Base station backup battery aggregation method, system and medium based on partial dimensional enhancement

Abstract

The present invention discloses a method, system, and medium for aggregating base station backup batteries based on partial dimensional enhancement, which belongs to the field of electrical engineering. The method includes: constructing the original scheduling feasible domain of the base station backup battery and describing it as a high-dimensional polyhedron, and further constructing the basic high-dimensional polyhedron of the aggregator; using the inner approximate feasible domain corresponding to the scaled and translated basic high-dimensional polyhedron as a subset of the original scheduling feasible domain, and constraining the decision variables of the base station backup battery within the inner approximate feasible domain, and constructing an optimization problem with the goal of maximizing the net profit of the base station backup battery and the inner approximate feasible domain under marginal price guidance; solving the optimization problem to obtain the partial dimensional enhanced inner approximate feasible domain of the base station backup battery, and further calculating the aggregated feasible domain of each base station backup battery under the aggregator to dispatch each base station backup battery. The flexible adjustment capability of the base station backup battery is fully utilized to improve the economic efficiency of power system operation.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering, and more specifically, relates to a base station backup battery aggregation method, system and medium based on partial dimensional enhancement. Background Art

[0002] The large-scale access of converter-based equipment (such as wind power and photovoltaics) to the power grid has made it difficult to absorb large-scale renewable energy and reduced system inertia, leading to prominent frequency security issues. Therefore, new power systems urgently need to tap into more flexibility resources to ensure the economic and reliable operation of their systems. With the development of information and communication technology, a large number of 5G base stations have been built and put into use. Their backup batteries remain idle for long periods of time, representing a potential load-side flexibility resource. However, the single-unit capacity of 5G base station backup batteries is small and the number is large. Due to limited computing resources, centralized scheduling by power system operators is not feasible.

[0003] Previous studies have proposed adopting an aggregation-based hierarchical scheduling framework to achieve the scheduling of massive individual loads. However, existing aggregation methods are typically based on the maximum inner approximation method. That is, within the same aggregator, the original feasible region of each base station backup battery is approximated from the inside by scaling and translating a common underlying high-dimensional polyhedron. However, this method, based on a purely mathematical theory - Farkas' lemma, cannot distinguish the physical meaning and importance of the decision variables in each dimension of the base station backup battery. This can easily lead to a loss of flexibility, unable to fully utilize the flexible adjustment capabilities of the massive base station backup batteries, and affecting the economic efficiency of power system operation. Summary of the Invention

[0004] In response to the defects of the existing technology and the need for improvement, the present invention provides a base station backup battery aggregation method, system and medium based on partial dimensional enhancement, which aims to solve the technical problem of flexibility loss caused by base station backup battery aggregation under the traditional maximum inner approximation method, thereby making full use of the flexibility adjustment potential of the backup battery and improving the economy of the power system operation.

[0005] To achieve the above-mentioned objectives, according to one aspect of the present invention, a method for aggregating base station backup batteries based on partial dimensionality enhancement is provided, comprising: S1, constructing an original scheduling feasible domain according to the operating constraints of the base station backup battery, and describing the original scheduling feasible domain as a high-dimensional polyhedron; S2, averaging the parameters of the high-dimensional polyhedrons of each base station backup battery under the aggregator to construct a basic high-dimensional polyhedron of the aggregator, and scaling and translating the basic high-dimensional polyhedron; S3, taking the inner approximate feasible domain corresponding to the scaled and translated basic high-dimensional polyhedron as a subset of the original scheduling feasible domain and the decision variables of the base station backup battery being constrained to be within the inner approximate feasible domain, and constructing an optimization problem with the goal of maximizing the net profit of the base station backup battery and the inner approximate feasible domain under marginal price guidance; S4, solving the optimization problem to obtain the partially dimensionality enhanced inner approximate feasible domain of the base station backup battery, calculating the Minkowski sum of the partially dimensionality enhanced inner approximate feasible domains of all base station backup batteries under the aggregator, and obtaining the aggregated feasible domain of each base station backup battery under the aggregator to schedule each base station backup battery.

[0006] Furthermore, the operating constraints of the base station backup battery include: upper and lower limit constraints on charging and discharging power, state of charge change constraints, upper and lower limit constraints on state of charge, net discharge power constraints, inertial response backup capacity and primary frequency modulation response backup capacity constraints, inertial response power constraints, and primary frequency modulation response power constraints.

[0007] Furthermore, the basic high-dimensional polyhedron is:

[0008]

[0009]

[0010]

[0011] in, is the basic high-dimensional polyhedron of aggregator k, X is the column vector consisting of the decision variables of the base station backup battery, is a set of real number column vectors in high-dimensional space, for The result after averaging is for The result after averaging is are the matrix and column vector consisting of the corresponding coefficients of the operation constraints of the base station backup battery b, Θ k is the set of backup batteries of all base stations under aggregator k, |Θ k | is the number of backup batteries of base stations under aggregator k.

[0012] Furthermore, the optimization problem includes:

[0013]

[0014]

[0015]

[0016]

[0017] φ b ·s b =1

[0018]

[0019] Among them, s b , G b 、r b are the three auxiliary decision variables introduced, λ en ,λ RoCoF ,λ nad ,λ ss They are the energy price, RoCoF price, frequency lowest point price and quasi-steady-state frequency price in the marginal price respectively. are the charging and discharging power of the base station backup battery b in time period t, is the inertial response power of the base station backup battery b in time period t, are the lowest frequency support power and quasi-steady-state frequency support power that the base station backup battery b can provide during time period t, respectively. T represents the transpose of a vector, is the unit energy cost, 1 T represents the transpose of the unit column vector, is the unit inertia response reserve capacity cost, are the inertial response reserve capacity and primary frequency modulation response reserve capacity of the base station backup battery b in time period t, is the unit primary frequency regulation response reserve capacity cost, δ is the preset coefficient, φ b 、 are the scaling coefficient and translation coefficient for scaling and translating the basic high-dimensional polyhedron, respectively. are the matrix and column vector respectively consisting of the corresponding coefficients of the operation constraints of the base station backup battery b, for The result after averaging is for The result after averaging is is the column vector consisting of all decision variables under the operating constraints of the base station backup battery b.

[0020] Furthermore, between S3 and S4, the following steps are further included: using binary expansion and big M method to solve the φb ·s b =1 and Perform linearization processing; solve the optimization problem after linearization processing in S4.

[0021] Furthermore, φ b ·s b =1 and After linearization, it becomes:

[0022]

[0023]

[0024]

[0025] -Mβ b,n ≤α b,n ≤Mβ b,n

[0026] -M(1-β b,n )+s b ≤α b,n ≤s b +M(1-β b,n )

[0027] -Mβ b,n ≤γ b,n ≤Mβ b,n

[0028] -M(1-β b,n )+r b ≤γ b,n ≤r b +M(1-β b,n )

[0029] Among them, N BE is the number of binary digits, n is the binary variable index, Δφ b is the discretization step size, β b,n is a binary variable, α b,n , γ b,n are two auxiliary variables introduced, and M is a preset positive real number.

[0030] Furthermore, the partially dimensional enhanced inner approximate feasible region of the base station backup battery b is:

[0031]

[0032] in, is the partially dimensional enhanced inner approximate feasible region of the base station backup battery b, X is the column vector consisting of the decision variables of the base station backup battery, is a set of real number column vectors in high-dimensional space, The optimal scaling coefficient and the optimal translation coefficient are obtained by solving the optimization problem.

[0033] Furthermore, the aggregation feasible domain is:

[0034]

[0035] in, is the aggregated feasible region of the backup batteries of each base station under aggregator k, Θ k is the set of backup batteries of all base stations under aggregator k, is a column vector consisting of the aggregator k decision variables.

[0036] According to another aspect of the present invention, a base station backup battery aggregation system based on partial dimensional enhancement is provided, including: a processor; a memory storing a computer executable program, which, when executed by the processor, enables the processor to execute the base station backup battery aggregation method based on partial dimensional enhancement as described above.

[0037] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the base station backup battery aggregation method based on partial dimensionality enhancement as described above is implemented.

[0038] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects: providing a base station backup battery aggregation method based on partial dimensional enhancement, using marginal price signals to reflect the scarcity of energy, inertia and primary frequency regulation resources in the power system, and guiding the formation of an approximate feasible domain within the base station backup battery. Compared with the traditional maximum inner approximation aggregation method, it can give full play to the flexibility of the base station backup battery, match it with the flexibility requirements of the power system, and improve the economy of the power system operation; and experiments show that this method can effectively ensure the economy of the power system operation under the premise of ensuring the optimality of actual scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of a base station backup battery aggregation method based on partial dimensional enhancement provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a modified IEEE 30-node system provided in an embodiment of the present invention;

[0041] Figure 3 A schematic diagram of wind curtailment and base station backup battery state of charge changes in an IEEE 30-node system provided by an embodiment of the present invention;

[0042] Figure 4A schematic diagram of inertial response spare capacity deployment provided by an embodiment of the present invention;

[0043] Figure 5 A schematic diagram of primary frequency regulation response spare capacity deployment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0045] In the present invention, the terms "first", "second", etc. (if any) in the present invention and the drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0046] Figure 1 This is a flow chart of a method for aggregating base station backup batteries based on partial dimensional enhancement provided by an embodiment of the present invention. Figure 1 , combined with Figure 2-Figure 5 , the base station backup battery aggregation method based on partial dimensional enhancement in this embodiment is described in detail, and the method includes operations S1 to S4.

[0047] Before executing operation S1, parameters of backup batteries of each base station under the aggregator, dynamic frequency security indicators of the power system, and marginal prices generated by clearing of the power system operator are obtained in advance.

[0048] The parameters of the backup batteries of each base station under the aggregator include: charging power capacity Discharge power capacity State of charge (SOC) upper limit Minimum state of charge requirement in each time period Charge and discharge efficiency Droop coefficient Unit energy cost Unit inertia response reserve capacity cost and the unit primary frequency response reserve capacity cost

[0049] Power system dynamic frequency security indicators include: rate of change of frequency (RoCoF) threshold Frequency minimum point deviation threshold Quasi-steady-state frequency deviation threshold

[0050] The marginal prices generated by the power system operator's clearing include: energy price λ en 、RoCoF price λ RoCoF , the lowest frequency price λ nad , quasi-steady-state frequency price λ ss .

[0051] Operation S1 constructs an original scheduling feasible domain according to the operating constraints of the base station backup battery, and describes the original scheduling feasible domain as a high-dimensional polyhedron.

[0052] The operating constraints of the base station backup battery include: upper and lower limit constraints on charge and discharge power, state of charge change constraints, upper and lower limit constraints on state of charge, net discharge power constraints, inertia response backup capacity and primary frequency regulation response backup capacity constraints, inertia response power constraints, and primary frequency regulation response power constraints.

[0053] The original scheduling feasible domain for single base station backup batteries to provide energy, inertial response, and primary frequency regulation response auxiliary services is composed of the following constraints:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] Among them, formulas (1)-(2) are the upper and lower limit constraints of the charging and discharging power of the base station backup battery b in time period t; formula (3) is the charge state change constraint of the base station backup battery b; formula (4) is the upper and lower limit constraints of the charge state of the base station backup battery b; formula (5) is the net discharge power constraint of the base station backup battery b in time period t; formula (6) is the inertial response reserve capacity and primary frequency modulation response reserve capacity constraint of the base station backup battery b in time period t; formulas (7)-(8) are the inertial response power constraints of the base station backup battery b in time period t; formulas (9)-(12) are the primary frequency modulation response power constraints of the base station backup battery b in time period t. b is the base station backup battery index; t is the time period index; is the charge / discharge power of the base station backup battery b during time period t; is the charge state of the base station backup battery b in time period t; Δt is the duration of a single time period, for example, 1 hour; is the net charging power of the base station backup battery b in time period t; is the inertial response backup capacity / primary frequency modulation response backup capacity of the base station backup battery b in time period t; is the inertial response power of the base station backup battery b in time period t; is the virtual inertia of the base station backup battery b in time period t; It is the lowest frequency support power / quasi-steady-state frequency support power that the base station backup battery b can provide during time period t.

[0067] For simplicity, the original scheduling feasible region of the base station backup battery b consisting of constraints (1)-(12) is described as a high-dimensional polyhedron in the high-dimensional decision variable space.

[0068]

[0069] in, represents the column vector consisting of all decision variables of the base station backup battery b; T is the number of scheduling periods; They are respectively the matrix and column vector consisting of the corresponding coefficients of the operating constraints of the base station backup battery b.

[0070] Operation S2 is to average the parameters of the high-dimensional polyhedron of the backup batteries of each base station under the aggregator to construct a basic high-dimensional polyhedron of the aggregator, and scale and translate the basic high-dimensional polyhedron.

[0071] According to an embodiment of the present invention, the basic high-dimensional polyhedron of the aggregation quotient k is constructed for:

[0072]

[0073]

[0074]

[0075] Where X is a column vector consisting of the base station backup battery decision variables, is a set of real number column vectors in high-dimensional space, for The result after averaging is for The result after averaging, Θ k is the set of backup batteries of all base stations under aggregator k, |Θ k | is the number of backup batteries of base stations under aggregator k.

[0076] By aggregating the basic high-dimensional polyhedrons of k By performing appropriate scaling and translation, the internal approximation of the feasible region of each base station backup battery b can be achieved. The corresponding scaling coefficient and translation coefficient decision variables are φ b and

[0077] Operation S3 takes the inner approximate feasible domain corresponding to the scaled and translated basic high-dimensional polyhedron as a subset of the original scheduling feasible domain, and the decision variables of the base station backup battery are constrained to be within the inner approximate feasible domain. The net profit of the base station backup battery under the guidance of marginal price and the maximum inner approximate feasible domain are taken as the objectives to construct an optimization problem.

[0078] According to an embodiment of the present invention, the optimization problem initially constructed includes:

[0079]

[0080]

[0081]

[0082]

[0083] φ b ·s b =1 (16)

[0084]

[0085] Among them, s b , G b 、r b are the three auxiliary decision variables introduced, λ en ,λ RoCoF ,λ nad ,λ ssThey are energy price, RoCoF price, frequency lowest point price and quasi-steady-state frequency price in marginal price respectively. are the charging and discharging power of the base station backup battery b in time period t, is the inertial response power of the base station backup battery b in time period t, are the lowest frequency support power and quasi-steady-state frequency support power that the base station backup battery b can provide during time period t, respectively. T represents the transpose of a vector, is the unit energy cost, 1 T represents the transpose of the unit column vector, is the unit inertia response reserve capacity cost, are the inertial response reserve capacity and primary frequency modulation response reserve capacity of the base station backup battery b in time period t, is the unit primary frequency regulation response reserve capacity cost; δ is the preset coefficient; φ b 、 are the scaling coefficient and translation coefficient of the basic high-dimensional polyhedron, respectively. are the matrix and column vector respectively consisting of the corresponding coefficients of the operation constraints of the base station backup battery b, for The result after averaging is for The result after averaging is is the column vector consisting of all decision variables under the operating constraints of the base station backup battery b.

[0086] In the objective function of the optimization problem above, maximizing the net profit of the base station backup battery is the primary objective, and maximizing the size of the inner approximate feasible region is the secondary objective. δ is a positive number whose value should be such that the secondary objective is 2-3 orders of magnitude smaller than the primary objective.

[0087] Among the constraints of the above optimization problem, constraints (13)-(14) are derived based on Farkas’ lemma and are used to ensure the inner approximate feasible region. The original scheduling feasible region Constraint (15) is used to ensure that the decision variables of the base station backup battery b Approximate feasible region within ; constraints (16)-(17) are auxiliary constraints.

[0088] Since there are bilinear terms in constraints (16)-(17), before executing operation S4, the method further includes: linearizing constraints (16)-(17) using binary expansion and the big M method to convert them into the following linear constraints:

[0089]

[0090]

[0091]

[0092] -Mβ b,n ≤α b,n ≤Mβ b,n (twenty one)

[0093] -M(1-β b,n )+s b ≤α b,n ≤s b +M(1-β b,n ) (twenty two)

[0094] -Mβ b,n ≤γ b,n ≤Mβ b,n (twenty three)

[0095] -M(1-β b,n )+r b ≤γ b,n ≤r b +M(1-β b,n ) (twenty four)

[0096] Among them, N BE is the number of binary digits, n is the binary variable index, Δφ b is the discretization step size, β b,n is a binary variable, α b,n , γ b,n are two auxiliary variables introduced; M is a preset positive real number and is large enough.

[0097] To sum up, the final optimization problem can be expressed as:

[0098]

[0099] st(13)-(15),(18)-(24)

[0100] Operation S4, solve the optimization problem to obtain the partial dimensional enhanced inner approximate feasible domain of the base station backup battery, calculate the Minkowski sum of the partial dimensional enhanced inner approximate feasible domain of all base station backup batteries under the aggregation quotient, and obtain the aggregated feasible domain of each base station backup battery under the aggregation quotient to schedule each base station backup battery.

[0101] By solving the above final optimization problem, the optimal scaling coefficient and the optimal translation coefficient can be solved. Furthermore, the partial dimensional enhanced inner approximate feasible region of the base station backup battery b can be expressed as:

[0102]

[0103] in, is the partially dimensional enhanced inner approximate feasible region of the base station backup battery b, X is the column vector consisting of the decision variables of the base station backup battery, is a set of real number column vectors in high-dimensional space, The optimal scaling coefficient and optimal translation coefficient are obtained to solve the optimization problem.

[0104] By solving the partial dimensions of all base station backup batteries b under the aggregator k, the inner approximate feasible domain is enhanced. The Minkowski sum of can be used to solve the aggregation feasible region of the backup batteries of each base station under the aggregator k:

[0105]

[0106] in, is the aggregated feasible region of the backup batteries of each base station under aggregator k, Θ k is the set of backup batteries of all base stations under aggregator k, is a column vector consisting of the decision variables of aggregator k. Further, aggregator k aggregates the feasible domain Uploaded to the power system operator to solve the dispatch instructions for each device in the entire system.

[0107] for Figure 2 The power system shown in Figure 1 uses the base station backup battery aggregation based on partial dimension enhancement in the embodiment of the present invention (Case 2) and compares it with the theoretical optimal scheduling method without aggregation and using centralized solution (Case 1). The operating results are shown in Table 1. The comparison between wind curtailment and base station backup battery charge state is as follows: Figure 3 As shown in the figure, the deployment of inertial response reserve capacity is compared with Figure 4 As shown in the figure, the primary frequency regulation response reserve capacity is compared with Figure 5 shown.

[0108] Table 1

[0109]

[0110] Referring to Table 1, it can be seen that compared with Case 1 (the theoretically optimal solution) in which the base station backup batteries are not aggregated and a centralized solution is used, the total operating cost of Case 2, the base station backup battery aggregation method based on partial dimensional enhancement proposed in the embodiment of the present invention, is only 0.08% higher. Figure 3 、 Figure 4 、 Figure 5As can be seen, the battery state-of-charge utilization level, wind curtailment, inertial response reserve, and primary frequency regulation response reserve deployment are similar in Case 1 and Case 2, proving that the proposed method has good approximate optimality. In addition, considering that aggregated scheduling can address the problem of massive base station backup batteries participating in centralized scheduling, it is proven that the proposed method can effectively ensure the economic operation of the power system while ensuring the optimality of actual scheduling.

[0111] An embodiment of the present invention also provides a base station backup battery aggregation system based on partial dimensional enhancement, including: a processor; a memory storing a computer executable program, which, when executed by the processor, enables the processor to execute the above-mentioned base station backup battery aggregation method based on partial dimensional enhancement.

[0112] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the above-mentioned base station backup battery aggregation method based on partial dimensional enhancement is implemented.

[0113] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for aggregating base station backup batteries based on partial dimensional enhancement, characterized in that: include: S1, constructing its original scheduling feasible domain according to the operating constraints of the base station backup battery, and describing the original scheduling feasible domain as a high-dimensional polyhedron; S2, averaging the parameters of the high-dimensional polyhedrons of the backup batteries of each base station under the aggregator to construct a basic high-dimensional polyhedron of the aggregator, and scaling and translating the basic high-dimensional polyhedron; the basic high-dimensional polyhedron is: is the basic high-dimensional polyhedron of the aggregator k, is a column vector consisting of the base station backup battery decision variables, is a set of real number column vectors in high-dimensional space, for The result after averaging is for The result after averaging is 、 are the matrix and column vector respectively consisting of the corresponding coefficients of the operation constraints of the base station backup battery b, is the set of backup batteries of all base stations under aggregator k, is the number of backup batteries for base stations under aggregator k; S3, using the inner approximate feasible domain corresponding to the scaled and translated basic high-dimensional polyhedron as a subset of the original scheduling feasible domain and the decision variables of the base station backup battery being constrained to be within the inner approximate feasible domain, constructing an optimization problem with the goal of maximizing the net profit of the base station backup battery under marginal price guidance and the inner approximate feasible domain; the optimization problem includes: 、 、 are the three auxiliary decision variables introduced, 、 、 、 They are the energy price, RoCoF price, frequency lowest point price and quasi-steady-state frequency price in the marginal price respectively. 、 are the charging and discharging power of the base station backup battery b in time period t, is the inertial response power of the base station backup battery b in time period t, 、 are the lowest frequency support power and quasi-steady-state frequency support power that the base station backup battery b can provide during time period t, respectively. represents the transpose of a vector, is the unit energy cost, represents the transpose of the unit column vector, is the unit inertia response reserve capacity cost, 、 are the inertial response reserve capacity and primary frequency modulation response reserve capacity of the base station backup battery b in time period t, is the unit primary frequency regulation response reserve capacity cost, is the preset coefficient, 、 are the scaling coefficient and translation coefficient for scaling and translating the basic high-dimensional polyhedron, respectively. is the column vector consisting of all decision variables under the operating constraints of the base station backup battery b; S4, solving the optimization problem to obtain the partial dimensional enhanced inner approximate feasible domain of the base station backup battery, calculating the Minkowski sum of the partial dimensional enhanced inner approximate feasible domain of all base station backup batteries under the aggregation quotient, and obtaining the aggregated feasible domain of each base station backup battery under the aggregation quotient to schedule each base station backup battery.

2. The method for aggregating base station backup batteries based on partial dimensional enhancement according to claim 1, characterized in that: The operating constraints of the base station backup battery include: upper and lower limit constraints on charge and discharge power, state of charge change constraints, upper and lower limit constraints on state of charge, net discharge power constraints, inertia response backup capacity and primary frequency regulation response backup capacity constraints, inertia response power constraints, and primary frequency regulation response power constraints.

3. The base station backup battery aggregation method based on partial dimension enhancement according to claim 1, characterized in that: The step between S3 and S4 also includes: using binary expansion and big M method to solve the optimization problem and Perform linearization processing; In S4, the optimization problem after linearization is solved.

4. The method for aggregating base station backup batteries based on partial dimensional enhancement according to claim 3, characterized in that: and After linearization processing: in, is the number of binary digits, is the binary variable index, is the discretization step size, is a binary variable, 、 are two auxiliary variables introduced, is a preset positive real number.

5. The method for aggregating base station backup batteries based on partial dimensional enhancement according to claim 1, wherein: The partial dimensional enhanced inner approximate feasible region of the base station backup battery b is: in, For the partial dimensional enhanced inner approximate feasible region of the base station backup battery b, 、 The optimal scaling coefficient and the optimal translation coefficient are obtained by solving the optimization problem.

6. The method for aggregating base station backup batteries based on partial dimensional enhancement according to claim 5, characterized in that: The aggregation feasible domain is: in, is the aggregated feasible domain of backup batteries of each base station under aggregator k, is a column vector consisting of the aggregator k decision variables.

7. A base station backup battery aggregation system based on partial dimensional enhancement, characterized in that: include: processor; A memory storing a computer-executable program, wherein when the program is executed by the processor, the processor executes the base station backup battery aggregation method based on partial dimension enhancement according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the base station backup battery aggregation method based on partial dimension enhancement is implemented as described in any one of claims 1 to 6.

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