CN115759508A

CN115759508A - Battery energy storage optimization planning method and device, electronic equipment and storage medium

Info

Publication number: CN115759508A
Application number: CN202211408884.5A
Authority: CN
Inventors: 胡泽春; 蔡福霖; 刘建琴; 蔡德福; 易海琼; 陈汝斯; 汪莹; 孙冠群; 吴界辰
Original assignee: State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office; Tsinghua University; Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
Current assignee: State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office; Tsinghua University; Electric Power Research Institute of State Grid Hubei Electric Power Co Ltd
Priority date: 2022-11-11
Filing date: 2022-11-11
Publication date: 2023-03-07

Abstract

The disclosure provides a battery energy storage optimization planning method and device, electronic equipment and a storage medium, and belongs to the technical field of power transmission network and battery energy storage optimization planning. Wherein the method comprises the following steps: acquiring an optimal installation node for battery energy storage in a power system; calculating the frequency modulation and voltage regulation total power and the frequency modulation and voltage regulation capacity of the battery energy storage of the optimal installation node, and considering the consumption total power of the new energy resource of the battery energy storage of the optimal installation node; summing the frequency modulation and modulation voltage total power and the absorption total power to obtain the total power of the battery energy storage of the optimal installation node; and updating the energy storage optimization planning model considering new energy consumption by using the frequency modulation and voltage regulation capacity, and then solving to obtain the final capacity of the battery energy storage of the optimal installation node so as to realize the optimization planning of the battery energy storage. The method and the device can improve the supporting capability of the voltage and frequency stability of the power transmission network in an emergency, promote large-scale new energy consumption in new energy enrichment areas, and reduce abandonment.

Description

Battery energy storage optimization planning method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of power transmission network and battery energy storage optimization planning, and in particular to a battery energy storage optimization planning method and device, electronic equipment and a storage medium.

Background

At present, new energy power generation is connected to a power grid in a large scale, and important support is provided for energy consumption of the whole society. However, since the new energy output is mainly determined by external meteorological conditions, strong uncontrollable performance and uncertainty often exist. Therefore, the change of the meteorological conditions can cause the output of the new energy to present stronger fluctuation and intermittence, which leads to a more serious wind and light abandoning condition. On the other hand, in a future high-proportion renewable energy and high-proportion power electronic leading power system, the proportion of the traditional synchronous unit is reduced, and the system has the characteristic of weak inertia; when unbalanced power occurs in the system, the system faces serious frequency and voltage safety problems due to low inertia and less controllable frequency modulation resources. Therefore, in the future, the power grid needs energy transfer equipment capable of restraining the fluctuation and intermittence of new energy, and fast frequency and voltage modulation resources are needed to support the safety and stability of the power grid.

The battery stores energy, so that on one hand, the energy can be transferred in time and space, and the consumption of new energy is effectively promoted; on the other hand, the frequency modulation and voltage regulation resource has the characteristics of good controllability, strong short-time charging and discharging capacity, capability of simultaneously regulating active power and reactive power and the like, and is a high-quality frequency modulation and voltage regulation resource. As is well known, the battery energy storage can be matched with the renewable energy in space-time scale, the output fluctuation of the power generation is effectively stabilized, and the transfer of energy in time and space is realized, so that the matching degree between the renewable energy power generation curve and the receiving end load is improved, and the abandonment of new energy is reduced. Compare in traditional unit, the battery energy storage is controlled through power electronic interface, and through customizing its flagging control parameter, the active power that the battery energy storage was increased and is sent out in the short time can reach 75 times of same capacity thermal power unit to prevent system's frequency from falling better. In addition, the battery energy storage simultaneously performs active and reactive regulation through power electronic equipment such as an inverter. Under emergency, through quick charge and discharge active and reactive power, the battery energy storage can improve the frequency supporting capacity of the power grid and can also improve the voltage supporting capacity of the power grid.

At present, the existing literature only considers the problem of singly improving the power grid frequency or singly improving the voltage supporting capacity of energy storage, and does not consider the problem of simultaneously improving the power grid frequency and the voltage supporting capacity of energy storage; the problem of improving the frequency and voltage supporting capability of a power grid by considering energy storage is a nonlinear integer optimization plan, the linearization and relevant relaxation of a model need to be considered, and a solution method considering both efficiency and quality needs to be considered. In addition, the energy storage optimization planning problem considering new energy consumption also fails to consider improving the voltage and frequency supporting capacity of the power grid at the same time. Under the condition of normal operation, the energy stored by a battery configured in a power grid mainly transfers energy and absorbs new energy; under the emergency condition of power grid faults, the energy storage fast response power grid active and reactive charge and discharge requirements preferentially meet the frequency modulation and voltage regulation requirements. Therefore, the consumption of new energy in the conventional case of the energy storage grid and the support of voltage and frequency in the case of grid faults can be completely considered at the same time. Therefore, currently, energy storage optimization planning independently considering improving the frequency supporting capability or the voltage supporting capability of a power grid or new energy consumption cannot fully utilize energy storage.

Disclosure of Invention

The purpose of the present disclosure is to provide a method and an apparatus for optimizing and planning battery energy storage, an electronic device, and a storage medium, in order to overcome the disadvantages of the prior art. According to the method, the battery energy storage is configured in the power transmission network connected with the high-proportion new energy, the supporting capacity of the voltage and frequency stability of the power transmission network in an emergency situation is improved, meanwhile, large-scale new energy consumption in a new energy enrichment area is promoted, and the abandon is reduced. By determining the optimal location and constant volume results of the battery energy storage, the battery energy storage and power grid operation modes are coordinated to improve the safety and comprehensive operation benefits of the power transmission network.

An embodiment of a first aspect of the present disclosure provides a battery energy storage optimization planning method, including:

acquiring an optimal installation node for battery energy storage in a power system;

calculating the frequency modulation and voltage regulation total power and the frequency modulation and voltage regulation capacity of the battery energy storage of the optimal installation node;

establishing an energy storage optimization planning model considering new energy consumption after configuring the battery energy storage at the optimal installation node, and solving to obtain the total consumption power of the battery energy storage of the optimal installation node;

summing the frequency modulation and voltage regulation total power and the absorption total power to obtain the total power of the battery energy storage of the optimal installation node; and updating the energy storage optimization planning model considering new energy consumption by using the frequency modulation and voltage regulation capacity, and solving to obtain the final capacity of the battery energy storage of the optimal installation node so as to realize the optimization planning of the battery energy storage.

In a specific embodiment of the present disclosure, the obtaining an optimal installation node of battery energy storage in an electric power system includes:

1) Building a simulation system corresponding to the power system;

2) Performing transient voltage stability time domain simulation under the condition of power grid faults in the simulation system, selecting sensitive nodes according to voltage fluctuation of each node in the simulation, recording low voltage duration of each sensitive node under each fault, and calculating a difference value between the low voltage duration of each sensitive node under each fault and the low voltage duration of other sensitive nodes;

wherein, the low voltage duration of the node i under the fault k is recorded as T _Ri,k The low voltage duration difference between nodes i, j at fault k is recorded as:

T _Ci,j,k ＝|T _Ri,k -T _Rj,k |,i≠j (1)

wherein, T _Ci,j,k Is the low voltage duration difference between nodes i, j at fault k;

3) Constructing a matrix T about the low voltage duration at node failure:

the subscript N is the number of faults set in simulation, and M is the number of sensitive nodes;

4) The battery energy storage and address selection sub-model is established as follows:

s.t.∑x _i ＝S,x _i ∈{0,1} (4)

wherein i ≠j；x _i For mounting the 0-1 variable, x, of battery energy storage at node i _i =1 indicates that the battery is installed at node i to store energy, and otherwise indicates that the battery is not installed; s represents the number of nodes for installing the battery energy storage device; i is not equal to j;

and solving the battery energy storage address selection sub-model to obtain the optimal installation node of the battery energy storage in the power system.

In a specific embodiment of the present disclosure, the calculation expression of the total power of frequency modulation and voltage regulation of the battery energy storage of the optimal installation node is as follows:

wherein, P _1,i Frequency and voltage modulated active power, Q, for battery energy storage of the ith optimal installation node _1,i Frequency and voltage modulation reactive compensation power for battery energy storage of ith optimal installation node, S _1,i And storing the total power of the frequency modulation and voltage regulation of the energy of the battery of the ith optimal installation node.

In a specific embodiment of the present disclosure, the method for obtaining fm voltage regulation reactive compensation power of battery energy storage of the optimal installation node includes:

1) Based on the optimal installation node, a battery energy storage reactive compensation power optimization model is constructed as follows:

wherein Q _m Configuring reactive power capacity for energy storage of a battery on a node m, wherein the node m is any optimal installation node; s is the number of nodes for installing the battery energy storage device; 0= g (x, y, u) and

the system is characterized in that the system is an algebraic equation and a differential equation of a simulation system respectively, x represents a differential state variable of the system, y represents an algebraic state variable of the system, and u represents a system parameter; q _max And Q _min Are respectively a batteryMaximum and minimum values of the energy storage reactive compensation power;

in the battery energy storage reactive compensation power optimization model, the load node voltage constraint is as follows:

U _i,k (t _c +t _lim )≥U _lim (5)

wherein, U _i,k The voltage value of the load node i is the fault k; t is t _c Is the fault clearing time, i.e. the sum of the fault occurrence time and the fault duration; t is t _lim Is the critical time; u shape _lim Judging the critical voltage;

the node voltage overshoot constraint is:

U _i,k (t _max )≤U _s (6)

wherein, t _max Time to reach voltage maximum after fault elimination; u shape _s Is the steady state upper voltage limit of the system;

2-2) respectively arranging the formula (5) and the formula (6) into:

and rewriting the battery energy storage reactive compensation power optimization model into:

solving the battery energy storage reactive compensation power optimization model to obtain Q of each optimal installation node _m Frequency and voltage modulation reactive compensation power Q used as battery energy storage of node _1,i 。

In a specific embodiment of the present disclosure, the method for obtaining the frequency modulation and voltage regulation active power and the frequency modulation and voltage regulation capacity of the battery energy storage of the optimal installation node includes:

1) Constructing system frequency safety constraint and linearization, and specifically comprising the following steps:

1-1) constructing a system frequency safety constraint, wherein the expression is as follows:

wherein, Δ f _max,set Is the maximum frequency change that the system is subjected to,

the maximum deviation of the system frequency when the unit k breaks down is obtained;

1-2) carrying out linear fitting on the system frequency safety constraint to obtain a corresponding linear constraint condition, wherein the specific method comprises the following steps:

let the initial power of the unit k be P _Gk0 When in use, will

Division into N _l A segment of which P _Eimax Battery energy storage rated power and system reference capacity S installed for node i _B Is calculated, then a linear fit model is constructed as follows:

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

under the condition that a unit k is in fault, the absolute value of the error between the linear fitting function and the original function on the ith section is obtained;

solving the linear fitting model to obtain the slope b and intercept a of each fitting line segment, and further converting the formula (22) into linearized frequency safety constraint shown as follows:

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

and

respectively fitting the slope and intercept of the segment i when the unit k fails;

2) Considering configuring the battery energy storage on the optimal installation node, and establishing an energy storage optimization planning model considering improving the frequency support capability based on linearized frequency safety constraint, wherein the model is composed of an objective function and constraint conditions, and specifically comprises the following steps:

2-1) determining an objective function:

in the formula, omega _Ess For optimal installation of node sets, G _inv The coefficient for converting the investment cost from the current value to the equal annual value in the planning period;

the capital cost of battery energy storage for the kth optimal installation node,

the cost per energy capacity of battery energy storage for the kth optimal mounting node,

a cost per power capacity of battery energy storage for the kth optimal installation node;E _k and P _k Respectively storing the built-in capacity and active power of the battery at a node k;

2-2) determining constraints, including:

energy storage investment capacity constraint;

E ^min ≤E _k ≤E ^max (27)

0≤P _k ≤P ^max (28)

C ^min E _k ≤P _k ≤C ^max E _k (29)

in the formula, E ^max And E ^min Maximum and minimum projected capacity, P, of battery energy storage ^max Maximum operating power, C, for battery energy storage ^max And C ^min Respectively the maximum multiplying power and the minimum multiplying power of the energy storage of the battery;

energy storage operation constraint;

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

a state flag 0-1 variable of charging active power of a battery stored at a t-th sampling point in a node k under a scene s is set, wherein 0 represents that the battery stored energy is not allowed to be charged, and 1 represents that the battery stored energy is allowed to be charged;

a state flag 0-1 variable of discharge active power of a battery energy storage at a t-th sampling point in a scene s is set, wherein 0 represents that the battery energy storage does not allow discharge, and 1 represents that the battery energy storage allows discharge;

and

respectively storing the charging active power and the discharging active power of the battery in the node k at the t-th sampling point under the scene s,

and

respectively storing reactive power absorbed and released at the t sampling point for the battery in the node k under the scene s;

wherein E is _s,k,t The storage electric quantity of the node k at the t-th sampling point under the scene s is obtained; eta _c And η _d Respectively representing the charging efficiency and the discharging efficiency of the battery energy storage, SOC _max And SOC _min Respectively representing the upper limit and the lower limit of the state of charge of the battery energy storage operation;

E _s,k,0 ＝E _s,k,T ＝E _k ·SOC _ini (39)

in the formula, E _s,k,0 And E _s,k,T Respectively storing the stored electric quantity and SOC of the node k under the scene s at the initial sampling point and the ending sampling point of each day _ini Representing the initial value of the state of charge of the battery energy storage operation;

power flow constraint;

in the formula, a corridor ij represents a power transmission line set from a node i to a node j in the system;

and

respectively setting the active power and the reactive power of the ith line on the corridor ij under the scene s at the t sampling point;

the square of the current amplitude of the ith line on the corridor ij under the scene s at the t sampling point is shown;

the current amplitude of the ith line on the corridor ij under the scene s at the t sampling point is shown; v _s,i,t The voltage amplitude of the ith node at the t-th sampling point under a scene s is shown;

and

the resistance and the reactance of the ith line on the corridor ij are respectively;

and

the active load and the reactive load of a jth node at a tth sampling point under a scene s are set;

the maximum value of the current, V, for the l-th line in corridor ij _i ^max And V _i ^min Is the voltage maximum of the ith node;

new energy output constraint;

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

and

respectively representing the maximum value and the minimum value of the renewable energy source internet active power of the node i at the t-th sampling point under the scene s;

the renewable energy source online reactive power of a node i at the t-th sampling point under a scene s is obtained;

and

respectively setting the maximum value and the minimum value of the renewable energy internet reactive power of the node i at the t-th sampling point under the scene s;

is the renewable energy capacity of node i;

system frequency safety constraints;

3) Solving the energy storage optimization planning model considering the frequency support capacity improvement to obtain P of each optimal installation node _k Frequency modulation and voltage regulation active power P as battery energy storage of the node _1,i Obtaining E of each optimal installation node _k Frequency and voltage modulation capacity E of battery energy storage as the node _1,i 。

In a specific embodiment of the present disclosure, the establishing an energy storage optimization planning model considering new energy consumption after configuring the battery energy storage at the optimal installation node, and solving to obtain a total consumption power of the battery energy storage of the optimal installation node includes:

1) Determining an objective function of an energy storage optimization planning model considering new energy consumption:

in the formula, C ^Inv Investment cost for the transmission grid; the total cost of the operation of the power transmission network in the scene s comprises the following steps: cost of generator generation in a power transmission network

Cost of new energy abandonment

Operating maintenance costs of stored energy

2) Determining constraint conditions of an energy storage optimization planning model considering new energy consumption, wherein the constraint conditions comprise the following steps:

energy storage investment capacity constraint is shown as a formula (27) to a formula (29);

energy storage operation constraint, as shown in formula (30) -formula (39);

a power flow constraint, as shown in equations (40) - (47);

the new energy output constraint is shown as a formula (48) to a formula (50);

and (3) restricting the consumption rate of new energy:

reserve capacity constraint of the system:

3) Solving the energy storage optimization planning model considering new energy consumption to obtain P of each optimal installation node _k Total power S consumed as battery energy storage for the node _2,i 。

In a specific embodiment of the present disclosure, the optimizing planning of battery energy storage includes:

1) Calculating the total power of the energy stored by the battery:

S _3,i ＝S _1,i +S _2,i (57)

wherein S is _3,i The total power of the energy stored by the battery of the ith optimal installation node;

2) Updating the energy storage optimization planning model considering new energy consumption, comprising:

2-1) newly adding an upper limit and a lower limit of the SOC as an optimized variable;

in the formula, SOC _s,k,t The state of charge value of the battery of the optimal installation node k at the t-th sampling point under the scene s is obtained;

2-2) increasing SOC _max Decrease SOC _min ；

2-3) utilizing the frequency modulation and voltage regulation capacity of the battery energy storage of each optimal installation node, wherein the new frequency modulation and voltage regulation energy storage capacity is as follows:

in the formula, E ₁ Optimizing results of the energy storage optimization planning model considering the frequency support capacity improvement;

3) Solving the energy storage optimization planning model which is updated in the step 2) and takes the new energy consumption into consideration,e obtaining each optimal installation node _k Final capacity E as battery energy storage for the node _3,i ；

S of each optimal installation node _3,i And E _3,i Namely the optimized planning result of the battery energy storage.

An embodiment of a second aspect of the present disclosure provides a battery energy storage optimization planning apparatus, including: (ii) a

The installation node acquisition module is used for acquiring an optimal installation node of battery energy storage in the power system;

the frequency modulation and voltage regulation optimizing module is used for calculating the frequency modulation and voltage regulation total power and the frequency modulation and voltage regulation capacity of the battery energy storage of the optimal installation node;

the new energy consumption optimization module is used for establishing an energy storage optimization planning model which is based on the fact that the battery energy storage is configured at the optimal installation node and takes the new energy consumption into consideration, and solving to obtain the total consumption power of the battery energy storage of the optimal installation node;

the battery energy storage optimization module is used for summing the frequency modulation and voltage regulation total power and the total power consumption to obtain the total power of the battery energy storage of the optimal installation node; and updating the energy storage optimization planning model considering new energy consumption by using the frequency modulation and voltage regulation capacity, and solving to obtain the final capacity of the battery energy storage of the optimal installation node so as to realize the optimization planning of the battery energy storage.

wherein, P ₁ I is the frequency modulation and voltage regulation active power of the battery energy storage of the ith optimal installation node, Q _1,i FM-PM reactive compensation power, S, for battery energy storage at ith optimal installation node _1,i And storing the total power of the frequency modulation and voltage regulation of the energy of the battery of the ith optimal installation node.

In a specific embodiment of the present disclosure, the method for obtaining frequency modulation and voltage regulation reactive compensation power of battery energy storage of the optimal installation node includes:

wherein Q _m Configuring reactive power capacity for battery energy storage on a node m, wherein the node m is any optimal installation node; s is the number of nodes for installing the battery energy storage device; 0= g (x, y, u) and

the system is characterized in that the system is an algebraic equation and a differential equation of a simulation system respectively, x represents a differential state variable of the system, y represents an algebraic state variable of the system, and u represents a system parameter; q _max And Q _min Respectively the maximum value and the minimum value of the energy storage reactive compensation power of the battery;

U _i,k (t _c +t _lim )≥U _lim (5)

the node voltage overshoot constraint is:

U _i,k (t _max )≤U _s (6)

wherein, t _max Time to reach voltage maximum after fault elimination; u shape _s Is the upper limit of the steady-state voltage of the system;

2-2) respectively arranging the formula (5) and the formula (6) into:

wherein, Δ f _max,set For the maximum frequency change that the system is subjected to,

the maximum deviation of the system frequency when the unit k fails;

let the initial power of the unit k be P _Gk0 When in use, will

Division into N _l A segment of which P _Eimax Battery energy storage rated power and system reference capacity S installed for node i _B At the maximum value of the ratio, a linear fitting model is constructed as follows:

in the formula, err _i ^k Under the condition that a unit k has a fault, the absolute value of the error between the linear fitting function and the original function on the ith section is calculated;

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

and

2-1) determining an objective function:

a cost per power capacity of battery energy storage for the kth optimal installation node; e _k And P _k Respectively storing the operating capacity and the active power of the battery at a node k;

2-2) determining constraints, including:

energy storage investment capacity constraint;

E ^min ≤E _k ≤E ^max (27)

0≤P _k ≤P ^max (28)

C ^min E _k ≤P _k ≤C ^max E _k (29)

in the formula, E ^max And E ^min Maximum and minimum projected capacity, P, of battery energy storage ^max Maximum built-in power for battery energy storage, C ^max And C ^min Respectively the maximum multiplying power and the minimum multiplying power of the energy storage of the battery;

energy storage operation constraint;

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

a state flag of charging active power of a battery energy storage at a t-th sampling point in a scene s is changed into a variable from 0 to 1, wherein 0 represents that the battery energy storage does not allow charging, and 1 represents that the battery energy storage allows charging;

and

and

respectively storing the reactive power absorbed and released at the t-th sampling point for the battery in the node k under the scene s;

E _s,k,0 ＝E _s,k,T ＝E _k ·SOC _ini (39)

flow constraint;

and

the current amplitude of the ith line on the corridor ij under the scene s at the t sampling point is shown; v _s,i,t The voltage amplitude of the ith node at the tth sampling point under a scene s is obtained;

and

and

the method comprises the steps that active load and reactive load of a jth node at a tth sampling point under a scene s are represented;

maximum value of current, V, for the first line in corridor ij _i ^max And V _i ^min Is the voltage maximum of the ith node;

new energy output constraint;

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

and

and

respectively setting the maximum value and the minimum value of the renewable energy source online reactive power of the node i at the t-th sampling point under the scene s;

renewable energy capacity for node i;

system frequency safety constraints;

3) Solving the energy storage optimization planning model considering the frequency support capacity improvement to obtain P of each optimal installation node _k Frequency modulation and voltage regulation active power P as battery energy storage of the node _1,i To obtain E of each optimal installation node _k Frequency and voltage modulation capacity E of battery energy storage as the node _1,i 。

in the formula, C ^Inv Investment cost for the transmission grid; the total cost of the operation of the power transmission network in the scene s comprises the following steps: power generation by generators in a power transmission networkCost of

Cost of new energy abandonment

Operating maintenance costs of stored energy

energy storage investment capacity constraint, as shown in formula (27) -formula (29);

energy storage operation constraint, as shown in formula (30) -formula (39);

a power flow constraint, as shown in equations (40) - (47);

the new energy output constraint is shown as a formula (48) to a formula (50);

and (3) restricting the consumption rate of new energy:

spare capacity constraint of the system:

In a specific embodiment of the present disclosure, the optimizing and planning of battery energy storage includes:

1) Calculating the total power of the energy stored by the battery:

S _3,i ＝S _1,i +S _2,i (57)

in the formula, SOC _s,k,t Setting the state of charge value of the battery of the optimal installation node k at the t-th sampling point under the scene s;

2-2) increasing SOC _max Decrease SOC _min ；

3) Solving the energy storage optimization planning model which is updated in the step 2) and takes the new energy consumption into consideration to obtain E of each optimal installation node _k Final capacity E as battery energy storage for the node _3,i ；

An embodiment of a third aspect of the present disclosure provides an electronic device, including:

at least one processor; and a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor and configured to perform a battery energy storage optimization planning method as described above.

A fourth aspect of the present disclosure is directed to a computer-readable storage medium, which stores computer instructions for causing a computer to execute the above-mentioned battery energy storage optimization planning method.

The characteristics and the beneficial effects of the disclosure are as follows:

1. the method and the device consider that the battery energy storage is configured in the power transmission network, so that the problems of insufficient voltage frequency supporting capacity and the like caused by the fact that a high proportion of new energy is accessed are solved, the large-scale access and consumption of new energy power generation are promoted, the power generation abandoning rate of the new energy is reduced, and the safety and the comprehensive operation benefit of the power transmission network are improved. The present disclosure considers the regulation of active and reactive power by battery storage while improving the voltage frequency support capability of the transmission grid and promoting new energy consumption.

2. The present disclosure will comprehensively consider improving voltage frequency support capability and new energy consumption; obtaining battery energy storage power and capacity considering improving voltage frequency supporting capacity based on time domain simulation of transient voltage stability, voltage track sensitivity and frequency safety constraint; meanwhile, optimizing and planning the energy storage of the battery based on the new energy consumption to obtain the energy storage power and capacity of another battery; finally, the two aspects are comprehensively considered, and the final battery energy storage power and capacity are obtained by adjusting the upper limit and the lower limit of the SOC, adding the battery power and the like, so that the voltage frequency supporting capacity of the power grid can be improved, and the consumption of new energy can be promoted.

Drawings

Fig. 1 is an overall flowchart of a battery energy storage optimization planning method in an embodiment of the present disclosure.

Detailed Description

The present disclosure provides a battery energy storage optimization planning method, apparatus, electronic device, and storage medium, which are described in further detail below with reference to the accompanying drawings and specific embodiments.

In an embodiment of the present disclosure, the overall process of the battery energy storage optimization planning method is shown in fig. 1, and includes the following steps:

1) Based on time domain simulation of transient voltage stability analysis and low voltage duration, obtaining an optimal installation node of battery energy storage considering improvement of voltage support capacity through optimization; the method comprises the following specific steps:

1-1) in power system transient simulation software (for example: PSASP, PSS/E, etc.) to build a simulation system of the power system to be optimized. In one particular embodiment of the present disclosure, an IEEE39 node system is selected.

1-2) performing transient voltage stability time domain simulation under the condition of power grid fault in the simulation system of the step 1-1); in this embodiment, the fault condition mainly includes a unit offline and a line disconnection.

After fault simulation, a part of nodes with the largest voltage fluctuation in the simulation system are determined as sensitive nodes, and in one specific embodiment of the disclosure, the number of the sensitive nodes is selected to be the first 10 nodes with the largest voltage fluctuation. And recording the low-voltage duration of each sensitive node under each fault, and calculating the difference value between the low-voltage duration of each sensitive node under each fault and the low-voltage duration of other sensitive nodes. In this embodiment, the low voltage duration of the node i under the fault k is denoted as T _Ri,k In one embodiment of the present disclosure, the low voltage is taken to be lower than 0.75p.u., and the difference between the low voltage durations of two nodes i, j under the same fault is defined as:

T _Ci,j,k ＝|T _Ri,k -T _Rj,k |，i≠j (1)

wherein, T _Ci,j,k Is the low voltage duration difference between nodes i, j at fault k and is used to describe the degree of synchronization instability between the two nodes.

1-3) constructing a matrix T about low voltage duration under node faults, wherein subscript N is the number of faults set in system simulation, and M is the number of sensitive nodes. Each sensitive node corresponds to a low voltage duration under a fault.

1-4) integrating the low voltage duration time and the low voltage duration time difference between different nodes, and establishing a battery energy storage address selection sub-model by taking the sum of the node recovery time and the recovery time difference under different faults as an optimization target to be maximized:

s.t.∑x _i ＝S,x _i ∈{0,1} (4)

wherein i is not equal to j; x is a radical of a fluorine atom _i Installing a 0-1 variable of battery energy storage at a node i; x is the number of _i And =1 represents that the battery energy storage is installed at node i, and otherwise represents that the battery energy storage is not installed. S represents the number of nodes available for energy storage by the installed battery.

The battery energy storage and address selection submodel is a mixed integer linear optimization model, and software Yalmip and Gurobi are selected to solve in a specific embodiment of the disclosure. And solving the battery energy storage site selection sub-model to obtain the optimal installation node of the battery energy storage, namely the installation position of the battery energy storage in the subsequent steps.

2) And calculating the frequency modulation and voltage regulation reactive compensation power of the battery energy storage of each optimal installation node by using the result of the step 1).

Considering the complex nonlinear relationship between the transient voltage stability and the battery compensation reactive capacity, the method directly obtains the change condition of the node voltage when the system parameter changes by using the approximately estimated voltage track sensitivity. The battery energy storage is configured on the optimal installation node in the step 1), and optimization planning is carried out based on the voltage track sensitivity, so that the battery energy storage reactive power considering the improvement of the voltage supporting capability is obtained. The method comprises the following specific steps:

2-1) based on the optimal installation node of the battery energy storage obtained in the step 1), constructing a battery energy storage reactive compensation power optimization model which can be expressed as follows:

wherein Q _m Configuring reactive power capacity for energy storage of a battery on a node m, wherein the node m is an optimal installation node obtained in the step 1); s is the number of the optimal installation nodes for storing energy of the battery selected in the step 1); 0= g (x, y, u) and

the method comprises the following steps that (1) an algebraic equation and a differential equation of a simulation system are respectively adopted, x represents a differential state variable of the system, and represents state variables of changed voltage, current, phase angle and the like in the embodiment of the disclosure; y represents an algebraic state variable of the system, and represents state variables such as voltage, current, phase angle and the like of a steady state in the disclosure; u represents a system parameter, which represents a system parameter such as a network structure and element properties of the system in the embodiment of the present disclosure; q _max And Q _min And respectively the maximum value and the minimum value of the reactive compensation power of the battery energy storage.

In the battery energy storage reactive compensation power optimization model, the load node voltage is guaranteed to be recovered to be above the critical voltage after the fault is eliminated, and the constraint can be expressed as the following inequality:

U _i,k (t _c +t _lim )≥U _lim (5)

wherein, U _i,k Is a fault kThe voltage value of the load node i; t is t _c The fault clearing time is the sum of the fault occurrence time and the fault duration time; t is t _lim For the critical time used in the criterion, 1s is taken in one specific embodiment of the disclosure; u shape _lim In one embodiment of the present disclosure, 0.75p.u. is taken as the threshold voltage used in the criterion.

After the system fails, the node voltage may exceed the upper voltage limit in the recovery process, and in order to limit the overshoot of the node voltage, the following constraint conditions are set:

U _i,k (t _max )≤U _s (6)

wherein, U _i,k The voltage value of the load node i is the voltage value of the fault k; t is t _max Time to reach voltage maximum after fault elimination; u shape _s For the upper limit of the steady-state voltage of the system, 1.1p.u. is taken in one specific embodiment of the present disclosure.

2-2) depending on the voltage trajectory sensitivity, equations (5) and (6) can be collated as:

in the formula (7), the summation term on the left of the unequal numbers is the track sensitivity of the optimal installation node i of the battery energy storage when each optimal installation node of the battery energy storage is in the fault k, and is multiplied by the reactive compensation power of the optimal installation node, namely the total voltage variation of the node i after all S battery energy storages are installed, and the second term content is the voltage of the node i when the node i is not compensated. The left-hand version is then the node i at t = t after all compensation equipment (i.e. battery storage) has been installed _c +t _lim The voltage estimate of (c). Similarly, in the formula (8), the node i is arranged at t = t after all battery energy storage devices are arranged in the left formula _max The voltage estimate of (a). The optimization model is then rewritten as:

the energy storage of the system can be correspondingly improved by t = t after the battery is installed _c +t _lim And t = t _max To satisfy transient voltage safety and stability constraints, i.e., equations (5) and (6).

The battery energy storage reactive compensation power optimization model is a mixed integer linear optimization model, and in a specific embodiment of the disclosure, the battery energy storage reactive compensation power optimization model is solved by software Yalmip and Gurobi to obtain Q of each optimal installation node _m Frequency and voltage modulation reactive compensation power Q used as battery energy storage of node _1,i Q of optimally installed nodes _1,i And forming an optimal installation node, and considering a frequency modulation and voltage regulation reactive compensation power set Q1 for improving the voltage support capability of the battery energy storage.

3) And calculating the frequency modulation and voltage regulation active power and the frequency modulation and voltage regulation capacity of the battery energy storage of each optimal installation node by using the result of the step 1).

In the embodiment, through constructing system frequency safety constraint and linearization, a battery energy storage active power planning model considering frequency support capacity improvement is further established, and the planning model is solved to obtain the frequency modulation and voltage regulation active power and the frequency modulation and voltage regulation capacity of the battery energy storage of each optimal installation node; the method comprises the following specific steps:

3-1) constructing system frequency safety constraint and linearization, and specifically comprising the following steps:

3-1-1) assuming rigid connection of the whole system, any node in the system and the unit have the same frequency dynamic process. Considering that renewable energy sources (wind power and photovoltaic) in the system have virtual inertia, when the synchronous conventional unit k is not shut down, for the rest synchronous conventional units in the system, a rotor motion equation is as follows:

wherein, Δ f is the variation of the system frequency; t is _Ji The inertia time constant of a conventional unit (mainly referring to a thermal generator unit) i is synchronized; i ≠ k represents that the unit i is not a synchronous conventional unit k in outage; t is _Vi A virtual inertia time constant of a renewable energy source unit i is obtained; p _Gk0 The method comprises the steps of synchronizing the initial active power of a conventional unit k;

increasing active power for the synchronous conventional unit i, wherein ng is the total number of the synchronous conventional units; delta P _Ei Storing energy and increasing active power for a battery of a node i, wherein nr is the total number of renewable energy units;

storing energy and increasing active power for a battery of a node i; delta P _L Is the amount of change in the load.

For a conventional unit and a renewable energy unit, a first-order simplified speed regulator model is adopted. Taking the synchronous conventional unit i as an example, the active power increase amount of the unit i

And the frequency deviation delta f is in a proportional relation, and the relation between the active power increased by the synchronous conventional unit and the system frequency variation is as follows:

in the formula, T _G Is the time constant of the speed regulator of the synchronous conventional unit; k is _Gi Synchronizing the power frequency response coefficient of the conventional unit i; p _Gi,max In order to synchronize the rated active power of the conventional unit i.

For battery energy storage, the control strategy of the conventional energy storage auxiliary frequency modulation is a virtual droop control strategy, so that an energy storage analog synchronous unit can participate in primary frequency modulation. The active power of battery energy storage and power increase under the virtual droop control strategy is in direct proportion to the system frequency variation:

ΔP _Ei ＝-K _Ei ×P _Ei ×Δf (13)

in the formula, P _Ei Battery energy storage rated active power and system reference capacity S installed for node i _B The ratio of (A) to (B); assuming in this disclosure a node i mounted battery stored functional power-frequency response coefficient K _Ei Are all K _E0 In which K is _E0 The initial battery energy storage active power-frequency response coefficient, taken in one embodiment of the present disclosure, is 0.1.

For the load, the frequency characteristic considering the load is:

ΔP _L ＝D×Δf (14)

in the formula, D is a work frequency response coefficient of the load.

3-1-2) from the results of step 3-1-1), a differential equation as shown in formula (15) can be obtained:

T _G T _J Δf”+[T _J +T _G (K _E +D)]Δf'+(K _G +K _E +D)Δf＝ΔP _Gk0 (15)

where Δ f "and Δ f' are the second and first derivatives of frequency f, respectively. Equation (17) is an initial condition of the differential equation.

Through the differential equation, the change process of the system frequency can be obtained as follows:

wherein:

wherein, alpha, w, M,

Gamma is an intermediate variable in the calculation process, so that the subsequent equation is simplified conveniently.

The maximum deviation delta f of the frequency in the primary frequency modulation process can be obtained by carrying out derivation on the formula (18) _max Comprises the following steps:

at the initial active power P of the known unit k _Gk0 When alpha, w,

Gamma (all can be expressed as

A function of, M and

and P _Gk0 In relation to the maximum deviation of the system frequency when the unit k fails

Only with

In this regard, the following function may be expressed:

the frequency safety constraints in the system are then:

in the formula,. DELTA.f _max,set The maximum frequency variation that the system can withstand.

Show about

Is used as the function of (g).

3-1-2) carrying out linear fitting on the function in the constraint condition of the formula (22) to obtain a group of linear constraint conditions, wherein the specific method comprises the following steps:

at the initial active power P of the known unit k _Gk0 When is at

Uniformly, equal step-size selecting a plurality of points so that

To form a fitted target curve, wherein P _Eimax Battery energy storage rated active power and system reference capacity S installed for node i _B Maximum value of the ratio of (a). In this embodiment, the

Division into N _l A segment of which N _l The larger the value of (A), the better the linear fitting accuracy, and N is suggested _l A value greater than 20, and in one embodiment of the disclosure, N _l =30. Linear fitting was performed on the different segments, the linear fitting model was as follows:

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

under the condition that a unit k is in fault, the absolute value of the error between the linear fitting function and the original function on the ith section is obtained; a specific embodiment of the present disclosure sets the linear fit errors to all be less than 5%.

In a specific embodiment of the present disclosure, the linear fitting models of equations (23) and (24) are solved by using software Yalmip and Gurobi, the slope b and intercept a of each fitting line segment are obtained by solving, and equation (22) is converted into a linearized frequency safety constraint as shown below:

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

and

and respectively fitting the slope and intercept of the segment i when the unit k breaks down.

To this end, the formula (22) for frequency safety constraint of the system when the unit k has a fault is converted into the formula (25) by the formulas (23) and (24), and the nonlinear constraint is converted into the linear constraint.

3-2) configuring the battery energy storage on the optimal installation node in the step 1), and establishing an energy storage optimization planning model considering improving the frequency support capability based on the linearized frequency safety constraint obtained in the step 3-1), wherein the model is composed of an objective function and a constraint condition. The method comprises the following specific steps:

3-2-1) determining an objective function:

the objective function is the minimization of the investment cost of battery energy storage; in the formula, omega _Ess Optimal set of installation nodes, G, for all battery energy storage _inv The coefficient for converting the investment cost from the current value to the equal annual value in the planning period;

the cost per energy capacity of the battery energy storage for the kth optimal installation node,

a cost per power capacity of battery energy storage for the kth optimal installation node; e _k And P _k And respectively storing the built-in capacity and the active power of the battery at the node k.

3-2-2) determining the constraint conditions of the model:

3-2-2-1) energy storage investment capacity constraint;

E ^min ≤E _k ≤E ^max (27)

0≤P _k ≤P ^max (28)

C ^min E _k ≤P _k ≤C ^max E _k (29)

in the formula, E ^max And E ^min Maximum and minimum projected capacity, P, of battery energy storage ^max And (4) establishing the maximum operating power for the battery to store energy. C ^max And C ^min The maximum multiplying power and the minimum multiplying power of the energy storage of the battery are respectively.

3-2-2-2) energy storage operation constraint;

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

a state mark 0-1 variable of discharge active power of a battery energy storage at the t-th sampling point in a node k under a scene s is represented, wherein 0 represents that the battery energy storage does not allow discharge, and 1 represents that the battery energy storage allows discharge;

and

and

and respectively storing the reactive power absorbed and released at the t-th sampling point by the battery in the node k under the scene s.

considering the continuity of the battery energy storage operation, the energy storage is returned to the initial energy storage state after one day of operation, namely:

E _s,k,0 ＝E _s,k,T ＝E _k ·SOC _ini (39)

in the formula, E _s,k,0 And E _s,k,T Respectively storing the stored electric quantity and SOC of the node k under the scene s at the initial sampling point and the ending sampling point of each day _ini And the initial value of the state of charge of the battery energy storage operation is represented.

3-2-2-3) power flow constraint at the moment of maximum frequency deviation, adopting an SOCP form of alternating current power flow:

(V _i ^min ) ² ≤U _s,i,t ≤(V _i ^max ) ² (47)

and

respectively setting the active power and the reactive power of the l line on the corridor ij under the scene s at the t sampling point;

the square of the current amplitude of the l line on the ij under the scene s at the t sampling point is obtained;

the current amplitude of the ith line on the ij under the scene s at the t sampling point is obtained; v _s,i,t For the ith node in the scene s, sampling at the t th nodeThe voltage amplitude of the sample point.

And

the resistance and reactance of the l-th line on corridor ij, respectively.

And

the real load and the reactive load of the jth node at the tth sampling point under the scene s are shown.

The maximum value of the current, V, for the l-th line in corridor ij _i ^max And V _i ^min Is the maximum voltage at the ith node.

3-2-2-4) new energy output constraint;

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

and

respectively representing the maximum value and the minimum value of the renewable energy internet active power of a node i in the system at the t-th sampling point under the scene s;

and

is the renewable energy capacity of node i.

3-2-2-5) system frequency safety constraint;

3-3) solving the energy storage optimization planning model which is established in the step 3-2) and takes the frequency supporting capacity into consideration. The model is a mixed integer second-order cone optimization model, and in a specific embodiment of the disclosure, software Yalmip and Gurobi are selected for solving. Solving the energy storage optimization planning model considering the improvement of the frequency supporting capacity to obtain P of each optimal installation node _k Frequency modulation and voltage regulation active power P used as battery energy storage of the node _1,i To obtain E of each optimal installation node _k Frequency and voltage modulation capacity E of battery energy storage as the node _1,i (ii) a P of each optimal installation node _1,i Frequency modulation and voltage regulation active power set P1 of battery energy storage considering improving frequency supporting capability and forming optimal installation nodes, and E of each optimal installation node _1,i And forming a frequency modulation and voltage regulation capacity set E1 of the optimal installation node considering battery energy storage for improving the frequency supporting capacity.

4) According to the battery energy storage active power set P of each optimal installation node obtained in the step 3) _1,i And the battery energy storage of each optimal installation node obtained in the step 2) is the mostOptimal reactive compensation power Q _1,i The total power S of the energy storage, frequency modulation and voltage regulation of each optimal installation node battery can be obtained _1,i ：

Wherein, P _1,i Storing, frequency and voltage modulating active power, Q, for the battery of the ith optimal installation node _1,i For the battery energy storage frequency modulation voltage regulation reactive compensation power of the ith optimal installation node, S _1,i And (4) storing energy, frequency and voltage for the battery of the ith optimal installation node.

S of each optimal installation node _1,i And forming a total power set S1 of the battery energy storage frequency modulation and voltage regulation of the optimal installation node.

So far, obtaining an optimal installation node for battery energy storage through the step 1); and obtaining a frequency modulation and voltage regulation capacity set E1 and a frequency modulation and voltage regulation total power set S1 of the battery energy storage comprehensively considering the improvement of the voltage and the frequency supporting capability of the power grid through the step 2) and the step 3).

5) And (2) considering the configuration of the battery energy storage on the optimal installation node in the step 1), establishing an energy storage optimization planning model which is composed of an objective function and constraint conditions and considers the new energy consumption, and solving to obtain the capacity and total consumption power of the battery energy storage of the optimal installation node considering the new energy consumption. The method comprises the following specific steps:

5-1) determining an objective function:

the objective function is the minimization of the investment and the total running cost F of the power transmission network;

in the formula, the investment cost of the power transmission network is the investment cost C of battery energy storage ^Inv (ii) a Transmission grid operation assembly under scene sThe cost includes the following items: cost of generator generation in a power transmission grid

Cost of new energy abandonment

Operating maintenance costs of stored energy

5-2) determining constraints of the model, including:

energy storage operation constraint, as shown in formula (30) -formula (39);

a power flow constraint, as shown in equations (40) - (47);

the new energy output constraint is shown as a formula (48) to a formula (50);

and (3) restricting the consumption rate of new energy:

spare capacity constraint of the system:

5-3) the energy storage optimization planning model considering new energy consumption is a mixed integer second-order cone optimization model, and in a specific embodiment of the disclosure, software Yalmip and Gurobi are selected to solve the energy storage optimization planning model considering new energy consumption to obtain P of each optimal installation node _k Total power S consumed as battery energy storage for the node _2,i Obtaining E of each optimal installation node _k Capacity E for taking into account consumption of new energy as battery energy storage of the node _2,i (ii) a S of each optimal installation node _2,i Battery energy storage total power set forming optimal installation node and considering new energy consumptionS2, E of each optimal installation node _2,i And forming a battery energy storage capacity set E2 of which the optimal installation node considers the consumption of new energy.

6) Step 2) and step 3) obtain the frequency modulation and voltage regulation capacity set E1 and the frequency modulation and voltage regulation total power set S1 of the battery energy storage which comprehensively considers the improvement of the voltage and the frequency support capability of the power grid; and 3) obtaining a consumption optimal capacity set E2 and a consumption total power set S2 of the battery energy storage considering new energy consumption. The two are comprehensively considered, and a final battery energy storage optimization planning result for improving the voltage frequency support of the power grid and the consumption of new energy can be obtained; the method comprises the following specific steps:

6-1) S1 and S2 are respectively energy storage power sets considering grid frequency voltage support and new energy consumption. Under the fault condition of the power grid (unit off-line, line disconnection and the like), the stored energy preferentially meets the support of the power grid frequency and voltage under the emergency condition, and then the consumption is considered. When the new energy high-power storage originally is in charging, the energy storage at the emergency time preferably meets the frequency and voltage regulation, and the new energy is allowed to be abandoned; when the new energy low-power storage originally is in discharge, the energy storage may be in a condition of simultaneously supplying power to the system and participating in frequency modulation and voltage regulation.

The total power calculation expression for the battery energy storage of each optimal installation node is as follows:

S _3,i ＝S _1,i +S _2,i (57)

wherein S is _3,i The total power of the energy stored by the battery of the ith optimal installation node; s of each optimal installation node _3,i And forming a total power set S2 of the battery energy storage of the optimal installation node.

6-2) optimally considering the capacity of the grid frequency voltage support, and updating an energy storage optimization planning model considering new energy consumption;

in this embodiment, E1 and E2 are capacity sets for each battery energy storage consideration for grid frequency voltage support and new energy consumption, and the energy storage support time required for frequency modulation and voltage regulation is short, so E1 is relatively small. Therefore, the energy storage optimization planning model considering new energy consumption is expanded as follows:

6-2-1) newly adding an upper limit and a lower limit of the SOC as optimization variables;

in the formula, SOC _s,k,t And the state of charge value of the battery of the optimal installation node k at the t-th sampling point under the scene s is obtained.

6-2-2) when the stored energy participates in frequency modulation and voltage regulation, the stored energy is allowed to briefly break through the upper and lower limits of the SOC within the short time of primary frequency modulation, namely the SOC is properly increased _max And reducing SOC _min . In one embodiment of the present disclosure, the SOC _max Increase from 0.85 to 0.9, SOC _min From 0.15 to 0.1.

6-2-3) utilizing the frequency and voltage regulation capacity of the battery energy storage of each optimal installation node, and newly adding frequency and voltage regulation energy storage capacity constraint;

namely, the reserved energy storage capacity is larger than the capacity required by frequency modulation and voltage regulation, as follows:

wherein E is _1,k Frequency and voltage regulation capacity of battery energy storage of the kth optimal installation node for providing power grid frequency and voltage support under consideration obtained in step 3).

6-3) solving the energy storage optimization planning model which is updated in the step 6-2) and takes the new energy consumption into consideration to obtain E of each optimal installation node _k Final capacity E of battery energy storage as the node _3,i (ii) a E of optimum installation nodes _3,i And forming a battery energy storage final capacity set E3 of the optimal installation node.

So far, the optimal installation node for battery energy storage is obtained through the step 1), and the battery energy storage is configured on the optimal installation node obtained in the step 1) in the rest steps; obtaining a frequency modulation and voltage regulation reactive compensation power set Q1 of battery energy storage considering improvement of voltage supporting capacity through the step 2); obtaining a frequency modulation and voltage regulation active power set P1 and a capacity set E1 of the battery energy storage considering the improvement of the frequency supporting capacity through the step 3); and (4) obtaining a total power set S1 of the battery energy storage frequency modulation and voltage regulation through the P1 and the Q1. The total power set S2 and the capacity set E2 of the battery energy storage considering the new energy consumption are obtained through the step 5). And finally, comprehensively considering the total power and the capacity of the two groups of battery energy storage through the step 6), and obtaining a final battery energy storage total power set S3 and a capacity set E3 which are used for improving the frequency and the voltage support capability of the power grid and absorbing new energy, namely, an optimal planning result of the battery energy storage.

In order to implement the foregoing embodiments, an embodiment of a second aspect of the present disclosure provides a battery energy storage optimization planning apparatus, including:

In a specific embodiment of the present disclosure, a calculation expression of the total power of frequency modulation and voltage regulation for battery energy storage of the optimal installation node is as follows:

wherein, P _1,i Frequency and voltage modulated active power, Q, for battery energy storage of the ith optimal installation node _1,i Frequency and voltage modulation reactive compensation power for battery energy storage of ith optimal installation node, S _1,i And the frequency modulation and voltage regulation total power for the energy storage of the battery of the ith optimal installation node.

wherein Q is _m Configuring reactive power capacity for battery energy storage on a node m, wherein the node m is any optimal installation node; s is the number of nodes for installing the battery energy storage device; 0= g (x, y, u) and

respectively representing an algebraic equation and a differential equation of the simulation system, wherein x represents a differential state variable of the system, y represents an algebraic state variable of the system, and u represents a system parameter; q _max And Q _min Respectively obtaining the maximum value and the minimum value of the energy storage reactive compensation power of the battery;

U _i,k (t _c +t _lim )≥U _lim (5)

wherein, U _i,k The voltage value of the load node i is the fault k; t is t _c The fault clearing time is the sum of the fault occurrence time and the fault duration time; t is t _lim Is the critical time; u shape _lim Judging the critical voltage;

the node voltage overshoot constraint is:

U _i,k (t _max )≤U _s (6)

2-2) respectively arranging the formula (5) and the formula (6) into:

let the initial power of the unit k be P _Gk0 When in use, will

in the formula, err _i ^k Under the condition that a unit k is in fault, the absolute value of the error between the linear fitting function and the original function on the ith section is obtained;

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

and

2-1) determining an objective function:

2-2) determining constraints, including:

energy storage investment capacity constraint;

E ^min ≤E _k ≤E ^max (27)

0≤P _k ≤P ^max (28)

C ^min E _k ≤P _k ≤C ^max E _k (29)

in the formula, E ^max And E ^min Maximum and minimum projected capacity, P, of battery energy storage ^max Maximum operating power, C, for battery energy storage ^max And C ^min Respectively for storing energy of batteriesMaximum magnification and minimum magnification;

energy storage operation constraint;

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

a state mark 0-1 variable of discharge active power of the battery energy storage at the t-th sampling point in a node k under a scene s, wherein 0 represents that the battery energy storage does not allow discharge, and 1 represents that the battery energy storage does not allow dischargeThe stored energy allows discharge;

and

and

E _s,k,0 ＝E _s,k,T ＝E _k ·SOC _ini (39)

in the formula, E _s,k,0 And E _s,k,T Respectively storing the stored electric quantity and SOC of the node k in the initial sampling point and the ending sampling point of each day under the scene s _ini Representing the initial value of the state of charge of the battery energy storage operation;

flow constraint;

and

the current amplitude of the ith line on the ij under the scene s at the t sampling point is obtained; v _s,i,t Is a fieldThe voltage amplitude of the ith node at the tth sampling point under the scene s;

and

and

new energy output constraint;

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

and

respectively is the maximum value and the minimum value of the active power of the renewable energy source on line of the node i at the t-th sampling point under the scene s；

The renewable energy online reactive power of the node i at the t-th sampling point under the scene s is obtained;

and

renewable energy capacity for node i;

system frequency safety constraints;

In a specific embodiment of the present disclosure, the establishing an energy storage optimization planning model considering new energy consumption after configuring battery energy storage at the optimal installation node, and solving to obtain total consumption power of the battery energy storage of the optimal installation node includes:

in the formula, C ^Inv Investment cost for the transmission grid; the total cost of the operation of the power transmission network under the scene s comprises the following steps: cost of generator generation in a power transmission network

Cost of new energy abandonment

Operating maintenance costs of stored energy

energy storage operation constraint, as shown in formula (30) -formula (39);

power flow constraints, as shown in equations (40) -47;

the new energy output constraint is shown as a formula (48) to a formula (50);

and (3) restricting the consumption rate of new energy:

reserve capacity constraint of the system:

In a specific embodiment of the present disclosure, the optimizing and planning for battery energy storage includes:

1) Calculating the total power of the energy stored by the battery:

S _3,i ＝S _1,i +S _2,i (57)

2) Updating the energy storage optimization planning model considering new energy consumption, including:

2-2) increasing SOC _max Decrease SOC _min ；

It should be noted that the above explanation of the embodiment of the battery energy storage optimization planning method is also applicable to a battery energy storage optimization planning apparatus of this embodiment, and is not repeated herein. According to the battery energy storage optimization planning device provided by the embodiment of the disclosure, the optimal installation node of the battery energy storage in the power system is obtained; calculating the frequency modulation and voltage regulation total power and the frequency modulation and voltage regulation capacity of the battery energy storage of the optimal installation node; establishing an energy storage optimization planning model considering new energy consumption after configuring the battery energy storage at the optimal installation node, and solving to obtain the total consumption power of the battery energy storage of the optimal installation node; summing the frequency modulation and voltage regulation total power and the absorption total power to obtain the total power of the battery energy storage of the optimal installation node; and updating the energy storage optimization planning model considering new energy consumption by using the frequency modulation and voltage regulation capacity, and solving to obtain the final capacity of the battery energy storage of the optimal installation node so as to realize the optimization planning of the battery energy storage.

Therefore, the battery energy storage is configured in the power transmission network accessed with the high-proportion new energy, the supporting capability of the voltage and frequency stability of the power transmission network in an emergency is improved, large-scale new energy consumption in a new energy enrichment area is promoted, and the abandon is reduced. By determining the optimal location and volume results of the battery energy storage, the safety and comprehensive operation benefits of the power transmission network are improved by coordinating the battery energy storage and the power grid operation modes.

In order to implement the foregoing embodiments, an embodiment of a third aspect of the present disclosure provides an electronic device, including:

wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a battery energy storage optimization planning method as described above.

To achieve the foregoing embodiments, a fourth aspect of the present disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to execute the foregoing battery energy storage optimization planning method.

It should be noted that the computer readable medium of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform a battery energy storage optimization planning method of the above embodiments.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.

The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.

It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.

It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.

In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium.

The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are exemplary and should not be construed as limiting the present application and that changes, modifications, substitutions and alterations in the above embodiments may be made by those of ordinary skill in the art within the scope of the present application.

Claims

1. A battery energy storage optimization planning method is characterized by comprising the following steps:

summing the frequency modulation and voltage regulation total power and the total power consumption to obtain the total power of the battery energy storage of the optimal installation node; and updating the energy storage optimization planning model considering new energy consumption by using the frequency modulation and voltage regulation capacity, and solving to obtain the final capacity of the battery energy storage of the optimal installation node so as to realize the optimization planning of the battery energy storage.

2. The method of claim 1, wherein obtaining the optimal installation node for battery energy storage in the power system comprises:

1) Building a simulation system corresponding to the power system;

2) Transient voltage stability time domain simulation under the condition of power grid faults is carried out in the simulation system, sensitive nodes are selected according to voltage fluctuation of each node in the simulation, the low voltage duration time of each sensitive node under each fault is recorded, and the difference value between the low voltage duration time of each sensitive node under each fault and the low voltage duration time of other sensitive nodes is calculated;

T _Ci,j,k ＝|T _Ri,k -T _Rj,k |,i≠j (1)

3) Constructing a matrix T about low voltage duration under node failure:

s.t.∑x _i ＝S,x _i ∈{0,1} (4)

wherein i is not equal to j; x is the number of _i For mounting the 0-1 variable, x, of battery energy storage at node i _i =1 indicates that the battery is installed at node i to store energy, and otherwise indicates that the battery is not installed; s represents the number of nodes for installing the battery energy storage device; i is not equal to j;

3. The method of claim 1, wherein the fm regulator total power calculation expression for battery energy storage of the optimal installation node is as follows:

4. The method of claim 3, wherein the FM reactive compensation power obtaining method for battery energy storage of the optimal installation node comprises:

0＝g(x,y,u)

s.t.U _i,k (t _c +t _lim )≥U _lim

U _i,k (t _max )≤U _s

Q _min ≤Q _m ≤Q _max

U _i,k (t _c +t _lim )≥U _lim (5)

the node voltage overshoot constraint is:

U _i,k (t _max )≤U _s (6)

2-2) respectively arranging the formula (5) and the formula (6) into:

5. The method according to claim 3, wherein the method for obtaining the FM active power and the FM capacity of the battery energy storage of the optimal installation node comprises:

the maximum deviation of the system frequency when the unit k fails;

let the initial power of the unit k be P _Gk0 When in use, will

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

and

2-1) determining an objective function:

a cost per power capacity of battery energy storage for the kth optimal installation node; e _k And P _k Respectively storing the built-in capacity and active power of the battery at a node k;

2-2) determining constraints, including:

energy storage investment capacity constraint;

E ^min ≤E _k ≤E ^max (27)

0≤P _k ≤P ^max (28)

C ^min E _k ≤P _k ≤C ^max E _k (29)

in the formula, E ^max And E ^min Maximum and minimum projected capacity, P, of the battery's stored energy, respectively ^max Maximum built-in power for battery energy storage, C ^max And C ^min Maximum energy storage of the battery respectivelyMultiplying power and minimum multiplying power;

energy storage operation constraint;

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

as a lower section of scene sThe battery energy storage in the point k is in a variable 0-1 of the state mark of the discharge active power of the t sampling point, wherein 0 represents that the battery energy storage does not allow discharge, and 1 represents that the battery energy storage allows discharge;

and

and

E _k ·SOC _min ≤E _s,k,t ≤E _k ·SOC _max (37)

wherein E is _s,k,t The storage capacity of the node k at the t sampling point under the scene s is obtained; eta _c And η _d Respectively representing the charging efficiency and the discharging efficiency of the battery energy storage, SOC _max And SOC _min Respectively representing the upper limit and the lower limit of the state of charge of the battery for energy storage operation;

E _s,k,0 ＝E _s,k,T ＝E _k ·SOC _ini (39)

power flow constraint;

(V _i ^min ) ² ≤U _s,i,t ≤(V _i ^max ) ² (47)

and

respectively the first line on the corridor ij under the scene s is sampled at the tThe active power and the reactive power of the sampling points;

the current amplitude of the ith line on the ij under the scene s at the t sampling point is obtained; v _s,i,t The voltage amplitude of the ith node at the tth sampling point under a scene s is obtained;

and

respectively the resistance and reactance of the l line on the corridor ij;

and

new energy output constraint;

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

and

respectively obtaining the maximum value and the minimum value of the active power of the node i on the network at the t-th sampling point under the scene s;

and

is the renewable energy capacity of node i;

system frequency safety constraints;

3) Solving the energy storage optimization planning model considering the frequency support capacity improvement to obtain P of each optimal installation node _k Frequency modulation and voltage regulation active power P as battery energy storage of the node _1,i Obtaining E of each optimal installation node _k Frequency and voltage modulation capacity of battery energy storage as nodeE _1,i 。

6. The method according to claim 5, wherein the establishing of the energy storage optimization planning model considering new energy consumption based on configuring the battery energy storage at the optimal installation node, and the solving to obtain the total consumption power of the battery energy storage of the optimal installation node comprises:

in the formula, C ^Inv Investment cost for the transmission grid; the total cost of the operation of the power transmission network under the scene s comprises the following steps: cost of generator generation in a power transmission grid

Cost of new energy abandonment

Operating maintenance costs of stored energy

energy storage operation constraint, as shown in formula (30) -formula (39);

power flow constraints, as shown in equations (40) -47;

the new energy output constraint is shown as a formula (48) to a formula (50);

and (3) restricting the consumption rate of new energy:

spare capacity constraint of the system:

7. The method of claim 6, wherein the optimizing the planning of the battery energy storage comprises:

1) Calculating the total power of the energy stored by the battery:

S _3,i ＝S _1,i +S _2,i (57)

2-1) newly adding an upper limit and a lower limit of the SOC as optimization variables;

2-2) increasing SOC _max Decrease SOC _min ；

8. A battery energy storage optimization planning apparatus, comprising:

9. The apparatus of claim 8, wherein the battery-powered FM total power calculation expression for the optimal installation node is as follows:

wherein, P _1,i FM-PM active power, Q, for battery energy storage at the ith optimal installation node _1,i Frequency and voltage modulation reactive compensation power for battery energy storage of ith optimal installation node, S _1,i And storing the total power of the frequency modulation and voltage regulation of the energy of the battery of the ith optimal installation node.

10. The apparatus of claim 9, wherein the battery-powered fm-regulator reactive compensation power harvesting method of the optimal installation node comprises:

0＝g(x,y,u)

s.t.U _i,k (t _c +t _lim )≥U _lim

U _i,k (t _max )≤U _s

Q _min ≤Q _m ≤Q _max

respectively representing an algebraic equation and a differential equation of the simulation system, wherein x represents a differential state variable of the system, y represents an algebraic state variable of the system, and u represents a system parameter; q _max And Q _min Respectively the maximum value and the minimum value of the energy storage reactive compensation power of the battery;

U _i,k (t _c +t _lim )≥U _lim (5)

the node voltage overshoot constraint is:

U _i,k (t _max )≤U _s (6)

2-2) respectively arranging the formula (5) and the formula (6) into:

solving the battery energy storage reactive compensation power optimization model to obtain Q of each optimal installation node _m As the nodeFrequency-modulation voltage-regulation reactive compensation power Q of battery energy storage _1,i 。

11. The apparatus of claim 9, wherein the method for obtaining the fm regulator active power and the fm regulator capacity of the battery energy storage of the optimal installation node comprises:

let the initial power of the unit k be P _Gk0 When in use, will

in the formula，Err _i ^k Under the condition that a unit k has a fault, the absolute value of the error between the linear fitting function and the original function on the ith section is calculated;

solving is carried out on the linear fitting model, slope b and intercept a of each fitting line segment are obtained through solving, and then the formula (22) is converted into linearized frequency safety constraint shown as follows:

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

and

2-1) determining an objective function:

specific energy capacity for storing energy of battery of kth optimal installation nodeThe cost of the amount of the catalyst is reduced,

2-2) determining constraints, including:

energy storage investment capacity constraint;

E ^min ≤E _k ≤E ^max (27)

0≤P _k ≤P ^max (28)

C ^min E _k ≤P _k ≤C ^max E _k (29)

in the formula, E ^max And E ^min Maximum and minimum projected capacity, P, of the battery's stored energy, respectively ^max Maximum built-in power for battery energy storage, C ^max And C ^min Respectively the maximum multiplying power and the minimum multiplying power of the energy storage of the battery;

energy storage operation constraint;

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

and

and

E _k ·SOC _min ≤E _s,k,t ≤E _k ·SOC _max (37)

E _s,k,0 ＝E _s,k,T ＝E _k ·SOC _ini (39)

power flow constraint;

(V _i ^min ) ² ≤U _s,i,t ≤(V _i ^max ) ² (47)

and

and

respectively the resistance and reactance of the l line on the corridor ij;

and

the maximum value of the current, V, for the l-th line in corridor ij _i ^max And V _i ^min Is the maximum voltage of the ith node;

new energy output constraint;

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

and

and

renewable energy capacity for node i;

system frequency safety constraints;

3) Solving the energy storage optimization planning model considering the frequency support capacity improvement to obtain P of each optimal installation node _k Frequency modulation and voltage regulation active power P used as battery energy storage of the node _1,i To obtain E of each optimal installation node _k Frequency and voltage modulation capacity E of battery energy storage as the node _1,i 。

12. The apparatus of claim 11, wherein the establishing an energy storage optimization planning model considering new energy consumption based on configuring battery energy storage at the optimal installation node, and solving to obtain total consumption power of the battery energy storage of the optimal installation node comprises:

in the formula, C ^Inv Investment costs for power transmission networks; grid operation in scene sThe total line cost includes: cost of generator generation in a power transmission network

Cost of new energy abandonment

Operating maintenance costs of stored energy

energy storage operation constraint, as shown in formula (30) -formula (39);

a power flow constraint, as shown in equations (40) - (47);

the new energy output constraint is shown as a formula (48) to a formula (50);

and (3) restricting the consumption rate of new energy:

spare capacity constraint of the system:

13. The apparatus of claim 12, wherein the optimizing the battery energy storage comprises:

1) Calculating the total power of the energy stored by the battery:

S _3,i ＝S _1,i +S _2,i (57)

2-2) increasing SOC _max Decrease SOC _min ；

in the formula, E ₁ Optimizing the optimization result of the energy storage optimization planning model considering the frequency support capacity improvement;

14. An electronic device, comprising:

wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1-7.

15. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-7.