CN115395539A

CN115395539A - Shared energy storage operation control method considering customized power service

Info

Publication number: CN115395539A
Application number: CN202210962690.3A
Authority: CN
Inventors: 方珺; 裴志刚; 陈晓宇; 高捷; 陈佳明; 唐志琼; 乔艳; 徐洋超; 周斌; 张津; 孔世炜; 尤俊杰; 范江鹏
Original assignee: Shaoxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Current assignee: Shaoxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date: 2022-08-11
Filing date: 2022-08-11
Publication date: 2022-11-25

Abstract

The invention provides a shared energy storage operation control method considering customized power service, which comprises the following steps: establishing a comprehensive performance index, and dividing shared energy storage resources and users into a plurality of load storage clusters by combining a community discovery algorithm; respectively carrying out node characteristic analysis on each load and reservoir group, and carrying out layered processing on shared energy storage resources and users by combining a k-means clustering algorithm; taking the users in each layer as a whole, and carrying out energy storage demand analysis by using the net effect of the whole energy utilization; respectively constructing a shared energy storage cost model, and solving the shared energy storage cost model under the constraint of an energy storage demand analysis result; and constructing a centralized optimization scheduling model, substituting the energy utilization net effect analyzed by the energy storage demand and the solved shared energy storage cost, applying a layered distributed scheduling optimization algorithm to solve the centralized optimization scheduling model, and allocating shared energy storage resources to the users according to the solution result. The invention can provide customized power service, realize economic operation of shared energy storage and improve the accuracy of active and reactive power distribution of the shared energy storage.

Description

Shared energy storage operation control method considering customized power service

Technical Field

The invention belongs to the technical field of power systems, and particularly relates to a shared energy storage operation control method considering customized power services.

Background

With the continuous progress of the energy power industry technology, the energy internet becomes an important development direction of the future power grid development. In the technology related to the energy internet, distributed energy storage operated in combination with renewable energy sources is an important development direction of the energy internet in the future. Unlike large-scale centralized energy storage installed investingly on the power supply side and the power grid, distributed energy storage is usually installed on the user side for storing excess electric energy generated by renewable energy sources of users or for reducing electricity charges by helping user load curve peak shifting. However, the cost of energy storage is still high, it is difficult to have general economy, and the wide application of distributed energy storage is limited.

The shared energy storage is a shared energy storage technology established based on the existing power grid, and a centralized energy storage facility or aggregated distributed energy storage resources are comprehensively utilized, so that a user can use the shared energy storage resources formed by the centralized or distributed energy storage facilities at any time and any place as required, the energy storage use cost of the user is reduced, and the investment recovery period of energy storage is shortened.

At present, research on shared energy storage is mainly focused at home and abroad on the aspects of operation control, transaction mode, capacity allocation and investment benefit, wherein the operation control on the shared energy storage only relates to an active charge-discharge control strategy interacted with a user, for example, a control strategy based on a principal-subordinate game theory and a control strategy based on a Lyapunov optimization theory, so that the power service requirement of the user is met, the four-quadrant power regulation control capability of the energy storage is neglected, customized power service cannot be provided for the user with special requirement on the power quality, and the user experience of the shared energy storage operation control is further reduced.

Disclosure of Invention

In order to solve the problem that the operation control of shared energy storage in the prior art cannot provide customized power service for users with different requirements, the invention provides a shared energy storage operation control method considering the customized power service.

The invention provides a shared energy storage operation control method considering customized power service, which comprises the following steps:

s1: establishing comprehensive performance indexes of shared energy storage resources and users at nodes where power grids are located, and dividing the shared energy storage resources and the users into a plurality of load storage clusters by combining a community discovery algorithm;

s2: respectively carrying out node characteristic analysis on each load and reservoir group, and carrying out layered processing on shared energy storage resources and users by combining a k-means clustering algorithm;

s3: taking the users in each layer as a whole, and carrying out energy storage demand analysis by using the net effect of the whole energy utilization;

s4: respectively constructing a shared energy storage cost model of each load storage cluster, and solving the shared energy storage cost model under the constraint of an energy storage demand analysis result;

s5: and constructing a centralized optimization scheduling model, substituting the energy utilization net effect analyzed by the energy storage demand and the solved shared energy storage cost, applying a layered distributed scheduling optimization algorithm to solve the centralized optimization scheduling model, and allocating shared energy storage resources to the users according to the solution result.

In this embodiment, the shared energy storage resource includes energy type energy storage elements such as a lithium iron phosphate battery, an all-vanadium redox flow battery, a sodium-sulfur battery, and a lead-acid battery, and power type energy storage elements such as an electrochemical super capacitor and a superconducting magnetic energy storage.

The shared energy storage operation control method provided by the invention can be based on a shared energy storage and distributed park user dynamic partitioning method of a community discovery algorithm, divides shared energy storage resources and distributed park users in the whole area into a plurality of internal tightly-connected and external non-interfering load storage clusters, and carries out layered processing on the users and the shared energy storage according to user behaviors and energy storage characteristics. Aiming at each load storage cluster, establishing a shared energy storage active and reactive power coordination optimization control method based on model predictive control based on the four-quadrant power regulation characteristic of energy storage, and making an active and reactive power distribution plan of shared energy storage resources according to the energy-saving service and the customized power service requirements of park users with the aim of minimizing the shared energy storage cost. Further considering comprehensive benefits of shared energy storage aggregators, energy storage users and distributed energy storage resources and requirements of differentiated user demand response privacy protection, a shared energy storage layered distributed system operation framework considering differentiated group user distributed demand response is provided, a shared energy storage layered distributed optimization scheduling model is established, shared energy storage resources are fully utilized, and social benefits are maximized.

Optionally, the comprehensive performance index includes an electrical coupling degree, a spatial geographic position, and an energy storage demand matching degree;

wherein the electrical coupling degree is an electrical distance between each node;

the spatial geographic position is the Euclidean distance between nodes in the geographic space;

the energy storage requirement matching degree is the amount of unbalance of power requirements of shared energy storage resources.

Optionally, the S1 includes:

s11: acquiring shared energy storage resources and nodes where users are located in a power grid, and constructing local comprehensive performance indexes and global comprehensive performance indexes according to the comprehensive performance indexes of all the nodes;

s12: initializing each node into an independent load storage cluster, and randomly selecting one node to sequentially move to the load storage clusters where other nodes are located;

s13: respectively calculating the increment of the local optimization index after the selected nodes are added into each load storage cluster, and dividing the selected nodes into the load storage clusters with the maximum local optimization index increment;

s14: and re-selecting new nodes and repeating S12-S13 until the global optimization index of all the divided load-storage clusters reaches the maximum, thereby obtaining the optimal division result of the load-storage clusters.

Optionally, the S11 includes:

respectively calculating local comprehensive performance indexes gamma based on the comprehensive performance indexes of all nodes in each load storage cluster _z Comprises the following steps:

wherein, tau _C 、τ _V 、τ _Q Is a weight factor of the index, and is,

the modularity index is further obtained by the electric coupling index and the space geographic position index,

representing the electrical distance between node m and node n,

representing the euclidean distance of node m and node n in geographic space,

for the amount of active power demand imbalance of the load storage cluster z,

the amount of reactive power demand unbalance of the load storage cluster z is obtained;

based on the comprehensive performance indexes of all nodes in the load storage cluster, calculating a global comprehensive performance index gamma as follows:

wherein z is _num Is the total number of charged and stored clusters.

Optionally, the S2 includes:

and taking the loss characteristic of the stored energy, the transient response characteristic index and the user behavior prediction index as k-means clustering evaluation indexes, carrying out k-means clustering on the shared stored energy and the park users in each charge storage cluster, and layering the user groups with differentiated power consumption behaviors and the shared stored energy with differentiated loss characteristic and transient response characteristic according to clustering results.

Optionally, the S3 includes:

taking users with similar energy consumption behaviors in the same node in each layer as a whole user u _i Based on u _i The net utility function of (2) and the establishment of the energy-saving service demand prediction model is as follows:

wherein the content of the first and second substances,

for u on node n in the cluster z _i At T _m The sum of the net effect in a day,

for u on node n in the charged reservoir group z _i Can be used as a function of the satisfaction degree,

for u on node n in the charged reservoir group z _i The cost of the amount of electricity in the battery,

ρ _de for u on node n in the charged reservoir group z _i The maximum demand at two electricity prices translates into cost,

to u on node n in the load-store cluster z _i The cost of the service of providing the shared energy storage,

for u on node n in the load-store cluster z _i The power quality governs the service cost, T _m To optimize the cycle.

And solving the energy-saving service demand prediction model under the condition of meeting the power balance constraint, and acquiring the charge and discharge power demand of the user on the shared energy storage resource according to the solving result.

For user u _i Maximum active demand of, ρ _de Converting the unit maximum demand price to the converted price at each moment, and defaulting the value to 0 when the user does not adopt two power generation prices to measure the electricity consumption; ρ is a unit of a gradient _ps,b (t)、ρ _ps,s (t) the price of buying and selling unit electricity to the upper-level power grid;

the power of purchasing and selling electricity from the superior power grid for the user is non-negative; rho _se,c (t)、ρ _se,d (t) charging and discharging service prices for the user to use the shared energy storage and energy saving service;

the charging and discharging power of the shared energy storage and energy saving service is used for users, and the value of the charging and discharging power is approximately the upper limit of the day

And lower limit

The range is changed, and the total charge and discharge amount of the shared energy storage in each user day is 0; t is t _cp Responding to demand time for customer's power generation service for different response timesThe voltage deviation and the voltage sag governance service share the stored energy to give different customized power service prices rho _pq (t _cp )；

Reactive power generated by the shared energy storage meeting the requirement for response speed;

the relaxation variables additionally added for calculating the maximum demand of the user are constantly non-negative numbers; eta _SHES,c And η _SHES,d And the charge and discharge loss coefficient negotiated with the user for the shared energy storage aggregator.

Under the energy-saving service scene of the shared energy storage, the user responds to the change of the time-of-use electricity price and the photovoltaic output of the user by adjusting the self load strategy and changing the shared energy storage using strategy, and the process follows the following power balance constraint:

in the formula (I), the compound is shown in the specification,

for user load, the load can be divided into adjustable loads according to load characteristics

Time-shiftable loading

And a fixed load

And providing photovoltaic power generation power for users. The photovoltaic generating power is limited within the maximum generating power at the moment, and the maximum generating power of the photovoltaic can be predicted by the following submodels:

in the formula (I), the compound is shown in the specification,

for user u _i The maximum value of the photovoltaic power generation of the user is related to the intensity of the solar radiation in the moment;

conversion efficiency for converting solar radiation into electrical energy for a photovoltaic panel;

area of the photovoltaic panel for the user; tau is _s Seasonal factors of solar radiation; tau. _w A weather effect factor that is solar radiation; tau. _p1 、τ _p2 A predicted correction factor for solar radiation; b ₁ 、b ₂ 、b ₃ 、b ₄ Fitting coefficients for solar radiation.

The reactive power required to be compensated by the user in the customized power service is obtained by calculating the voltage of the public node where the user is located, and based on the reactive power compensation submodel, a customized power service demand prediction model is established as follows:

wherein the content of the first and second substances,

is a charge and reserveU on node n in group z _i Transformer ratio, V, from the incoming line of the common bus _z,n (t) is the voltage of a common bus where a node n in the load storage cluster z is located, the voltage of the common bus is calculated through a distribution network linearization power flow sub-model,

for u on node n in the charged reservoir group z _i The voltage of (a) is set to be,

and

respectively a common bus to u _i The line resistance and the reactance of (a),

and

is u _i The active and reactive loads of the load are controlled,

solving the customized power service demand prediction model, and obtaining the reactive power compensation demand of the user on the shared energy storage resource according to the solving result;

and taking the charging and discharging power demand and the reactive power demand as the result of energy storage demand analysis.

Optionally, the respectively constructing a shared energy storage cost model of each energy storage cluster includes:

establishing an objective function as a shared energy storage cost model by taking the minimization of the shared energy storage cost as a target, wherein the objective function is as follows:

wherein, the first and the second end of the pipe are connected with each other,C _SHES,z for sharing the total cost of operation within the energy storage day within the charge-storage group z,

for the total life loss cost of the shared energy storage resource within the charge-storage cluster z at time t,

for the real-time response cost of the shared energy storage resource within the charge-storage cluster z at time t,

for the day-ahead response cost of the shared energy storage resource within the load-reservoir cluster z at time t,

for sharing the energy storage resource and the electricity purchasing cost of the power grid at the moment t in the charge storage group z, c _mt For a fixed operating maintenance cost per time,

for the service benefit of sharing energy storage resources in the load storage group z at the moment T, T _m To optimize the cycle.

Optionally, the solving the shared energy storage cost model by combining the result of the energy storage demand analysis includes:

determining power balance constraint, energy storage electric quantity constraint, energy storage four-quadrant operation constraint and energy storage charging and discharging constraint in a charge and storage group, combining the result of energy storage demand analysis as constraint conditions together, and solving a shared energy storage cost model under the condition of meeting the constraint conditions;

wherein the intra-load-pool group power balance constraint comprises: the total charge-discharge power of all node users in the charge storage cluster is equal to the total charge-discharge power of shared energy storage resources in the charge storage cluster, and meanwhile, the reactive power demand in the energy storage demand of each user is equal to the sum of the reactive powers of the converters sharing the energy storage resources in the charge storage cluster:

the energy storage capacity constraint comprises: for the self-built energy storage unit, the sum of the energy storage charge and discharge amounts within one day is equal to 0; for the energy storage unit responding to the day ahead, the sum of the energy storage charge and discharge amount in the allowed use period is equal to 0; for the real-time response energy storage unit, the sum of the energy storage charge and discharge amounts in the emergency call time period is equal to 0; meanwhile, the stored electric quantity of all the shared energy storage resources does not exceed the preset allowable electric quantity;

the energy storage four quadrant operation constraints include: the active power of the shared energy storage resource and the apparent power formed by the reactive power of the converter in the shared energy storage resource are within the allowable capacity of the converter;

the energy storage charging and discharging constraint comprises: the charging and discharging power of the shared energy storage resource is within a preset allowable limit range, and the same shared energy storage resource cannot be in a charging state and a discharging state at the same time.

Optionally, the S5 includes:

target function F for constructing centralized optimization scheduling model _total Comprises the following steps:

wherein, F _total The sum of the net utility of users, the operation cost of a shared energy storage aggregator, the benefit of a distributed energy storage resource provider and the ecological environmental protection benefit in a plurality of load storage clusters;

for u on node n in the cluster z _i The sum of net effects of;

for the whole user u on the node n in the load-and-store group z _i Environmental protection benefit generated by improving photovoltaic consumption rate to increase photovoltaic utilization rate in each time period in the day

The sum is represented quantitatively; c _SHES,z Sharing the total operation cost in the energy storage day for the charge storage group z; f _r,z For distributed energy storage day-ahead response to the benefit of the resource, F _re,z Responding to the benefit of the resource in real time for distributed energy storage; tau is _u A net utility weight coefficient for the user; tau is _pv A weight coefficient which is an ecological environmental benefit; tau is _SHES A weight coefficient for the shared energy storage aggregator operating cost; tau is _r Weighting coefficients for distributed energy storage day-ahead response resource benefits; tau is _re A weight coefficient for the benefit of the distributed energy storage real-time response resource; z _lb A load storage cluster set served by a shared energy storage aggregator; n is a radical of _z For a set of nodes belonging to the load store cluster z,

the method comprises the steps of collecting power utilization behaviors of users after users are layered in a charge storage group z; u shape _z,n,su The energy consumption behaviors are classified into su user clusters on the node n, su is a user energy consumption behavior type number, each su represents a class of user energy consumption behaviors, and users with the same energy consumption behavior have the same energy consumption satisfaction function;

f is to be _total Decomposition into upper-layer shared energy storage sub-problem G _SHES,z And underlying user sub-problems

Introducing a Lagrange operator lambda, and solving an augmented Lagrange function of the node n structure in the cluster z as follows:

wherein x is _n For all the set of optimization variables contained in the underlying user sub-problem,

the information of the interaction of the energy storage aggregators is shared to the upper layer for the users of the node n,

representing the active load set of the user of node n,

respectively representing the electricity purchasing set and the electricity selling set of the electric energy transaction between the user of the node n and the power grid,

an active output power set representing the household's photovoltaic;

y is the set of all optimization variables contained in the upper-layer shared energy storage sub-problem,

auxiliary decision variable vectors corresponding to the energy storage aggregators are shared for the upper layer,

respectively representing energy storage unit charging and discharging power sets of the shared energy storage aggregation quotient of the node n,

respectively representing the charging power and the discharging power of the shared energy storage aggregator of the node n in transaction with the power grid,

respectively representing the charging state and the discharging state, alpha, of the shared energy storage aggregator of the node n _re，b 、

Respectively representing a distributed energy storage calling strategy set, theta is a penalty coefficient, and theta is>0；U _n Representing a set of users, B _z Represent a sum ofA shared energy storage aggregation quotient set;

and the iteration step based on the ADMM algorithm applies a layered distributed scheduling optimization algorithm, the upper-layer shared energy storage subproblem and the lower-layer user subproblem are solved based on an augmented Lagrange function, the interactive information of the user to the upper-layer shared energy storage aggregator and the auxiliary decision corresponding to the upper-layer shared energy storage aggregator are called according to the solving result, and the shared energy storage resources are distributed.

Optionally, the shared energy storage resource includes an energy type energy storage element and a power type energy storage element.

The technical scheme provided by the invention has the following beneficial effects:

(1) The technical scheme provided by the invention is characterized in that a dynamic hierarchical model of shared energy storage and users based on a community discovery algorithm and self-adaptive clustering is established, a local comprehensive index and a global comprehensive performance index of cluster division are established according to an electrical coupling index, a spatial geographic position index and a storage load demand matching index, and the community discovery algorithm based on modularity is applied to perform cluster division on the shared energy storage and the garden users with the aim of maximizing the global comprehensive performance index; according to the power consumption behavior index of the user, the loss characteristic and the transient response characteristic index of the stored energy, the distributed park users and the shared energy storage resources in the cluster are processed in a layered mode by using a self-adaptive k-means clustering algorithm, a user group with differentiated power consumption behavior and a set of the shared energy storage resources with differentiated transient response characteristics are obtained, accordingly, customized power service can be provided for users with special requirements on the quality of the electric energy, the active and reactive requirements of the users are reasonably distributed to each shared energy storage unit, the economic operation of the shared energy storage is achieved, and the active and reactive distribution accuracy of the shared energy storage is improved.

(2) According to the technical scheme provided by the invention, differentiated user demand response privacy protection demands during multi-cluster shared energy storage optimization scheduling are further considered, a shared energy storage layered distributed system operation framework is constructed, a centralized scheduling model is converted into an upper and lower layer one-to-many cooperative layered distributed optimization scheduling model by using an alternating direction multiplier method and mutual iterative computation is carried out, so that the independence and privacy of differentiated group user main bodies are ensured, and meanwhile, the efficient and accurate solution of the scheduling scheme with maximized benefit is realized.

Drawings

In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.

Fig. 1 is a schematic flowchart of a shared energy storage operation control method considering a customized power service according to an embodiment of the present invention;

FIG. 2 is a flow diagram illustrating a dynamic partitioning and layering process for sharing energy storage resources and users

Fig. 3 is a diagram of a shared energy storage hierarchical distributed operation framework according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.

It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of three of A, B, C is comprised, "comprises A, B and/or C" means that any 1 or any 2 or 3 of the three of A, B, C is comprised.

It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.

As used herein, "if" can be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on context.

The technical means of the present invention will be described in detail with reference to specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.

Example (c);

as shown in fig. 1, the present embodiment provides a method for controlling shared energy storage operation in consideration of customized power service, including:

s2: respectively carrying out node characteristic analysis on each load and storage cluster, and carrying out hierarchical processing on shared energy storage resources and users by combining a k-means clustering algorithm;

s5: and constructing a centralized optimization scheduling model, substituting the energy utilization net effect analyzed by the energy storage requirement and the solved shared energy storage cost, solving the centralized optimization scheduling model, and allocating shared energy storage resources to the users according to the solution result.

The invention aims to overcome the defects of the prior art and provides a distributed park customized power technology based on shared energy storage. A shared energy storage and distributed park user dynamic partitioning method based on a community discovery algorithm is provided, shared energy storage resources and distributed park users in the whole area are divided into a plurality of load storage clusters which are closely connected inside and do not interfere with each other outside, and the users and the shared energy storage are respectively processed in a layered mode according to user behaviors and energy storage characteristics. Aiming at each load and storage cluster, establishing a shared energy storage active and reactive power coordination optimization control method based on model predictive control based on the four-quadrant power regulation characteristic of energy storage, and making an active and reactive power distribution plan of shared energy storage resources according to the energy-saving service and the customized power service requirements of users in a park with the aim of minimizing the shared energy storage cost. Further considering comprehensive benefits of shared energy storage aggregators, energy storage users and distributed energy storage resources and requirements of differentiated user demand response privacy protection, a shared energy storage layered distributed system operation framework considering differentiated group user distributed demand response is provided, a shared energy storage layered distributed optimization scheduling model is built, shared energy storage resources are fully utilized, and social benefits are maximized.

In this embodiment, the comprehensive performance index includes an electrical coupling degree, a spatial geographic location, and an energy storage requirement matching degree;

the electrical coupling degree is an electrical distance between nodes, and is used for measuring the mutual influence degree of electrical quantities between nodes where distributed garden users and shared energy storage resources are located in a network, a distribution network is divided into modules, the load flow analysis complexity of a subsequent sub-distribution network is simplified, the electrical distance between the nodes is used as an electrical coupling degree index in the embodiment, and the specific calculation process is as follows:

by using a linear Newton-Raphson power flow equation, the sensitivity matrix of the obtained voltage to active power and reactive power is as follows:

in the formula, H, N, K, L is four sub-matrices of Jacobian matrix J of the power flow equation, S _VP 、S _VQ The m-th row and n-th column of the sensitivity matrix are respectively active and inactive _VP.mn 、S _VQ.mn And the unit change of the active power and the reactive power of the node n is represented by the change value of the voltage of the node m. The electrical active sensitivity index and the electrical reactive sensitivity index are respectively expressed as:

in the formula, sensitivity index

The ratio of the voltage change value of the node m to the voltage change value of the node n when the reactive power of the node m changes is shown, and the larger the value of the ratio is, the smaller the influence of the node n on the node m is, namely, the farther the distance between the two nodes is. Considering that the relationship between two nodes is not only of itselfAnd also to other nodes in the network. Setting the comprehensive electrical distance between the node n and the node m

Comprises the following steps:

in the formula (I), the compound is shown in the specification,

is the active electrical distance with respect to nodes n, m, wherein

The maximum active electrical distance for all nodes;

is a reactive electrical distance with respect to the nodes n, m, wherein

The maximum reactive electrical distance for all nodes; tau is _VP Is the active electrical distance weight; tau is _VQ Is the reactive electrical distance weight.

The spatial geographic position is the Euclidean distance between nodes in the geographic space and is used for describing the geographic spatial correlation degree of distributed park users and shared energy storage resources, the power waveform similarity of distributed energy sources close to the geographic position is high, the approach of the users on the spatial geographic position is convenient for uniform prediction of the distributed energy sources on the user side, the approach of the users on the spatial geographic position is convenient for uniform acquisition of energy storage information, and real-time transmission of user demand data and timely response of shared energy storage services are facilitated. The patent uses the Euclidean distance of the geographic space as the index of the spatial geographic position, namely

In the formula (I), the compound is shown in the specification,

for the purpose of the spatial geographical distance,

is the actual longitude position of the node m,

is the actual latitudinal position of the node m,

the maximum and minimum longitude positions of all nodes of the distribution network,

the maximum and minimum latitude positions of all nodes of the distribution network are obtained.

The energy storage requirement matching degree is the amount of power requirement unbalance of the shared energy storage resources, and the shared energy storage resources in the cluster can meet active and reactive requirements of users as much as possible. According to the minimum limit value of the energy storage active power negotiated by the shared energy storage aggregator and the distributed park users in the day, the unbalance amount of the energy storage in any cluster z meeting the active charging/discharging requirement can be obtained:

in the formula (I), the compound is shown in the specification,

if the quantity of the active charge-discharge unbalance of the energy storage in the cluster z is greater than 0, the shared energy storage in the cluster can meet the basic requirements of the users of the cluster, otherwise, the shared energy storage cannot meet the basic requirements; n is a radical of _z Is the node set contained by the cluster z; b _z A shared energy storage resource set which can be called in a cluster z; u shape _n A user set of a node n;

for sharing stored energy b _i The maximum active power of (a), the power can be used as charging power or discharging power;

for user u on node n _i Negotiated minimum charge and discharge power. The active demand matching degree index of the cluster z can be obtained based on the charge-discharge unbalance amount of the stored energy by setting the time length in the day as T

Comprises the following steps:

besides the active demand, the reactive power of each node in the cluster should meet the local balance as much as possible, and the reactive power transmission across the cluster is reduced. When the voltage of each node has the maximum historical deviation, the reactive sensitivity matrix in the previous step can obtain the minimum reactive requirement required in the cluster as follows:

in the formula,. DELTA.V _n,max Is the historical maximum deviation for node n.

Based on the minimum reactive power requirement, the reactive power requirement matching degree index of the cluster z can be obtained

Comprises the following steps:

in the formula (I), the compound is shown in the specification,

reflects the reactive power balance capability of the cluster, and the capacity of the inverter for storing energy in the cluster

When the sum is larger than the reactive demand, the energy storage in the cluster completely meets the reactive demand, the reactive balance capacity of the cluster reaches the maximum value of 1, and otherwise, the reactive balance capacity is represented by a positive real number smaller than 1.

The S1 comprises:

The specific flow of load and reservoir cluster partitioning is shown in fig. 2, and first, when the partitioning is started, a local optimization target and a global optimization target of a cluster are constructed, in this embodiment, the local optimization target is measured by a local comprehensive performance index, the global optimization target is measured by a global comprehensive performance index, the local comprehensive index is used to adjust the local optimization target of cluster partitioning, and the global comprehensive indexes of all clusters are used as a global optimization target. Specifically, the S11 includes:

wherein, tau _C 、τ _V 、τ _Q Is a weight factor of the index, and is,

representing the electrical distance between node m and node n,

representing the euclidean distance of node m and node n in geographic space,

for the amount of active power demand imbalance of the charge storage cluster z,

wherein z is _num Is the total number of charged and stored clusters.

In this embodiment, load and storage group division is performed through a community discovery algorithm to maximize a cluster division optimization target to detect an optimal cluster division result, and a specific flow is shown in a load and storage group division part of fig. 2: each node is initialized to a cluster, and the nodes are selected as the initial clusters. And selecting other nodes, moving the nodes to the cluster, for example, moving any node m to the cluster where the node n is located in sequence, calculating and recording the added local optimization target increment respectively, if the added local optimization target increment is the largest, dividing the nodes into the clusters, selecting a new point to move to each cluster, and otherwise, returning to the step of selecting other nodes. And judging whether the global optimization target increment is maximum or not until no node can be merged, if so, obtaining the optimal load storage cluster result, otherwise, returning to the step of selecting other nodes, adjusting the node division mode and re-dividing the load storage cluster.

And after the dividing result of the charge storage clusters is obtained, the relevant characteristic data of the mobile phone energy storage and the historical data of the user load are input, and then the user behavior index, the energy storage loss characteristic, the transient response index, the upper and lower k value limits of the user and the energy storage cluster in each cluster are input to prepare for subsequent sharing of energy storage resources and layering of the user.

Specifically, the S2 includes:

and taking the loss characteristic, the transient response characteristic index and the user behavior prediction index of the stored energy as k-means clustering evaluation indexes, carrying out k-means clustering on shared stored energy and park users in each charge storage cluster, and layering the user groups with differentiated power utilization behaviors and the shared stored energy with differentiated loss characteristic and transient response characteristic according to clustering results.

In this embodiment, a specific process of hierarchically dividing the shared energy storage resource and the user is implemented, as shown in the adaptive k-means clustering hierarchical portion of fig. 2: firstly, selecting an upper limit and a lower limit of a K value, initializing the K value, then applying a K-means clustering algorithm, and reserving the SSE of a K value clustering result, if the K value reaches the upper limit, selecting the K value with the maximum SSE reduction contribution rate, and outputting an energy storage and user hierarchical clustering result corresponding to the K value, otherwise, executing K = K +1, and returning to the K-means clustering algorithm for repeated execution.

SSE is a clustering evaluation index, and the number of the optimal clustering clusters is judged by reducing the contribution rate through the SSE, so that self-adaptive clustering is realized. Clustering evaluation index SSE and contribution rate reduction thereof

The calculation formula is as follows:

in the formula (I), the compound is shown in the specification,

the evaluation result is the evaluation result under the minimum cluster number;

the evaluation result under the maximum cluster number is obtained; SSE _k The evaluation result under the cluster number k can be divided into energy storage cluster evaluation results

And user cluster evaluation results

C _j A sample point set in the jth clustering cluster; t is _m The last moment in the day.

In the embodiment, dynamic clusters with mutual influence of node voltages in a plurality of clusters and sparse inter-cluster node voltage association are obtained by dividing the load storage clusters, and then, on the basis of a traditional k-means clustering algorithm, according to the electricity utilization behavior index of a user, the loss characteristic of energy storage and the transient response characteristic index, the reduction contribution rate of the k-means clustering evaluation index is introduced to realize self-adaptive judgment of the optimal cluster number, so that the hierarchical processing of distributed park users and shared energy storage resources in the clusters is completed, and a user cluster with differentiated electricity utilization behavior and a shared energy storage set with differentiated loss characteristic and transient response characteristic are obtained.

The S3 comprises the following steps:

taking users with similar energy consumption behaviors in the same node in each layer as a whole user u _i Based on u _i The net utility function of (2) and the establishment of an energy-saving service demand prediction model are as follows:

wherein the content of the first and second substances,

for u on node n in the cluster z _i At T _m The sum of the net effect in a day,

for u on node n in the charged reservoir group z _i The function of the satisfaction degree of the user,

for u on node n in the charged reservoir group z _i The maximum demand at two electricity prices translates into cost,

for u on node n in the charged reservoir group z _i The power quality of (1) governs the service cost, T _m To optimize the cycle.

In this embodiment, the building of the energy saving service demand prediction model further includes:

in the formula, ρ _ps,b (t)、ρ _ps,s (t) the price of buying and selling unit electricity to the upper-level power grid;

the power of buying and selling electricity to the superior power grid for the user is non-negative; rho _se,c (t)、ρ _se,d (t) charging and discharging service prices for the user to use the shared energy storage and energy saving service;

the charging and discharging power of the shared energy storage and energy saving service is used by the user, and the value of the charging and discharging power is about the upper limit of the daily appointment

And lower limit

The total charge and discharge amount of the shared energy storage in each user day is 0; rho _pq (t _cp ) Different customized power service prices are given for the shared energy storage resources;

the relaxation variables additionally added for calculating the maximum demand of the user are constantly non-negative numbers; eta _SHES,c And η _SHES,d Charge-discharge loss factor, T, negotiated for shared energy storage aggregator and user _m To optimize the cycle, Δ t is the optimization unit period.

And solving the energy-saving service demand prediction model under the condition of meeting the power balance constraint, and acquiring the charge and discharge power demand of the user on the shared energy storage resource according to the solving result. In this embodiment, the power balance constraint is:

in the formula (I), the compound is shown in the specification,

Time-shiftable loads

And a fixed load

；

And photovoltaic power generation power is provided for users. The photovoltaic power generation power is limited within the maximum power generation power at the moment, and the maximum power generation power of the photovoltaic can be predicted by the following submodels:

in the formula (I), the compound is shown in the specification,

area of the photovoltaic panel for the user; tau is _s Seasonal factors of solar radiation; tau is _w A weather effect factor that is solar radiation; tau is _p1 、τ _p2 A predicted correction factor for solar radiation; b ₁ 、b ₂ 、b ₃ 、b ₄ Fitting coefficients for solar radiation.

In the embodiment, the reactive power required by the user for compensation in the customized power service is calculated by the voltage of the common node where the user is located. Specifically, based on the reactive compensation submodel, the customized power service demand prediction model is established as follows:

wherein the content of the first and second substances,

for u on node n in the charged reservoir group z _i Transformer ratio, V, from common bus incoming line _z,n (t) is the voltage of a common bus where a node n in the load storage cluster z is located, the voltage of the common bus is calculated through a distribution network linearization power flow sub-model,

and

respectively a common bus to u _i The line resistance and reactance of (a) is,

and

is u _i The active load and the reactive load of the system,

reactive power generated by the shared energy storage meeting the requirements for response speed. The common bus voltage can be calculated by a distribution network linearization power flow equation as follows:

in the formula (I), the compound is shown in the specification,

active and reactive power for distributed generators;

for user u _i Interacting power with a power grid;

charging and discharging active power to a power grid through a node n for shared energy storage;

the input power is the active power and the reactive power flowing out of a node n through a line l, and when the value is a negative number, the input power is the input power; l (n,: e n represents the set of lines flowing out of the node n; v _z,n,r Is the nominal voltage of node n; r is _l 、x _l Resistance and reactance of the line l; l (m, n) is a line from node m to n; l is a radical of an alcohol _z,DN Is the set of lines contained by cluster z.

And solving the customized power service demand prediction model, and obtaining the reactive power compensation demand of the user on the shared energy storage resource according to the solving result.

And finally, taking the charging and discharging power demand and the reactive power demand as the result of energy storage demand analysis. According to the embodiment, an energy storage demand analysis model based on user demand characteristics is constructed by taking the net effectiveness maximization of users as a target according to the provided shared energy storage service framework, a satisfaction function and a photovoltaic prediction submodel of the users are established according to the demand response characteristics of the users and the operation characteristics of the users' photovoltaics, and the energy-saving service demands of user groups are predicted; according to the power flow change condition of the distribution network where the user is located, a distribution network linearization power flow submodel and a reactive compensation submodel are established, and the customized power service requirements of the user group are predicted.

The method for respectively constructing the shared energy storage cost model of each energy storage cluster comprises the following steps:

establishing an objective function as a shared energy storage cost model by taking the minimization of the shared energy storage cost as an objective, wherein the objective function is as follows:

wherein, C _SHES,z For sharing the total cost of operation within the energy storage day within the charge-storage group z,

sharing energy storage resources and the electricity purchasing cost of the power grid at the moment t in the charge and storage group z, c _mt For a fixed operating maintenance cost per time,

In this embodiment, the shared energy storage cost model further includes:

after S2 layering, the total energy storage set B in the charge storage group z _z The energy storage set is divided into a plurality of converter response speed grades and loss characteristic grades, and the energy storage response speed grade set is

The energy storage loss characteristic class set is

Power type energy storage set

From self-built energy storage sets

Day-ahead response energy storage set

And real-time response energy storage set

Composition, rational, energy-type energy storage assembly

From self-built energy storage sets

Day-ahead response energy storage set

And real-time response energy storage set

And (4) forming.

Representing stored energy b _i The operating costs resulting from the cyclic losses of (c),

and

to store energy b _i The charging and discharging power of the battery pack,

and

to store energy b _i The charge and discharge state variables of the battery,

to store energy b _i The state of charge of;

to store energy b _i The price is negotiated the day before (c),

respectively an energy storage real-time response mark and an energy storage calling permission mark, wherein the energy storage real-time response mark and the energy storage calling permission mark are 01 variables;

for distributed energy storage b _i Allowing the flag to be invoked after a negotiation ahead of date;

for distributed energy storage b _i The price of the response.

The solving of the shared energy storage cost model in combination with the result of the energy storage demand analysis includes:

and determining power balance constraint, energy storage electric quantity constraint, energy storage four-quadrant operation constraint and energy storage charging and discharging constraint in the charge and storage cluster, combining the result of energy storage demand analysis as constraint conditions together, and solving the shared energy storage cost model under the condition of meeting the constraint conditions. In this embodiment, the shared energy storage cost model is required to follow the trend constraints (19) - (23) on the user side in addition to satisfying the constraints.

The intra-charged-pool-group power balance constraint comprises: the total charge-discharge power of all node users in the charge storage cluster is equal to the total charge-discharge power of shared energy storage resources in the charge storage cluster, and meanwhile, the reactive power demand in the energy storage demand of each user is equal to the sum of the reactive powers of the converters sharing the energy storage resources in the charge storage cluster, namely:

in the formula (I), the compound is shown in the specification,

and

are respectively stored energy b _i The charging and discharging power of (1);

to store energy b _i Reactive power provided by the converter; t is _cp To satisfy the response time t _cp The corresponding time constant; t is _cp A time constant less than T _cp The set of response speed levels of.

The energy storage capacity constraint comprises: for the self-built energy storage unit, the sum of the energy storage charge and discharge amounts within one day is equal to 0; for the energy storage unit responding to the day ahead, the sum of the energy storage charge and discharge amount in the allowed use period is equal to 0; for the real-time response energy storage unit, the sum of the energy storage charge and discharge amounts in the emergency call time period is equal to 0; meanwhile, the stored electric quantity of all the shared energy storage resources must not exceed the preset allowable electric quantity, that is:

in the formula (I), the compound is shown in the specification,

to store energy b _i The total energy stored at time t;

to store energy b _i The energy stored at the initial moment.

The energy storage four quadrant operation constraint comprises: the apparent power composed of the active power of the shared energy storage resource and the reactive power of the converter in the shared energy storage resource is within the allowable capacity of the converter, namely:

the energy storage charging and discharging constraint comprises: the charge and discharge power of the shared energy storage resource should be within a preset allowable limit range, and the same shared energy storage resource cannot be in a charge state and a discharge state at the same time, that is:

the coordination optimization of the active and reactive power distribution of the shared energy storage relates to the benefits of a shared energy storage aggregator, energy storage users and distributed energy storage resource providers, the power distribution of the shared energy storage is optimized only from the perspective of the shared energy storage aggregator, the shared energy storage is caused to perform electric energy transaction with a power grid among a plurality of nodes to disturb node tide distribution, further additional treatment cost is brought to each customized power user, meanwhile, the shared energy storage aggregator only considers the life loss of self-built energy storage, the shared energy storage aggregator does not consider the loss cost and uses other distributed energy storage, the benefits of the distributed energy storage resource providers are damaged, and the sharing enthusiasm of the providers is reduced. In addition, by directly using the centralized optimization scheduling model, energy storage facility parameters, user electricity consumption behavior information and weather prediction information of each cluster need to be collected at the same time, the information transmission amount is large, the autonomy is poor, and each user exposes privacy information such as own electricity consumption data to the outside, and the confidentiality is poor. Aiming at the problem, in the embodiment, a user side coordination management center which is managed by user groups is arranged in each cluster, coordination users make a shared energy storage use strategy, the coordination management center is used as a middle medium, a shared energy storage aggregator is used as an upper layer system, each type of user groups with similar behaviors is used as a lower layer autonomous system, an upper layer and a lower layer one-to-many cooperative shared energy storage layered distributed operation frame are constructed, so that shared energy storage resources are fully and reasonably utilized, the win-win is promoted, and further the maximum of the general social benefit is realized, the embodiment uses the shared energy storage layered distributed operation frame shown in figure 3 to perform reactive power distribution of the shared energy storage resources, and comprises the shared energy storage aggregator on the upper layer and a plurality of users on the lower layer, in this embodiment, taking n users in a cluster 1 and a cluster 2 and corresponding shared energy storage as an example, a shared energy storage control center receives a power allocation policy provided by a shared energy storage aggregator through a communication network, and the shared energy storage control center executes the power allocation policy, so that the shared energy storage provides services for the users in a load storage cluster, and a user-side coordination management center exists between the shared energy storage aggregator and the users, specifically, a centralized optimization scheduling model that considers user net utility, shared energy storage aggregator operation cost, distributed energy storage resource provider benefits, and ecological environmental protection benefits in a plurality of load storage clusters simultaneously is provided in this embodiment, and a target function of the model is obtained by introducing a weight coefficient, specifically, S5 includes:

for node n u in the cluster z _i The sum of net effects of (a);

for the whole user u on the node n in the load-storage group z _i Environmental protection benefits due to the improvement of photovoltaic absorption rate in every daySegment photovoltaic utilization

The sum is represented quantitatively; c _SHES,z Sharing the total operation cost in the energy storage day for the charge storage group z; f _r,z Benefits of day-ahead response resources for distributed energy storage, F _re,z Responding to the benefit of resources in real time for distributed energy storage; tau is _u A net utility weight coefficient for the user; tau is _pv A weight coefficient which is an ecological environmental benefit; tau is _SHES A weight coefficient for the shared energy storage aggregator operating cost; tau is _r Weighting coefficients for distributed energy storage day-ahead response resource benefits; tau. _re A weight coefficient for the benefit of the distributed energy storage real-time response resource; z _lb A load storage cluster set served by a shared energy storage aggregator; n is a radical of _z For a set of nodes belonging to the load store cluster z,

in this embodiment, the centralized optimized scheduling model further includes:

in this embodiment, the constraint conditions of the centralized optimization scheduling model are equations (19) - (27) and equations (36) - (48) presented above, and the distributed energy storage calling strategy of the shared energy storage aggregator is changed by adjusting the distributed demand response of the differentiated group users, the power utilization strategy and the shared energy storage use strategy between the users are coordinated, the power allocation strategy of the shared energy storage resources is optimized, and further, the comprehensive benefit maximization is realized.

As shown in the following formula:

G _SHES,z ＝τ _r F _r,z +τ _re F _re,z -τ _SHES C _SHES,z (54)

in the formula, the optimization target G of the upper layer _SHES,z The sum of the weighted benefits of the shared energy storage aggregators and the distributed energy storage resource providers in the cluster z is responsible for the operation of the shared energy storage resources in the whole area, and the operation cost of the energy storage side is minimized; optimization objectives of the lower layer

And the weighted sum of the total user utility and the environmental protection benefit at the user side in the cluster z and at the node n is responsible for optimizing the self energy storage use strategy and maximizing the self utility and photovoltaic consumption level. The coupling variable between the upper layer objective function and the lower layer objective function is

According to ADMM originAnd (3) processing the constraint expression of the coupling variables of the upper layer and the lower layer:

in the formula (I), the compound is shown in the specification,

sharing information of interaction of the energy storage aggregators for the lower-layer node n users to the upper layer, wherein the information comprises expected energy storage active charging power, active discharging power and reactive power demand respectively;

and the vector of the auxiliary decision variable corresponding to the upper layer.

Introducing a Lagrange operator lambda, and solving the structure augmentation Lagrange function of the node n in the cluster z as follows:

representing the active load set of the user of node n,

respectively represents an electricity purchasing set and an electricity selling set of the electric energy transaction between the user of the node n and the power grid,

an active output power set representing the household's photovoltaic; y is the set of all optimization variables contained in the upper-layer shared energy storage subproblem,

the auxiliary decision variable vector corresponding to the energy storage aggregator is shared by the upper layer,

Respectively representing a distributed energy storage calling strategy set, theta is a penalty coefficient, and theta is>0；U _n Representing a set of users, B _z Representing a set of shared energy storage aggregators.

Specifically, by fixing two variables and updating the third variable, the iteration steps of the ADMM algorithm in the complete form can be derived, that is:

in the formula (I), the compound is shown in the specification,

representing a set of optimization variables x _n At the k-th iteration result, the same is true,

for coupling variables to upper layers

The result of the kth iteration of (1). Lagrange multipliers with further introduction of scaling

After that, the upper and lower sub-problems become:

1) Lower node n user sub-problem:

the constraints of the lower sub-problem are equations (19) - (27);

2) The upper layer shares the energy storage sub-problem:

the constraints of the upper sub-problem are equations (36) - (48).

And comprises the following components:

therefore, the original centralized model is decomposed into equations (65) to (66) for iterative solution, and the interactive process is as follows: the lower-layer user formulates a power utilization strategy considering both the photovoltaic consumption level and the maximization of self utility according to the power price information, the weather condition and the self power utilization behavior under the assistance of the user-side coordination management center, and reports expected use information of energy storage under the power utilization strategy to the upper-layer shared energy storage aggregator

The upper-layer shared energy storage aggregator changes a trading strategy with a power grid according to the reported expected use information and the existing information such as energy storage supply capacity, the charge state in energy storage, energy storage characteristics and the like, makes an energy storage control strategy considering the maximization of shared energy storage operation benefits and distributed energy storage resource benefits, and provides modified energy storage suggested use information for users

Updating Lagrange multipliers according to formulas (67) - (70) every time when the interaction process is finished once, and finally adopting an original residual r and a dual residual s as convergence criteria, wherein an original residual threshold and a dual residual threshold are according to a given error precision epsilon _pre Square root of the sum coupled variable dimension

|N _z And l is the number of elements of the z node set of the cluster, when the coupling variable error between the upper layer and the lower layer is in a small range, the coupling constraint is established, and at the moment, the whole distributed algorithm process is finished to obtain a final scheduling result.

The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components. The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A shared energy storage operation control method considering customized power service is characterized by comprising the following steps:

s1: establishing comprehensive performance indexes of shared energy storage resources and users at nodes of a power grid, and dividing the shared energy storage resources and the users into a plurality of load storage clusters by combining a community discovery algorithm;

2. The method of claim 1, wherein the overall performance index includes an electrical coupling degree, a spatial geographic location, and an energy storage requirement matching degree;

wherein the electrical coupling degree is the electrical distance between each node;

the matching degree of the energy storage requirements is the amount of unbalance of the power requirements of the shared energy storage resources.

3. The method of claim 2, wherein the S1 comprises:

4. The method of claim 3, wherein the S11 comprises:

wherein, tau _C 、τ _V 、τ _Q Is used as a weight factor of the index,

representing the electrical distance between node m and node n,

representing the euclidean distance of node m and node n in geographic space,

wherein z is _num Is the total number of charged and stored clusters.

5. The method of claim 1, wherein the step S2 comprises:

6. The method of claim 1, wherein the step S3 comprises:

wherein the content of the first and second substances,

for u on node n in the cluster z _i At T _m The sum of the net effect in a day,

for u on node n in the charged reservoir group z _i The power quality governs the service cost, T _m To optimize the cycle;

solving the energy-saving service demand prediction model under the condition of meeting the power balance constraint, and acquiring the charge-discharge power demand of the user on the shared energy storage resource according to the solving result;

based on the reactive compensation submodel, establishing a customized power service demand prediction model as follows:

wherein the content of the first and second substances,

for u on node n in the charged reservoir group z _i Transformer ratio, V, from the incoming line of the common bus _z,n (t) is the voltage of a common bus where a node n in the load storage cluster z is located, the voltage of the common bus is calculated through a distribution network linearization power flow sub model,

for u on node n in the load-store cluster z _i The voltage of (a) is set to be,

and

respectively a common bus to u _i The line resistance and reactance of (a) is,

and

is u _i The active load and the reactive load of the system,

7. The method of claim 1, wherein the separately constructing a shared energy storage cost model for each energy storage cluster comprises:

for the total life loss cost of the shared energy storage resource within the charge storage cluster z at time t,

8. The method of claim 1, wherein the solving a shared energy storage cost model in combination with the result of the energy storage demand analysis comprises:

determining power balance constraint, energy storage electric quantity constraint, energy storage four-quadrant operation constraint and energy storage charging and discharging constraint in the charge and storage cluster, combining the result of energy storage demand analysis as constraint conditions, and solving a shared energy storage cost model under the condition of meeting the constraint conditions;

wherein the intra-load-reservoir group power balance constraint comprises: the total charge-discharge power of all node users in the charge storage cluster is equal to the total charge-discharge power of shared energy storage resources in the charge storage cluster, and meanwhile, the reactive power demand in the energy storage demand of each user is equal to the sum of the reactive powers of the converters sharing the energy storage resources in the charge storage cluster:

the energy storage capacity constraint comprises: for the self-built energy storage unit, the sum of the energy storage charge and discharge amount in one day is equal to 0; for the energy storage unit responding to the day ahead, the sum of the energy storage charge and discharge amount in the allowed use period is equal to 0; for the real-time response energy storage unit, the sum of the energy storage charge and discharge amounts in the emergency call time period is equal to 0; meanwhile, the stored electric quantity of all the shared energy storage resources does not exceed the preset allowable electric quantity;

the energy storage four quadrant operation constraint comprises: the apparent power composed of the active power of the shared energy storage resource and the reactive power of the converter in the shared energy storage resource is within the allowable capacity of the converter;

9. The method of claim 1, wherein the step S5 comprises:

for u on node n in the cluster z _i The sum of net effects of;

for the whole user u on the node n in the load-storage group z _i Environmental protection benefit generated by improving photovoltaic consumption rate to increase photovoltaic utilization rate in each time period in the day

The sum is represented quantitatively; c _SHES,z Sharing the total operation cost in the energy storage day for the charge storage group z; f _r,z Benefits of day-ahead response resources for distributed energy storage, F _re,z Responding to the benefit of the resource in real time for distributed energy storage; tau is _u A net utility weight coefficient for the user; tau is _pv A weight coefficient which is an ecological environmental benefit; tau. _SHES A weight coefficient for the shared energy storage aggregator operating cost; tau is _r Weighting coefficients for distributed energy storage day-ahead response resource benefits; tau. _re A weight coefficient for the benefit of the distributed energy storage real-time response resource; z _lb A load storage cluster set served by a shared energy storage aggregator; n is a radical of _z For a set of nodes belonging to the load store cluster z,

the method comprises the steps of collecting power utilization behaviors of users after users are layered in a charge storage group z; u shape _z,n,su The energy consumption behaviors on the nodes n belong to su-type user clusters, su is a user energy consumption behavior type number, each su represents a user energy consumption behavior of one type, and users with the same energy consumption behavior have the same energy consumption satisfaction degree function;

up for the user of node nThe layers share information of the energy storage aggregator interaction,

representing the active load set of the user of node n,

representing the active output power set of the household photovoltaic;

Respectively representing a distributed energy storage calling strategy set, theta is a penalty coefficient, and theta is>0；U _n Represents a set of users, B _z Representing a shared energy storage aggregate set;

10. The method of claim 1, wherein the shared energy storage resources comprise energy type energy storage elements and power type energy storage elements.