CN110059840B - Method and system for selecting address of battery energy storage system in receiving-end power grid - Google Patents
Method and system for selecting address of battery energy storage system in receiving-end power grid Download PDFInfo
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Abstract
The invention provides a method and a system for selecting a battery energy storage system in a receiving end power grid, wherein the method comprises the following steps: acquiring basic data of an energy storage system; and taking the acquired basic data into a battery energy storage construction model which is built in advance, and calculating to obtain the optimal site of the energy storage system after the economy is considered. According to the invention, the site selection and the economy of battery energy storage in the receiving end power grid are combined, and the economic benefit maximization is used as an objective function, so that the practical feasibility of adding the energy storage system into the power distribution network is greatly improved, the impact caused by serious faults is reduced, the operation stability and reliability are improved, and the calculation capability and the operation efficiency of an algorithm are greatly improved. Meanwhile, the energy storage system is added to bring the advantages of the energy storage system into play, so that the receiving capacity of the receiving end power grid to new energy sources is increased, and the power grid system can be supported and regulated to a certain extent.
Description
Technical field:
the invention belongs to the technical field of energy storage, and particularly relates to a method and a system for selecting a battery energy storage system in a receiving-end power grid.
The background technology is as follows:
In recent years, due to the large amount of grid connection of new energy sources with strong volatility and randomness, the power grid at the receiving end of the new energy sources faces the large amount of problems of further increase of peak-valley difference, low investment and extension income of the power distribution network, low utilization rate of main equipment and the like, and the power supply reliability of the power distribution network is greatly reduced. The energy storage system is connected with the system to play a role in supporting and adjusting the system to a certain extent. The reasonable planning layout in the power grid not only can effectively absorb the distributed energy, but also can be used as a means for providing emergency power support, reducing power impact caused by serious faults, improving the running stability and reliability of the power grid, adjusting the frequency and compensating the load fluctuation, however, the research on the location method of the battery energy storage system in the receiving-end power grid is single.
The large-scale receiving end power grid is primarily formed through nationwide interconnection, the characteristics of the urban receiving end power grid are considered, the original power grid planning mode and method cannot completely adapt to the future development trend, and particularly the threat that the operation of the power system faces the problem of voltage stability is increasingly prominent along with the continuous development of power grid interconnection, the increasing expansion of the load scale of the power receiving end system and the market reformation of the power constitution. The rapid development of battery energy storage technology, the equivalent full life cycle electricity cost of lithium ion batteries and lead carbon batteries is reduced, the application conditions of peak-valley electricity difference arbitrage are provided, and the energy storage site selection established in economic optimization in the existing research is less mentioned. In addition, in the site selection layout of battery energy storage, the layout of a battery energy storage system is complex, the calculated amount is large, and the defect of great existence exists.
The invention comprises the following steps:
In order to overcome the above-mentioned drawbacks, the present invention provides a method for locating a battery energy storage system in a receiving-end power grid, the method comprising:
Acquiring basic data of an energy storage system;
And taking the acquired basic data into a battery energy storage construction model which is built in advance, and calculating to obtain the optimal site of the energy storage system after the economy is considered.
Preferably, the step of bringing the obtained basic data into a pre-established battery energy storage construction model, and the step of calculating includes:
assigning values for an objective function and constraint conditions in a pre-established battery energy storage construction model by using the acquired basic data;
Initializing an ant colony by using preset initial data;
performing ant colony iterative computation on the battery energy storage construction model by adopting an ant colony algorithm;
Recording iteration result data;
If the ant colony algorithm reaches the maximum iteration number, outputting the iteration result data as an optimal address selection position; otherwise, generating next generation ant colony based on the iteration result data, selecting a moving position according to the probability, updating initial data, and continuing to perform iteration calculation until the maximum iteration number is reached.
The selecting a direction of movement according to the probability includes:
The probability of moving to any direction is calculated as follows:
Wherein: Representing the probability of ant k transitioning from object i to object j; τ ij (t) represents the pheromone concentration on the paths of the target i and j at the moment t; η ij (t) is a heuristic function representing the expected degree of ants from i to j; allow k represents the set of targets to be accessed by ant k; alpha and beta are used to control the relative importance of the pheromone and heuristic functions, respectively;
The direction with the highest probability is selected for movement.
Preferably, the objective function of the battery energy storage construction model is as follows:
Wherein Z min is the minimum value of economic cost; i 0 is the expected yield; sr c,i is the installation unit capacity of the ith node of the energy storage device; k 1 is the cost price of the energy storage unit capacity; n 1 is the life cycle of the battery energy storage system obtained according to the net benefit analysis; sr s,i is the installation unit capacity of the ith node of the converter device; k 2 is the cost price of the converter in unit capacity; n 2 is the life cycle of the converter obtained according to net income analysis; sr p,i is the installation unit capacity of the ith node of the power distribution device; k 3 is the cost price per unit capacity of the switchgear; n 3 is the life span of the power distribution device obtained according to the net income analysis;
The direction with the highest probability is selected for movement.
Preferably, the constraint conditions of the battery energy storage construction model include:
The method comprises the following steps of improving power constraint, node voltage fluctuation value constraint, line maximum transmission power constraint, transient stability constraint, energy storage battery capacity constraint and energy storage discharge capacity constraint of a power grid at a receiving end.
Preferably, the power constraint of the power grid at the receiving end is calculated according to the following formula:
Wherein: p rt is the actual received power of the receiving end power grid after energy storage is installed; beta-mounting the lifting coefficient after energy storage; and receiving power before the energy storage is not installed on the receiving-end power grid.
Preferably, the node voltage fluctuation constraint is calculated according to the following formula:
dUk％≤dUmax％
Wherein: k is an energy storage installation node; where dU k% is the voltage ripple value at node k.
Preferably, the line maximum transmission power constraint is calculated as follows:
Pi'≤Pimax
wherein P imax is the maximum allowed power of line imax.
Preferably, the transient stability constraint comprises: voltage stabilization and frequency stabilization;
The voltage is stable and is calculated according to the following formula:
Wherein: is the relation expression of the power and the voltage of the receiving end network;
the frequency is stable and calculated according to the following formula:
|Δf|≤ε
Wherein: the I delta f I is a frequency fluctuation value in the power distribution network; epsilon is the critical value for frequency stability.
Preferably, the energy storage battery capacity constraint is calculated according to the following formula:
Wherein P ESS、QESS is the active power and the reactive power of the battery energy storage device respectively; p PCS、QPCS is the active and reactive power of the converter device, respectively.
Preferably, the energy storage and discharge amount constraint is calculated according to the following formula:
0≤E(t)≤Ecmax
Wherein: e cmax is the maximum charge-discharge energy of the energy storage battery system.
Preferably, the acquiring basic information includes: the expected yield, the unit capacity of the energy storage device, the service life of the battery energy storage system, the unit capacity of the converter device, the service life of the converter, the unit capacity of the distribution device and the service life of the distribution device.
Preferably, the preset initial data includes: the ant colony algorithm comprises the individual number, information element volatilization factors, constant coefficients, heuristic coefficients, information element importance factors, information element matrixes, a path record table, information of optimal paths of each generation and iteration times.
A battery energy storage system site selection system in a receiving-side power grid, the system comprising:
the acquisition module is used for: the method comprises the steps of acquiring basic data of an energy storage system;
And (3) an address selecting module: and the method is used for bringing the acquired basic data into a pre-established battery energy storage construction model, and carrying out iterative calculation to obtain the optimal address of the energy storage system.
Preferably, the addressing module includes: the computing unit is used for assigning values for objective functions and constraint conditions in a pre-established battery energy storage construction model by using the acquired basic data;
Initializing an ant colony by using preset initial data;
performing ant colony iterative computation on the battery energy storage model by adopting an ant colony algorithm;
Recording iteration result data;
If the ant colony algorithm reaches the maximum iteration number, outputting the iteration result data as an optimal address selection position; otherwise, generating next generation ant colony based on the iteration result data, selecting a moving position according to the probability, updating initial data, and continuing to perform iteration calculation until the maximum iteration number is reached.
And the address selecting unit is used for determining iteration result data reaching the maximum iteration number as the optimal address of the energy storage system.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a method for selecting the address of a battery energy storage system in a receiving end power grid, which outputs the optimal address of energy storage when economy is optimal, realizes the combination of the address selection of the battery energy storage in the receiving end power grid and economy, ensures that the economic benefit is maximum, and ensures that the power grid added with the battery energy storage can run safely, stably and reliably.
2. The invention provides a method for selecting the address of a battery energy storage system in a receiving-end power grid, which takes the maximization of economic benefit as an objective function to greatly improve the practical feasibility of adding the energy storage system into a power distribution network. Meanwhile, the energy storage system is added to exert the advantages of the energy storage system, so that the receiving capacity of the receiving end power grid to new energy sources is increased, the power grid system can be supported and regulated to a certain extent, and the energy storage system is used as a means for providing emergency power support, reducing impact caused by serious faults, improving the running stability and reliability of the power grid, adjusting the frequency and compensating load fluctuation.
3. The invention provides a method for selecting the address of a battery energy storage system in a receiving end power grid, which takes the complex layout and large calculation amount of the battery energy storage system into consideration on the site selection layout of battery energy storage, and adopts a distributed calculation mode in the ant colony algorithm searching process, so that a plurality of individuals perform parallel calculation at the same time, and the calculation capacity and the operation efficiency of an algorithm are greatly improved.
Description of the drawings:
FIG. 1 is a flow chart of an implementation method of the present invention;
fig. 2 is a flow chart of a method for locating a battery energy storage system in a receiving-end power grid according to the present invention.
The specific embodiment is as follows:
For a better understanding of the present invention, reference is made to the following description, taken in conjunction with the accompanying drawings and examples, in which:
example 1
The invention provides a method for selecting a location of a battery energy storage system in a receiving-end power grid, as shown in fig. 1, the method can comprise the following steps:
Acquiring basic data of an energy storage system;
And taking the acquired basic data into a battery energy storage construction model which is built in advance, and calculating to obtain the optimal site of the energy storage system after the economy is considered.
As shown in fig. 2, in the flow chart of the method for selecting the address of the battery energy storage system in the receiving-end power grid provided by the invention, under the condition that the safe, stable and reliable operation of the power grid added with energy storage is ensured, a distributed computing mode is adopted in the searching process of an ant colony algorithm, and a plurality of individuals perform parallel computation at the same time, so that the characteristics of high computing capacity and operating efficiency of the algorithm are greatly improved, the complexity of solving is greatly reduced, and the optimal address of energy storage with economic optimization is also met. The method comprises the following steps:
Step 1: basic data of the energy storage system are obtained, and the ant colony is initialized by using preset initial data;
step 2: and the obtained basic data is brought into a battery energy storage construction model which is built in advance, calculation is carried out, and the optimal site of the energy storage system after the economy is considered is obtained.
The pre-established battery energy storage construction model comprises
Step 2.1: inputting an objective function and a network constraint condition in an ant colony algorithm;
step 2.2: the ant colony algorithm starts to iterate;
step 2.3: evaluating ant colony information and constructing an optimal position of a solution space;
Step 2.4: and if the ant colony algorithm reaches the maximum iteration number, outputting a result, and if the ant colony algorithm does not reach the maximum iteration number, returning to the step 2.2, and finally realizing the optimal site of battery energy storage in the economic optimal state.
And step1, initializing and setting the individual number of the ant colony algorithm, the pheromone volatilization factor, the constant coefficient, the heuristic coefficient, the pheromone importance factor, the pheromone matrix, the path record table, the information of each generation of optimal paths and the maximum iteration number.
The basic data in the step 1 includes:
the expected yield, the unit capacity of the energy storage device, the service life of the battery energy storage system, the unit capacity of the converter device, the service life of the converter, the unit capacity of the distribution device and the service life of the distribution device.
And 2.1, establishing a battery energy storage construction model taking the optimal economical efficiency as an objective function, and giving the optimal position of the energy storage layout according to the total objective function. The specific calculation method is as follows:
Wherein Z min is the minimum value of economic cost; i 0 is the expected yield; sr c,i is the installation unit capacity of the ith node of the energy storage device; k 1 is the cost price of the energy storage unit capacity; n 1 is the life cycle of the battery energy storage system obtained according to the net benefit analysis; sr s,i is the installation unit capacity of the ith node of the converter device; k 2 is the cost price of the converter in unit capacity; n 2 is the life cycle of the converter obtained according to net income analysis; sr p,i is the installation unit capacity of the ith node of the power distribution device; k 3 is the cost price per unit capacity of the switchgear; n 3 is the life span of the power distribution device based on the net benefit analysis.
The network constraint condition in the step 2.1 includes:
Node voltage ripple value constraint:
dUk％≤dUmax％ (2)
Line maximum transmission power constraint:
Pi'≤Pimax (3)
dU k% is the voltage fluctuation value of the node k, and the maximum value of the voltage fluctuation of the reference national standard is dU max%;
P imax is the maximum allowed power of line i;
Transient stability constraints under active power flow conditions are satisfied:
Voltage stabilization:
Frequency stabilization:
|Δf|≤ε (5)
is the relation expression of the power and the voltage of the receiving end network;
The I delta f I is a frequency fluctuation value in the power distribution network; epsilon is a critical value of frequency stability;
Energy storage battery capacity constraint
Wherein P ESS QESS is the active power and the reactive power of the battery energy storage device respectively;
wherein P PCS QPCS is the active power and the reactive power of the converter device respectively;
Energy storage and discharge capacity constraint:
0≤E(t)≤Ecmax (7)
e cmax is the maximum charge-discharge energy of the energy storage battery system.
The initial iteration is performed in the step 2.2, including: according to the set maximum iteration times, starting ant colony iteration calculation based on the objective function and the constraint condition;
The iteration result data is then recorded.
And 2.3, evaluating the ant colony, and extracting the optimal position of the solution space. The energy storage site selection under the optimal economy is extracted, and the ant colony is evaluated, namely, the ant colony algorithm automatically evaluates one of the calculation results of each iteration to evaluate whether the optimal output result is reached.
Each time an iteration of the ant colony algorithm is performed, a group of iteration results appear, and the results are a group of address selecting positions of the battery energy storage obtained in the iteration. This set of results is called the solution space.
Evaluation criteria: related to the initial data set. For example: maximum number of iterations.
Each time an iteration of the ant colony algorithm is performed, a group of iteration results appear, and the results are a group of address selecting positions of the battery energy storage obtained in the iteration. This set of results is called the solution space.
Each iteration produces a new set of data information for the addressed location.
And 2.4, outputting a result if the ant colony algorithm reaches the maximum iteration number, and returning to the step 2.2 if the ant colony algorithm does not reach the maximum iteration number, so as to finally realize the optimal site of battery energy storage in the economic optimal state. The method specifically comprises the following steps:
If the iteration number of the ant colony algorithm reaches the set maximum iteration number, outputting a result, if the iteration number of the ant colony algorithm does not reach the set maximum iteration number, generating a next generation population by an initial individual, selecting a moving direction according to probability, updating initial data, and repeating the step 2.2 to finally realize the optimal site for energy storage when the economy is optimal.
The probability calculation method is as follows:
The ant colony algorithm simulates the ant foraging process.
Wherein the method comprises the steps ofRepresenting the probability that ant k transitions from object i to object j.
Τ ij (t) represents the pheromone concentration on the paths of the targets i and j at time t. At the initial time, the pheromone concentration on each target connection path is the same, and τ ij(0)＝τ0 is set.
Η ij (t) is a heuristic function representing the expected degree of ants from i to j.
Allow k represents the set of objects to be accessed by ant k, α and β being used to control the relative importance of pheromones and heuristic functions, respectively. For example, when the value of α is larger, it means that the pheromone concentration plays a larger role in the transfer.
The probability value is updated once every time an iterative calculation is performed, and is different.
And updating the initial data, namely generating a group of new address selecting positions for each iteration and updating in real time.
Example 2
Based on the same inventive concept, the embodiment of the invention also provides a battery energy storage system address selection system in the receiving end power grid, wherein the principle of the battery energy storage system address selection system in the receiving end power grid is similar to that of the battery energy storage system address selection method in the receiving end power grid, and the repeated parts are not repeated; the following describes a battery energy storage system site selection system in the receiving-end power grid.
The system may include:
the acquisition module is used for: the method comprises the steps of acquiring basic data of an energy storage system;
And (3) an address selecting module: and the method is used for bringing the acquired basic data into a pre-established battery energy storage construction model, and carrying out iterative calculation to obtain the optimal address of the energy storage system.
The addressing module comprises: the computing unit is used for assigning values for objective functions and constraint conditions in a pre-established battery energy storage construction model by using the acquired basic data;
Initializing an ant colony by using preset initial data;
performing ant colony iterative computation on the battery energy storage model by adopting an ant colony algorithm;
Recording iteration result data;
If the ant colony algorithm reaches the maximum iteration number, outputting the iteration result data as an optimal address selection position; otherwise, generating next generation ant colony based on the iteration result data, selecting a moving position according to the probability, updating initial data, and continuing to perform iteration calculation until the maximum iteration number is reached.
And the address selecting unit is used for determining iteration result data reaching the maximum iteration number as the optimal address of the energy storage system.
The objective function of the pre-established battery energy storage construction model is as follows:
Wherein Z min is the minimum value of economic cost; i 0 is the expected yield; sr c,i is the installation unit capacity of the ith node of the energy storage device; k 1 is the cost price of the energy storage unit capacity; n 1 is the life cycle of the battery energy storage system obtained according to the net benefit analysis; sr s,i is the installation unit capacity of the ith node of the converter device; k 2 is the cost price of the converter in unit capacity; n 2 is the life cycle of the converter obtained according to net income analysis; sr p,i is the installation unit capacity of the ith node of the power distribution device; k 3 is the cost price per unit capacity of the switchgear; n 3 is the life span of the power distribution device based on the net benefit analysis.
The acquisition module is used for basic information, and the basic information can comprise: the expected yield, the unit capacity of the energy storage device, the service life of the battery energy storage system, the unit capacity of the converter device, the service life of the converter, the unit capacity of the distribution device, the service life of the distribution device and the like.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and block diagrams of methods, systems, and computer program products according to embodiments of the application. It will be understood that each flowchart and block of the flowchart and block diagrams, and combinations of flowcharts and block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and block diagram block or blocks.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (7)
1. A method for locating a battery energy storage system in a receiving-end power grid, the method comprising:
Acquiring basic data of an energy storage system;
The obtained basic data are brought into a battery energy storage construction model which is built in advance, calculation is carried out, and the optimal site of the energy storage system after economy is considered is obtained;
The objective function of the battery energy storage construction model is as follows:
Wherein Z min is the minimum value of economic cost; i 0 is the expected yield; sr c,i is the installation unit capacity of the ith node of the energy storage device; k 1 is the cost price of the energy storage unit capacity; n 1 is the life cycle of the battery energy storage system obtained according to the net benefit analysis; sr s,i is the installation unit capacity of the ith node of the converter device; k 2 is the cost price of the converter in unit capacity; n 2 is the life cycle of the converter obtained according to net income analysis; sr p,i is the installation unit capacity of the ith node of the power distribution device; k 3 is the cost price per unit capacity of the switchgear; n 3 is the life span of the power distribution device obtained according to the net income analysis;
the constraint conditions of the battery energy storage construction model comprise:
The method comprises the following steps of improving power constraint of a power grid at a receiving end, constraint of a node voltage fluctuation value, constraint of a maximum transmission power of a line, constraint of transient stability, constraint of energy storage battery capacity and constraint of energy storage discharge;
The power constraint of the power grid lifting at the receiving end is calculated according to the following formula:
Wherein: p rt is the actual received power of the receiving end power grid after energy storage is installed; beta-mounting the lifting coefficient after energy storage; Receiving power before energy storage is not installed for a receiving-end power grid;
the node voltage fluctuation value constraint is calculated according to the following formula:
dUk≤dUmax％
Wherein: k is an energy storage installation node; wherein dU k% is the voltage fluctuation value of the node k;
the maximum transmission power constraint of the line is calculated according to the following formula:
Pi′≤Pimax
Wherein P imax is the maximum allowable passing power of the line i;
the transient stability constraint comprises: voltage stabilization and frequency stabilization;
The voltage is stable and is calculated according to the following formula:
Wherein: Is a relational expression of power and voltage of the receiving end power grid;
the frequency is stable and calculated according to the following formula:
|Δf|≤ε
wherein: the I delta f I is a frequency fluctuation value in the power distribution network; epsilon is a critical value of frequency stability;
The energy storage battery capacity constraint is calculated according to the following formula:
wherein P ESS、QESS is the active power and the reactive power of the battery energy storage device respectively; p PCS、QPCS is the active power and reactive power of the converter device respectively;
the energy storage and discharge capacity constraint is calculated according to the following formula:
0≤E(t)≤Ecmax
Wherein: e cmax is the maximum charge-discharge energy of the energy storage battery system.
2. The method for locating a battery energy storage system in a receiving-end power grid according to claim 1, wherein said bringing the obtained basic data into a pre-established battery energy storage construction model, and performing the calculation comprises:
assigning values for an objective function and constraint conditions in a pre-established battery energy storage construction model by using the acquired basic data;
Initializing an ant colony by using preset initial data;
performing ant colony iterative computation on the battery energy storage construction model;
Recording iteration result data;
if the ant colony algorithm reaches the maximum iteration number, outputting the iteration result data as an optimal address selection position; otherwise, generating next generation ant colony based on the iteration result data, selecting a moving direction according to the probability, updating initial data, and continuing to perform iteration calculation until the maximum iteration number is reached.
3. The method for locating a battery energy storage system in a power grid according to claim 2, wherein the selecting a direction of movement according to probability comprises:
the probability of moving to any direction is calculated as:
Wherein: Representing the probability of ant k transitioning from object i to object j; τ ij (t) represents the pheromone concentration on the paths of the target i and j at the moment t; η ij (t) is a heuristic function representing the expected degree of ants from i to j; allow k represents the set of targets to be accessed by ant k; alpha and beta are used to control the relative importance of the pheromone and heuristic functions, respectively;
The direction with the highest probability is selected for movement.
4. The method for locating a battery energy storage system in a power grid of claim 1, wherein said base information comprises: the expected yield, the unit capacity of the energy storage device, the service life of the battery energy storage system, the unit capacity of the converter device, the service life of the converter, the unit capacity of the distribution device and the service life of the distribution device.
5. The method for locating a battery energy storage system in a power grid of claim 2, wherein the predetermined initial data comprises: the ant colony algorithm comprises the individual number, information element volatilization factors, constant coefficients, heuristic coefficients, information element importance factors, information element matrixes, a path record table, information of optimal paths of each generation and iteration times.
6. A battery energy storage system site selection system in a receiving-side power grid, the system comprising:
the acquisition module is used for: the method comprises the steps of acquiring basic data of an energy storage system;
And (3) an address selecting module: the method comprises the steps of carrying the acquired basic data into a pre-established battery energy storage construction model, and carrying out iterative calculation to obtain the optimal address of an energy storage system;
The objective function of the battery energy storage construction model is as follows:
Wherein Z min is the minimum value of economic cost; i 0 is the expected yield; sr c,i is the installation unit capacity of the ith node of the energy storage device; k 1 is the cost price of the energy storage unit capacity; n 1 is the life cycle of the battery energy storage system obtained according to the net benefit analysis; sr s,i is the installation unit capacity of the ith node of the converter device; k 2 is the cost price of the converter in unit capacity; n 2 is the life cycle of the converter obtained according to net income analysis; sr p,i is the installation unit capacity of the ith node of the power distribution device; k 3 is the cost price per unit capacity of the switchgear; n 3 is the life span of the power distribution device obtained according to the net income analysis;
the constraint conditions of the battery energy storage construction model comprise:
The method comprises the following steps of improving power constraint of a power grid at a receiving end, constraint of a node voltage fluctuation value, constraint of a maximum transmission power of a line, constraint of transient stability, constraint of energy storage battery capacity and constraint of energy storage discharge;
The power constraint of the power grid lifting at the receiving end is calculated according to the following formula:
Wherein: p rt is the actual received power of the receiving end power grid after energy storage is installed; beta-mounting the lifting coefficient after energy storage; Receiving power before energy storage is not installed for a receiving-end power grid;
the node voltage fluctuation value constraint is calculated according to the following formula:
dUk≤dUmax％
Wherein: k is an energy storage installation node; wherein dU k% is the voltage fluctuation value of the node k;
the maximum transmission power constraint of the line is calculated according to the following formula:
Pi′≤Pimax
Wherein P imax is the maximum allowable passing power of the line i;
the transient stability constraint comprises: voltage stabilization and frequency stabilization;
The voltage is stable and is calculated according to the following formula:
Wherein: Is a relational expression of power and voltage of the receiving end power grid;
the frequency is stable and calculated according to the following formula:
|Δf|≤ε
wherein: the I delta f I is a frequency fluctuation value in the power distribution network; epsilon is a critical value of frequency stability;
The energy storage battery capacity constraint is calculated according to the following formula:
wherein P ESS、QESS is the active power and the reactive power of the battery energy storage device respectively; p PCS、QPCS is the active power and reactive power of the converter device respectively;
the energy storage and discharge capacity constraint is calculated according to the following formula:
0≤E(t)≤Ecmax
Wherein: e cmax is the maximum charge-discharge energy of the energy storage battery system.
7. The system of claim 6, wherein the location module comprises: the computing unit is used for assigning values for objective functions and constraint conditions in a pre-established battery energy storage construction model by using the acquired basic data; initializing an ant colony by using preset initial data; performing ant colony iterative computation on the battery energy storage model by adopting an ant colony algorithm; recording iteration result data; if the ant colony algorithm reaches the maximum iteration number, outputting the iteration result data as an optimal address selection position; otherwise, generating next generation ant colony based on the iteration result data, selecting a moving position according to the probability, updating initial data, and continuing to perform iteration calculation until the maximum iteration number is reached; and the address selecting unit is used for determining iteration result data reaching the maximum iteration number as the optimal address of the energy storage system.
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