposed on the margin weighting coefficient to form a comprehensive allocation weight; Step 4.5: Dynamically adjust the basic allocation power based on the comprehensive allocation weight to obtain the pre-allocated power.

[0019] In one embodiment of the present invention, in step S1, the reference center value for:

[0020] The single-unit SOC deviation for:

[0021] The average SOC deviation of the system is:

[0022] in, For the first The upper limit of SOC for energy storage-like devices For the first Lower limit of SOC for energy storage-like systems; For the first The state of charge of energy storage at time t. The amount of energy stored in the system; In step 4.2, the objective function of the SOC equalization control is:

[0023] The dynamic priority base factor is:

[0024] in, The total number of scheduling periods. It is a positive number; In step 4.3, the corrected dynamic priority factor is:

[0025] in, For the current power demand, The margin weighting factor at time t; In step 4.4, the overall weight allocation is as follows:

[0026] In step 4.5, the pre-allocated power is:

[0027] In the formula, The average of the priority factors. This refers to the intraday trend base load component in the net power command.

[0028] In one embodiment of the present invention, step 4 further includes: Half of the sum of squares of the normalized individual SOC deviations of each energy storage unit is selected as the Lyapunov function; The first derivative of the Lyapunov function is calculated, and the power allocation weight is adjusted by a dynamic priority factor to determine whether the first derivative of the Lyapunov function is always not greater than zero. If it is true, the system is asymptotically stable at the equilibrium point. The pre-allocated power can ensure that the energy storage SOC eventually converges to the reference center value during long-term operation.

[0029] Another embodiment of the present invention provides an operation and control system based on a unified characterization of the feasible domain of multi-element energy storage, for performing the above method, including: The feasible domain construction module is used to obtain the operating parameters of each energy storage unit, establish a single feasible domain based on the physical model and operating constraints of each energy storage unit and perform geometric characterization, and linearly aggregate the single feasible domains of each energy storage unit in the power dimension to construct a joint feasible domain of the multi-element energy storage system that characterizes the boundary of the overall charging and discharging regulation capability of the system. The preliminary allocation and role division module is used to divide the functional roles of each energy storage unit based on the different frequency band components in the system net power command, combined with the response speed and continuous charging and discharging characteristics of each energy storage unit, and obtain a preliminary power allocation scheme. The power deficit redistribution module is used to combine the functional roles and the margin weighting coefficients of the joint feasible region to formulate multi-level progressive power allocation priority rules, and to redistribute the power deficit of the preliminary power allocation scheme to obtain the basic allocated power. The dynamic balancing correction module is used to construct a dynamic state variable balancing mechanism based on the real-time state variables of the energy storage unit, calculate the deviation-driven dynamic priority factor, and dynamically correct the weight of the basic allocated power to obtain the pre-allocated power. The control command generation and execution module is used to verify whether the pre-allocated power meets the individual feasible domain of each energy storage unit. If it does, it outputs the final power command of each energy storage unit. If it does not meet, it truncates according to the boundary of the individual feasible domain and redistributes the power deficit, and executes the operation control of the multi-element energy storage system.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention introduces feasible region theory to characterize the state-power coupling constraints of different energy storage systems, transforming the originally dispersed physical models and operational constraints into intuitive geometric boundaries, thus improving the accuracy and versatility of assessing the actual regulation capabilities of heterogeneous energy storage. By decomposing the system's net power command into frequency bands and matching energy storage functional roles, different energy storage units can operate at optimal frequency bands and optimal time scales, fully leveraging the complementary advantages of various energy storage types. Furthermore, by designing a dynamic state variable balancing mechanism driven by state variable deviation and controlling it based on Lyapunov stability theory, this invention solves the problem of unidirectional state drift that easily occurs in single-unit energy storage systems during long-term continuous operation. This method effectively ensures the safety margin and bidirectional adjustment capability of the system in the long term. By combining feasible region constraint verification with a multi-level deficit redistribution mechanism, it avoids the power truncation abrupt change caused by directly touching the hard boundary in traditional methods, greatly improving the power tracking accuracy and system operation stability. Therefore, by combining dynamic feasible region construction, functional role division, multi-level progressive power allocation priority rules and dynamic state variable balancing mechanism, this method achieves accurate characterization and refined coordinated control of the real operating boundary of the multi-element energy storage system, improving the accuracy of energy storage system operation regulation, long-term continuous operation stability and dynamic adjustment margin. Attached Figure Description

[0031] Figure 1 A flowchart illustrating an operation control method based on a unified characterization of the feasible domain of multi-element energy storage provided in an embodiment of the present invention; Figure 2 A flowchart illustrating another operation control method based on a unified characterization of the feasible domain of multi-element energy storage provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for geometrically representing a single feasible region. Detailed Implementation

[0032] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0033] Example 1 This embodiment provides an operation and control method based on the unified characterization of the feasible domain of multi-element energy storage, which solves the problem that existing operation and control technologies are unable to accurately characterize the real operating boundary of heterogeneous energy storage, and that the accuracy of control, operation stability and long-term regulation capability need to be improved.

[0034] Please see Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating an operation and control method based on a unified characterization of the feasible domain of multi-element energy storage, as provided in an embodiment of the present invention. Figure 2 This is a flowchart illustrating another operation and control method based on a unified characterization of the feasible domain of multi-element energy storage, provided in an embodiment of the present invention.

[0035] The method includes the following steps: Step 1: Obtain the operating parameters of each energy storage unit, establish the individual feasible domain based on the physical model and operating constraints of each energy storage unit and perform geometric characterization, and linearly aggregate the individual feasible domains of each energy storage unit in the power dimension to construct a joint feasible domain of the multi-element energy storage system that characterizes the boundary of the overall charging and discharging regulation capability of the system.

[0036] This embodiment introduces the feasible region theory, the basic idea of ​​which is to project the reachable states of energy storage under all operational constraints onto a state-power two-dimensional plane, forming a closed region. Any point inside the region represents a physically realizable combination, while the outside of the region is unreachable. This allows the constraint information, which was originally scattered in multiple inequalities, to be compressed into a geometric shape. The differences in the regulation capabilities between different types of energy storage can be directly observed by comparing their shape, area, and boundary slope.

[0037] Step 1 specifically includes: Step 1.1: Obtain the operating parameters of each energy storage unit, and use the numerical sampling method to dynamically establish a single feasible domain mathematical model based on the physical model and operating constraints of each energy storage unit.

[0038] Specifically, for a single type of energy storage device, its feasible operating region is defined as the set of possible combinations of energy storage state variables and control variables that satisfy all constraints under given operating conditions, mathematically expressed as:

[0039] in, It is a vector of state variables, including state of charge, rotational speed, pressure, etc. This is a vector of control variables, primarily consisting of charging and discharging power; This is an inequality constraint function; This is the equality constraint function.

[0040] For a single energy storage state-power plane, its single-cell feasible region at time t Represented as:

[0041] in, For the first State variables for energy storage; This refers to the charging and discharging power. This is the upper limit for the safe operation of state variables. This is the lower bound for the safe operation of state variables; The lower boundary of power related to the state, This is the upper boundary of the power related to the state.

[0042] Step 1.2, State space discretization: The range of state variables of the energy storage unit is uniformly discretized into multiple sampling points.

[0043] Specifically, the state variables The range of values Uniformly discretized One sampling point. Sampling step size. To balance computational accuracy and efficiency, this paper determines the number of sampling points through convergence analysis. This ensures discrete accuracy while controlling the computational load.

[0044] Step 1.3, Power Boundary Calculation: For each state sampling point, numerical solutions are performed based on the physical model and operating constraints of the corresponding energy storage unit to obtain the upper limit of the maximum discharge power and the lower limit of the maximum charging power for each state.

[0045] Specifically, for each state sampling point Based on the various energy storage physical models and operational constraints, the upper limit of the maximum discharge power under this state is numerically solved. With the lower limit of maximum charging power This ensures that the solution results satisfy all operational constraints. The physical models and operational constraints for each energy storage device are constructed using a general method, which will not be elaborated upon in this embodiment.

[0046] Step 1.4, Calculation of feasible region area: Substitute the discrete power boundary sequence into the trapezoidal integral formula to calculate the feasible region area. :

[0047] in, The number of sampling points. The sampling step size, State variables The range of values ​​for , For the first Power adjustment range at each sampling point; Step 1.5, Feasible Region Boundary Output: Output the sequence of upper boundary points. and lower boundary point sequence Connecting the beginning and end points forms a closed polygon, providing a geometric representation of the feasible region of a single energy storage unit. Connecting the beginning and end points means linking the starting points of the upper boundary point sequence to the starting points of the lower boundary point sequence, and connecting the ending points of the upper boundary point sequence to the ending points of the lower boundary point sequence. For example... Figure 3 As shown, Figure 3 This is a schematic diagram of the process for geometrically representing a single feasible region.

[0048] Step 1.6: Linearly aggregate the individual feasible regions of each energy storage unit along the power dimension to construct a joint feasible region of the multi-element energy storage system that characterizes the boundary of the overall charge-discharge regulation capability of the system, i.e.:

[0049] in, The total charging and discharging power of the energy storage system; For the first The feasible domain of a single energy storage system under the current state is defined by its upper boundary being the system's maximum discharge capacity and its lower boundary being the system's maximum charging capacity. This provides the core constraint envelope range for the subsequent collaborative allocation of multiple types of energy storage.

[0050] Step 2: Based on the different frequency band components in the system net power command, and combined with the response speed and continuous charging and discharging characteristics of each energy storage unit, the functional roles of each energy storage unit are divided to obtain a preliminary power allocation scheme.

[0051] Step 2 includes: Step 2.1: Extract the intraday trend baseload component from the system net power command and allocate it to long-duration energy storage, matching the characteristics of large capacity and long discharge duration of long-duration energy storage; the intraday trend baseload component for long-duration energy storage is:

[0052] in, The intraday trend base load component in the net power instruction can be extracted from the 15-minute series using methods such as moving averages.

[0053] Step 2.2: Extract the power change of the system's net power command between adjacent time periods as the slope compensation component, and allocate it to the fast response energy storage to match the characteristics of the fast response energy storage in smoothing power steps; the slope compensation component undertaken by the fast response energy storage is:

[0054] in, This is the net power instruction for the current time period. This is the net power instruction from the previous time period.

[0055] Step 2.3: Subtract the intraday trend base load component and slope compensation component from the system net power command, and allocate the remaining residual component as a fluctuation smoothing component to the medium-time energy storage to match the hourly fluctuation regulation characteristics of the medium-time energy storage; the fluctuation smoothing component undertaken by the medium-time energy storage is: .

[0056] With a 15-minute step size, the residual component after deducting the intraday trend base load component and the inter-period slope compensation from the net power instruction is the hourly fluctuation component.

[0057] The functional role division determines the initial power allocation scheme for each energy storage unit, but the allocated power needs to be verified and corrected under feasible domain constraints, and must simultaneously satisfy two types of basic constraints: 1) Power balance constraint: The total output power of multi-element energy storage must be able to meet the power requirements of the system.

[0058]

[0059] 2) Individual feasible region constraints of energy storage units: The output power of each energy storage unit must be within its operational feasible region.

[0060]

[0061] in, This refers to the single feasible region in step 1.1. . Step 3: Combining functional roles and margin weighting coefficients of the joint feasible region, formulate multi-level progressive power allocation priority rules, and redistribute the power deficit of the initial power allocation scheme to obtain the basic allocated power.

[0062] When a single feasible energy storage domain cannot cover the power demand, power balance needs to be achieved through the coordinated cooperation of multiple feasible energy storage domains. Specifically, step 3 includes: Step 3.1: Preliminary power allocation scheme after functional role division Projecting the data onto the joint feasible domain of the multi-element energy storage system, we verify whether the individual feasible domain constraints and power balance constraints of the energy storage unit are met.

[0063] The joint feasible region of multi-element energy storage is defined as:

[0064] Step 3.2, calculate the margin weighting coefficient; where, the margin weighting coefficient The remaining usable energy, rated capacity, and economic efficiency weights of a single energy storage unit are all considered.

[0065] in, For the first The remaining usable energy in the form of energy storage; For the first Rated capacity of energy storage devices; Weighted by economic efficiency; for discharge demand: , For the first State of charge of energy storage For the first The state of charge limit for energy storage systems; regarding charging requirements: , For the first Upper limit of the state of charge of energy storage devices; Step 3.3: Based on the multi-level progressive power allocation priority rule, the power deficit exceeding the feasible domain boundary of a single unit is distributed among all other energy storage units with adjustment margins according to their respective margin weighting coefficients, ensuring that the total system output power meets the command requirements and does not exceed the safe operating boundary, thus obtaining the basic allocated power; wherein, the power deficit allocation formula is:

[0066] in, This is due to a power deficit. For the initial allocation of power, This represents the actual output power. The upper and lower bounds of the feasible region. For the first Type of energy storage.

[0067] Meanwhile, based on functional role division and feasible domain margin matching, power allocation follows the following three-level progressive criteria: The first priority criterion is the functional role matching criterion: based on the functional roles of various energy storage units, the control demand is allocated to the energy storage units in the corresponding frequency bands, and the power allocation range of each energy storage unit is initially determined. Specifically, the ramp rate compensation component is allocated to fast response energy storage (flywheel), the hourly fluctuation component is allocated to medium-term energy storage (LFP), and the intraday trend baseload component is allocated to long-term energy storage (CAES).

[0068] The second priority criterion, namely the feasible domain margin matching criterion, is to verify whether the power allocation range exceeds the individual feasible domain boundary of the energy storage unit based on role matching; and to redistribute the excess power deficit to other energy storage units with adjustment margins according to the margin weighting coefficient ratio, so as to ensure that the power is always within the feasible domain constraints.

[0069] The third priority criterion, also known as the efficiency priority criterion, prioritizes energy storage units with higher overall efficiency to undertake power tasks, provided that the first and second priority criteria are met, in order to reduce operating costs. Among these, overall efficiency... Defined as:

[0070] in, For the first i The charging / discharging efficiency of energy storage devices For the first i Self-discharge loss rate of energy storage devices.

[0071] Step 4: Based on the real-time state variables of the energy storage unit, construct a dynamic state variable balancing mechanism, calculate the deviation-driven dynamic priority factor, dynamically correct the weight of the basic allocated power, and obtain the pre-allocated power.

[0072] Step 4 includes: Step 4.1: Define the midpoint of the safe operating range of the State of Charge (SOC) of each energy storage unit as the reference center value, and calculate the individual SOC deviation of each energy storage unit and the system average SOC deviation.

[0073] Specifically, refer to the central value for:

[0074] Monomer SOC deviation for:

[0075] The average SOC deviation of each energy storage unit in the system is:

[0076] in, For the first The upper limit of SOC for energy storage-like devices For the first Lower limit of SOC for energy storage-like systems; For the first The state of charge of energy storage at time t. This represents the amount of energy stored in the system.

[0077] Step 4.2: Calculate the dynamic priority base factor so that energy storage units with individual SOC deviations greater than the system average SOC deviation receive a priority factor greater than 1, and energy storage units with individual SOC deviations less than the system average SOC deviation receive a priority factor less than 1, in order to achieve the objective function of SOC balance control.

[0078] Specifically, the objective function of SOC equalization control is:

[0079] in, This represents the total number of scheduling periods.

[0080] The objective function aims to minimize the normalized SOC deviation of each energy storage unit. To achieve consistency and minimize the size of the single energy storage state of charge (SOC), it is important to avoid excessive deviation of the SOC from the reference center.

[0081] To achieve the goal of SOC balance, a deviation-driven dynamic priority factor is designed to correct the basic allocation weights. In discharge scenarios, energy storage with a higher SOC should receive a higher discharge priority, and in charging scenarios, energy storage with a lower SOC should receive a higher charging priority. The priority adjustment should be smooth and gradual to avoid abrupt changes in power allocation.

[0082] The dynamic priority base factor is:

[0083] in, The value is a small positive number to prevent the denominator from being zero and to ensure numerical robustness. Energy storage with a SOC deviation greater than the average deviation receives a priority factor greater than 1, and vice versa.

[0084] Step 4.3: Adjust the dynamic priority base factor based on the charging and discharging direction of the current period. In the discharging scenario, increase the discharge priority of energy storage with a state of charge (SOC) higher than the reference center value. In the charging scenario, increase the charging priority of energy storage with a SOC lower than the reference center value to suppress unidirectional SOC drift.

[0085] Specifically, taking into account the charging and discharging direction, the dynamic priority base factor is modified as follows:

[0086] in, For the current power demand, is the margin weighting coefficient at time t.

[0087] When the SOC is higher than the reference center value and discharge is required. Take the larger value to allow for more energy storage and discharge; when the SOC is below the reference center value and charging is required... Similarly, a larger value will result in more energy storage charging. Conversely, a smaller value will suppress its participation.

[0088] Step 4.4: The corrected dynamic priority factor is superimposed on the margin weighting coefficient to form the comprehensive allocation weight, expressed as:

[0089] Step 4.5: Dynamically adjust the basic allocation power based on the comprehensive allocation weight to obtain the pre-allocated power.

[0090] Specifically, no. The basic allocated power of each energy storage unit obtained using the above method is: The pre-allocated power after dynamic correction is:

[0091] In the formula, This is the average priority factor, used to ensure the total power is conserved before and after the correction. The corrected power allocation must still satisfy the feasible region constraint. If If the value exceeds the feasible domain boundary, the boundary value will be output and the difference will be redistributed to other energy storage.

[0092] Furthermore, step 4 also includes verifying the convergence and stability of the strategy. The specific steps are as follows: Step 4.6: Select half of the sum of squares of the normalized single-unit SOC deviations of each energy storage unit as the Lyapunov function:

[0093] Obviously, only If and only if the SOC of all stored energy is equal to the reference center value hour, This satisfies the positive definite requirement of the Lyapunov function.

[0094] Step 4.7: Calculate the first derivative of the Lyapunov function. Use the dynamic priority factor to correct the power allocation weights to determine whether the first derivative of the Lyapunov function is always not greater than zero. If it is true, the system is asymptotically stable at the equilibrium point. The pre-allocated power can ensure that the energy storage SOC eventually converges to the reference center value during long-term operation.

[0095] Specifically, for Find the first derivative:

[0096] From the SOC dynamic equation and power distribution correction logic, it can be seen that... and Negative correlation: when When the priority factor increases, the energy storage output power increases. The absolute value decreases. and opposite signs; when When the priority factor decreases, the energy storage output power decreases. The absolute value of no longer increases. Therefore, always satisfy According to Lyapunov's stability theorem, the system at the equilibrium point The proposed dynamic SOC balancing strategy is asymptotically stable, meaning that the SOC of each energy storage unit will eventually converge to the reference center value. During long-term operation, there will be no unidirectional SOC drift problem, and the strategy has theoretical stability.

[0097] Step 5: Verify whether the pre-allocated power meets the individual feasible domain of each energy storage unit. If it does, output the final power command for each energy storage unit. If it does not meet, truncate according to the boundary of the individual feasible domain and redistribute the power deficit, and execute the operation and control of the multi-energy storage system.

[0098] Step 5 specifically includes: using the system's total power conservation as a constraint, correcting the initial power to obtain the pre-allocated power; determining whether the pre-allocated power of each energy storage unit is within its individual feasible region; if satisfied, directly outputting the final power command for each energy storage unit; if exceeding the feasible region boundary, truncating it as output according to the feasible region boundary constraint, calculating the power deficit, and redistributing the deficit to other energy storage units according to a multi-level progressive power allocation priority rule.

[0099] This embodiment also provides an operation and control system based on a unified characterization of the feasible domain of multi-element energy storage. This system is used to execute the methods described above, including: The feasible domain construction module is used to obtain the operating parameters of each energy storage unit, establish the individual feasible domain based on the physical model and operating constraints of each energy storage unit and perform geometric characterization, and linearly aggregate the individual feasible domains of each energy storage unit in the power dimension to construct a joint feasible domain of the multi-element energy storage system that characterizes the boundary of the overall charging and discharging regulation capability of the system. The preliminary allocation and role division module is used to divide the functional roles of each energy storage unit based on the different frequency band components in the system net power command, combined with the response speed and continuous charging and discharging characteristics of each energy storage unit, and obtain a preliminary power allocation scheme. The power deficit redistribution module is used to combine the margin weighting coefficients of functional roles and joint feasible regions to formulate multi-level progressive power allocation priority rules, redistribute the power deficit of the initial power allocation scheme, and obtain the basic allocated power. The dynamic balancing correction module is used to construct a dynamic state variable balancing mechanism based on the real-time state variables of the energy storage unit, calculate the deviation-driven dynamic priority factor, and dynamically correct the weight of the base allocation power to obtain the pre-allocated power. The control command generation and execution module is used to verify whether the pre-allocated power meets the individual feasible domain of each energy storage unit. If it does, it outputs the final power command of each energy storage unit. If it does not meet, it truncates according to the boundary of the individual feasible domain and redistributes the power deficit, and executes the operation control of the multi-energy storage system.

[0100] This invention introduces feasible region theory to characterize the state-power coupling constraints of different energy storage systems, transforming the originally dispersed physical models and operational constraints into intuitive geometric boundaries, thereby improving the accuracy and versatility of the actual regulation capability assessment of heterogeneous energy storage. By decomposing the system's net power command into frequency bands and matching energy storage functional roles, different energy storage units can operate at the optimal frequency band and optimal time scale, fully leveraging the complementary advantages of various energy storage types. By designing a dynamic state variable balancing mechanism driven by state variable deviation and controlling it based on Lyapunov stability theory, the invention solves the problem of unidirectional state drift that easily occurs in single energy storage units during long-term continuous operation, effectively ensuring the safety margin and bidirectional regulation capability of the system in long-term operation. By combining feasible region constraint verification with a multi-level deficit redistribution mechanism, the invention avoids the power truncation abrupt change caused by directly touching hard boundaries in traditional methods, greatly improving power point tracking accuracy and system operational stability.

[0101] Therefore, the method in this embodiment, by combining the dynamic construction of feasible domains, functional role division, multi-level progressive power allocation priority rules, and dynamic state variable balancing mechanism, achieves accurate characterization and refined coordinated control of the real operating boundary of the multi-element energy storage system, thereby improving the accuracy of energy storage system operation regulation, long-term continuous operation stability, and dynamic adjustment margin.

[0102] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. An operation and control method based on a unified characterization of the feasible domain of multi-element energy storage, characterized in that, Including the following steps: Step 1: Obtain the operating parameters of each energy storage unit, establish a single feasible domain based on the physical model and operating constraints of each energy storage unit and perform geometric characterization, and linearly aggregate the single feasible domains of each energy storage unit in the power dimension to construct a joint feasible domain of the multi-element energy storage system that characterizes the boundary of the overall charging and discharging regulation capability of the system. Step 2: Based on the different frequency band components in the system net power command, and combined with the response speed and continuous charging and discharging characteristics of each energy storage unit, the functional roles of each energy storage unit are divided to obtain a preliminary power allocation scheme. Step 3: Combining the functional roles and the margin weighting coefficients of the joint feasible region, formulate multi-level progressive power allocation priority rules, and redistribute the power deficit of the preliminary power allocation scheme to obtain the basic allocated power. Step 4: Based on the real-time state variables of the energy storage unit, construct a dynamic state variable balancing mechanism, calculate the deviation-driven dynamic priority factor, and dynamically correct the weight of the basic allocated power to obtain the pre-allocated power. Step 5: Verify whether the pre-allocated power meets the individual feasible domain of each energy storage unit. If it does, output the final power command for each energy storage unit. If it does not meet, truncate according to the boundary of the individual feasible domain and redistribute the power deficit, and execute the operation and control of the multi-energy storage system.

2. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 1, characterized in that, Step 1 involves obtaining the operating parameters of each energy storage unit, establishing a single-unit feasible region based on the physical model and operating constraints of each energy storage unit, and performing geometric characterization, including: Step 1.1: Obtain the operating parameters of each energy storage unit and establish the individual feasible region based on the physical model and operating constraints of each energy storage unit; wherein, for a single energy storage state-power plane, its individual feasible region at time t is... Represented as: in, For the first State variables for energy storage; This refers to the charging and discharging power. This is the upper limit for the safe operation of state variables. This is the lower bound for the safe operation of state variables; The lower boundary of power related to the state, The upper boundary of power is state-dependent; Step 1.2: The range of state variables of the energy storage unit is uniformly discretized into multiple sampling points; Step 1.3: For each state sampling point, numerical solutions are performed based on the physical model and operating constraints of the corresponding energy storage unit to obtain the upper limit of the maximum discharge power and the lower limit of the maximum charging power for each state. Step 1.4: Substitute the discrete power boundary sequence into the trapezoidal integral formula to calculate the feasible region area. : in, The number of sampling points. The sampling step size, State variables The range of values ​​for , For the first Power adjustment range at each sampling point; Step 1.5, sequence the upper boundary points and lower boundary point sequence Connecting the ends together forms a closed polygon, thus obtaining the geometric representation of the feasible region of the single entity.

3. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 2, characterized in that, The joint feasible domain of the multi-element energy storage system described in step 1 for: in, The total charging and discharging power of the energy storage system; For the first The feasible domain of a single energy storage system under the current state is defined by its upper boundary being the maximum discharge capacity of the system and its lower boundary being the maximum charging capacity of the system.

4. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 1, characterized in that, Step 2 includes: Step 2.1: Extract the intraday trend baseload component from the system net power command and allocate it to long-term energy storage; the intraday trend baseload component of the long-term energy storage is: in, This refers to the intraday trend base load component in the net power instruction; Step 2.2: Extract the power change of the system net power command in adjacent time periods as the slope compensation component, and allocate it to the fast response energy storage; the slope compensation component undertaken by the fast response energy storage is: in, This is the net power instruction for the current time period. This refers to the net power command from the previous time period; Step 2.3: Subtract the intraday trend base load component and the slope compensation component from the system net power command, and allocate the remaining residual component as a fluctuation smoothing component to the medium-time energy storage; the fluctuation smoothing component of the medium-time energy storage is: 。 5. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 1, characterized in that, Step 3 includes: Step 3.1: Project the preliminary power allocation scheme onto the joint feasible domain of the multi-element energy storage system and verify whether it meets the individual feasible domain constraints and power balance constraints of the energy storage unit. Step 3.2, calculate the margin weighting coefficient; wherein, the margin weighting coefficient The remaining usable energy, rated capacity, and economic efficiency weights of a single energy storage unit are all considered. in, For the first The remaining usable energy in the form of energy storage; For the first i Rated capacity of energy storage devices; Weighted by economic efficiency; for discharge demand: , For the first State of charge of energy storage For the first The state of charge limit for energy storage systems; regarding charging requirements: , For the first Upper limit of the state of charge of energy storage devices; Step 3.3: Based on the multi-level progressive power allocation priority rule, the power deficit exceeding the boundary of a single feasible region is distributed among all other energy storage units with adjustment margins according to their respective margin weighting coefficients to obtain the basic allocated power; wherein, the power deficit allocation formula is: in, This is due to a power deficit. For the initial allocation of power, This represents the actual output power. The upper and lower bounds of the feasible region. For the first Type of energy storage.

6. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 5, characterized in that, The multi-level progressive power allocation priority rules include: First priority criterion: Based on the functional roles of various energy storage units, allocate the control power to the energy storage units in the corresponding frequency bands to initially determine the power allocation range of each energy storage unit; Second priority criterion: Verify whether the power allocation range exceeds the feasible domain boundary of a single energy storage unit; redistribute the excess power deficit to other energy storage units with adjustment margins according to the margin weighting coefficient ratio. The third priority criterion: Provided that the first and second priority criteria are met, select energy storage units with higher overall efficiency to undertake the power tasks. (The overall efficiency is...) Defined as: in, For the first i The charging / discharging efficiency of energy storage devices For the first i Self-discharge loss rate of energy storage devices.

7. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 1, characterized in that, Step 4 includes: Step 4.1: Define the midpoint of the safe operating range of the State of Charge (SOC) of each energy storage unit as the reference center value, and calculate the individual SOC deviation of each energy storage unit and the system average SOC deviation. Step 4.2: Calculate the dynamic priority base factor so that energy storage units with individual SOC deviations greater than the system average SOC deviation receive a priority factor greater than 1, and energy storage units with individual SOC deviations less than the system average SOC deviation receive a priority factor less than 1, in order to achieve the objective function of SOC balance control. Step 4.3: Modify the dynamic priority base factor based on the charging and discharging direction of the current time period. In the discharging scenario, increase the discharge priority of energy storage with a state of charge (SOC) higher than the reference center value. In the charging scenario, increase the charging priority of energy storage with a SOC lower than the reference center value. Step 4.4: The modified dynamic priority factor is superimposed on the margin weighting coefficient to form a comprehensive allocation weight; Step 4.5: Dynamically adjust the basic allocation power based on the comprehensive allocation weight to obtain the pre-allocated power.

8. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 7, characterized in that, In step S1, the reference center value for: The single-unit SOC deviation for: The average SOC deviation of the system is: in, For the first The upper limit of SOC for energy storage-like devices For the first Lower limit of SOC for energy storage-like systems; For the first The state of charge of energy storage at time t. The amount of energy stored in the system; In step 4.2, the objective function of the SOC equalization control is: The dynamic priority base factor is: in, The total number of scheduling periods. It is a positive number; In step 4.3, the corrected dynamic priority factor is: in, For the current power demand, The margin weighting factor at time t; In step 4.4, the overall weight allocation is as follows: In step 4.5, the pre-allocated power is: In the formula, The average of the priority factors. This refers to the intraday trend base load component in the net power command.

9. The operation and control method based on unified characterization of the feasible domain of multi-element energy storage according to claim 7, characterized in that, Step 4 also includes: Half of the sum of squares of the normalized individual SOC deviations of each energy storage unit is selected as the Lyapunov function; The first derivative of the Lyapunov function is calculated, and the power allocation weight is adjusted by a dynamic priority factor to determine whether the first derivative of the Lyapunov function is always not greater than zero. If it is true, the system is asymptotically stable at the equilibrium point. The pre-allocated power can ensure that the energy storage SOC eventually converges to the reference center value during long-term operation.

10. An operation and control system based on a unified characterization of the feasible domain of multi-element energy storage, characterized in that, For performing the method as described in any one of claims 1-9, comprising: The feasible domain construction module is used to obtain the operating parameters of each energy storage unit, establish a single feasible domain based on the physical model and operating constraints of each energy storage unit and perform geometric characterization, and linearly aggregate the single feasible domains of each energy storage unit in the power dimension to construct a joint feasible domain of the multi-element energy storage system that characterizes the boundary of the overall charging and discharging regulation capability of the system. The preliminary allocation and role division module is used to divide the functional roles of each energy storage unit based on the different frequency band components in the system net power command, combined with the response speed and continuous charging and discharging characteristics of each energy storage unit, and obtain a preliminary power allocation scheme. The power deficit redistribution module is used to combine the functional roles and the margin weighting coefficients of the joint feasible region to formulate multi-level progressive power allocation priority rules, and to redistribute the power deficit of the preliminary power allocation scheme to obtain the basic allocated power. The dynamic balancing correction module is used to construct a dynamic state variable balancing mechanism based on the real-time state variables of the energy storage unit, calculate the deviation-driven dynamic priority factor, and dynamically correct the weight of the basic allocated power to obtain the pre-allocated power. The control command generation and execution module is used to verify whether the pre-allocated power meets the individual feasible domain of each energy storage unit. If it does, it outputs the final power command of each energy storage unit. If it does not meet, it truncates according to the boundary of the individual feasible domain and redistributes the power deficit, and executes the operation control of the multi-element energy storage system.

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