High-voltage distribution network reactive voltage optimization method and device based on time scale, equipment and medium

Abstract

The invention discloses a high-voltage distribution network reactive voltage optimization method, device, equipment and medium based on a time scale, and relates to the technical field of distribution network optimization, and the method comprises the following steps: obtaining the real-time operation state data and prediction information of a high-voltage distribution network, dividing the operation time of the high-voltage distribution network, obtaining the divided data, and calculating the real-time operation state data of the high-voltage distribution network; the division data comprises a long time scale and a short time scale; under a long time scale, based on load prediction and distributed power supply output prediction, a mixed integer programming model is established, and a capacitor bank switching scheme and a transformer tap reference position are obtained; under a short time scale, constructing an optimal power flow model with minimum voltage deviation as a target, and dynamically calling the reactive capacity of the distributed energy converter for real-time correction; and establishing a cross-time-scale coordination control mechanism, triggering advanced re-optimization of a long time scale when the accumulated reactive power regulation quantity under a short time scale reaches a preset reactive power capacity threshold value, and updating an equipment action constraint condition.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network optimization, and more specifically, to a method, device, equipment and medium for optimizing reactive voltage of a high-voltage power distribution network based on a time scale. Background Art

[0002] In modern power systems, high-voltage distribution networks are the key link between transmission networks and users, and their operational stability and efficiency are crucial to the entire power system. With the rapid development of social economy, various types of power loads are growing and showing diversified and complex characteristics, which brings unprecedented challenges to the reactive voltage control of high-voltage distribution networks. The reactive voltage optimization method of high-voltage distribution networks based on time scale has emerged.

[0003] In the early high-voltage distribution network, the load type was relatively single, mainly industrial and residential basic electricity consumption, and the load fluctuation was relatively regular. At that time, relatively simple reactive power compensation and voltage regulation methods, such as switching on and off of fixed capacitor banks and manual adjustment of on-load tap-changing transformer taps, were able to meet basic operating requirements. However, the large-scale access of distributed power sources in recent years has changed this situation. Distributed power sources such as photovoltaic power generation and wind power generation are intermittent and random, and their output power is significantly affected by natural factors such as weather, resulting in complex and changeable power flow distribution in the power grid. For example, during the day when there is sufficient sunlight, distributed photovoltaics generate a large amount of power and inject power into the power grid, which may cause the voltage of local nodes to increase; on cloudy days or at night, the photovoltaic output drops sharply, which may cause the voltage to drop.

[0004] At the same time, the power demand at the user end is also undergoing profound changes. The widespread popularity of electric vehicles, whose charging behavior is centralized and random, may increase the load burden of the power grid during peak hours and cause voltage fluctuations; the increase in nonlinear loads such as smart home appliances and frequency conversion equipment has generated a large number of harmonics, further affecting the power quality of the power grid and making the reactive voltage problem more prominent.

[0005] Traditional reactive voltage control methods often lack effective consideration of the time dimension and are difficult to adapt to such rapidly changing operating scenarios. They are usually based on fixed operating modes and parameter settings and cannot track the dynamic changes of loads and power sources in real time. For example, the switching strategy of fixed capacitor banks cannot be flexibly adjusted according to the reactive power demand in different time periods. It is either under-compensated or over-compensated, making it difficult to ensure voltage quality and may also cause unnecessary active power loss.

[0006] For example, the method, device, medium, equipment and product for combined analog-digital voltage control of a distribution network disclosed in the invention patent announcement with the announcement number of CN118449149A. The method includes the following steps: obtaining distribution network data; constructing a global-local two-stage voltage control model for a flexible interconnected distribution network based on analog-digital combination; by constructing agents corresponding to the distribution network equipment on the AC side or the DC side of the flexible interconnected distribution network, transforming the voltage control model into a partitioned Markov game model; according to the distribution network data, training the partitioned Markov game model based on the multi-agent flexible actor-critic algorithm to obtain multiple agent decision models; sending the agent decision models to the corresponding edge computing devices, and realizing the combined analog-digital voltage control of the distribution network based on the local autonomy of the edge computing devices. The present invention gives play to the adjustment capabilities of different devices at different time scales, and at the same time separates integer and continuous variables to facilitate the solution of the optimization algorithm, and can greatly improve the control efficiency of the distribution network.

[0007] For example, a method, device, equipment and medium for reactive power optimization and coordinated control of the voltage of a distribution network disclosed in the invention patent announcement with the announcement number of CN116505542A. The method includes: obtaining photovoltaic power data and load prediction data; according to the photovoltaic power data and the load prediction data, performing hourly integrated scheduling with the optimal comprehensive cost as the first control target to determine the active power output of DPV, the first-stage voltage distribution and the switching results of discrete variables; according to the active power output of DPV, the first-stage voltage distribution and the switching results of discrete variables, performing comprehensive coordinated control with the minimum voltage deviation as the second control target to determine the reactive power output of DPV, the reactive power compensation of the flexible OLTC and the switching correction of the CB. Thus, the voltage reactive power optimization that comprehensively considers the time scale characteristics and regulation characteristics of the regulating equipment is realized, the cost of coordinated control is greatly reduced, and at the same time, the DPV accommodation and voltage quality are improved, and the requirements of the new distribution network can be better met.

[0008] In the above disclosed technical solutions, there are at least the following technical problems: the regulation of traditional capacitor banks, transformers and new energy converters lacks time sequence coordination, and static equipment cannot follow minute-level fluctuations, resulting in redundant reserve capacity, and the reactive power capacity of new energy converters is not dynamically called and is only used as a backup.

[0009] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0010] In order to overcome the above defects of the prior art, the embodiments of the present invention provide a method, device, equipment and medium for reactive power and voltage optimization of a high-voltage distribution network based on time scales. By dividing time scales, a cross-time-scale coordinated control mechanism is established to solve the problem that the regulation of traditional capacitor banks, transformers and new energy converters lacks time sequence coordination and static equipment cannot follow minute-level fluctuations.

[0011] To achieve the above object, the present invention provides the following technical solutions: A reactive power and voltage optimization method for a high-voltage distribution network based on time scales, comprising the following steps: obtaining real-time operation state data and prediction information of the high-voltage distribution network, and dividing the operation time of the high-voltage distribution network to obtain division data, where the division data includes long time scales and short time scales; at long time scales, based on load forecasting and distributed power generation output forecasting, establishing a mixed-integer programming model to obtain a capacitor bank switching scheme and a reference position of a transformer tap; at short time scales, constructing an optimal power flow model with the minimum voltage deviation as the objective, and dynamically invoking the reactive power capacity of a distributed energy converter for real-time correction; establishing a cross-time scale coordination control mechanism, and when the cumulative reactive power regulation amount at short time scales reaches a preset reactive power capacity threshold, triggering an early re-optimization at long time scales and updating the equipment action constraint conditions.

[0012] In a preferred embodiment, the dividing the operation time of the high-voltage distribution network to obtain division data is specifically: setting an initial time window length of the division data, calculating the volatility of distributed power generation, where the division data includes long time scales and short time scales; comparing the volatility of distributed power generation with a set distributed power generation volatility threshold to obtain a comparison result, where the comparison result includes adjustments and optimizations of long time scales and short time scales.

[0013] In a preferred embodiment, the establishing a mixed-integer programming model based on load forecasting and distributed power generation output forecasting at long time scales is specifically: calculating the power loss cost during the operation of the power grid based on the active power loss of each line; constructing an objective function based on the power loss cost and the manufacturing cost of relevant electrical equipment; obtaining the decision variables of the objective function based on correlation analysis according to the equipment control characteristics and the objective function; and obtaining the mixed-integer programming model by combining the constraint conditions of each variable, the decision variables, and the objective function.

[0014] In a preferred embodiment, the obtaining the capacitor bank switching scheme and the reference position of the transformer tap is specifically: performing linearization modeling on the influence of discrete positions of the transformer tap, converting the mixed-integer programming model from a non-linear model to a mixed-integer linear programming model; solving the mixed-integer linear programming model based on CPLEX, and obtaining the capacitor bank switching scheme and the reference position of the transformer tap according to the objective function and the constraint conditions; after each long time scale optimization is completed, updating the prediction information according to the actual operation data and re-solving the decision for the next time window.

[0015] In a preferred embodiment, in the short time scale, an optimal power flow model with the minimum voltage deviation as the target is constructed, and the reactive power capacity of the distributed energy converter is dynamically called for real-time correction. Specifically: according to the grid characteristics and control requirements, the short time scale is set, and the sliding window is optimized; in the short time scale, with the goal of minimizing the sum of the squares of the voltage deviations of all network nodes, and at the same time introducing a penalty term for the reactive power regulation rate of the distributed energy converter as the second objective function; the decision variables of the second objective function are set, the constraint conditions are established, and the optimal power flow model is obtained; the optimal power flow model is solved based on the interior point method to obtain the reactive power regulation amount of each converter, and the regulation instruction is issued; the voltage deviation after regulation is monitored. If the voltage deviation change rate is greater than the preset change rate threshold, the voltage weight coefficient recalibration is triggered.

[0016] In a preferred embodiment, when the cumulative reactive power regulation amount reaches the preset reactive power capacity threshold in the short time scale, the long time scale is triggered for early re-optimization, and the equipment action constraint conditions are updated. Specifically: calculate the cumulative value of the reactive power regulation amount of the distributed energy converter, and set the reactive power capacity threshold; based on the short time scale optimization period, compare and analyze the cumulative value of the reactive power regulation amount with the reactive power capacity threshold; if the cumulative value of the reactive power regulation amount is greater than the reactive power capacity threshold, trigger the cross-time scale coordinated control mechanism, perform early re-optimization on the long time scale, and update the equipment action constraint conditions.

[0017] In a preferred embodiment, the coordinated control mechanism further includes a conflict resolution strategy. When there is a control conflict between the long time scale equipment regulation and the short time scale converter regulation, the short time scale real-time correction instruction is preferentially executed, and at the same time, the conflict event is recorded and the optimization model parameters of the subsequent time window are corrected.

[0018] The technical effects and advantages of the reactive power and voltage optimization method, device, equipment and medium for high-voltage distribution network based on time scale of the present invention: 1. The present invention achieves a balance between real-time performance and equipment lifespan by establishing a coordinated control mechanism across time scales. On a short time scale, with the goal of minimizing voltage deviation and combining a regulation rate penalty term, a distributed energy converter is used to perform real-time correction of reactive power capacity, ensuring the stability of the grid voltage. On a long time scale, a mixed-integer programming model based on load forecasting and distributed power output forecasting incorporates the number of equipment operations into the cost function, limiting the number of operations of capacitor banks and transformer tap changers, and extending the service life of the equipment. When the cumulative reactive power regulation amount reaches a preset reactive power capacity threshold on the short time scale, for example, when the cumulative regulation amount reaches 30% of the total converter capacity, it will trigger an early re-optimization on the long time scale and update the equipment operation constraints. In this way, both the real-time voltage quality of the grid is guaranteed, and the equipment is prevented from accelerating aging due to excessive regulation, achieving a win-win situation for the economic operation of the grid and the reliability of the equipment.

[0019] 2. The present invention realizes dynamic adjustment of time scales through real-time calculation of the volatility of distributed power sources. It not only ensures that when the output of new energy fluctuates violently, the optimization period of the long time scale can be shortened to timely adjust equipment operations, such as the switching of capacitor banks and the position of transformer tap changers, so as to effectively respond to the rapidly changing grid conditions; but also when the fluctuations are small, the time scale can be appropriately extended to reduce the frequent operations of the equipment and lower the equipment losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic flow chart of the reactive power and voltage optimization method for a high-voltage distribution network based on time scales according to the present invention.

[0021] Figure 2 It is a schematic structural diagram of the reactive power and voltage optimization device for a high-voltage distribution network based on time scales according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Embodiment 1 Figure 1 The reactive power and voltage optimization method for a high-voltage distribution network based on time scales according to the present invention is given, including the following steps: S1, Obtain the real-time operation state data and prediction information of the high-voltage distribution network, and divide the operation time of the high-voltage distribution network into a long time scale and a short time scale according to the real-time operation state data and prediction information; The real-time operating state data includes system voltage, current, and power parameters, and the prediction information includes load prediction and distributed power generation output prediction; The acquisition of the real-time operating state data and prediction information of the high-voltage distribution network is specifically as follows: Through a synchronized phasor measurement device, the system voltage, current, and power parameters are collected in real time; Input the historical load curve and meteorological data (temperature, humidity), and generate a 24-hour load prediction through an LSTM neural network; Based on irradiance, cloud cover prediction combined with numerical weather prediction to obtain the distributed power generation output prediction.

[0024] The division of the operating time of the high-voltage distribution network into long time scales and short time scales is specifically as follows: Set the initial time window lengths of the long time scale and short time scale, as well as the distributed power generation volatility threshold; Calculate the distributed power generation volatility in each short time scale time window. If the distributed power generation penetration rate is greater than the distributed power generation volatility threshold, the long time scale time window will be dynamically compressed, and the short time scale time window will be adjusted synchronously; After the long time scale optimization, roll the time window forward and refit it with the latest prediction data, update the distributed power generation volatility, and re-divide the long time scale and short time scale.

[0025] The distributed power generation volatility is specifically as follows:

[0026] Among them, is the difference in distributed power generation output between adjacent short time scale time windows, is the rated capacity of the distributed power generation, is the distributed power generation volatility.

[0027] Furthermore, the long time scale is set to 4 hours, the short time scale is set to 15 minutes, and the distributed power generation volatility threshold is set to 15%.

[0028] S2. Under the long time scale, based on the load prediction and distributed power generation output prediction, establish a mixed-integer programming model to obtain the switching scheme of the capacitor bank and the reference position of the transformer tap; The establishment of the mixed-integer programming model based on the load prediction and distributed power generation output prediction under the long time scale is specifically as follows: By obtaining the active power loss of each line and multiplying it by the corresponding electricity price to calculate the power loss cost during the operation of the power grid; Obtain the capacitor bank operation cost and transformer tap operation cost respectively, and add them to the power loss cost to obtain the objective function; Based on the device control characteristics and the objective function, decision variables of the objective function are obtained through correlation analysis. The decision variables include the switching states of capacitor banks, the tap positions of transformers, the node voltage magnitudes, the active power of lines, and the reactive power. The constraint conditions of each variable are obtained, and combined with the decision variables and the objective function, a mixed-integer programming model is obtained.

[0029] The power loss cost is specifically:

[0030] Where, is the power loss cost, is the electricity price coefficient, is the total number of time periods divided by the long time scale, is line 's resistance, is line 's active power at time period t, is line 's voltage magnitude at the first section node i at time period t, is the set of lines.

[0031] The capacitor bank operation cost is specifically:

[0032] Where, is the capacitor bank operation cost, is the set of capacitor banks, is the penalty coefficient for each operation, is the switching state variable of capacitor bank c at time period t, is the switching state variable of capacitor bank c at time period t - 1, is the total number of time periods divided by the long time scale.

[0033] The objective function is specifically:

[0034] Where, is the power loss cost, is the capacitor bank operation cost, is the set of transformer taps, is the penalty coefficient for each tap adjustment, is the tap variable of the transformer at time period t, is the tap variable of the transformer at time period t - 1.

[0035] The constraint conditions of each variable are specifically: Active power balance constraint:

[0036] Reactive power balance constraint:

[0037] Voltage constraint:

[0038] Line capacity constraint:

[0039] Action times constraint of capacitor bank:

[0040] Action times constraint of transformer tap:

[0041] Output constraint of distributed power source: ,

[0042] Among them, and are the predicted values of active and reactive power loads of node i at time period t respectively, and are the active and reactive powers obtained by node i from the main grid respectively, and are the predicted values of active and reactive power outputs of the distributed power source at node i at time period t respectively, is the reactive power capacity of capacitor bank c, and are the lower and upper limits of the voltage amplitude of node i respectively, is the line rated capacity, is the maximum allowable action times of capacitor bank c, is the maximum allowable action times of transformer tap g, is the maximum active power output of the distributed power source at node i at time period t, and are the minimum and maximum reactive power outputs of the distributed power source at node i at time period t respectively.

[0043] The obtained switching scheme of capacitor bank and the reference position of transformer tap are specifically as follows: Linearize the influence of discrete positions of transformer taps to transform the mixed-integer programming model from non-linear to mixed-integer linear programming model; Solve the mixed-integer linear programming model based on CPLEX, and obtain the switching scheme of capacitor bank and the reference position of transformer tap according to the objective function and constraint conditions; After each long-time scale optimization is completed, update the prediction information according to the actual operation data and re-solve the decision of the next time window.

[0044] CPLEX is a commercial-grade mathematical optimization solver developed by IBM and is widely used to solve complex linear programming (LP), integer programming (IP), mixed integer programming (MIP), quadratic programming (QP / QCP), etc.

[0045] Transforming non-linear problems into linear / mixed integer linear problems: Limitations of non-linear terms: Non-linear terms (such as quadratic terms, exponential terms, or complex functions) in the original model can cause the problem to become a non-linear programming (NLP) or mixed integer non-linear programming (MINLP). Solving such problems is difficult: High computational complexity: The solution algorithms for NLP / MINLP (such as the interior point method, branch and bound combined with NLP sub-problems) are less efficient, especially in large-scale problems.

[0046] Poor convergence: It may fall into local optima and cannot guarantee the global optimal solution.

[0047] Limited solver support: Commercial solvers (such as CPLEX) have less mature support for NLP / MINLP than for MILP.

[0048] The role of piecewise linearization: Approximate non-linear functions (such as quadratic functions, piecewise functions) as multiple linear segments and transform the problem into a mixed integer linear programming (MILP). For example: Quadratic function: Approximated by a piecewise linear function, such as decomposing a parabola into multiple linear intervals.

[0049] Impact of discrete tap positions: Convert the non-linear voltage regulation effect of tap positions (such as the non-linear relationship between transformation ratio and tap position) into linear constraints.

[0050] S3. On a short time scale, construct an optimal power flow model with the goal of minimizing voltage deviation, and dynamically call the reactive power capacity of distributed energy converters for real-time correction; The construction of the optimal power flow model with the goal of minimizing voltage deviation on a short time scale is specifically as follows: According to the grid characteristics and control requirements, set the short time scale to 15 - 30 minutes. After each time window optimization is completed, roll forward by 5 minutes; On a short time scale, with the goal of minimizing the sum of squares of voltage deviations of all network nodes, and at the same time introducing a penalty term for the reactive power regulation rate of distributed energy converters as the second objective function; Set the decision variables of the second objective function and establish constraint conditions based on the basic operation principle of the power grid to obtain the optimal power flow model.

[0051] The real-time correction by dynamically invoking the reactive power capacity of the distributed energy converter is specifically as follows: Solve the optimal power flow model based on the interior point method to obtain the reactive power regulation amount of each converter, and issue a regulation command; Monitor the voltage deviation after regulation. If the voltage deviation change rate is greater than the preset change rate threshold, trigger the recalibration of the voltage weight coefficient.

[0052] The second objective function is specifically:

[0053] where, is the total number of nodes, is the voltage weight coefficient of node i, is the voltage amplitude of node i, is the voltage reference value, is the regulation rate penalty coefficient, is the reactive power regulation change of converter j at time period t, G is the set of converters, is the total number of time periods.

[0054] The constraint conditions are specifically: Converter capacity limit:

[0055] Regulation rate constraint:

[0056] Grid safe operation constraint:

[0057] where, and are respectively the lower and upper limits of the reactive power output allowed for converter j, is the reactive power currently output by the j-th converter, is the reactive power regulation amount of the j-th distributed energy converter, is the maximum allowed reactive power regulation rate of the converter, is the control period, is the real-time voltage measurement value of the i-th node.

[0058] The recalibration of the voltage weight coefficient is specifically:

[0059] where, is the voltage change rate, is the updated voltage weight coefficient, and sgn is the sign function.

[0060] S4. Establish a coordinated control mechanism across time scales. When the cumulative reactive power regulation amount reaches the preset reactive power capacity threshold at a short time scale, trigger the early re-optimization at a long time scale and update the device operation constraint conditions.

[0061] The coordinated control mechanism also includes a conflict resolution strategy. When there is a control conflict between the device regulation at a long time scale and the converter regulation at a short time scale, give priority to executing the short time scale real-time correction instruction, and at the same time record the conflict event and correct the optimization model parameters of the subsequent time window.

[0062] When the cumulative reactive power regulation amount reaches the preset reactive power capacity threshold at a short time scale, trigger the early re-optimization at a long time scale and update the device operation constraint conditions, specifically: Calculate the cumulative value of the reactive power regulation amount of the distributed energy converter in real time within a short time scale and set the reactive power capacity threshold; After each short time scale optimization cycle, analyze the cumulative value of the reactive power regulation amount and the reactive power capacity threshold; If the cumulative value of the reactive power regulation amount is greater than the reactive power capacity threshold, trigger the coordinated control mechanism across time scales, perform the early re-optimization at a long time scale, and update the device operation constraint conditions.

[0063] The cumulative value of the reactive power regulation amount of the distributed energy converter is specifically:

[0064] Where is the cumulative value of the reactive power regulation amount of the distributed energy converter, is the total duration of the short time scale, is the converter set, is the reactive power regulation amount of converter j at time t, is the reactive power regulation amount of converter j at time t - 1.

[0065] The early re-optimization at a long time scale and the update of the device operation constraint conditions are specifically:

[0066]

[0067]

[0068] Where is the new time window, is the current time, is the update deviation, generally 2 hours, is the remaining operation times of the capacitor bank, is the maximum allowable operation times of capacitor bank c For the switching state change of the capacitor bank C, For the remaining adjustment times of the transformer tap, For the maximum allowable operation times of the transformer tap g, For the tap position change of the transformer tap g.

[0069] Embodiment 2. A reactive power and voltage optimization device for a high-voltage distribution network based on a time scale, characterized by comprising the following modules: Time scale division module: used to obtain the real-time operation state data and prediction information of the high-voltage distribution network, and divide the operation time of the high-voltage distribution network into a long time scale and a short time scale according to the real-time operation state data and prediction information; Long time scale optimization decision module: used to establish a mixed integer programming model based on load prediction and distributed power output prediction under the long time scale, and obtain the switching scheme of the capacitor bank and the reference position of the transformer tap; Short time scale real-time correction module: used to construct an optimal power flow model with the minimum voltage deviation as the target under the short time scale, and dynamically call the reactive power capacity of the distributed energy converter for real-time correction; Cross-time scale coordinated control module: used to establish a cross-time scale coordinated control mechanism. When the cumulative reactive power regulation amount under the short time scale reaches the preset reactive power capacity threshold, trigger the early re-optimization of the long time scale and update the device action constraint conditions.

[0070] The present invention further includes an electronic device, and the electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the reactive power and voltage optimization method for the high-voltage distribution network based on the time scale.

[0071] The present invention includes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the reactive power and voltage optimization method for the high-voltage distribution network based on the time scale is realized.

[0072] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0073] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0074] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0075] In addition, the functional modules in each embodiment of the present invention can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0076] As described above, only the specific implementation manners of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0077] Finally: The above description is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A reactive voltage optimization method for high-voltage distribution networks based on time scales, characterized in that, It includes the following steps: Obtain the real-time operation status data and prediction information of the high-voltage distribution network, and divide the operation time of the high-voltage distribution network to obtain division data, where the division data includes long time scales and short time scales; Under the long time scale, based on load prediction and distributed power generation output prediction, establish a mixed-integer programming model to obtain the switching scheme of capacitor banks and the reference positions of transformer tap changers; Under the short time scale, construct an optimal power flow model with the minimum voltage deviation as the goal, and dynamically call the reactive power capacity of distributed energy converters for real-time correction; Establish a cross-time-scale coordinated control mechanism. When the cumulative reactive power regulation amount under the short time scale reaches the preset reactive power capacity threshold, trigger the early re-optimization of the long time scale and update the equipment action constraint conditions.

2. The reactive power voltage optimization method for high-voltage distribution network based on time scale according to claim 1, wherein The division of the operation time of the high-voltage distribution network to obtain division data is specifically as follows: Set the initial time window length of the division data, calculate the volatility of distributed power generation, and the division data includes long time scales and short time scales; Compare the volatility of distributed power generation with the set distributed power generation volatility threshold to obtain a comparison result, where the comparison result includes the adjustment and optimization of long time scales and short time scales.

3. The reactive power and voltage optimization method for high-voltage distribution network based on time scale according to claim 2, characterized in that Under the long time scale, based on load prediction and distributed power generation output prediction, establishing a mixed-integer programming model is specifically as follows: Based on the active power loss of each line, calculate the power loss cost during the operation of the power grid; Construct an objective function based on the power loss cost and the manufacturing cost of related electrical equipment; Based on the device control characteristics and the objective function, obtain the decision variables of the objective function through correlation analysis; Obtain the constraint conditions of each variable, and combine the decision variables and the objective function to obtain a mixed-integer programming model.

4. The method for optimizing the reactive power voltage of a high-voltage distribution network based on a time scale according to claim 3, characterized in that, The obtaining of the switching scheme of capacitor banks and the reference positions of transformer tap changers is specifically as follows: Conduct linearization modeling on the influence of the discrete tap positions of transformers, and convert the mixed-integer programming model from a non-linear model to a mixed-integer linear programming model; Based on CPLEX, solve the mixed-integer linear programming model, and according to the objective function and constraint conditions, obtain the switching scheme of capacitor banks and the reference positions of transformer tap changers; After each long time scale optimization is completed, update the prediction information according to the actual operation data and re-solve the decision for the next time window.

5. The method for optimizing the reactive power and voltage of a high-voltage distribution network based on a time scale according to claim 4, characterized in that Under the short time scale, constructing an optimal power flow model with the minimum voltage deviation as the goal and dynamically calling the reactive power capacity of distributed energy converters for real-time correction is specifically as follows: According to the grid characteristics and control requirements, set the short time scale time and optimize the sliding window; Under the short time scale, with the goal of minimizing the sum of the squares of the voltage deviations of all network nodes, and at the same time introduce a penalty term for the reactive power regulation rate of distributed energy converters as the second objective function; Set the decision variables of the second objective function, establish constraint conditions, and obtain an optimal power flow model; Based on the interior point method, solve the optimal power flow model to obtain the reactive power regulation amount of each converter and issue a regulation command; Monitor the voltage deviation after regulation. If the voltage deviation change rate is greater than the preset change rate threshold, trigger the re-calibration of the voltage weight coefficient.

6. The method for optimizing reactive power and voltage of a high-voltage distribution network based on time scale according to claim 5, characterized in that, When the cumulative reactive power regulation amount reaches the preset reactive power capacity threshold on a short - time scale, trigger the early re - optimization on a long - time scale and update the device operation constraint conditions. Specifically: Calculate the cumulative value of the reactive power regulation amount of the distributed energy converter and set the reactive power capacity threshold; Based on the short - time scale optimization period, compare and analyze the cumulative value of the reactive power regulation amount with the reactive power capacity threshold; If the cumulative value of the reactive power regulation amount is greater than the reactive power capacity threshold, trigger the cross - time - scale coordinated control mechanism, perform the early re - optimization on a long - time scale, and update the device operation constraint conditions.

7. The reactive power voltage optimization method for high-voltage distribution network based on time scale according to claim 6, characterized in that, The coordinated control mechanism also includes a conflict resolution strategy. When there is a control conflict between the long - time scale device regulation and the short - time scale converter regulation, preferentially execute the short - time scale real - time correction instruction, record the conflict event, and correct the optimization model parameters of the subsequent time window.

8. An apparatus using the time-scale-based reactive power and voltage optimization method for high-voltage distribution networks according to any one of claims 1-7, characterized in that, It includes the following modules: Time scale division module: used to obtain the real - time operation state data and prediction information of the high - voltage distribution network, and divide the operation time of the high - voltage distribution network into a long - time scale and a short - time scale according to the real - time operation state data and prediction information; Long - time scale optimization decision - making module: used to establish a mixed - integer programming model based on load prediction and distributed power output prediction on a long - time scale, and obtain the switching scheme of capacitor banks and the reference position of transformer tap changers; Short - time scale real - time correction module: used to construct an optimal power flow model with the minimum voltage deviation as the goal on a short - time scale, and dynamically call the reactive power capacity of the distributed energy converter for real - time correction; Cross - time - scale coordinated control module: used to establish a cross - time - scale coordinated control mechanism. When the cumulative reactive power regulation amount reaches the preset reactive power capacity threshold on a short - time scale, trigger the early re - optimization on a long - time scale and update the device operation constraint conditions.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the time - scale - based reactive power and voltage optimization method for a high - voltage distribution network according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the time - scale - based reactive power and voltage optimization method for a high - voltage distribution network according to any one of claims 1 to 7.

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