Energy storage configuration method and system for transformer capacity optimization

Abstract

The application discloses a kind of energy storage configuration methods for transformer capacity optimization, including obtaining the data information of target power system and target transformer;Fitting target transformer user side's load data curve and calculating to obtain energy storage virtual configuration capacity;The valley period charging capacity and peak period discharging capacity of energy storage system are calculated;Energy storage configuration optimization model is constructed and the final energy storage configuration scheme for transformer capacity optimization is solved.The application also discloses a kind of system for realizing the energy storage configuration method for transformer capacity optimization.The application considers various types of influencing factors comprehensively, not only realizes the energy storage configuration for transformer capacity optimization, but also the reliability of the application is higher, the accuracy is better, and it is more objective and scientific.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, and specifically relates to an energy storage configuration method and system for transformer capacity optimization. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, power system loads are growing rapidly, but the capacity of power system transformers cannot be changed quickly. This is because replacing transformers is too costly and time-consuming, and larger capacity transformers will operate under light load during off-peak hours, thus affecting their lifespan. Therefore, optimizing the capacity of existing transformers has become an important research topic in power systems.

[0004] In recent years, with the development of energy storage technology, the use of energy storage systems to optimize transformer capacity can alleviate the impact on users' electricity consumption caused by insufficient transformer capacity during peak electricity demand periods. It can also charge the energy storage system from the grid during off-peak electricity demand periods, playing a role in peak shaving and valley filling, thereby ensuring the stable operation of the power system.

[0005] Currently, although there are energy storage configuration schemes for transformer capacity optimization, these schemes have poor reliability and accuracy due to the limited number of influencing factors considered, and thus cannot meet the needs of practical applications. Summary of the Invention

[0006] One of the objectives of this invention is to provide a reliable, accurate, and objective scientific method for optimizing transformer capacity through energy storage configuration.

[0007] A second objective of this invention is to provide a system for implementing the energy storage configuration method for transformer capacity optimization.

[0008] The energy storage configuration method for transformer capacity optimization provided by this invention includes the following steps:

[0009] S1. Obtain data information about the target power system and the target transformer;

[0010] S2. Based on the data information obtained in step S1, fit the load data curve of the target transformer user side and calculate the virtual configuration capacity of energy storage;

[0011] S3. Based on the data obtained in step S2, calculate the charging capacity during off-peak hours and the discharging capacity during peak hours of the energy storage system;

[0012] S4. Based on the data obtained in step S3, construct an energy storage configuration optimization model with the objective function of maximizing the overall efficiency of the energy storage system and with energy storage capacity, line current and line voltage as constraints.

[0013] S5. Combining the particle swarm optimization algorithm and the gray wolf algorithm, the energy storage configuration optimization model constructed in step S4 is solved to obtain the final energy storage configuration scheme for transformer capacity optimization.

[0014] Step S2, which involves fitting the load data curve of the target transformer user side based on the data information obtained in step S1 and calculating the virtual energy storage configuration capacity, specifically includes the following steps:

[0015] Obtain the capacity S of the target transformer N ;

[0016] Obtain the full-day load capacity data of the target transformer's user side on a typical day, fit the load data curve, and obtain the maximum load capacity on the load data curve.

[0017] The calculated virtual configuration capacity S of energy storage is obtained. v for

[0018] Step S3, which involves calculating the off-peak charging capacity and peak-peak discharging capacity of the energy storage system based on the data obtained in step S2, specifically includes the following steps:

[0019] The off-peak charging capacity q is calculated using the following formula. c :

[0020]

[0021] In the formula T i,N For the target transformer capacity S N Greater than the load demand capacity S l The start time of the i-th time period, T i,M For the target transformer capacity S N Greater than the load demand capacity S l The end time of the i-th time period, Power factor;

[0022] The peak discharge capacity q is calculated using the following formula. f :

[0023]

[0024] In the formula T j,W For the target transformer capacity S N Less than the load demand capacity S l The start time of the j-th time period, T j,V For the target transformer capacity S N Less than the load demand capacity S l The end time of the j-th time period.

[0025] Step S4, which uses maximizing the overall efficiency of the energy storage system as the objective function, specifically includes the following steps:

[0026] The following formula is used as the objective function:

[0027] maxH = -A + BCDE

[0028] In the formula, H represents the overall benefit of the energy storage system; A represents the charging cost of the energy storage system during the off-peak electricity load period throughout the year, and A = q c *e d *h,e d The off-peak electricity price is given by h, which represents the number of hours throughout the year; B represents the cost of the energy storage system discharging electricity during peak electricity load periods throughout the year, and B = q. f *e g *h,e g C is the peak electricity price; D is the construction investment cost of the energy storage system; E is the cost of power loss for users caused by insufficient rated capacity of the target transformer during peak electricity load periods throughout the year; and E is the annual maintenance cost of the energy storage system.

[0029] Step S4, which uses energy storage capacity, line current, and line voltage as constraints, specifically includes the following steps:

[0030] The following formula is used as the constraint condition:

[0031]

[0032] In the formula C min C represents the lowest construction investment cost for energy storage systems. max This represents the highest construction investment cost for energy storage systems; K B S is the configuration coefficient; C To optimize the capacity of the energy storage system; i min i is the lower limit of the line current; i is the line current value; i max This represents the upper limit of the line current; u min The lower limit of the line voltage; u is the line voltage value; u max This is the upper limit of the line voltage.

[0033] Step S5, which combines particle swarm optimization and gray wolf algorithm to solve the energy storage configuration optimization model constructed in step S4, specifically includes the following steps:

[0034] The Grey Wolf algorithm is used to solve the energy storage configuration optimization model constructed in step S4. During the solution process, the Particle Swarm Optimization algorithm is used to optimize the Grey Wolf algorithm to improve the solution speed.

[0035] Step S5, which combines particle swarm optimization and gray wolf algorithm to solve the energy storage configuration optimization model constructed in step S4, specifically includes the following steps:

[0036] A. Set the number of particles N, the number of iterations K, and the number of variables nar for the particle swarm optimization algorithm;

[0037] B. Set the fitness function to...

[0038] C. Under constraints, calculate the fitness value pbest for each particle. ii K ii takes values ​​from 1 to N; obtain the optimal fitness value gbest K gbest K =min[pbest ii K ];

[0039] D. Update the particle's position and velocity using the following formula:

[0040]

[0041] In the formula ω represents the velocity of particle ii in the (K+1)th iteration; ω is the inertia factor. c1 is the velocity of particle ii in the Kth iteration; c1 is the first learning factor; rand(*) is a random number between 0 and 1; X ii K c1 represents the position of particle ii in the Kth iteration; c2 represents the second learning factor; X represents the position of particle ii in the Kth iteration. ii K+1 This represents the position of particle ii during the (K+1)th iteration.

[0042] E. Recalculate the fitness values ​​of each particle and update the best fitness value gbest. K Select the optimal fitness value gbest K The three optimal fitness values ​​in the vector are used as the initial optimal solution α, the initial suboptimal solution β, and the initial candidate solution δ of the gray wolf individual in the gray wolf algorithm, respectively, and the remaining solutions are used as the ordinary solutions ω.

[0043] F. Update the distance between the current solution and the optimal solution of the model using the following formula:

[0044]

[0045] In the formula D Z To solve for the distance the individual gray wolf moves corresponding to Z; C Z The second coefficient vector; Z = α, β, δ;

[0046] G. Update the gray wolf's position using the following formula:

[0047]

[0048] In the formula To find the position of the gray wolf corresponding to Z after K+1 iterations; X Z K To find the position of the gray wolf corresponding to Z after K iterations, and X Z K =|gbest Z -A Z *D Z |,A Z Let A be the first coefficient vector and A Z = 2*a*rand(*) - a, where a is the convergence factor;

[0049] H. After updating the gray wolf's position, recalculate the fitness function value, update the optimal fitness value, and then make a decision:

[0050] If the optimal fitness value meets the set requirements, the global optimal position corresponding to the optimal fitness value is output, and the calculation ends; the global optimal position corresponds to several energy storage configuration optimization schemes;

[0051] If the optimal fitness value does not meet the set requirements, return to step D for the next round of iteration calculation;

[0052] G. Based on the obtained energy storage configuration optimization schemes, the final energy storage configuration optimization scheme is determined using the following formula:

[0053]

[0054] In the formula S c Optimize capacity scheme for final energy storage configuration; C max (S c ) represents the functional relationship between energy storage capacity and cost.

[0055] This invention also provides a system for implementing the energy storage configuration method for transformer capacity optimization, comprising a data acquisition module, a virtual capacity calculation module, a charge / discharge calculation module, a model building module, and an energy storage configuration module; the data acquisition module, virtual capacity calculation module, charge / discharge calculation module, model building module, and energy storage configuration module are connected in series; the data acquisition module is used to acquire data information of the target power system and data information of the target transformer, and upload the data to the virtual capacity calculation module; the virtual capacity calculation module is used to fit the load data curve of the user side of the target transformer based on the received data, calculate the virtual energy storage configuration capacity, and then... The data is uploaded to the charging and discharging calculation module; the charging and discharging calculation module is used to calculate the charging capacity during off-peak hours and the discharging capacity during peak hours of the energy storage system based on the received data, and uploads the data to the model building module; the model building module is used to construct an energy storage configuration optimization model based on the received data, with the overall efficiency of the energy storage system as the objective function and energy storage capacity, line current and line voltage as constraints, and uploads the data to the energy storage configuration module; the energy storage configuration module is used to solve the constructed energy storage configuration optimization model based on the received data, combining the particle swarm optimization algorithm and the gray wolf algorithm, to obtain the final energy storage configuration scheme for transformer capacity optimization.

[0056] The energy storage configuration method and system for transformer capacity optimization provided by this invention, by comprehensively considering various influencing factors, not only achieves energy storage configuration for transformer capacity optimization, but also has higher reliability, better accuracy, and is more objective and scientific. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0058] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0059] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The energy storage configuration method for transformer capacity optimization disclosed in this invention includes the following steps:

[0060] S1. Obtain data information about the target power system and the target transformer;

[0061] S2. Based on the data obtained in step S1, fit the load data curve of the target transformer user side and calculate the virtual energy storage configuration capacity; specifically including the following steps:

[0062] Obtain the capacity S of the target transformer N ;

[0063] Obtain the full-day load capacity data of the target transformer's user side on a typical day, fit the load data curve, and obtain the maximum load capacity on the load data curve.

[0064] The calculated virtual configuration capacity S of energy storage is obtained. v for

[0065] S3. Based on the data obtained in step S2, calculate the off-peak charging capacity and peak-peak discharging capacity of the energy storage system; specifically including the following steps:

[0066] The off-peak charging capacity q is calculated using the following formula. c :

[0067]

[0068] In the formula T i,N For the target transformer capacity S N Greater than the load demand capacity S l The start time of the i-th time period, T i,M For the target transformer capacity S N Greater than the load demand capacity S l The end time of the i-th time period, Power factor;

[0069] The peak discharge capacity q is calculated using the following formula. f :

[0070]

[0071] In the formula T j,W For the target transformer capacity S N Less than the load demand capacity S l The start time of the j-th time period, T j,V For the target transformer capacity S N Less than the load demand capacity S l The end time of the j-th time period;

[0072] S4. Based on the data obtained in step S3, construct an energy storage configuration optimization model with the objective function of maximizing the overall efficiency of the energy storage system and with energy storage capacity, line current and line voltage as constraints.

[0073] In practice, the objective function of maximizing the overall efficiency of the energy storage system includes the following steps:

[0074] The following formula is used as the objective function:

[0075] maxH = -A + BCDE

[0076] In the formula, H represents the overall benefit of the energy storage system; A represents the charging cost of the energy storage system during the off-peak electricity load period throughout the year, and A = q c *e d *h,e d The off-peak electricity price is given by h, which represents the number of hours throughout the year; B represents the cost of the energy storage system discharging electricity during peak electricity load periods throughout the year, and B = q. f *e g *h,e g C is the peak electricity price; D is the construction investment cost of the energy storage system; E is the cost of power loss for users caused by insufficient rated capacity of the target transformer during peak electricity load periods throughout the year; and E is the annual maintenance cost of the energy storage system.

[0077] The aforementioned constraints, including energy storage capacity, line current, and line voltage, specifically include the following steps:

[0078] The following formula is used as the constraint condition:

[0079]

[0080] In the formula C min C represents the lowest construction investment cost for energy storage systems. max This represents the highest construction investment cost for energy storage systems; K B The configuration coefficient is preferably set to 0.9; S C To optimize the capacity of the energy storage system; i min i is the lower limit of the line current; i is the line current value; i max This represents the upper limit of the line current; u min The lower limit of the line voltage; u is the line voltage value; u max This is the upper limit of the line voltage.

[0081] S5. Combining the particle swarm optimization algorithm and the gray wolf algorithm, solve the energy storage configuration optimization model constructed in step S4 to obtain the final energy storage configuration scheme for transformer capacity optimization; specifically including the following steps:

[0082] The gray wolf algorithm is used to solve the energy storage configuration optimization model constructed in step S4. During the solution process, the particle swarm optimization algorithm is used to optimize the gray wolf algorithm to improve the solution speed.

[0083] The specific implementation includes the following steps:

[0084] A. Set the number of particles N (preferably 30), the number of iterations K (preferably 200), and the number of variables nar (preferably 2) for the particle swarm optimization algorithm;

[0085] B. Set the fitness function to...

[0086] C. Under constraints, calculate the fitness value pbest for each particle. ii K ii takes values ​​from 1 to N; obtain the optimal fitness value gbest K gbest K =min[pbest ii K ];

[0087] D. Update the particle's position and velocity using the following formula:

[0088]

[0089] In the formula ω is the velocity of particle ii in the (K+1)th iteration; ω is the inertia factor, ranging from 0 to 1, with a preferred value of 0.8. is the velocity of particle ii in the Kth iteration; c1 is the first learning factor; rand(*) is a random number between 0 and 1; c1 represents the position of particle ii in the Kth iteration; c2 represents the second learning factor; X represents the position of particle ii in the Kth iteration. ii K+1 Let c1 be the position of particle ii in the (K+1)th iteration; the preferred values ​​for c1 and c2 are c1 = c2 = 2.

[0090] E. Recalculate the fitness values ​​of each particle and update the best fitness value gbest. K Select the optimal fitness value gbest K The three optimal fitness values ​​in the vector are used as the initial optimal solution α, the initial suboptimal solution β, and the initial candidate solution δ of the gray wolf individual in the gray wolf algorithm, respectively, and the remaining solutions are used as the ordinary solutions ω.

[0091] F. Update the distance between the current solution and the optimal solution of the model using the following formula:

[0092]

[0093] In the formula D Z To solve for the distance the individual gray wolf moves corresponding to Z; C Z The second coefficient vector; Z = α, β, δ;

[0094] G. Update the gray wolf's position using the following formula:

[0095]

[0096] In the formula To find the position of the gray wolf corresponding to Z after K+1 iterations; X ZK To find the position of the gray wolf corresponding to Z after K iterations, and X Z K =|gbest Z -A Z *D Z |,A Z Let A be the first coefficient vector and A Z = 2*a*rand(*) - a, where a is the convergence factor;

[0097] H. After updating the gray wolf's position, recalculate the fitness function value, update the optimal fitness value, and then make a decision:

[0098] If the optimal fitness value meets the set requirements, the global optimal position corresponding to the optimal fitness value is output, and the calculation ends; the global optimal position corresponds to several energy storage configuration optimization schemes;

[0099] If the optimal fitness value does not meet the set requirements, return to step D for the next round of iteration calculation;

[0100] G. Based on the obtained energy storage configuration optimization schemes, the final energy storage configuration optimization scheme is determined using the following formula:

[0101]

[0102] In the formula S c Optimize capacity scheme for final energy storage configuration; C max (S c ) represents the functional relationship between energy storage capacity and cost.

[0103] like Figure 2The diagram shows the functional modules of the system of the present invention: The system disclosed in this invention for implementing the energy storage configuration method for transformer capacity optimization includes a data acquisition module, a virtual capacity calculation module, a charge / discharge calculation module, a model building module, and an energy storage configuration module; these modules are connected in series. The data acquisition module acquires data information of the target power system and the target transformer, and uploads the data to the virtual capacity calculation module. The virtual capacity calculation module fits the load data curve of the user side of the target transformer based on the received data and calculates the virtual capacity configuration of the energy storage. The system configures the capacity and uploads the data to the charge / discharge calculation module. The charge / discharge calculation module calculates the low-peak charging capacity and peak-peak discharging capacity of the energy storage system based on the received data and uploads the data to the model building module. The model building module constructs an energy storage configuration optimization model based on the received data, with the overall efficiency of the energy storage system as the objective function and energy storage capacity, line current, and line voltage as constraints. This model is then uploaded to the energy storage configuration module. The energy storage configuration module solves the constructed energy storage configuration optimization model based on the received data, using both particle swarm optimization and gray wolf algorithms, to obtain the final energy storage configuration scheme for transformer capacity optimization.

Claims

1. A method for optimizing transformer capacity through energy storage configuration, comprising the following steps: S1. Obtain data information about the target power system and the target transformer; S2. Based on the data obtained in step S1, fit the load data curve of the target transformer user side and calculate the virtual energy storage configuration capacity; specifically including the following steps: Obtain the capacity of the target transformer ; Obtain the full-day load capacity data of the target transformer's user side on a typical day, fit the load data curve, and obtain the maximum load capacity on the load data curve. ; Calculate the virtual configuration capacity of energy storage for ; S3. Based on the data obtained in step S2, calculate the charging capacity during off-peak hours and the discharging capacity during peak hours of the energy storage system; S4. Based on the data obtained in step S3, construct an energy storage configuration optimization model with the objective function of maximizing the overall efficiency of the energy storage system and with energy storage capacity, line current, and line voltage as constraints; specifically, this includes the following steps: The following formula is used as the objective function: In the formula, H represents the overall benefit of the energy storage system; The cost of charging the energy storage system during off-peak electricity demand periods throughout the year, and , For off-peak electricity pricing, The total number of hours throughout the year; The cost of the energy storage system's discharge during peak electricity load periods throughout the year, and , Peak electricity price; The construction investment cost of the energy storage system; The cost of power loss for users caused by insufficient rated capacity of the target transformer during peak electricity load periods throughout the year; This refers to the annual maintenance and upkeep costs of the energy storage system. Charging capacity during off-peak hours; This refers to the discharge capacity during peak hours. S5. Combining the particle swarm optimization algorithm and the gray wolf algorithm, the energy storage configuration optimization model constructed in step S4 is solved to obtain the final energy storage configuration scheme for transformer capacity optimization.

2. The energy storage configuration method for transformer capacity optimization according to claim 1, characterized in that... Step S3, which involves calculating the off-peak charging capacity and peak-peak discharging capacity of the energy storage system based on the data obtained in step S2, specifically includes the following steps: The off-peak charging capacity is calculated using the following formula. : In the formula For the target transformer capacity Greater than the load demand capacity The start time of the i-th time period, For the target transformer capacity Greater than the load demand capacity The end time of the i-th time period, Power factor; The peak discharge capacity is calculated using the following formula. : In the formula For the target transformer capacity Less than the load demand capacity The start time of the j-th time period, For the target transformer capacity Less than the load demand capacity The end time of the j-th time period.

3. The energy storage configuration method for transformer capacity optimization according to claim 2, characterized in that... Step S4, which uses energy storage capacity, line current, and line voltage as constraints, specifically includes the following steps: The following formula is used as the constraint condition: In the formula This represents the lowest construction investment cost for energy storage systems. This represents the highest construction investment cost for energy storage systems. For configuration coefficients; To optimize the capacity of the energy storage system; This is the lower limit value of the line current; This is the line current value; This is the upper limit of the line current. This is the lower limit of the line voltage. This refers to the line voltage value. This is the upper limit of the line voltage.

4. The energy storage configuration method for transformer capacity optimization according to claim 3, characterized in that... Step S5, which combines particle swarm optimization and gray wolf algorithm to solve the energy storage configuration optimization model constructed in step S4, specifically includes the following steps: The Grey Wolf algorithm is used to solve the energy storage configuration optimization model constructed in step S4. During the solution process, the Particle Swarm Optimization algorithm is used to optimize the Grey Wolf algorithm to improve the solution speed.

5. The energy storage configuration method for transformer capacity optimization according to claim 4, characterized in that... Step S5, which combines particle swarm optimization and gray wolf algorithm to solve the energy storage configuration optimization model constructed in step S4, specifically includes the following steps: A. Set the number of particles N, the number of iterations K, and the number of variables nar for the particle swarm optimization algorithm; B. Set the fitness function for ; C. Under constraints, calculate the fitness value of each particle. ii takes values ​​from 1 to N; obtain the optimal fitness value. for ; D. Update the particle's position and velocity using the following formula: In the formula Let be the velocity of particle ii in the (K+1)th iteration; Inertia factor; Let be the velocity of particle ii in the Kth iteration; As the first learning factor; A random number between 0 and 1; This represents the position of particle ii during the Kth iteration. As the second learning factor; This represents the position of particle ii during the (K+1)th iteration. E. Recalculate the fitness values ​​of each particle and update the optimal fitness value. Select the optimal fitness value The three optimal fitness values ​​in the vector are used as the initial optimal solutions for individual gray wolves in the gray wolf algorithm. The initial suboptimal solution for individual gray wolves and initial gray wolf individual candidate solutions And the remaining solutions are taken as ordinary solutions. ; F. Update the distance between the current solution and the optimal solution of the model using the following formula: In the formula To solve for the distance the individual gray wolf moves corresponding to Z; This is the second coefficient vector; ; G. Update the gray wolf's position using the following formula: In the formula To find the position of the gray wolf corresponding to Z after K+1 iterations; To solve for the position of the gray wolf corresponding to Z after K iterations, and , The first coefficient vector and , The convergence factor; H. After updating the gray wolf's position, recalculate the fitness function. The value of is used to update the optimal fitness value, and a decision is made: If the optimal fitness value meets the set requirements, the global optimal position corresponding to the optimal fitness value is output, and the calculation ends; the global optimal position corresponds to several energy storage configuration optimization schemes; If the optimal fitness value does not meet the set requirements, return to step D for the next round of iteration calculation; G. Based on the obtained energy storage configuration optimization schemes, the final energy storage configuration optimization scheme is determined using the following formula: In the formula Optimize capacity scheme for final energy storage configuration; This is a functional relationship between energy storage capacity and cost.

6. A system for implementing the energy storage configuration method for transformer capacity optimization as described in any one of claims 1 to 5, characterized in that... It includes a data acquisition module, a virtual capacity calculation module, a charge / discharge calculation module, a model building module, and an energy storage configuration module; these modules are connected in series. The data acquisition module acquires data information from the target power system and the target transformer, and uploads the data to the virtual capacity calculation module. The virtual capacity calculation module fits the load data curve of the target transformer's user side based on the received data, calculates the virtual energy storage configuration capacity, and uploads the data to the charge / discharge calculation module. The charge / discharge calculation module calculates the energy storage system's charging capacity during off-peak hours and its discharging capacity during peak hours based on the received data, and uploads the data to the model building module. The model building module is used to construct an energy storage configuration optimization model based on the received data, with the objective function of maximizing the overall efficiency of the energy storage system and the constraints of energy storage capacity, line current and line voltage, and then upload the data to the energy storage configuration module. The energy storage configuration module is used to solve the constructed energy storage configuration optimization model based on the received data, combining the particle swarm optimization algorithm and the gray wolf algorithm, to obtain the final energy storage configuration scheme for transformer capacity optimization.

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