Power control optimization method and system considering line loss and random load fluctuation

Abstract

The present invention belongs to the field of power control technology, and provides a power control optimization method and system that considers line loss and random load fluctuations. The power control optimization method includes: S1, obtaining line topology, constructing a node admittance matrix and a power transfer distribution factor; S2, obtaining historical data of load nodes, and analyzing load fluctuation characteristics; S3, constructing a control optimization model that considers line loss and random load fluctuations in multiple scenarios; S4, solving the optimization model and determining the optimal power control strategy. The present invention constructs a flow constraint model based on the node admittance matrix and the power transfer distribution factor, combines load fluctuation data analysis, considers generator loss, line loss value and load reduction value, establishes a control optimization model, introduces conditional expected loss CVaR into power control optimization, and accurately measures the average loss of each line in special scenarios through multi-scenario modeling and sampling approximation, enhances the robustness and security of the control strategy, and meets complex power control needs.

Description

Technical Field

[0001] The present invention belongs to the technical field of power regulation, and in particular relates to a power regulation optimization method and system taking into account line loss and random load fluctuations. Background Art

[0002] With the increasing penetration of renewable energy and the growing uncertainty in power loads, the challenges facing power system regulation are becoming increasingly complex. Traditional optimization methods assume a relatively stable power load, ignoring the impact of random load fluctuations and line losses. This assumption no longer meets the actual needs of modern power systems, especially when faced with the integration of large-scale, unstable energy sources such as wind and solar power.

[0003] In order to improve the reliability and economy of regulation, optimization methods that consider load fluctuations and line losses have gradually gained attention. In uncertain scenarios, loss management tools such as conditional expected loss (CVaR, Conditional Value at Risk) have been introduced to measure regulation problems that may arise under special circumstances. The regulation optimization method based on CVaR can better cope with load fluctuations and other random factors, so that the power system can maintain a stable operating state under special conditions. In addition, the loss of power lines also has an important impact on the operation of the entire system. Traditional regulation methods have failed to effectively combine the calculation of line losses, resulting in the regulation results deviating from the actual optimal state. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a power control optimization method that takes into account line loss and random load fluctuations. Based on the node admittance matrix and the power transfer distribution factor, a power flow constraint model is constructed. Combined with the analysis of historical load fluctuation data, the generator loss, line loss value and load reduction value are comprehensively considered to establish a control optimization model. The conditional expected loss CVaR is introduced into the power control optimization. Through multi-scenario modeling and sampling approximation, the average loss in special scenarios is accurately measured. The control optimization model is efficiently solved through optimization tools such as Gurobi, thereby enhancing the robustness and security of the control strategy, ensuring power balance while meeting complex power control needs.

[0005] The present invention provides a power control optimization method considering line loss and random load fluctuations, which includes the following steps:

[0006] S1. Obtain the line topology and construct the node admittance matrix Y bus and calculate the power transfer distribution factor PTDF;

[0007] S2. Obtain historical data of load nodes and analyze load fluctuation characteristics;

[0008] S3. Based on the node admittance matrix and the power transfer distribution factor (PTDF), a power flow constraint model is constructed. Combined with the analysis of historical load fluctuation data, multi-scenario load fluctuation samples are generated. Considering the generator loss, line loss value, and load reduction value, a control optimization model that considers random fluctuations in line loss and load is constructed. The specific sub-steps include:

[0009] S31. Calculate the total operating loss value L of s scenarios s ;

[0010] S32. Use scene sampling to approximate the conditional expected loss CVaR, specifically:

[0011]

[0012] Where v is the VaR auxiliary variable, β is the confidence level, and s is the number of scenarios;

[0013] S33. Inputting the s scenario constraints into the constructed control optimization model to obtain a control optimization model that considers line loss and random load fluctuations;

[0014] S4. Solve the control optimization model and determine the optimal power control strategy. The specific sub-steps include:

[0015] S41. The objective function F of the control optimization model is solved by Gurobi solver, specifically:

[0016]

[0017] Where C genti is the unit power generation of the i-th generator, is the line loss value of the jth line in scenario s, N G is the number of generator nodes, N L is the number of load nodes, C shedti is the unit load reduction value of the kth node, P G,i Output power for the generator;

[0018] S42. Set the maximum number of iterations and convergence tolerance parameters to solve the output power P of the generator under s scenarios. G,i and load reduction values;

[0019] S43. Update the generator output power P according to the control requirements G,i , adjust power distribution and load reduction values;

[0020] S44. Iteratively solve the objective function F according to the optimization algorithm to obtain the optimal power control strategy, specifically:

[0021] Generator output power P G,i :

[0022] Reduce load value P shed，k :

[0023] Optimal power control value CVaR β :

[0024] Where, are the generator output power and load reduction of scenario s, respectively;

[0025] By adjusting the generator output power and load reduction value parameters, the average loss of each line in special scenarios can be accurately measured, and the system power distribution can be dynamically updated to achieve optimal power regulation.

[0026] Preferably, the sub-steps of step S1 include:

[0027] S11. Collecting line topology information of the power grid, obtaining connection relationships between nodes and branches, and branch impedance information;

[0028] S12. Construct a node-branch association matrix A to describe the association relationship between each node and branch. The construction formula of the node-branch association matrix A is:

[0029]

[0030] S13. Calculate the branch admittance matrix Y b , the expression is:

[0031] Y b =diag(1 / Z i ),Z i =R i +jX i

[0032] Where Z i is the branch impedance, R i and X i are the resistance and reactance of the branch respectively;

[0033] S14, according to the node-branch association matrix A and the branch admittance matrix Y b , calculate the node admittance matrix Y bus , whose expression is:

[0034] Y bus =A T Y b A

[0035] S15. Calculate the power transfer distribution factor PTDF, which is used to describe the relationship between the power flow of each line and the node injection power. The expression of the power transfer distribution factor PTDF is:

[0036]

[0037] Preferably, the sub-steps of step S2 include:

[0038] S21. Obtain historical load data P of each load node L,i (t), statistically analyze the load fluctuation characteristics of each load node;

[0039] S22. Calculate the load fluctuation mean μ of each load node i and variance

[0040] S23. Construct a load fluctuation model based on the load fluctuation mean and variance;

[0041]

[0042] Where,∈ i is a standard normal distribution;

[0043] S24. Generate s load fluctuation scenarios using Monte Carlo or Latin hypercube sampling methods

[0044] Preferably, the load fluctuation mean μ in step S22 is i and variance Specifically:

[0045]

[0046]

[0047] Preferably, the total running loss L of the scene s in step S31 is s for:

[0048]

[0049] Preferably, the CVaR in step S32 β The auxiliary variable ν is introduced in , specifically:

[0050]

[0051] Where, CVaR β is the quantile loss value of the loss, E is the expected value, L is the random variable of the total loss, which includes the generator loss, line loss and load reduction value, (L-ν) + is the positive part of Lv.

[0052] Preferably, the constraints in step S33 include:

[0053] Power balance constraints:

[0054] Power generation constraints:

[0055] Load shedding constraints:

[0056] Power flow constraint, power flow of the jth line Specifically:

[0057]

[0058] Where, PTDF jti is the power transfer distribution factor of the i-th generator to line j, PTDF jtk is the power transfer distribution factor of the kth load node to line j.

[0059] In the second aspect, the present invention also provides an electric power control optimization system that takes into account line loss and random fluctuations in load, which includes a data acquisition module and a model optimization module. The data acquisition module is used to obtain historical data of the line topology structure and load nodes of the power grid to generate a load fluctuation model; the model optimization module is used to input the line topology information and load fluctuation data into the optimization model. The optimization model includes an evaluation of power generation and load schemes for various scenarios, and ensures that power balance, power generation limit and flow constraint conditions are met. The optimization model calculates the DC flow, combines the generator loss, line loss value and load reduction value, uses the conditional expected loss CVaR to solve the objective function, and outputs the optimal electric power control strategy.

[0060] In a third aspect, the present invention further provides a computer-readable storage medium, which, when a computer program is running, controls the device where the computer-readable storage medium is located to execute the steps of a control optimization method that takes into account line loss and random load fluctuations.

[0061] Compared with the prior art, the present invention has the following advantages:

[0062] 1. The present invention proposes an electric power control optimization method that takes into account random fluctuations in line losses and loads. A power flow constraint model is constructed based on the node admittance matrix and the power transfer distribution factor (PTDF). Combined with analysis of historical load fluctuation data, multi-scenario load fluctuation samples are generated. Generator losses, line losses, and load reduction values ​​are considered to establish a unified optimization model. This model is efficiently solved using optimization tools such as Gurobi. This method can optimize economic efficiency and avoid losses while ensuring power balance, meeting complex electric power control requirements and possessing broad application value in uncertain scenarios.

[0063] 2. This invention proposes a power control optimization method that considers random fluctuations in line losses and loads, introduces conditional expected loss (CVaR) into power control optimization, and accurately measures the average loss in special scenarios through multi-scenario modeling and sampling approximation, thereby enhancing the robustness and security of the control scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of the power control optimization method considering line loss and random load fluctuations of the present invention;

[0065] Figure 2 Schematic diagram of the power control optimization system considering line loss and random load fluctuations of the present invention;

[0066] Figure 3 This is a schematic diagram of parameter adjustment in the power control optimization method of the present invention that takes into account line loss and random load fluctuations. DETAILED DESCRIPTION

[0067] To fully describe the technical content, structural features, objectives and effects of the present invention, the following is a detailed description with reference to the accompanying drawings.

[0068] The present invention considers the power control optimization method of line loss and random load fluctuation, such as Figure 1 As shown, it includes the following steps:

[0069] S1. Obtain the line topology and construct the node admittance matrix Y bus and calculating the power transfer distribution factor PTDF; the sub-steps of step S1 include:

[0070] S11. Collecting line topology information of the power grid, obtaining connection relationships between nodes and branches, and branch impedance information;

[0071] S12. Construct a node-branch association matrix A to describe the association relationship between each node and branch. The construction formula of the node-branch association matrix A is:

[0072]

[0073] S13. Calculate the branch admittance matrix Y b , the expression is:

[0074] Y b =diag(1 / Z i ),Z i =R i +jX i

[0075] Where Z i is the branch impedance, R i and Xi are the resistance and reactance of the branch respectively;

[0076] S14, according to the node-branch association matrix A and the branch admittance matrix Y b , calculate the node admittance matrix Y bus , whose expression is:

[0077] Y bus =A T Y b A

[0078] S15. Calculate the power transfer distribution factor PTDF, which is used to describe the relationship between the power flow of each line and the node injection power. The expression of the power transfer distribution factor PTDF is:

[0079]

[0080] S2. Obtain historical data of load nodes and analyze load fluctuation characteristics. The sub-steps of step S2 include:

[0081] S21. Obtain historical load data P of each load node L,i (t), statistically analyze the load fluctuation characteristics of each load node;

[0082] S22. Calculate the load fluctuation mean μ of each load node i and variance

[0083] The load fluctuation mean μ in step S22 i and variance Specifically:

[0084]

[0085]

[0086] S23. Construct a load fluctuation model based on the load fluctuation mean and variance;

[0087]

[0088] Where,∈ i is a standard normal distribution;

[0089] S24. Generate s load fluctuation scenarios using Monte Carlo or Latin hypercube sampling methods

[0090] S3. Based on the node admittance matrix and the power transfer distribution factor PTDF, a power flow constraint model is constructed. Combined with the analysis of historical load fluctuation data, multi-scenario load fluctuation samples are generated. Considering the generator loss, line loss value and load reduction value, a control optimization model considering random fluctuations of line loss and load in multiple scenarios is constructed, such as Figure 3 As shown, the specific sub-steps include:

[0091] S31. Calculate the total operating loss value L of s scenarios s ;

[0092] The total running loss L of scene s in step S31 s for:

[0093]

[0094] S32. Use scene sampling to approximate the conditional expected loss CVaR, specifically:

[0095]

[0096] Where v is the VaR auxiliary variable, β is the confidence level, and s is the number of scenarios;

[0097] CVaR in step S32 β The auxiliary variable ν is introduced in , specifically:

[0098]

[0099] Where, CVaR β is the quantile loss value of the loss, E is the expected value, L is the random variable of the total loss, which includes the generator loss, line loss and load reduction value, (L-ν) + is the positive part of Lv.

[0100] S33. Inputting the s scenario constraints into the constructed control optimization model to obtain a control optimization model that considers line loss and random load fluctuations;

[0101] The constraints in step S33 include:

[0102] Power balance constraints:

[0103] Power generation constraints:

[0104] Load shedding constraints:

[0105] Power flow constraint, power flow of the jth line Specifically:

[0106]

[0107] Where, PTDF jti is the power transfer distribution factor of the i-th generator to line j, PTDF jtk is the power transfer distribution factor of the kth load node to line j.

[0108] S4. Solve the control optimization model and determine the optimal power control strategy. The specific sub-steps include:

[0109] S41. The objective function F of the control optimization model is solved by Gurobi solver, specifically:

[0110]

[0111] Where C genti is the unit power generation of the i-th generator, is the line loss value of the jth line in scenario s, N G is the number of generator nodes, N L is the number of load nodes, C shedtk is the unit load reduction value of the kth node, P G,i Output power for the generator;

[0112] S42. Set the maximum number of iterations and convergence tolerance parameters to solve the output power P of the generator under s scenarios. G,i and load reduction values;

[0113] S43. Update the generator output power P according to the control requirements G,i , adjust power distribution and load reduction values;

[0114] S44. Iteratively solve the objective function F according to the optimization algorithm to obtain the optimal power control strategy, specifically:

[0115] Generator output power P G,i :

[0116] Reduce load value P shedtk :

[0117] Optimal power control value CVaR β :

[0118] Where, are the generator output power and load reduction of scenario s, respectively;

[0119] By adjusting the generator output power and load reduction value parameters, the average loss of each line in special scenarios can be accurately measured, and the system power distribution can be dynamically updated to achieve optimal power regulation.

[0120] Second, as Figure 2 As shown, the present invention also provides a power control optimization system considering line loss and random load fluctuations, including a data acquisition module 201 and a model optimization module 202.

[0121] The data acquisition module 201 is used to obtain historical data on the line topology and load nodes of the power grid and generate a load fluctuation model. The model optimization module 202 is used to input the line topology information and load fluctuation data into the optimization model. The optimization model includes evaluating the power generation and load plans of various scenarios and ensuring that the power balance, power generation limit and power flow constraint conditions are met. The optimization model calculates the DC power flow, combines the generator loss, line loss value and load reduction value, uses the conditional expected loss CVaR to solve the objective function, and outputs the optimal power control strategy.

[0122] In a third aspect, the present invention further provides a computer-readable storage medium, which, when a computer program is running, controls the device where the computer-readable storage medium is located to execute the steps of a control optimization method that takes into account line loss and random load fluctuations.

[0123] The following further describes the power control optimization method and system of the present invention taking into account line loss and random load fluctuations in conjunction with embodiments:

[0124] This embodiment provides a computer device, which includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the aforementioned method of the embodiment is implemented.

[0125] A computer device may be a desktop computer, laptop, PDA, server, or cloud server, among other computing devices. A computer device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that a computer device may include more or fewer components than shown, or a combination of certain components, or different components. For example, a computer device may also include input / output devices, network access devices, and buses.

[0126] The processor is a central processing unit (CPU), and may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0127] Memory can be an internal storage unit of a computer device, such as a computer device's hard drive or memory. It can also be an external storage device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, memory can include both internal storage units and external storage devices. Memory is used to store computer programs and other programs and data required by the computer device. Memory can also be used to temporarily store data that has been output or is about to be output.

[0128] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some of the steps of the above-mentioned methods of various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0129] The present invention proposes an electric power control optimization method that takes into account random fluctuations in line losses and loads. A power flow constraint model is constructed based on the node admittance matrix and the power transfer distribution factor PTDF. Combined with the analysis of historical load fluctuation data, multi-scenario load fluctuation samples are generated. Generator losses, line losses, and load reduction values ​​are considered to establish a unified optimization model. The conditional expected loss CVaR is introduced into the electric power control optimization. Through multi-scenario modeling and sampling approximation, the average loss in special scenarios is accurately measured, thereby enhancing the robustness and security of the control scheme. Efficiently solved through optimization tools such as Gurobi, it can optimize economy and avoid losses while ensuring power balance, thus meeting complex electric power control needs.

[0130] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A power control optimization method considering line loss and random load fluctuations, characterized in that: It includes the following steps: S1. Obtain the line topology and construct the node admittance matrix Y bus and calculate the power transfer distribution factor PTDF; S2. Obtain historical data of load nodes and analyze load fluctuation characteristics; S3. Based on the node admittance matrix and the power transfer distribution factor (PTDF), a power flow constraint model is constructed. Combined with the analysis of historical load fluctuation data, multi-scenario load fluctuation samples are generated. Considering the generator loss, line loss value, and load reduction value, a control optimization model that considers random fluctuations in line loss and load is constructed. The specific sub-steps include: S31. Calculate the total operating loss value L of s scenarios s ; S32. Use scene sampling to approximate the conditional expected loss CVaR, specifically: Where v is the VaR auxiliary variable, β is the confidence level, and s is the number of scenarios; S33. Inputting the scenario constraints into the constructed control optimization model to obtain a control optimization model that considers line loss and random load fluctuations; S4. Solve the control optimization model and determine the optimal power control strategy. The specific sub-steps include: S41. The objective function F of the control optimization model is solved by Gurobi solver, specifically: Where C genti is the unit power generation of the i-th generator, is the line loss value of the jth line in scenario s, N G is the number of generator nodes, N L is the number of load nodes, C shedtk is the unit load reduction value of the kth node, P G,i Output power for the generator; S42. Set the maximum number of iterations and convergence tolerance parameters to solve the output power P of the generator under s scenarios. G,i and load reduction values; S43. Update the generator output power P according to the control requirements G,i , adjust power distribution and load reduction values; S44. Iteratively solve the objective function F according to the optimization algorithm to obtain the optimal power control strategy, specifically: Generator output power P G ,i: Reduce load value P shedtk : Optimal power control value CVaR β : Where, are the generator output power and load reduction of scenario s, respectively; By adjusting the generator output power and load reduction value parameters, measuring the average loss of each line, and dynamically updating the system power distribution, optimal power regulation can be achieved.

2. The power control optimization method considering line loss and random load fluctuation according to claim 1 is characterized in that: The sub-steps of step S1 include: S11. Collecting line topology information of the power grid, obtaining connection relationships between nodes and branches, and branch impedance information; S12. Construct a node-branch association matrix A to describe the association relationship between each node and branch. The construction formula of the node-branch association matrix A is: S13. Calculate the branch admittance matrix Y b , the expression is: Y b =diag(1 / Z i ),Z i =R i +jX i Where Z i is the branch impedance, R i and X i are the resistance and reactance of the branch respectively; S14, according to the node-branch association matrix A and the branch admittance matrix Y b , calculate the node admittance matrix Y bus , whose expression is: AND bus =A T AND b TO S15. Calculate the power transfer distribution factor PTDF, which is used to describe the relationship between the power flow of each line and the node injection power. The expression of the power transfer distribution factor PTDF is: 。 3. The power control optimization method considering line loss and random load fluctuation according to claim 1 is characterized in that: The sub-steps of step S2 include: S21. Obtain historical load data P of each load node L,i (t), statistically analyze the load fluctuation characteristics of each load node; S22. Calculate the load fluctuation mean μ of each load node i and variance S23. Construct a load fluctuation model based on the load fluctuation mean and variance; Where, ∈i is the standard normal distribution; S24. Generate s load fluctuation scenarios using Monte Carlo or Latin hypercube sampling methods 4. The power control optimization method considering line loss and random load fluctuation according to claim 3 is characterized in that: The load fluctuation mean μ in step S22 i and variance Specifically: 。 5. The power control optimization method considering line loss and random load fluctuation according to claim 1 is characterized in that: The total running loss L of the scenario s in step S31 s for: 。 6. The power control optimization method considering line loss and random load fluctuation according to claim 1 is characterized in that: CVaR of step S32 β The auxiliary variable ν is introduced in , specifically: Where, CVaR β is the quantile loss value of the loss, E is the expected value, L is the random variable of the total loss, which includes the generator loss, line loss and load reduction value, (L-ν) + is the positive part of Lv.

7. The power control optimization method considering line loss and random load fluctuation according to claim 1 is characterized in that: The constraints in step S33 include: Power balance constraints: Power generation constraints: Load shedding constraints: Power flow constraint, power flow of the jth line Specifically: Where, PTDF jti is the power transfer distribution factor of the i-th generator to line j, PTDF jtk is the power transfer distribution factor of the kth load node to line j.

8. A control optimization system for the power control optimization method considering line loss and random load fluctuations according to any one of claims 1 to 7, characterized in that: It includes data acquisition module and model optimization module. The data acquisition module is used to acquire the line topology structure and historical data of the load nodes of the power grid and generate a load fluctuation model; The model optimization module is used to input line topology information and load fluctuation data into the optimization model. The optimization model includes evaluating power generation and load plans for various scenarios and ensuring that power balance, power generation limit and power flow constraints are met. The optimization model calculates DC power flow, combines generator losses, line losses and load reduction values, uses conditional expected loss (CVaR) to solve the objective function, and outputs the optimal power regulation strategy.

9. A computer-readable storage medium, characterized in that When the computer program is running, it controls the device where the computer-readable storage medium is located to execute the steps of the control optimization method considering line loss and random load fluctuations according to any one of claims 1 to 7.

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