Power distribution network distributed energy storage aggregation regulation and control method based on sino polyhedron

Abstract

The invention discloses a power distribution network distributed energy storage aggregation regulation and control method based on a chinonol polyhedron, and the method comprises the steps: building a single operation model of a distributed energy storage resource, depicting a feasible region space of the distributed energy storage resource through employing a convex polyhedron, and obtaining the single power feasible region space of a plurality of devices of the distributed energy storage resource; carrying out internal approximation and Minkowski summation on single power feasible region spaces of the plurality of devices based on a chinonol polyhedron, and determining a power aggregation feasible region of the distributed energy storage resources; according to the power aggregation feasible region, a power distribution network optimization scheduling model containing aggregation distributed energy storage resources is established and solved, and a power distribution network optimization scheduling result in which the distributed energy storage resources participate is obtained; and obtaining a power regulation and control instruction of the distributed energy storage cluster according to the optimization scheduling result of the power distribution network containing the aggregated distributed energy storage resources, decomposing the regulation and control instruction in the distributed energy storage cluster by adopting a water injection algorithm, and determining the charging power and the discharging power of each energy storage in the distributed energy storage resources.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to a distributed energy storage aggregation control method for a distribution network based on Chino polyhedron. Background Art

[0002] With the advancement of the electric energy substitution strategy, the power load has been rising year by year, bringing major challenges to the safe and economical operation of the power system. Exploiting the distributed energy storage resources in the distribution network to participate in the optimal dispatching of the distribution network can delay power investment and is an effective way to improve the safe and economical operation of the power system. However, the flexible characteristic curve of the single energy storage device has the characteristics of periodic jump and small adjustable power. In contrast, the distributed energy storage resource group has the characteristics of considerable quantity, flexible dispatching mode and huge potential. Therefore, how to aggregate the adjustable capacity of distributed energy storage resources to participate in the optimal dispatching of the distribution network has become a technical problem that needs to be solved urgently.

[0003] However, in actual use, there is a problem: the capacity of individual distributed energy storage resources in the distribution network in the existing technology is small and cannot directly participate in grid regulation, resulting in the problem that its flexibility potential cannot be tapped. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a distributed energy storage aggregation control method for a distribution network based on Chino polyhedron.

[0005] According to one aspect of the present invention, a distributed energy storage aggregation control method for a distribution network based on Chino polyhedron is provided, comprising:

[0006] Establish a single-unit operation model of distributed energy storage resources and use convex polytopes to characterize its feasible domain space, and obtain the single-unit power feasible domain space of multiple devices of distributed energy storage resources;

[0007] Based on Chino polyhedron, the feasible domain space of the power of multiple devices is approximated internally and Minkowski summation is performed to determine the power aggregation feasible domain of distributed energy storage resources.

[0008] According to the power aggregation feasible domain, the distribution network optimization dispatching model containing aggregated distributed energy storage resources is established and solved, and the distribution network optimization dispatching result involving distributed energy storage resources is obtained;

[0009] According to the optimized dispatching results of the distribution network containing aggregated distributed energy storage resources, the power control instructions of the distributed energy storage cluster are obtained, and the control instructions are decomposed inside the distributed energy storage cluster using the water injection algorithm to determine the charging power and discharging power of each energy storage in the distributed energy storage resources.

[0010] According to another aspect of the present invention, a distributed energy storage aggregation control device for a distribution network based on Chino polyhedron is provided, comprising:

[0011] Establish a module for establishing a single-unit operation model of distributed energy storage resources and using convex polytopes to characterize its feasible domain space, thereby obtaining the single-unit power feasible domain space of multiple devices of distributed energy storage resources;

[0012] An aggregation module is used to perform inner approximation and Minkowski summation on the feasible power domain space of multiple devices based on Chino polyhedrons to determine the power aggregation feasible domain of distributed energy storage resources;

[0013] A solution module is used to establish and solve the distribution network optimization dispatching model containing aggregated distributed energy storage resources according to the power aggregation feasible domain, and obtain the distribution network optimization dispatching result involving distributed energy storage resources;

[0014] The acquisition module is used to obtain the power control instructions of the distributed energy storage cluster based on the optimized dispatching results of the distribution network containing aggregated distributed energy storage resources, and use the water injection algorithm to decompose the control instructions inside the distributed energy storage cluster to determine the charging power and discharging power of each energy storage in the distributed energy storage resources.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of the above aspects of the present invention.

[0017] Therefore, the present invention establishes a feasible domain of single-unit power regulation based on the single-unit operation model of distributed energy storage resources, and then characterizes the approximate feasible domain within it in the form of a Chino polyhedron and obtains the aggregated feasible domain of the distributed energy storage resource group to be aggregated through the Minkowski sum. According to the aggregation result of the feasible domain, with the goal of minimizing the operating cost, considering the operating constraints of various devices and the network topology constraints, an optimal scheduling model of the regional power grid containing aggregated distributed energy storage resources is established. According to the solution result of the scheduling model, the scheduling instructions of the distributed energy storage cluster are obtained, and the instructions are decomposed within the cluster through the water injection algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0019] Figure 1It is a flow chart of a distributed energy storage aggregation control method for a distribution network based on Chino polyhedron provided by an exemplary embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of quantifying the approximate similarity of a two-dimensional Chino polyhedron provided by an exemplary embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of an IEEE33 node provided by an exemplary embodiment of the present invention;

[0022] Figure 4 is a system optimization scheduling result diagram provided by an exemplary embodiment of the present invention;

[0023] Figure 5 It is a power decomposition result diagram of a distributed energy storage cluster provided by an exemplary embodiment of the present invention;

[0024] Figure 6 This is a graph showing SOC changes of a distributed energy storage cluster provided by an exemplary embodiment of the present invention;

[0025] Figure 7 It is a structural schematic diagram of a distributed energy storage aggregation control device for a distribution network based on Chino polyhedron provided by an exemplary embodiment of the present invention;

[0026] Figure 8 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0027] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0028] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0029] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0030] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0031] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0032] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0033] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0034] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0035] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0036] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0037] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0038] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0039] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0040] Exemplary Methods

[0041] Figure 1 1 is a flow chart of a distributed energy storage aggregation control method for a distribution network based on Chino polyhedron provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the distribution network distributed energy storage aggregation control method 100 based on Chino polyhedron includes the following steps:

[0042] Step 101, establishing a single-unit operation model of distributed energy storage resources and using convex polytopes to characterize its feasible domain space, to obtain a single-unit power feasible domain space of multiple devices of distributed energy storage resources;

[0043] Step 102, based on the Chino polyhedron, inner approximation is performed on the feasible power domain space of the individual devices and Minkowski summation is performed to determine the power aggregation feasible domain of the distributed energy storage resources;

[0044] Step 103, establishing and solving a distribution network optimization dispatching model including aggregated distributed energy storage resources according to the power aggregation feasible region, and obtaining a distribution network optimization dispatching result involving distributed energy storage resources;

[0045] Step 104, based on the distribution network optimization dispatch result containing aggregated distributed energy storage resources, obtain the power control instructions of the distributed energy storage cluster, use the water injection algorithm to decompose the control instructions inside the distributed energy storage cluster, and determine the charging power and discharging power of each energy storage in the distributed energy storage resources.

[0046] Specifically, the present invention provides a distributed energy storage aggregation control method for distribution networks based on Chino polyhedrons. First, a single-unit operation model of distributed energy storage resources is established and a convex polyhedron is used to characterize its feasible domain space. Secondly, a feasible domain approximation and efficient aggregation method for distributed energy storage resources based on Chino polyhedrons is proposed. Then, the aggregated cluster of distributed energy storage resources is applied to the optimization and control of the distribution network, and a power decomposition method based on a water injection algorithm is proposed. The present invention solves the problem in the prior art that the single-unit capacity of distributed energy storage resources in the distribution network is small and cannot directly participate in the control of the power grid, resulting in the problem that its flexibility potential cannot be tapped. The specific implementation steps are as follows:

[0047] Step S100: Establishing a single-unit operation model of distributed energy storage resources and using convex polytopes to characterize its feasible domain space, thereby obtaining a single-unit power feasible domain space of multiple devices of distributed energy storage resources;

[0048] Step S200: performing inner approximation on the power feasible domain space of multiple devices based on Chino polyhedron and calculating the Minkowski sum to determine the power aggregation feasible domain of the distributed energy storage resource cluster;

[0049] Step S300: establishing a distribution network optimization dispatching model containing aggregated distributed energy storage resources according to the power aggregation feasible domain of the distributed energy storage resource cluster, and solving it to obtain a distribution network optimization dispatching result;

[0050] Step S400: According to the optimized dispatch result of the distribution network containing aggregated distributed energy storage resources, the power control instructions of the distributed energy storage cluster are obtained, and the instructions are decomposed within the cluster using a water injection algorithm.

[0051] In a specific embodiment, the feasible domain of a single distributed energy storage device is first characterized to obtain a feasible domain model that reflects the overall response capability of a distributed energy storage device cluster.

[0052] The charging and discharging of energy storage equipment can be freely controlled and is not affected by environmental factors. Its charging and discharging model is constructed as follows:

[0053] Formula 1:

[0054] Formula 2:

[0055] Formula 3:

[0056] Formula 4:

[0057] Formula 5:

[0058] In the formula, and are the energy storage levels of the energy storage device r at time t and t-1 respectively; and Respectively charge efficiency coefficient and discharge efficiency coefficient; and are the charging and discharging power of the energy storage device r at time t respectively; and are the minimum and maximum values ​​of the charging power of the energy storage device r, respectively; and are the minimum and maximum values ​​of the discharge power of the energy storage device r, respectively; and are the minimum capacity and maximum capacity of the energy storage device r, respectively; and are the energy storage levels of energy storage device r at the beginning and end of the scheduling period, respectively.

[0059] Based on the above modeling, the operation model of each energy storage unit can be obtained. In order to obtain the mapping of the operation model of the energy storage unit to the feasible domain, the operation model of the rth energy storage is represented in the form of a convex polyhedron (that is, the feasible domain space of a single device) as follows:

[0060] Formula 6:

[0061] In the formula, and are the flexible feasible domain of the r-th energy storage and the power matrix in the dispatch period; and The inequality coefficient matrix and column vector representing the feasible region of the r-th energy storage; represents the power constraint, represents the energy boundary constraint; E is the unit matrix, By charge and discharge efficiency and Decide, Determined by the power boundary of the rth energy storage, The energy upper and lower boundaries of the rth energy storage and Sure.

[0062] Furthermore, step S200 includes: performing inner approximation on multiple single-cell power regulation feasible regions based on Chino polyhedrons and calculating the Minkowski sum to determine the power aggregation feasible region of the distributed energy storage resource cluster.

[0063] Specifically, the Minkowski sum is an addition applicable to Euclidean space, and its physical essence is the expansion set of multiple spaces. By using the Minkowski sum method to sum the feasible domains of single-cell power regulation of energy storage clusters, the aggregated feasible domain of the aggregated energy storage resource group can be determined.

[0064] Furthermore, step S200 further includes:

[0065] Step S210: Establishing a Chino polyhedron model for distributed energy storage resources;

[0066] Step S220: Approximate the feasible domain of the distributed energy storage resources based on the Chino polyhedron model and calculate the Minkowski sum;

[0067] Among them, the Chino polyhedron is a polyhedron with a special mathematical expression, which is expressed as:

[0068] Formula 7:

[0069] Formula 8: G = [g (1) ,g (1) ,…,g (M) ]∈R N×M ;

[0070] Formula 9: ||g (m) ||2=1 m=1,2,…,M;

[0071] Formula 10: β=[β1,β1,…,β M ] T ;

[0072] Where: N is the spatial dimension; c is the coordinate of the center point of the polyhedron; G is the generator matrix, which consists of M generator vectors g (m) It means that the polyhedron extends from the center point in all directions; β is the extension length of the generator in all directions, which is determined by the maximum extension length Restriction; ||·||2 represents 2-norm operation.

[0073] The generator G represents the extension of the Chino polyhedron in all directions. In the problem of the Chino polyhedron approximating the feasible domain of distributed energy storage, the generator matrix G is considered as a known quantity, and the corresponding generator matrices are designed for the power constraints and power constraints of the distributed energy storage equipment as shown below:

[0074] Formula 11:

[0075] Formula 12:

[0076] Where: Formula 11 is the first N generators, corresponding to the N power constraints of the energy storage device; Formula 12 contains N-1 generators, corresponding to the N-1 power constraints of the load device; thus, the generator matrix is ​​G∈R N×(2N-1) .

[0077] For the Chino polyhedra with the same generator matrix G, the Minkowski sum process of these Chino polyhedra is simplified because their extension directions are consistent:

[0078] Formula 13:

[0079] Formula 14:

[0080] Formula 15:

[0081] Where: Z agg is the total Chino polyhedron after polymerization; is the XOR operation; N c is the number of aggregated feasible domains, that is, the number of aggregated resources; c agg is the coordinate of the center point of the aggregated Chino polyhedron; c s is the coordinate of the sth center point; is the total extension length of the aggregated Chino polyhedron in the direction of the mth generator; β m,s is the extension length of the s-th Chino polyhedron in the direction of the m-th generator before aggregation.

[0082] Step S220: Characterizing the feasible domain of distributed energy storage resources based on Chino polyhedron;

[0083] (1) Objective function

[0084] The feasible domain approximation index Λ is defined to measure the difference between the approximate feasible domain of a single energy storage resource and the original feasible domain. Taking two-dimensional space as an example, the similarity between the approximate feasible domain and the original feasible domain is as follows: Figure 2 As shown in the figure, Ω represents the original feasible domain of single energy storage; Z represents the approximate feasible domain of single energy storage. Select several unit normal vectors to represent a certain direction of the feasible domain, and calculate the ratio of the width of Z to Ω in each unit normal vector direction to measure the degree of approximation.

[0085] Formula 16:

[0086] Where N f is the number of unit normal vectors; Δ Ω,k and Δ Z,k are the widths of the original feasible domain (convex polyhedron) and the approximate feasible domain (Qino polyhedron) in the direction of the kth unit normal vector. The energy storage feasible domain consists of power boundary and energy boundary constraints. Each normal vector can describe the upper and lower bounds of a power or energy boundary constraint. Therefore, in the N-dimensional space, we select 2N-1 normal vectors.

[0087] The closer Λ is to 1, the higher the similarity between the Chino polyhedron and the original feasible domain. For the width Δ of the Chino polyhedron in all unit normal vector directionsZ According to mathematical derivation, we can know that:

[0088] Formula 17:

[0089] Where: F is N f The matrix composed of normal vectors. Substituting formula 17 into formula 16, the objective function is:

[0090] Formula 18:

[0091] (2) Calculation of parameters related to the original feasible domain approximation index

[0092] Assume Δ Ω is the maximum distance between two tangent planes in the original feasible domain in the direction of each unit normal vector, which can be solved by optimization method. The original feasible domain Ω is represented by a half-space polyhedron as follows:

[0093] Formula 19: Ω = {x∈R N :Ax≤b};

[0094] For a unit normal vector f k =[a1,a2,…,a N ], and its corresponding N-dimensional hyperplane is:

[0095] Formula 20: a1x1+a2x2+…+a N a N +b l =0;

[0096] In the formula, x1, x2, …, x N is the position in each dimension; b l Is a constant.

[0097] Assumptions:

[0098] Formula 21: X P =[x 1p ,x 2p ,…,x Np ];

[0099] Formula 22: X Q =[x 1q ,x 2q ,…,x Nq ];

[0100] Where, X P and X Q They represent a point outside the hyperplane and a point inside the hyperplane respectively.

[0101] The unit normal vector f k Δ onΩ,k , which can be solved according to the following optimization model:

[0102] Formula 23:

[0103] (3) Constraints

[0104] Since the model adopts the idea of ​​inner approximation, the constraint condition is that the approximate Chino polyhedron is inside the feasible domain of the original device. According to mathematical derivation, the constraint condition of the optimization problem is equivalently processed, that is:

[0105] Formula 24:

[0106] So far, the feasible domain optimization model of the equipment based on Chino polyhedron is:

[0107] Formula 25:

[0108] After completing the feasible domain approximation, the feasible domain of the aggregated cluster can be obtained according to Formula 13-Formula 15:

[0109] Formula 26:

[0110] Furthermore, step S300 includes:

[0111] According to the power aggregation feasible domain of distributed energy storage clusters, with the goal of minimizing operating costs, a regional power grid optimization scheduling model is established by considering the operating constraints of photovoltaic devices and network topology constraints.

[0112] Specifically, step S300 is as follows: based on the aggregation results of the feasible domain of the distributed energy storage cluster, with the goal of minimizing the operating cost, considering the operating constraints of the photovoltaic devices and the network topology constraints, a distribution network optimization scheduling model containing aggregated distributed energy storage resources is established and solved.

[0113] (1) Objective function

[0114] Formula 27:

[0115] In the formula, C tot is the total operating cost of the system; C grid,t is the electricity price in period t; P grid,t is the power purchased during period t; C PV is the unit power generation cost of the photovoltaic unit; C ESS The regulation costs of distributed energy storage are respectively.

[0116] (2) Distribution network flow constraints

[0117] This paper adopts the DC power flow constraints of the distribution network and converts the power flow constraints into MISOCP constraints using the second-order cone relaxation method:

[0118] Formula 28:

[0119] Formula 29:

[0120] Formula 30:

[0121] Formula 31:

[0122] Formula 32:

[0123] Formula 33:

[0124] Formula 34:

[0125] Formula 35:

[0126] In the formula, represents the power of the line with p as the inflow node; q represents the outflow node; represents the power of the line with q as the outflow node; represents the resistance of the line with q as the outflow node; and are the branches starting and ending from node n in DN; Ω node and Ω line are the sets of nodes and branches in DN respectively; is the output power of node n at time t; and are the voltage at node n, the current, power, and resistance of branch L at time t, respectively; represents the square of the current in line L; represents the square of the voltage at node n; and They are and The upper limit of Represents the square of the voltage at node i.

[0127] (3) Constraints on photovoltaic units

[0128] Formula 36: P PVtot,t =η PV I t ;

[0129] Formula 37: P PVtot,t =PPV,t +P PVa,t ;

[0130] Formula 38: |P PV,t -P PV,t-1 |≤ΔP PVcl ;

[0131] Where P PVtot,t is the total output power of the photovoltaic power station at time t; I t is the light intensity at time t; η PV is the photoelectric conversion efficiency of the photovoltaic power station; P PV,t and P PVa,t Indicates the power supplied by the photovoltaic unit to the load and the power abandoned; ΔP PVcl Indicates the upper limit of the unit's climbing rate.

[0132] Furthermore, step S400 includes:

[0133] The power instructions of the distributed energy storage cluster are obtained according to the optimized dispatch results of the distribution network containing aggregated distributed energy storage resources, and the instructions are decomposed within the cluster using a water injection algorithm.

[0134] By solving the above optimization scheduling model, we can get the charging and discharging instructions of the distributed energy storage cluster in each period, and use the water injection algorithm to distribute the instructions. In the decomposed power control of the energy storage cluster, if the aggregator receives a charging command Then solve the following problem to get the charging power of each energy storage

[0135] Formula 39:

[0136] Formula 40:

[0137] In the formula, E r is the capacity of the rth energy storage; is the aggregate charging power at time t; The objective function formula 39 strives to maintain a consistent storage level in all energy storage, and the constraint formula 40 indicates that partial response is allowed, but excessive response is prohibited. The solution to the problem can be obtained as follows:

[0138] Formula 40:

[0139] In the formula, The rth energy storage is The increase in SOC during charging time Δt; Indicates that the rth energy storage is at rated power The increase in SOC during charging time Δt; represents the SOC of the rth energy storage at the end of period t-1. Written as is a piecewise linear function of 1 / v, then:

[0140] Formula 41:

[0141] Formula 41 has a unique root v * , which can be found by Algorithm 1.

[0142] If the aggregator receives a discharge command Then solve the following problem to get the discharge power of each EV:

[0143] Formula 42:

[0144] Formula 43:

[0145] In the formula, is the aggregate discharge power at time t. The solution to the problem can be obtained from the following formula:

[0146] Formula 44:

[0147] at this time The rth energy storage is The SOC reduced by the discharge time Δt; Indicates that the rth energy storage is at rated power The SOC reduced by the discharge time Δt; Written as is a piecewise linear function of 1 / v, then:

[0148] Formula 45:

[0149] Similarly, the decomposition of discharge power can also be solved by Algorithm 1.

[0150]

[0151]

[0152] So far, the distributed energy storage aggregation control model of the distribution network based on Chino polyhedron has been established and can be applied to actual examples. In order to verify the effectiveness of the proposed method, the example is set as follows:

[0153] Example: Figure 3As shown in the figure, taking the IEEE 33-node distribution network as an example, the proposed distribution network optimization operation model considering distributed ESS aggregation is studied. Table 1 shows the parameters of distributed ESS equipment.

[0154] Table 1 Parameters of various equipment in the system

[0155]

[0156] Figure 4 In order to consider the optimized operation results of the distribution network with distributed ESS resource aggregation, the electricity price is low from 0:00 to 6:00, and the electric load is all supplied by purchasing electricity from the upper power grid except for the period from 5:00 to 6:00, and more electricity will be purchased to charge the ESS; from 6:00 to 10:00, the photovoltaic output gradually increases, and the electric load is jointly supplied by purchasing electricity and photovoltaics. Since the electricity price is high during this period, ESS discharges to supply the load during this period to reduce the operating cost; from 10:00 to 17:00, photovoltaic power generation is large, and the load is all supplied by photovoltaics at this time, and the excess power is used to charge the ESS; from 17:00 to 20:00, the photovoltaic output gradually decreases, and the electricity price is high at this time, and the load is supplied by photovoltaics, purchasing electricity and ESS discharge. By aggregating distributed ESS to participate in distribution network scheduling, its low charging and high discharging characteristics can reduce the electricity cost by 27,650 yuan, and the consumption rate increases by 1.88%, which improves the economy and flexibility of the power grid.

[0157] Figure 5 The power decomposition results of six representative ESS devices are shown in Figure 2. Figure 6 The SOC variation trend of six representative ESS devices during the scheduling period is shown in Figure 1. It can be seen that from 00:00 to 06:00, the ESS cluster is all charging instructions. ESS1 is in the charging state during this period because of its lowest initial SOC. ESS3-ESS5 has a higher initial SOC and is not charged during this period. ESS2 and ESS6 with moderate initial SOC are only charged during the 0:00-06:00 period. From 06:00 to 07:00, it is a discharge instruction. Contrary to charging, those with higher initial SOC will be allocated more discharge power. At the end of the 7th period, the SOC of all ESSs has reached a consistent level. This power decomposition method can coordinate all ESSs in the cluster to make their SOC as close as possible, so as to retain the total power flexibility to a greater extent.

[0158] Therefore, the present invention establishes a feasible domain of single-unit power regulation based on the single-unit operation model of distributed energy storage resources, and then characterizes the approximate feasible domain within it in the form of a Chino polyhedron and obtains the aggregated feasible domain of the distributed energy storage resource group to be aggregated through the Minkowski sum. According to the aggregation result of the feasible domain, with the goal of minimizing the operating cost, considering the operating constraints of various devices and the network topology constraints, an optimal scheduling model of the regional power grid containing aggregated distributed energy storage resources is established. According to the solution result of the scheduling model, the scheduling instructions of the distributed energy storage cluster are obtained, and the instructions are decomposed within the cluster through the water injection algorithm.

[0159] In one example of the present invention, the power regulation feasible domains of multiple single distributed energy storages are summed to determine the power aggregation feasible domain of the resource cluster, including: performing inner approximation on multiple single power regulation feasible domains based on Chino polyhedrons and calculating the Minkowski sum to determine the power aggregation feasible domain of the distributed energy storage resource cluster. The Minkowski sum is an addition applicable to Euclidean space, and its physical essence is the expansion set of multiple spaces. The Minkowski sum method is used to sum the single power regulation feasible domains of distributed energy storage resources to obtain the aggregation feasible domain of the distributed energy storage resource group to be aggregated. The present invention establishes a distribution network optimization scheduling model containing aggregated distributed energy storage resources based on the power aggregation feasible domain of the distributed energy storage resource cluster, including: based on the power aggregation feasible domain of the distributed energy storage cluster, with the goal of minimizing the operating cost of the power system, considering the operating constraints and network topology constraints of the new energy power generation device; establishing a regional power grid optimization scheduling model based on the operating constraints and network topology constraints.

[0160] After adopting the technical solution of the present invention, the following technical effects can be achieved:

[0161] (1) Determine the power aggregation feasible domain of distributed energy storage resource clusters, and use resource groups to participate in the optimal dispatch of distribution networks, which will result in a larger number of resources, more flexible dispatch methods, and greater potential;

[0162] (2) The present invention can aggregate the adjustable capacity of distributed energy storage resources to participate in the optimal dispatching of distribution networks, delay power investment, and improve the safety and economy of power system operation.

[0163] (3) The power decomposition method of the present invention can make the SOC of the energy storage in the cluster tend to be consistent while satisfying the power instruction, thereby retaining the total power flexibility to a greater extent. This method does not require optimization solution, reducing the installation cost of the control equipment.

[0164] Exemplary Devices

[0165] Figure 7 1 is a schematic diagram of a distributed energy storage aggregation control device for a distribution network based on a Chino polyhedron provided by an exemplary embodiment of the present invention. Figure 7 As shown, the apparatus 700 includes:

[0166] Establishing module 710, for establishing a single-unit operation model of distributed energy storage resources and using convex polytopes to characterize its feasible domain space, to obtain a single-unit power feasible domain space of multiple devices of distributed energy storage resources;

[0167] Aggregation module 720, used for performing inner approximation and Minkowski summation on the power feasible domain space of multiple devices based on Chino polyhedron, and determining the power aggregation feasible domain of distributed energy storage resources;

[0168] A solution module 730 is used to establish and solve a distribution network optimization dispatching model containing aggregated distributed energy storage resources according to the power aggregation feasible domain, and obtain a distribution network optimization dispatching result involving distributed energy storage resources;

[0169] The acquisition module 740 is used to obtain the power control instructions of the distributed energy storage cluster according to the distribution network optimization scheduling result containing the aggregated distributed energy storage resources, and use the water injection algorithm to decompose the control instructions inside the distributed energy storage cluster to determine the charging power and discharging power of each energy storage in the distributed energy storage resources.

[0170] Optionally, the expression of the single-unit operation model of the distributed energy storage resource is:

[0171]

[0172]

[0173] In the formula, and are the energy storage levels of the energy storage device r at time t and t-1 respectively; and Respectively charge efficiency coefficient and discharge efficiency coefficient; and are the charging and discharging power of the energy storage device r at time t respectively; and are the minimum and maximum values ​​of the charging power of the energy storage device r, respectively; and are the minimum and maximum values ​​of the discharge power of the energy storage device r, respectively; and are the minimum capacity and maximum capacity of the energy storage device r, respectively; and are the energy storage levels of energy storage device r at the beginning and end of the scheduling period, respectively.

[0174] Optionally, the expression of the feasible domain space of single power is:

[0175]

[0176] In the formula, and are the flexible feasible domain of the r-th energy storage and the power matrix in the dispatch period; and The inequality coefficient matrix and column vector representing the feasible region of the r-th energy storage; represents the power constraint, represents the energy boundary constraint; E is the unit matrix, By charge and discharge efficiency and Decide, Determined by the power boundary of the rth energy storage, The energy upper and lower boundaries of the rth energy storage and Sure.

[0177] Optionally, based on the Chino polyhedron, the power feasible domain space of multiple devices is inner-approximated and Minkowski summation is performed to determine the power aggregation feasible domain of the distributed energy storage resources, including:

[0178] Establish a Chino polyhedron model for distributed energy storage resources;

[0179] Based on the Chino polyhedron model, the feasible domain space of the single power of multiple devices is inner approximated and Minkowski summation is performed to determine the power aggregation feasible domain of distributed energy storage resources.

[0180] Alternatively, the expression for the Qino polyhedron model is:

[0181]

[0182] G=[g (1) ,g (1) ,…,g (M) ]∈R N×M

[0183] ||g (m) ||2=1 m=1,2,…,M

[0184] β=[β1,β1,…,β M ] T

[0185] Where N is the spatial dimension; c is the coordinate of the center point of the polyhedron; G is the generator matrix, which consists of M generator vectors g (m) It means that the polyhedron extends from the center point in all directions; β is the extension length of the generator in all directions, which is determined by the maximum extension length Restriction; ||·||2 represents the 2-norm operation, where the generator matrix expression is:

[0186]

[0187] In the formula, g (m) are the first N generators, corresponding to the N power constraints of the energy storage device; g (N+m) Contains N-1 generators, corresponding to N-1 power constraints of load equipment; generator matrix G∈R N×(2N-1) ;

[0188] The Minkowski sum solution process of the Chino polyhedron model is:

[0189]

[0190] Where: Z agg is the total Chino polyhedron after polymerization; is the XOR operation; N c is the number of aggregated feasible domains, that is, the number of aggregated resources; c agg is the coordinate of the center point of the aggregated Chino polyhedron; c s is the coordinate of the sth center point; is the total extension length of the aggregated Chino polyhedron in the direction of the mth generator; β m,s is the extension length of the s-th Chino polyhedron in the direction of the m-th generator before aggregation.

[0191] Optionally, based on the Chino polyhedron model, the power feasible domain space of multiple devices is inner-approximated and Minkowski summed to determine the power aggregation feasible domain of the distributed energy storage resources, including:

[0192] Based on the Chino polyhedron model, the feasible domain space of the single power of multiple devices is internally approximated to obtain the approximate feasible domain of the single energy storage of multiple devices;

[0193] According to the feasible domain space of single power and the approximate feasible domain of single energy storage, the objective function of Minkowski sum is determined;

[0194] Calculate the parameters related to the approximation index of the feasible domain space of single power;

[0195] According to the objective function, the parameters related to the approximation index and the preset constraints, an approximate feasible domain optimization model is obtained, and the approximate feasible domain of each energy storage unit in the form of a Chino polyhedron is obtained by solving the approximate feasible domain optimization model;

[0196] The Minkowski sum of the approximate feasible domain is performed according to the Minkowski sum solution process of the Chino polyhedron, and the aggregated power aggregation feasible domain is obtained.

[0197] Optionally, the objective function of the distribution network optimization dispatching model is:

[0198]

[0199] In the formula, C tot is the total operating cost of the system; C grid,t is the electricity price in period t; P grid,t is the power purchased during period t; C PV is the unit power generation cost of the photovoltaic unit; C ESS The regulation cost of distributed energy storage respectively;

[0200] The power flow constraints of the distribution network optimization dispatching model are:

[0201]

[0202] In the formula, represents the power of the line with p as the inflow node; q represents the outflow node; represents the power of the line with q as the outflow node; represents the resistance of the line with q as the outflow node; and are the branches starting and ending from node n in DN; Ω node and Ω line are the sets of nodes and branches in DN respectively; is the output power of node n at time t; and are the voltage at node n, the current, power, and resistance of branch L at time t, respectively; represents the square of the current in line L; represents the square of the voltage at node n; and They are and The upper limit of represents the square of the voltage at node i;

[0203] The constraints of the photovoltaic units in the distribution network optimization dispatch model are:

[0204] P PVtot,t =η PV I t

[0205] P PVtot,t =P PV,t +P PVa,t

[0206] |P PV,t -P PV,t-1 |≤ΔP PVcl

[0207] Where P PVtot,t is the total output power of the photovoltaic power station at time t; It is the light intensity at time t; η PV is the photoelectric conversion efficiency of the photovoltaic power station; P PV,t and P PVa,t Indicates the power supplied by the photovoltaic unit to the load and the power abandoned; ΔP PVcl Indicates the upper limit of the unit's climbing rate.

[0208] Optionally, a water injection algorithm is used to decompose the control instructions within the distributed energy storage cluster to determine the charging power and discharging power of each energy storage in the distributed energy storage resource, including:

[0209] The water injection algorithm is used to distribute and solve the charging commands in the control instructions according to the preset charging power solution model to obtain the charging power of each energy storage;

[0210] The water injection algorithm is used to distribute and solve the discharge commands in the control instructions according to the preset discharge power solution model to obtain the discharge power of each energy storage.

[0211] Optionally, the objective function expression of the charging power solution model is:

[0212]

[0213] The constraint expression of the charging power solution model is:

[0214]

[0215] In the formula, E r is the capacity of the rth energy storage; is the aggregate charging power at time t;

[0216]

[0217] The solution expression of the charging power solution model is:

[0218]

[0219] In the formula, The rth energy storage is The increase in SOC during charging time Δt; Indicates that the rth energy storage is at rated power The increase in SOC during charging time Δt; represents the SOC of the rth energy storage at the end of period t-1; Written as is a piecewise linear function of 1 / v,

[0220] The objective function expression of the discharge power solution model is:

[0221]

[0222] The constraint expression of the discharge power solution model is:

[0223]

[0224] In the formula, is the aggregate discharge power at time t;

[0225] The solution expression of the discharge power solution model is:

[0226]

[0227] In the formula, The rth energy storage is The SOC reduced by the discharge time Δt; Indicates that the rth energy storage is at rated power The SOC reduced by the discharge time Δt; Written as is a piecewise linear function of 1 / v, then:

[0228] Exemplary Electronic Devices

[0229] Figure 8 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 8 As shown, the electronic device 80 includes one or more processors 81 and a memory 82 .

[0230] The processor 81 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0231] The memory 82 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 81 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 83 and an output device 84, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0232] In addition, the input device 83 may also include, for example, a keyboard, a mouse, etc.

[0233] The output device 84 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0234] Of course, to simplify, Figure 8 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application conditions.

[0235] Exemplary computer program products and computer-readable storage media

[0236] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0237] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0238] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0239] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0240] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0241] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0242] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0243] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0244] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0245] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A distributed energy storage aggregation control method for distribution network based on Chino polyhedron, characterized in that: include: Establish a single-unit operation model of distributed energy storage resources and use convex polytopes to characterize its feasible domain space, so as to obtain the single-unit power feasible domain space of multiple devices of the distributed energy storage resources; Based on Chino polyhedron, inner approximation is performed on the feasible domain space of the power of multiple devices and Minkowski summation is performed to determine the power aggregation feasible domain of the distributed energy storage resource; Establishing and solving a distribution network optimization dispatching model containing aggregated distributed energy storage resources according to the power aggregation feasible domain, and obtaining a distribution network optimization dispatching result involving the distributed energy storage resources; According to the optimized dispatching result of the distribution network containing aggregated distributed energy storage resources, the power control instructions of the distributed energy storage cluster are obtained, and the control instructions are decomposed within the distributed energy storage cluster using a water injection algorithm to determine the charging power and discharging power of each energy storage in the distributed energy storage resources.

2. The method according to claim 1, characterized in that The expression of the single-unit operation model of the distributed energy storage resource is: In the formula, and are the energy storage levels of the energy storage device r at time t and t-1 respectively; and Respectively charge efficiency coefficient and discharge efficiency coefficient; and are the charging and discharging power of the energy storage device r at time t respectively; and are the minimum and maximum values ​​of the charging power of the energy storage device r, respectively; and are the minimum and maximum values ​​of the discharge power of the energy storage device r, respectively; and are the minimum capacity and maximum capacity of the energy storage device r, respectively; and are the energy storage levels of energy storage device r at the beginning and end of the scheduling period, respectively.

3. The method according to claim 1, characterized in that The expression of the feasible domain space of the single power is: In the formula, and are the flexible feasible domain of the r-th energy storage and the power matrix in the dispatch period; and The inequality coefficient matrix and column vector representing the feasible region of the r-th energy storage; represents the power constraint, represents the energy boundary constraint; E is the unit matrix, By charge and discharge efficiency and Decide, Determined by the power boundary of the rth energy storage, The energy upper and lower boundaries of the rth energy storage and Sure.

4. The method according to claim 1, characterized in that: Based on Chino polyhedron, the feasible power domain space of the single power of multiple devices is inner approximated and Minkowski summation is performed to determine the power aggregation feasible domain of the distributed energy storage resource, including: Establish a Chino polyhedron model for distributed energy storage resources; Based on the Chino polyhedron model, the power feasible domain space of the individual devices is inner-approximated and Minkowski summed to determine the power aggregation feasible domain of the distributed energy storage resources.

5. The method according to claim 4, characterized in that The expression of the Chino polyhedron model is: G=[g (1) ,g (1) ,…,g (M) ]∈R N×M ||g (m) ||2=1m=1,2,…,M β＝[β1,β1,…,β M ] T Where N is the spatial dimension; c is the coordinate of the center point of the polyhedron; G is the generator matrix, which consists of M generator vectors g (m) It means that the polyhedron extends from the center point in all directions; β is the extension length of the generator in all directions, which is determined by the maximum extension length Restriction; ||·||2 represents the 2-norm operation, where the generator matrix expression is: In the formula, g (m) are the first N generators, corresponding to the N power constraints of the energy storage device; g (N+m) Contains N-1 generators, corresponding to N-1 power constraints of load equipment; generator matrix G∈R N×(2N-1) ; The Minkowski sum solution process of the Chino polyhedron model is: Where: Z agg is the total Chino polyhedron after polymerization; is the XOR operation; N c is the number of aggregated feasible domains, that is, the number of aggregated resources; c agg is the coordinate of the center point of the aggregated Chino polyhedron; c s is the coordinate of the sth center point; is the total extension length of the aggregated Chino polyhedron in the direction of the mth generator; β m,s is the extension length of the s-th Chino polyhedron in the direction of the m-th generator before aggregation.

6. The method according to claim 5, characterized in that Based on the Chino polyhedron model, the power feasible domain space of multiple devices is internally approximated and Minkowski summed to determine the power aggregation feasible domain of the distributed energy storage resources, including: Based on the Chino polyhedron model, an inner approximation is performed on the feasible region space of the power of multiple devices, so as to obtain the approximate feasible region of the energy storage of multiple devices; Determining the objective function of the Minkowski sum according to the monomer power feasible domain space and the monomer energy storage approximate feasible domain; Calculating parameters related to the approximation index of the feasible domain space of the single power; According to the objective function, the parameters related to the approximation index and the preset constraints, an approximate feasible domain optimization model is obtained, and the approximate feasible domain of each energy storage unit in the form of a Chino polyhedron is obtained by solving the approximate feasible domain optimization model; The Minkowski sum is performed on the approximate feasible region according to the Minkowski sum solution process of the Chino polyhedron to obtain the aggregated power aggregation feasible region.

7. The method according to claim 1, characterized in that The objective function of the distribution network optimization dispatching model is: In the formula, C tot is the total operating cost of the system; C grid,t is the electricity price in period t; P grid,t is the power purchased during period t; C PV is the unit power generation cost of the photovoltaic unit; C ESS The regulation cost of distributed energy storage respectively; The power flow constraint of the distribution network optimization dispatching model is: In the formula, represents the power of the line with p as the inflow node; q represents the outflow node; represents the power of the line with q as the outflow node; represents the resistance of the line with q as the outflow node; and are the branches starting and ending from node n in DN; Ω node and Ω line are the sets of nodes and branches in DN respectively; is the output power of node n at time t; and are the voltage at node n, the current, power, and resistance of branch L at time t, respectively; represents the square of the current in line L; represents the square of the voltage at node n; and They are and The upper limit of represents the square of the voltage at node i; The photovoltaic unit constraints of the distribution network optimization dispatch model are: P PVtot,t =the PV I t P PVtot,t =P PV,t +P PVa,t |P PV,t -P PV,t-1 |≤ΔP PVcl Where P PVtot,t is the total output power of the photovoltaic power station at time t; I t is the light intensity at time t; η PV is the photoelectric conversion efficiency of the photovoltaic power station; P PV,t and P PVa,t Indicates the power supplied by the photovoltaic unit to the load and the power abandoned; ΔP PVcl Indicates the upper limit of the unit's climbing rate.

8. The method according to claim 1, characterized in that The control instructions are decomposed within the distributed energy storage cluster using a water injection algorithm to determine the charging power and discharging power of each energy storage in the distributed energy storage resource, including: A water injection algorithm is used to distribute and solve the charging commands in the control instructions according to a preset charging power solution model to obtain the charging power of each energy storage; The water injection algorithm is used to distribute and solve the discharge commands in the control instructions according to a preset discharge power solution model to obtain the discharge power of each energy storage.

9. The method according to claim 8, characterized in that The objective function expression of the charging power solution model is: The constraint expression of the charging power solution model is: In the formula, E r is the capacity of the rth energy storage; is the aggregate charging power at time t; The solution expression of the charging power solution model is: In the formula, The rth energy storage is The increase in SOC during charging time Δt; Indicates that the rth energy storage is at rated power The increase in SOC during charging time Δt; represents the SOC of the rth energy storage at the end of period t-1; Written as is a piecewise linear function of 1 / v, The objective function expression of the discharge power solution model is: The constraint expression of the discharge power solution model is: In the formula, is the aggregate discharge power at time t; The solution expression of the discharge power solution model is: In the formula, The rth energy storage is The SOC reduced by the discharge time Δt; Indicates that the rth energy storage is at rated power The SOC reduced by the discharge time Δt; Written as is a piecewise linear function of 1 / v, then:

10. A distributed energy storage aggregation control device for distribution network based on Chino polyhedron, characterized in that: include: Establish a module for establishing a single-unit operation model of distributed energy storage resources and using convex polytopes to characterize its feasible domain space, so as to obtain the single-unit power feasible domain space of multiple devices of the distributed energy storage resources; An aggregation module, used for performing inner approximation and Minkowski summation on the power feasible domain space of multiple devices based on Chino polyhedron, to determine the power aggregation feasible domain of the distributed energy storage resources; A solution module is used to establish and solve a distribution network optimization dispatching model containing aggregated distributed energy storage resources according to the power aggregation feasible domain, and obtain a distribution network optimization dispatching result involving the distributed energy storage resources; The acquisition module is used to obtain the power control instructions of the distributed energy storage cluster according to the optimization scheduling results of the distribution network containing the aggregated distributed energy storage resources, and use the water injection algorithm to decompose the control instructions inside the distributed energy storage cluster to determine the charging power and discharging power of each energy storage in the distributed energy storage resources.

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