A method and system for acquiring phase shifter adaptation line scene

Abstract

The present application relates to the field of power grid technology, and discloses a method and system for acquiring phase shifter adaptation line scenarios, including: acquiring annual load forecast data for future time periods; based on the annual load forecast data, determining optimal power flow data with the total cost of minimizing power generation and power flow network loss as the first objective function; based on the optimal power flow data, screening out a set of lines with limited transmission capacity; with the total cost of minimizing the phase shifter as the second objective function, determining the adaptation line scenario with the lowest cost of configuring the phase shifter for each line in the line set. Its beneficial effect is to obtain the adaptation line scenario of the phase shifter in the actual power grid to achieve the most cost-effective power grid operation.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a method and system for acquiring a phase shifter adaptation line scenario. Background Art

[0002] As a transmission carrier between sources and loads, the power grid undertakes the key conversion function, which is crucial to the safe and reliable balance between sources and loads. Phase shifters, as an important means of steady-state power flow control in power grids, have been increasingly widely used in power grids. In the process of power generation and load balance, power flow control grids containing phase shifters play an important supporting role.

[0003] However, the current related technologies have only been simulated and analyzed in traditional models, but lack application analysis in actual power grids. Therefore, how to obtain the adaptation line scenario of phase shifters in actual power grids to achieve the most cost-effective power grid operation is a key issue that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method and system for acquiring a phase shifter adaptation line scenario, which solves the technical problem of how to acquire a phase shifter adaptation line scenario in an actual power grid, thereby achieving the most cost-effective technical effect of power grid operation.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a method for acquiring a phase shifter adaptation line scenario, the method comprising:

[0007] Obtain annual load forecast data for future periods of electricity;

[0008] Based on the annual load forecast data, the optimal power flow data is determined by minimizing the total cost of power generation and power flow network loss as the first objective function;

[0009] Based on the optimal power flow data, a set of lines with limited transmission capacity are screened out;

[0010] Taking minimizing the total cost of the phase shifter as the second objective function, for each line in the line set, determining an adapted line scenario with the lowest cost for configuring the phase shifter.

[0011] This embodiment provides a method for obtaining a phase shifter adaptation line scenario, which can optimize the power grid by obtaining annual load forecast data for future time periods. Based on these forecast data, the power grid's power flow and power generation cost are adjusted to ensure that supply and demand are balanced at the lowest cost, thereby obtaining the optimal power flow data. Next, these optimal power flow data are used to screen out lines with limited transmission capacity. Finally, suitable lines are selected from these restricted lines to install phase shifters, and their configurations (such as capacity and angle) are optimized to minimize the total cost of the phase shifters. In this way, adjusting the phase shifter can improve line load distribution, reduce line congestion, and thus reduce the operating cost of the power grid.

[0012] Optionally, the determining of the optimal power flow data based on the annual load forecast data and taking the total cost of minimizing power generation and power flow network loss as the first objective function includes:

[0013] The load constraint condition is constructed by taking the sum of the total active output of all generators and the power flow network loss as equal to the annual load forecast data;

[0014] Taking minimizing the total cost of power generation and power flow network loss as the first objective function, the optimal power flow data is determined by the load constraint condition.

[0015] This embodiment constructs a clear load constraint condition by ensuring that the sum of the total active output of all generators and the power flow network loss is equal to the annual load forecast data. It ensures that the total active output of all generators in the power grid and the sum of the power flow network loss are consistent with the annual load forecast data, thereby ensuring power supply balance. By constructing the first objective function including the power generation cost and the power flow network loss cost, and optimizing it in combination with the load constraint condition, the total cost can be minimized while satisfying the supply and demand balance of the power grid.

[0016] Optionally, the first objective function is:

[0017]

[0018] Among them, c k is the cost coefficient of the kth generator; p k is the active power output of the kth generator; p i,l loss is the power flow network loss on the phase shifter branch i and the AC branch l.

[0019] Optionally, taking minimizing the total cost of the phase shifter as the second objective function and determining, for each line in the line set, an adapted line scenario with the lowest cost for configuring the phase shifter, includes:

[0020] Obtaining a reference phase shifter price, a reference phase shift angle, and a reference phase shifter capacity of the phase shifter;

[0021] For each estimated phase shift angle of the phase shifter, determining an estimated phase shifter price corresponding to the estimated phase shift angle according to the reference phase shifter price, the reference phase shift angle and the reference phase shifter capacity;

[0022] For each line in the line set, adaptation is performed according to the estimated phase shifter price, and the line scenario with the lowest cost for configuring the phase shifter is determined as the adapted line scenario.

[0023] This embodiment calculates the estimated phase shifter price corresponding to each phase shift angle by obtaining the benchmark phase shifter price, benchmark phase shift angle, and benchmark phase shifter capacity, and evaluates the phase shifter cost under different angle conditions. The actual cost of configuring the phase shifter is calculated by applying the estimated phase shifter price to each line. By comparing the different costs of installing a phase shifter on each line and selecting the line scenario with the lowest cost as the phase shifter adaptation line scenario, cost-effectiveness is maximized.

[0024] Optionally, the second objective function is:

[0025]

[0026]

[0027] in, is the estimated phase shifter price; π is the benchmark phase shifter price; To estimate the phase shift angle; To install a phase shift angle estimation device on the AC branch l between the AC branch starting point m and the AC branch end point n Estimated phase shifter price; θ is the reference phase shift angle; u m,n,l is the installation position of the phase shifter in the AC branch l at the AC branch starting point m and the AC branch end point n; is the estimated phase shifter capacity; s is the reference phase shifter capacity.

[0028] Optionally, in determining the optimal power flow data and determining the adapted line scenario, the method further includes a node power balance constraint:

[0029]

[0030]

[0031]

[0032]

[0033] Among them, p k is the active output of the generator at node k; p dis the active power demand of the generator at node k; q k is the reactive power output of the generator at node k; q d is the reactive power demand of the generator at node k; p m,n,l (θ, ν, λ) is the active power flow through the AC branch l from the starting point m of the AC branch to the end point n of the AC branch; q m,n,l (θ, ν, λ) is the reactive power flow through the AC branch l from the starting point m to the end point n of the AC branch; v m is the voltage at the starting point m of the AC branch; v n is the voltage at the end point n of the AC branch; r m,n,l is the resistance of the line; is the reactance after line compensation, , x m,n,l is the line reactance, λ m,n,l The level of reactance compensation provided to the phase shifter; θ m is the phase shift angle of the starting point m of the AC branch; θ n is the phase shift angle of the AC branch terminal n; bc m,n,l is the line susceptance.

[0034] Optionally, the stable power flow on the adapted line scenario is less than or equal to the corresponding thermal stability limit.

[0035] Optionally, the obtaining of annual load forecast data of electricity in a future time period includes:

[0036] Obtaining historical annual data of power grid operation; wherein the historical annual data includes circuit load data, holiday data, temperature and climate;

[0037] Based on the historical annual data, a preset artificial intelligence algorithm is used to predict the power load data for future time periods to obtain the annual load forecast data.

[0038] This embodiment combines circuit load data, holiday data, temperature and climate, and uses artificial intelligence algorithms to improve the prediction accuracy of future power loads.

[0039] Optionally, the screening out a set of lines with limited transmission capacity based on the optimal power flow data includes:

[0040] Obtaining the conductor cross-sectional area and corresponding transmission capacity of each of the lines;

[0041] According to the optimal power flow data, the conductor cross-sectional area and the transmission capacity, a preset artificial intelligence algorithm is used to screen the lines with limited transmission capacity to obtain the line set.

[0042] This embodiment obtains the conductor cross-sectional area and the corresponding transmission capacity of each line, and automatically screens the lines with limited transmission capacity through an artificial intelligence algorithm based on the optimal power flow data, so as to accurately identify which lines have actual loads exceeding their transmission capacity, thereby improving the recognition efficiency and accuracy.

[0043] In a second aspect, an embodiment of the present application provides a phase shifter adaptation line scene acquisition system, the system comprising:

[0044] The load data acquisition module is used to obtain the annual load forecast data of electricity in the future time period;

[0045] A power flow data determination module, configured to determine optimal power flow data based on the annual load forecast data and taking minimizing the total cost of power generation and power flow network loss as a first objective function;

[0046] A line screening module, used for screening out a set of lines with limited transmission capacity based on the optimal power flow data;

[0047] The line scenario determination module is used to determine, for each line in the line set, an adapted line scenario with the lowest cost for configuring the phase shifter, taking minimizing the total cost of the phase shifter as the second objective function.

[0048] This embodiment provides a phase shifter adaptation line scenario acquisition system, which can optimize the power grid by acquiring annual load forecast data for future time periods. Based on these forecast data, the power grid's power flow and power generation cost are adjusted to ensure that supply and demand are balanced at the lowest cost, thereby obtaining the optimal power flow data. Next, these optimal power flow data are used to screen out lines with limited transmission capacity. Finally, suitable lines are selected from these restricted lines to install phase shifters, and their configurations (such as capacity and angle) are optimized to minimize the total cost of the phase shifters. In this way, adjusting the phase shifter can improve line load distribution, reduce line congestion, and thus reduce the operating cost of the power grid.

[0049] Optionally, the flow data determination module includes:

[0050] The load constraint condition is constructed by taking the sum of the total active output of all generators and the power flow network loss as equal to the annual load forecast data;

[0051] Taking minimizing the total cost of power generation and power flow network loss as the first objective function, the optimal power flow data is determined by the load constraint condition.

[0052] This embodiment constructs a clear load constraint condition by ensuring that the sum of the total active output of all generators and the power flow network loss is equal to the annual load forecast data. It ensures that the total active output of all generators in the power grid and the sum of the power flow network loss are consistent with the annual load forecast data, thereby ensuring power supply balance. By constructing the first objective function including the power generation cost and the power flow network loss cost, and optimizing it in combination with the load constraint condition, the total cost can be minimized while satisfying the supply and demand balance of the power grid.

[0053] Optionally, the first objective function is:

[0054]

[0055] Among them, c k is the cost coefficient of the kth generator; p k is the active power output of the kth generator; p i,l loss is the power flow network loss on the phase shifter branch i and the AC branch l.

[0056] Optionally, the line scene determination module includes:

[0057] Obtaining a reference phase shifter price, a reference phase shift angle, and a reference phase shifter capacity of the phase shifter;

[0058] For each estimated phase shift angle of the phase shifter, determining an estimated phase shifter price corresponding to the estimated phase shift angle according to the reference phase shifter price, the reference phase shift angle and the reference phase shifter capacity;

[0059] For each line in the line set, adaptation is performed according to the estimated phase shifter price, and the line scenario with the lowest cost for configuring the phase shifter is determined as the adapted line scenario.

[0060] This embodiment calculates the estimated phase shifter price corresponding to each phase shift angle by obtaining the benchmark phase shifter price, benchmark phase shift angle, and benchmark phase shifter capacity, and evaluates the phase shifter cost under different angle conditions. The actual cost of configuring the phase shifter is calculated by applying the estimated phase shifter price to each line. By comparing the different costs of installing a phase shifter on each line and selecting the line scenario with the lowest cost as the phase shifter adaptation line scenario, cost-effectiveness is maximized.

[0061] Optionally, the second objective function is:

[0062]

[0063]

[0064] in, is the estimated phase shifter price; π is the benchmark phase shifter price; To estimate the phase shift angle; To install a phase shift angle estimation device on the AC branch l between the AC branch starting point m and the AC branch end point n Estimated phase shifter price; θ is the reference phase shift angle; u m,n,l is the installation position of the phase shifter in the AC branch l at the AC branch starting point m and the AC branch end point n; is the estimated phase shifter capacity; s is the reference phase shifter capacity.

[0065] Optionally, in determining the optimal power flow data and determining the adapted line scenario, the method further includes a node power balance constraint:

[0066]

[0067]

[0068]

[0069]

[0070] Among them, p k is the active output of the generator at node k; p d is the active power demand of the generator at node k; q k is the reactive power output of the generator at node k; q d is the reactive power demand of the generator at node k; p m,n,l (θ, ν, λ) is the active power flow through the AC branch l from the starting point m of the AC branch to the end point n of the AC branch; q m,n,l (θ, ν, λ) is the reactive power flow through the AC branch l from the starting point m to the end point n of the AC branch; v m is the voltage at the starting point m of the AC branch; v n is the voltage at the end point n of the AC branch; r m,n,l is the resistance of the line; is the reactance after line compensation, , x m,n,l is the line reactance, λ m,n,l The level of reactance compensation provided to the phase shifter; θ m is the phase shift angle of the starting point m of the AC branch; θ n is the phase shift angle of the AC branch terminal n; bc m,n,l is the line susceptance.

[0071] Optionally, the stable power flow on the adapted line scenario is less than or equal to the corresponding thermal stability limit.

[0072] Optionally, the load data acquisition module includes:

[0073] Obtaining historical annual data of power grid operation; wherein the historical annual data includes circuit load data, holiday data, temperature and climate;

[0074] Based on the historical annual data, a preset artificial intelligence algorithm is used to predict the power load data for future time periods to obtain the annual load forecast data.

[0075] This embodiment combines circuit load data, holiday data, temperature and climate, and uses artificial intelligence algorithms to improve the prediction accuracy of future power loads.

[0076] Optionally, the line screening module includes:

[0077] Obtaining the conductor cross-sectional area and corresponding transmission capacity of each of the lines;

[0078] According to the optimal power flow data, the conductor cross-sectional area and the transmission capacity, a preset artificial intelligence algorithm is used to screen the lines with limited transmission capacity to obtain the line set.

[0079] This embodiment obtains the conductor cross-sectional area and the corresponding transmission capacity of each line, and automatically screens the lines with limited transmission capacity through an artificial intelligence algorithm based on the optimal power flow data, so as to accurately identify which lines have actual loads exceeding their transmission capacity, thereby improving the recognition efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0081] Figure 1 A flow chart of a method for acquiring a phase shifter adaptation line scenario provided in an embodiment of the present application;

[0082] Figure 2 A flowchart of step S3 provided in an embodiment of the present application;

[0083] Figure 3 A flowchart of step S7 provided in an embodiment of the present application;

[0084] Figure 4 A flowchart of step S1 provided in an embodiment of the present application;

[0085] Figure 5 A flowchart of step S5 provided in an embodiment of the present application;

[0086] Figure 6 Adapt line scenarios and investment relationship diagrams for phase shifters;

[0087] Figure 7 A block diagram of a phase shifter adaptation line scene acquisition system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0089] With the explosive development of "highly uncertain" resources such as distributed new energy and electric vehicles, power grid planning faces greater challenges and puts forward higher requirements for power demand forecasting. High-precision power demand forecasting requires comprehensive consideration of multi-dimensional big data such as climate, meteorology, population and industrial structure. The data is high in dimension and difficult to process. Traditional forecasting technology is difficult to cope with such large data volumes and high-precision requirements. At the same time, the backbone grid of the power grid is also becoming mature. How to improve the transmission capacity of the existing grid through demand forecasting without changing the existing grid structure, especially the mutual assistance capacity of inter-provincial channels and equipment utilization efficiency, is a key research issue in the current stage of power grid development.

[0090] Accurate load forecasting can guide the deployment of controllable phase shifting transformers (PSTs) technology, thereby improving equipment utilization, balancing power flow, and providing new ideas for the current main grid planning. At present, the optimal location allocation of controllable phase shifters on the transmission network has been analyzed, but these analyses are limited to the simulation of traditional models and have not yet been applied in actual power grids. Therefore, how to obtain the adaptation line scenario of phase shifters in the actual power grid environment and achieve economical and efficient power grid operation is still an urgent problem to be solved.

[0091] According to an embodiment of the present application, an embodiment of a method for acquiring a phase shifter adaptation line scene is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0092] In this embodiment, a method for acquiring a phase shifter adaptation line scenario is provided. Figure 1A flow chart of a method for acquiring a phase shifter adaptation line scenario provided in an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0093] Step S1, obtaining annual load forecast data of electricity in future time periods.

[0094] Specifically, in order to obtain annual load forecast data, it is necessary to first collect historical annual data, including total social electricity consumption, circuit load, holiday information, temperature, climate conditions, gross domestic product (GDP), added value of various industries, per capita GDP, total population, consumer price index, and electricity consumption elasticity coefficient, etc. Subsequently, these data are cleaned, missing values ​​and outliers are processed, and standardized or normalized to eliminate the impact of different dimensions.

[0095] Next, based on the processed data, select a suitable forecasting model for training. Common forecasting models include time series models (such as ARIMA), machine learning models (such as random forests, gradient boosting trees, etc.), and deep learning models (such as LSTM, convolutional neural networks, etc.). The forecasting accuracy and generalization ability of the model are evaluated through methods such as cross-validation and goodness of fit tests.

[0096] Finally, the trained model is applied to the power load forecasting in the future time period to obtain the annual load forecast data. In order to improve the accuracy of the forecast, it is preferred to adopt the "Medium- and Long-Term Load Forecasting Method of Multi-Time Scale Data" (reference: Luo Shuxin et al., "Proceedings of the CSEE", Vol. 40 Supplement, 2020.8.31, pp. 11-19) to obtain more accurate future power load forecast data.

[0097] Step S3, based on the annual load forecast data, the optimal power flow data is determined by taking the total cost of minimizing power generation and power flow network loss as the first objective function.

[0098] Specifically, a detailed model of the power grid is constructed based on the annual load forecast data, including the parameters of components such as generators, loads, transmission lines and transformers, and the network topology. For each generator, a cost model needs to be established, which is usually expressed as the proportional relationship between the power generation cost and the power generation. The cost model is C(p k )=c k ·p k ; Among them, C(p k ) is the power generation cost of the kth generator; c k is the cost coefficient of the kth generator; p k is the active power output of the kth generator. Next, based on the resistance and reactance parameters of the transmission line, the power loss during the power flow is calculated. For example, the power flow network loss p on the line is i,lloss=I 2 ·R, R=ρ·L / A, where I is the current in the line; R is the resistance in the line; ρ is the resistivity of the line; A is the cross-sectional area of ​​the conductor; and L is the length of the line.

[0099] The first objective function is defined as minimizing the total cost, which includes the sum of the power generation costs of all generators and the power flow network losses. In addition, various constraints need to be considered in the model, such as power balance constraints, generator output constraints, and line capacity constraints. The power balance constraint ensures that the power supply in the power grid is balanced with the demand. For each node, the injected power (including generator output and adjacent node input) minus the output power (including load demand and power flowing to other nodes) plus the loss should be equal to zero. The generator output constraint is determined by the design parameters of the generator, such as maximum capacity, minimum stable operation limit, etc. The line capacity constraint is determined by the thermal capacity of the line or its thermal stability limit. It is necessary to ensure that at any time, the power flow on the line will not exceed this maximum capacity to avoid equipment damage or system instability.

[0100] Finally, a planning algorithm is used to solve the optimization problem and find the optimal power flow data that meets all constraints.

[0101] Step S5: based on the optimal power flow data, screen out a set of lines with limited transmission capacity.

[0102] Specifically, based on the optimal power flow data, the operating status data of the power grid can be obtained, including the active and reactive power flow values ​​of each line. Next, the technical parameters of each transmission line need to be obtained, such as line length, conductor type and cross-sectional area. These parameters determine the thermal stability limit of the line, that is, the maximum capacity of the line to safely transmit electrical energy in long-term operation, usually expressed in megawatts (MW) or amperes (A).

[0103] In order to evaluate the transmission capacity of the line, the optimal power flow data needs to be compared with the thermal stability limit of each line. If the power flow value of a line exceeds its thermal stability limit, the transmission capacity of the line is considered to be limited. In addition, artificial intelligence algorithms, such as machine learning, can be used to automatically screen out lines with limited transmission capacity based on optimal power flow data and line parameters, thereby obtaining a set of lines with limited transmission capacity.

[0104] Step S7: taking minimizing the total cost of the phase shifter as the second objective function, for each line in the line set, determining an adapted line scenario with the lowest cost for configuring the phase shifter.

[0105] Specifically, in order to minimize the total cost of the phase shifter, it is necessary to construct a second objective function, which contains decision variables such as line selection, phase shifter capacity, phase shift angle and installation location. First, the cost of installing a phase shifter is estimated for each line in the line set, and the adaptation line scenario with the lowest cost of configuring the phase shifter is solved, and the installation position and angle of the phase shifter on each adaptation line scenario are determined. In the solution process, power balance constraints, generator output constraints and line capacity constraints need to be considered. The power balance constraint ensures that the power supply of the power grid matches the demand, and the difference between the injected power and the output power of each node plus the loss should be equal to zero. The generator output constraint is determined by design parameters such as the maximum capacity and minimum stable operation limit of the generator. The line capacity constraint is determined by the thermal capacity or thermal stability limit of the line, ensuring that the power flow on the line does not exceed the maximum capacity at any time to avoid equipment damage or system instability.

[0106] This embodiment provides a method for obtaining a phase shifter adaptation line scenario, which can optimize the power grid by obtaining annual load forecast data for future time periods. Based on these forecast data, the power grid's power flow and power generation cost are adjusted to ensure that supply and demand are balanced at the lowest cost, thereby obtaining the optimal power flow data. Next, these optimal power flow data are used to screen out lines with limited transmission capacity. Finally, suitable lines are selected from these restricted lines to install phase shifters, and their configurations (such as capacity and angle) are optimized to minimize the total cost of the phase shifters. In this way, adjusting the phase shifter can improve line load distribution, reduce line congestion, and thus reduce the operating cost of the power grid.

[0107] Figure 2 The flowchart of step S3 provided in the embodiment of the present application may include the following steps:

[0108] Step S31, constructing load constraint conditions by taking the sum of the total active output of all generators and the power flow network loss as the annual load forecast data.

[0109] Specifically, the following load constraints are constructed:

[0110]

[0111] Among them, P is the annual load forecast data; p k is the active power output of the kth generator; p i,l loss is the power flow network loss on the phase shifter branch i and the AC branch l.

[0112] Step S33, taking the total cost of minimizing power generation and power flow network loss as the first objective function, and determining the optimal power flow data through the load constraint condition.

[0113] Specifically, the first objective function is constructed, including the power generation cost and the power flow network loss cost. Then, the load constraint condition is applied in the solution process to ensure that the power grid can meet the power supply balance. Finally, by calculating and optimizing the first objective function, it is ensured that the total cost of power generation and power flow network loss is minimized under the premise of meeting the load constraint condition.

[0114] and Figure 1 Compared with the embodiment shown in the figure, this embodiment constructs a clear load constraint condition by ensuring that the sum of the total active output of all generators and the power flow network loss is equal to the annual load forecast data. It is ensured that the total active output and the sum of the power flow network loss of all generators in the power grid are consistent with the annual load forecast data, thereby ensuring power supply balance. By constructing a first objective function including the power generation cost and the power flow network loss cost, and optimizing it in combination with the load constraint condition, the total cost can be minimized while satisfying the balance of power supply and demand in the power grid.

[0115] In some embodiments, the first objective function is:

[0116]

[0117] Among them, c k is the cost coefficient of the kth generator; p k is the active power output of the kth generator; p i,l loss is the power flow network loss on the phase shifter branch i and the AC branch l.

[0118] Specifically, when calculating the cost, the power flow loss can be multiplied by the cost per unit of energy (such as per megawatt hour MWh) C m , C m is the network loss cost coefficient, which can be based on the electricity price in the power market or the marginal cost of the power grid to obtain the monetary cost of the power flow network loss, and integrate the monetary cost into the first objective function, and use the optimization algorithm to solve it to obtain the optimal power flow data corresponding to the lowest cost. That is, when optimizing and calculating the first objective function, the function can be converted into:

[0119]

[0120] Figure 3 The flowchart of step S7 provided in the embodiment of the present application may include the following steps:

[0121] Step S71, obtaining a reference phase shifter price, a reference phase shift angle, and a reference phase shifter capacity of the phase shifter.

[0122] Specifically, the manufacturer or supplier provides the benchmark phase shifter price of the phase shifter, as well as the benchmark phase shift angle and benchmark phase shifter capacity corresponding to the phase shifter. The benchmark phase shift angle generally refers to the maximum phase shift that the phase shifter can provide under standard or rated conditions. For example, some phase shifters may be designed to provide a continuously variable phase response from 0° to 360°. The benchmark phase shifter capacity refers to the benchmark input power that can be handled, usually expressed in watts (W) or kilowatts (kW). Different models of phase shifters have corresponding gear shifter capacities for a variety of different gears, covering different input frequency ranges from low frequency to high frequency, and have different input power capacities. These data can be obtained by referring to the technical manuals, product specifications provided by the manufacturer or by directly consulting the manufacturer.

[0123] Step S73: for each estimated phase shift angle of the phase shifter, determine the estimated phase shifter price corresponding to the estimated phase shift angle according to the reference phase shifter price, the reference phase shift angle and the reference phase shifter capacity.

[0124] Specifically, when considering installing phase shifters in the power grid for steady-state power flow control, it is necessary to consider the price of phase shifters installed on the line. However, the price of installing phase shifters is affected by many factors and cannot be priced solely based on unit capacity. It is necessary to consider the benchmark phase shifter price, benchmark phase shift angle, and benchmark phase shifter capacity, and establish a cost model based on these benchmark data to estimate the price of phase shifters at different phase shift angles. This model may be based on empirical formulas or trained with historical data.

[0125] Step S75: For each line in the line set, adaptation is performed according to the estimated phase shifter price, and the line scenario with the lowest cost for configuring the phase shifter is determined as the adapted line scenario.

[0126] Specifically, for each line in the line set, the cost impact of the phase shifter on the line after installation is evaluated, and the cost of each line after being configured with phase shifters of different models, different capacities, and different phase shifting angles is obtained. The costs corresponding to the line are sorted from low to high, and the line scenario for installing the phase shifter with the lowest cost is determined as the adapted line scenario for the phase shifter.

[0127] and Figure 1 Compared with the embodiment shown in the figure, this embodiment calculates the estimated phase shifter price corresponding to each phase shift angle by obtaining the benchmark phase shifter price, benchmark phase shift angle and benchmark phase shifter capacity, and evaluates the phase shifter cost under different angle conditions. By applying the estimated phase shifter price to each line, the actual cost of configuring the phase shifter is calculated. By comparing the different costs of installing the phase shifter on each line and selecting the line with the lowest cost as the phase shifter adaptation line scenario, the cost-effectiveness is maximized.

[0128] In some embodiments, the second objective function is:

[0129]

[0130]

[0131] in, is the estimated phase shifter price; π is the benchmark phase shifter price; To estimate the phase shift angle; To install a phase shift angle estimation device on the AC branch l between the AC branch starting point m and the AC branch end point n Estimated phase shifter price; θ is the reference phase shift angle; u m,n,l is the installation position of the phase shifter in the AC branch l at the AC branch starting point m and the AC branch end point n; is the capacity of the gear phase shifter; s is the capacity of the reference phase shifter.

[0132] Specifically, each phase shifter has different gears and the gear shifter capacity corresponding to each gear Including the effect of the gear shifter capacity in the second objective function ensures that the optimization process takes into account the cost of different gear selections while minimizing the cost.

[0133] In some embodiments, in determining the optimal power flow data and determining the adapted line scenario, the method further includes a node power balance constraint:

[0134]

[0135]

[0136]

[0137]

[0138] Among them, p k is the active output of the generator at node k; p d is the active power demand of the generator at node k; q k is the reactive power output of the generator at node k; q d is the reactive power demand of the generator at node k; p m,n,l (θ, ν, λ) is the active power flow through the AC branch l from the starting point m of the AC branch to the end point n of the AC branch; q m,n,l (θ, ν, λ) is the reactive power flow through the AC branch l from the starting point m to the end point n of the AC branch; v m is the voltage at the starting point m of the AC branch; v n is the voltage at the end point n of the AC branch; rm,n,l is the resistance of the line; is the reactance after line compensation, , x m,n,l is the line reactance, λ m,n,l The level of reactance compensation provided to the phase shifter; θ m is the phase shift angle of the starting point m of the AC branch; θ n is the phase shift angle of the AC branch terminal n; bc m,n,l is the line susceptance.

[0139] Specifically, for each node k, the active power and reactive power must satisfy the balance constraint. This means that the total active and reactive power entering the node must be equal to the total active and reactive power leaving the node. With these constraints, the operation of the power grid can be optimized, the cost can be minimized, and the stability and reliability requirements of the power grid can be met. Preferably, λ m,n,l is [0,2.33].

[0140] In some embodiments, the stable power flow on the adapted line scenario is less than or equal to the corresponding thermal stability limit.

[0141] Specifically, the thermal stability limit refers to the maximum power that the transmission line can safely transmit without exceeding its thermal capacity limit. This limit is determined by the physical properties of the conductor, including the cross-sectional area, material, ambient temperature, etc. of the conductor. When current passes through the conductor, heat is generated. If there is too much heat, it may cause the conductor to overheat, affecting the safety and reliability of the line. Therefore, ensure that the stable power flow on the adapted line scenario is less than or equal to the corresponding thermal stability limit to ensure that the transmission line can operate safely under various working conditions and avoid equipment damage or power outages due to overload.

[0142] Figure 4 The flowchart of step S1 provided in the embodiment of the present application may include the following steps:

[0143] Step S11, obtaining historical annual data of power grid operation; wherein the historical annual data includes circuit load data, holiday data, temperature and climate.

[0144] Step S13, based on the historical annual data, a preset artificial intelligence algorithm is used to predict the power load data for a future time period to obtain the annual load forecast data.

[0145] Specifically, past grid operation data, including total load, peak load time, and load curve, are collected and organized, which will serve as the basis for load forecasting. The impact of holidays on load patterns needs to be recorded in particular, as holidays can significantly change load demand. At the same time, temperature changes, especially extreme weather conditions, can also have an impact on electricity demand. Therefore, both long-term climate patterns and short-term climate changes must be taken into account.

[0146] Next, use the collected historical data to perform load forecasting through artificial intelligence algorithms. Common forecasting models include time series models (such as ARIMA), machine learning models (such as random forests, gradient boosting trees, etc.), and deep learning models (such as LSTM, convolutional neural networks, etc.). In order to ensure the accuracy and generalization of the forecast, methods such as cross-validation and goodness of fit tests are used to evaluate the model. Finally, the trained model is used to generate future annual load forecast data. In order to improve the accuracy of the forecast, it is preferred to use the "Medium- and Long-Term Load Forecasting Method for Multi-Time Scale Data" (reference: Luo Shuxin et al., "Proceedings of the CSEE", Volume 40 Supplement, 2020.8.31, pages 11-19) to obtain more accurate future power load forecast data.

[0147] and Figure 1 Compared with the embodiment shown, this embodiment combines circuit load data, holiday data, temperature and climate, and uses artificial intelligence algorithms to improve the prediction accuracy of future power loads.

[0148] Figure 5 The flowchart of step S5 provided in the embodiment of the present application may include the following steps:

[0149] Step S51, obtaining the conductor cross-sectional area and corresponding transmission capacity of each of the lines.

[0150] Step S53, based on the optimal power flow data, the conductor cross-sectional area and the transmission capacity, a preset artificial intelligence algorithm is used to screen the lines with limited transmission capacity to obtain the line set.

[0151] Specifically, the conductor cross-sectional area is a key factor affecting the transmission capacity of the line. The transmission capacity is the maximum power that the line can safely transmit under specific conditions, that is, the thermal stability limit, which is affected by many factors, including conductor material, cross-sectional area, line length, ambient temperature, etc. It is necessary to collect the conductor cross-sectional area and corresponding transmission capacity of each transmission line, and the relevant parameters can be obtained from the manufacturer or grid operator. Then, the flow value of each line is obtained according to the optimal power flow data, and the flow value of each line is compared with the transmission capacity of each line. If the flow value of a line exceeds its transmission capacity, the transmission capacity of the line is considered to be limited. In addition, artificial intelligence algorithms, such as machine learning, can be used to automatically screen out lines with limited transmission capacity based on optimal power flow data and line parameters, thereby obtaining a set of lines with limited transmission capacity.

[0152] and Figure 1 Compared with the embodiment shown, this embodiment obtains the conductor cross-sectional area and the corresponding transmission capacity of each line, and automatically screens the lines with limited transmission capacity through an artificial intelligence algorithm based on the optimal power flow data, so as to accurately identify which lines have actual loads exceeding their transmission capacity, thereby improving the recognition efficiency and accuracy.

[0153] In order to illustrate the technical effect achieved by the embodiment of the present invention, a 220 kV power grid consisting of 266 220 kV substations in Shanxi Province was selected as basic data to evaluate the feasibility and adaptability of the phase shifter adaptation line scenario acquisition method.

[0154] The price of the benchmark phase shifter is 33.6 million yuan, the benchmark phase shift angle is 6°, and the benchmark phase shifter capacity is 350MVA. According to the phase shifter adaptation line scene acquisition method of the embodiment of the present application, the double-circuit line in Jinzhong City is screened out. There may be an overload risk in the N-1 situation. Therefore, the phase shifter is selected to be implemented on the double-circuit line, and different schemes are compared. The Jinzhong double-circuit line is a 220 kV overhead line with a line length of 18.3 kilometers. The conductor cross-section of the I-circuit line is 2×150mm² (transmission capacity 275,000 kilowatts), and the cross-section of the II-circuit line is 2×300mm² (transmission capacity 454,000 kilowatts). During normal operation, the transmission power of the I-circuit line is 204,000 kilowatts, and the transmission power of the II-circuit line is 192,000 kilowatts. During the peak of new energy power generation at noon in winter, if an N-1 fault occurs, the transmission power of the I-circuit line will reach 368,000 kilowatts, an overload of 34%. It is proposed to use phase shifters to improve the transmission capacity of the line. Phase shifters can improve the power flow distribution of the line by adjusting the voltage phase, thereby increasing the transmission capacity of the line. When a phase shifter with a specification of 350MVA / 230kV is selected, the phase shifter angle is 9 degrees, and the power flow capacity is increased by about 164,000 kilowatts, which is the most economical and safe operation mode.

[0155] It should be noted that Table 1 shows the technical and economic indicators of 220 kV phase shifters. As shown in Table 1, as the phase shift angle increases, although the line transmission capacity increases, the cost of the phase shifter also increases. It is necessary to compare the unit investment increase flow under different phase shift angles to determine the most economical solution.

[0156] Table 1 Technical and economic indicators of 220 kV phase shifter

[0157]

[0158] By comparing the unit investment and transmission capacity improvement in different scenarios, the suitable line scenario can be determined. See Table 2 for the phase shifter suitable line scenario and investment relationship. Figure 6 The following scenarios are line sets selected from the optimal power flow data obtained through the embodiments of the present application, and the minimum cost of adding a phase shifter, i.e. the corresponding investment amount, is calculated for each line in the following scenarios.

[0159] Scenario 1: Meeting the requirements of line N-1, the phase shifter angle is 6 degrees, the investment is 33.6 million yuan, and the unit investment increases the flow by 299,000 kilowatts.

[0160] Scenario 2: Double-circuit power flow balance, phase shifter angle is 10 degrees, investment is 47.69 million yuan, and the unit investment increases the power flow by 267,000 kilowatts.

[0161] Scenario 3: To meet the line reconstruction standards, the phase shifter angle is 16 degrees, the investment is 68.52 million yuan, and the unit investment increases the flow by 257,000 kilowatts.

[0162] Table 2 Phase shifter adaptation line scenarios and investment relationships

[0163]

[0164] According to the above results, subsequent staff can use it to deploy phase shifters for different line adaptation scenarios. For example, for double-circuit line power flow balance, these solutions perform well in terms of economy and long-term applicability. In the 220 kV application scenario, phase shifters are more suitable for balancing the power flow of double-circuit lines rather than significantly improving the line transmission capacity. If line reconstruction is considered, the I-circuit line will be transformed into a 2×400mm² cross-section conductor. Although it can significantly improve the transmission capacity, the cost per kilometer is relatively high, with a total investment of approximately 54.9 million yuan, which is less economical than other solutions. Therefore, in terms of economy and applicability, the phase shifter solution is obviously more advantageous, and it is recommended to give priority to the phase shifter solution for deployment in this scenario.

[0165] Accordingly, please refer to Figure 7, a block diagram of a phase shifter adaptation line scene acquisition system provided in an embodiment of the present application, the system comprising:

[0166] The load data acquisition module 101 is used to obtain the annual load forecast data of the power in the future time period;

[0167] A power flow data determination module 103 is used to determine the optimal power flow data based on the annual load forecast data and taking the total cost of minimizing power generation and power flow network loss as the first objective function;

[0168] A line screening module 105, configured to screen out a set of lines with limited transmission capacity based on the optimal power flow data;

[0169] The adapted line scenario determining module 107 is configured to determine, for each line in the line set, an adapted line scenario with the lowest cost for configuring the phase shifter, taking minimizing the total cost of the phase shifter as the second objective function.

[0170] In some embodiments, the flow data determination module 103 includes:

[0171] The load constraint condition is constructed by taking the sum of the total active output of all generators and the power flow network loss as equal to the annual load forecast data;

[0172] Taking minimizing the total cost of power generation and power flow network loss as the first objective function, the optimal power flow data is determined by the load constraint condition.

[0173] In some embodiments, the first objective function is:

[0174]

[0175] Among them, c k is the cost coefficient of the kth generator; p k is the active power output of the kth generator; p i,l loss is the power flow network loss on the phase shifter branch i and the AC branch l.

[0176] In some embodiments, the adaptation route scenario determination module 107 includes:

[0177] Obtaining a reference phase shifter price, a reference phase shift angle, and a reference phase shifter capacity of the phase shifter;

[0178] For each estimated phase shift angle of the phase shifter, determining an estimated phase shifter price corresponding to the estimated phase shift angle according to the reference phase shifter price, the reference phase shift angle and the reference phase shifter capacity;

[0179] For each line in the line set, adaptation is performed according to the estimated phase shifter price, and the line scenario with the lowest cost for configuring the phase shifter is determined as the adapted line scenario.

[0180] In some embodiments, the second objective function is:

[0181]

[0182]

[0183] in, is the estimated phase shifter price; π is the benchmark phase shifter price; To estimate the phase shift angle; To install a phase shift angle estimation device on the AC branch l between the AC branch starting point m and the AC branch end point n Estimated phase shifter price; θ is the reference phase shift angle; u m,n,l is the installation position of the phase shifter in the AC branch l at the AC branch starting point m and the AC branch end point n; is the estimated phase shifter capacity; s is the reference phase shifter capacity.

[0184] In some embodiments, in determining the optimal power flow data and determining the adapted line scenario, the method further includes a node power balance constraint:

[0185]

[0186]

[0187]

[0188]

[0189] Among them, p k is the active output of the generator at node k; p d is the active power demand of the generator at node k; q k is the reactive power output of the generator at node k; q d is the reactive power demand of the generator at node k; p m,n,l (θ, ν, λ) is the active power flow through the AC branch l from the starting point m of the AC branch to the end point n of the AC branch; q m,n,l (θ, ν, λ) is the reactive power flow through the AC branch l from the starting point m to the end point n of the AC branch; v m is the voltage at the starting point m of the AC branch; v n is the voltage at the end point n of the AC branch; r m,n,l is the resistance of the line; is the reactance after line compensation, , x m,n,l is the line reactance, λ m,n,l The level of reactance compensation provided to the phase shifter; θ m is the phase shift angle of the starting point m of the AC branch; θ n is the phase shift angle of the AC branch terminal n; bc m,n,l is the line susceptance.

[0190] In some embodiments, the stable power flow on the adapted line scenario is less than or equal to the corresponding thermal stability limit.

[0191] In some embodiments, the load data acquisition module 101 includes:

[0192] Obtaining historical annual data of power grid operation; wherein the historical annual data includes circuit load data, holiday data, temperature and climate;

[0193] Based on the historical annual data, a preset artificial intelligence algorithm is used to predict the power load data for future time periods to obtain the annual load forecast data.

[0194] In some embodiments, the line screening module 105 includes:

[0195] Obtaining the conductor cross-sectional area and corresponding transmission capacity of each of the lines;

[0196] According to the optimal power flow data, the conductor cross-sectional area and the transmission capacity, a preset artificial intelligence algorithm is used to screen the lines with limited transmission capacity to obtain the line set.

[0197] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0198] A phase shifter adaptation line scene acquisition system in this embodiment is presented in the form of a functional module, where the module refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0199] Although the embodiments of the present application are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

[0200] The systems, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0201] For the convenience of description, the above system is described by dividing the functions into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0202] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0203] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0204] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0205] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for acquiring a phase shifter adaptation line scenario, characterized in that: The method comprises: Obtain annual load forecast data for future periods of electricity; Based on the annual load forecast data, the optimal power flow data is determined by minimizing the total cost of power generation and power flow network loss as the first objective function; Based on the optimal power flow data, a set of lines with limited transmission capacity are screened out; Taking minimizing the total cost of the phase shifter as the second objective function, for each line in the line set, determining an adapted line scenario with the lowest cost for configuring the phase shifter; In determining the optimal power flow data and determining the adapted line scenario, the method further includes a node power balance constraint: Among them, p k is the active output of the generator at node k; p d is the active power demand of the generator at node k; q k is the reactive power output of the generator at node k; q d is the reactive power demand of the generator at node k; p m,n,l (θ, ν, λ) is the active power flow through the AC branch l from the starting point m of the AC branch to the end point n of the AC branch; q m,n,l (θ, ν, λ) is the reactive power flow through the AC branch l from the starting point m to the end point n of the AC branch; v m is the voltage at the starting point m of the AC branch; v n is the voltage at the end point n of the AC branch; r m,n,l is the resistance of the line; is the reactance of the line after compensation, , x m,n,l is the line reactance, λ m,n,l The level of reactance compensation provided to the phase shifter; θ m is the phase shift angle of the starting point m of the AC branch; θ n is the phase shift angle of the AC branch terminal n; bc m,n,l is the line susceptance.

2. The method according to claim 1, characterized in that The method of determining the optimal power flow data based on the annual load forecast data and taking the total cost of minimizing power generation and power flow network loss as the first objective function includes: The load constraint condition is constructed by taking the sum of the total active output of all generators and the power flow network loss as equal to the annual load forecast data; Taking minimizing the total cost of power generation and power flow network loss as the first objective function, the optimal power flow data is determined by the load constraint condition.

3. The method according to claim 1 or 2, characterized in that: The first objective function is: Among them, c k is the cost coefficient of the kth generator; p k is the active power output of the kth generator; p i,l loss is the power flow network loss on the phase shifter branch i and the AC branch l.

4. The method according to claim 1, characterized in that: The minimizing the total cost of the phase shifter as the second objective function, and determining, for each line in the line set, an adapted line scenario with the lowest cost of configuring the phase shifter, comprises: Obtaining a reference phase shifter price, a reference phase shift angle, and a reference phase shifter capacity of the phase shifter; For each estimated phase shift angle of the phase shifter, determining an estimated phase shifter price corresponding to the estimated phase shift angle according to the reference phase shifter price, the reference phase shift angle and the reference phase shifter capacity; For each line in the line set, adaptation is performed according to the estimated phase shifter price, and the line scenario with the lowest cost for configuring the phase shifter is determined as the adapted line scenario.

5. The method according to claim 1 or 4, characterized in that: The second objective function is: in, is the estimated phase shifter price; π is the benchmark phase shifter price; To estimate the phase shift angle; To install a phase shift angle estimation device on the AC branch l between the AC branch starting point m and the AC branch end point n Estimated phase shifter price; θ is the reference phase shift angle; u m,n,l is the installation position of the phase shifter in the AC branch l at the AC branch starting point m and the AC branch end point n; is the estimated phase shifter capacity; s is the reference phase shifter capacity.

6. The method according to claim 1, characterized in that The stable power flow on the adapted line scenario is less than or equal to the corresponding thermal stability limit.

7. The method according to claim 1, characterized in that The obtaining of annual load forecast data of electricity in future time periods includes: Obtaining historical annual data of power grid operation; wherein the historical annual data includes circuit load data, holiday data, temperature and climate; Based on the historical annual data, a preset artificial intelligence algorithm is used to predict the power load data for future time periods to obtain the annual load forecast data.

8. The method according to claim 1, characterized in that The step of screening out a set of lines with limited transmission capacity based on the optimal power flow data includes: Obtaining the conductor cross-sectional area and corresponding transmission capacity of each of the lines; According to the optimal power flow data, the conductor cross-sectional area and the transmission capacity, a preset artificial intelligence algorithm is used to screen the lines with limited transmission capacity to obtain the line set.

9. A phase shifter adaptation line scene acquisition system, characterized in that: The system comprises: The load data acquisition module is used to obtain the annual load forecast data of electricity in the future time period; A power flow data determination module, configured to determine optimal power flow data based on the annual load forecast data and taking minimizing the total cost of power generation and power flow network loss as a first objective function; A line screening module, used for screening out a set of lines with limited transmission capacity based on the optimal power flow data; A line scenario determination module, configured to determine, for each line in the line set, an adapted line scenario with the lowest cost for configuring the phase shifter, taking minimizing the total cost of the phase shifter as a second objective function; In the process of determining the optimal power flow data and determining the adapted line scenario, a node power balance constraint is also included: Among them, p k is the active output of the generator at node k; p d is the active power demand of the generator at node k; q k is the reactive power output of the generator at node k; q d is the reactive power demand of the generator at node k; p m,n,l (θ, ν, λ) is the active power flow through the AC branch l from the starting point m of the AC branch to the end point n of the AC branch; q m,n,l (θ, ν, λ) is the reactive power flow through the AC branch l from the starting point m to the end point n of the AC branch; v m is the voltage at the starting point m of the AC branch; v n is the voltage at the end point n of the AC branch; r m,n,l is the resistance of the line; is the reactance of the line after compensation, , x m,n,l is the line reactance, λ m,n,l The level of reactance compensation provided to the phase shifter; θ m is the phase shift angle of the starting point m of the AC branch; θ n is the phase shift angle of the AC branch terminal n; bc m,n,l is the line susceptance.

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