Adaptive power distribution network topology optimization configuration method for novel power system

Abstract

The invention discloses a novel power system-oriented adaptive power distribution network topology optimization configuration method. The method comprises the steps of obtaining multi-source operation data of a power system; constructing a power distribution network topology multi-objective optimization model, and determining an objective function and constraint conditions of the power distribution network topology multi-objective optimization model; solving the power distribution network topology multi-objective optimization model according to the multi-source operation data to obtain a power distribution network topology configuration scheme; and performing a pressure simulation test on the power distribution network topology configuration scheme, and performing power distribution network topology optimization configuration on the power system according to the power distribution network topology configuration scheme passing the pressure simulation test.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a self-adaptive power distribution network topology optimization configuration method for a new-type power system. BACKGROUND

[0002] With the continuous optimization of energy structure and the rapid development of new-type power systems, the operation efficiency, flexibility and reliability of power distribution networks, as key links connecting power transmission networks and terminal users, have a crucial impact on the stability of the entire power system. In recent years, with the wide access of multiple resources such as distributed power sources (e.g. wind power, photovoltaic), energy storage systems and load-side responses to power distribution networks, the traditional power distribution network has evolved from a single power supply function to a multi-source coordination, bidirectional flow and flexible adjustment. This increase in complexity and dynamics puts higher adaptability requirements on the topology structure of power distribution networks.

[0003] In the current operation and management of power distribution networks, the traditional fixed topology structure configuration method has been difficult to meet the comprehensive needs of flexibility, economy and reliability of new-type power systems. Most power distribution network topology optimization methods rely on static or periodic updating of operation data, and cannot adaptively adjust according to real-time changes in operation state, resulting in an optimization scheme that lags behind actual operation needs. SUMMARY

[0004] In view of this, the embodiments of the present application provide a self-adaptive power distribution network topology optimization configuration method for a new-type power system.

[0005] According to one aspect of the present application, a self-adaptive power distribution network topology optimization configuration method for a new-type power system is provided, which comprises:

[0006] obtaining multi-source operation data of a power system;

[0007] constructing a power distribution network topology multi-objective optimization model, and determining objective functions and constraint conditions of the power distribution network topology multi-objective optimization model;

[0008] solving the power distribution network topology multi-objective optimization model according to the multi-source operation data to obtain a power distribution network topology configuration scheme;

[0009] performing stress simulation testing on the power distribution network topology configuration scheme, and performing power distribution network topology optimization configuration on the power system according to the power distribution network topology configuration scheme that passes the stress simulation testing.

[0010] According to still another aspect of the present application, a storage medium having a computer program stored thereon is provided, and the program is executed by a processor to implement the above-mentioned self-adaptive power distribution network topology optimization configuration method for a new-type power system.

[0011] According to another aspect of the present application, a computer device is provided, comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein the processor implements the above-mentioned method for adaptive power distribution network topology optimization configuration for new power systems when executing the program.

[0012] By means of the above technical solution, the present application provides a method for adaptive power distribution network topology optimization configuration for new power systems, which balances economic cost, renewable energy consumption and voltage stability through a multi-objective optimization model of power distribution network topology, and improves the adaptability of the model by combining model correction based on error feedback; the model is solved by using multi-source operation data to obtain an optimization configuration scheme, and a mixed integer second-order cone programming solver is used to process complex problems; stress simulation tests are carried out to verify the reliability of the scheme; user adjustable loads are modeled, and differential incentives are formulated according to response potential and cost, and users are guided to adjust loads in combination with dynamic electricity prices and APP notifications, so as to finally realize flexible, stable and economic operation of the power system, improve renewable energy consumption capacity, and enhance user participation and system reliability.

[0013] The above description is only a summary of the technical solutions of the present application. In order to enable the technical means of the present application to be more clearly understood, and to be implemented according to the content of the description, and in order to enable the above and other purposes, features and advantages of the present application to be more apparent and easy to understand, the specific embodiments of the present application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0014] The drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0015] Figure 1 A flowchart of a method for adaptive power distribution network topology optimization configuration for new power systems provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0016] The present application will be described in detail below with reference to the drawings and in combination with embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the present embodiment, a method for adaptive power distribution network topology optimization configuration for new power systems is provided, as shown in Figure 1 The method comprises the following steps:

[0018] In step 101, multi-source operation data of the power system is obtained. The multi-source operation data comprises real-time photovoltaic / wind power output data, energy storage state data, user load demand data and power grid topology structure.

[0019] In the embodiments of the present application, the new power system involves multiple energy forms and complex operating states, and needs to collect multi-source operating data to fully understand the system status. Real-time photovoltaic / wind power output data reflects the power generation capacity of renewable energy. Since photovoltaic and wind power are greatly affected by natural factors such as weather, the output is volatile and uncertain. Obtaining these data helps accurately grasp the energy supply situation. Energy storage state data can reflect the charging and discharging state, remaining power and other information of the energy storage device. Energy storage devices play a key role in balancing power supply and demand and smoothing the fluctuation of renewable energy output. User load demand data represents the demand for electricity by users. The load demand of different users at different times is quite different. Accurate acquisition of these data is crucial for rational allocation of power resources. Power grid topology data describes the connection mode and layout of each element (such as lines, transformers, etc.) in the power grid, and is the basis for topology optimization configuration.

[0020] In the embodiments of the present application, optionally, the multi-source operating data of the power system is acquired, including: setting a collection time window length T, rolling updating the multi-source operating data according to the time window length T; based on the 3σ principle, the multi-source operating data exceeding μ±3σ is taken as abnormal data, and the abnormal data is filled by interpolation using an interpolation method, μ represents the mean of the multi-source operating data, and σ represents the standard deviation of the multi-source operating data.

[0021] In this embodiment, during the operation of the power system, the operation data is dynamically changing. In order to ensure that the obtained data can reflect the real-time operation state of the power system, a collection time window length T is set, and the multi-source operation data is updated in rolling manner according to the time window length. That is, every T, T = 15 min, the system collects new operation data and updates the original data set. In this way, the freshness and timeliness of the data can be ensured, so that the subsequent optimization model can be solved based on the latest state of the power system. Further, during the data collection process, abnormal data may be generated due to equipment failure, communication interference or other abnormal situations. If these abnormal data are not processed, they may have a negative impact on the subsequent optimization model solving. In order to identify and process these abnormal data, the 3σ principle is adopted. Specifically, the mean μ and standard deviation σ of the multi-source operation data are calculated, and the data beyond the range of μ ± 3σ is regarded as abnormal data. The 3σ principle is based on the characteristics of normal distribution, and believes that under normal circumstances, most of the data (about 99.7%) should fall within the range of μ ± 3σ, and the data beyond this range is likely to be abnormal. Specifically, real-time photovoltaic / wind power output data, energy storage state data, and user load demand data are analyzed for abnormal data. For the identified abnormal data, an interpolation method is used for filling. The interpolation method is a technique for estimating the value of unknown data points from known data points. For example, linear interpolation, polynomial interpolation, spline interpolation, etc. Through interpolation filling, the reasonable value of the abnormal data point can be restored without introducing significant error, thereby ensuring the integrity and continuity of the data set.

[0022] Step 102, a power distribution network topology multi-objective optimization model is constructed, and a target function and a constraint condition of the power distribution network topology multi-objective optimization model are determined.

[0023] In the embodiment of the application, the power distribution network topology optimization is a complex problem, and multiple objectives need to be considered, such as minimizing network loss, improving power supply reliability, and reducing investment cost. The multi-objective optimization model is to express these objectives in mathematical form to form a target function. At the same time, in order to ensure the feasibility and safety of the optimization result, a series of constraint conditions also need to be determined. By constructing such a model, the actual power distribution network topology optimization problem can be converted into a mathematical optimization problem, which is convenient for subsequent solving.

[0024] In the embodiment of the application, optionally, the target function of the power distribution network topology multi-objective optimization model is represented as: min (αC 经济 + β (1-η 消纳 ) + γ ΔV max ), in the formula, C 经济 represents economic cost,

[0025] C 经济 = C线路 +C 储能 +C 运维 ,C 线路 representing line construction cost, C 储能 representing energy storage system cost, C 运维 representing operation and maintenance cost, η 消纳 representing renewable energy consumption rate, P

[0026] ∑P RE,实际 and renewable energy predicted output ∑P RE,预测 , voltage deviation penalty ΔV max = max |V i -1.0|, V i representing the voltage of different nodes in the power system, α, β, γ representing economic cost weight, renewable energy consumption weight, and voltage deviation penalty weight, respectively. The constraint condition of the distribution network topology multi-objective optimization model is represented as: renewable energy consumption constraint:

[0027] ∑P RE,实际 ≥ 0.85·∑P RE,预测 ; energy storage dynamic balance constraint:

[0028] SOC t+1 , SOC t representing the state of charge at t+1 and t, η 充 , η 放 representing charging efficiency and discharging efficiency, P 充,t , P 放,t representing charging power and discharging power at t, Δt representing time interval, SOC ∈ [0.2, 0.9], SOC representing the state of charge at any time, charging and discharging power ≤ P 额定 ; voltage stability constraint: 0.95 ≤ V i ≤ 1.05.

[0029] In this embodiment, the objective function is min (αC 经济 + β (1-η 消纳 ) + γΔV max ), which integrates three key objectives: economic cost objective: C 经济 = C 线路 + C 储能 + C 运维 , covering line construction cost C 线路 , energy storage system cost C 储能 , and operation and maintenance cost C 运维 , pursuing the economy of distribution network construction and operation by minimizing economic cost. Renewable energy consumption objective: η 消纳For the core, η is the actual renewable energy output-to-predicted output ratio. Minimizing (1-η 消纳 ) means maximizing renewable energy consumption rate, promoting the effective use of renewable energy. Voltage deviation penalty target ΔV max = max |V i -1.0| (unit value), where V i is the voltage of different nodes in the power system. By minimizing the voltage deviation penalty, it ensures that the system voltage is stable within a reasonable range. α, β, γ are the weights of the above three objectives, respectively, and the importance of each objective can be adjusted according to actual needs. The constraints include: renewable energy consumption constraint: ∑P RE,实际 ≥ 0.85∑P RE,预测 , which ensures that the actual renewable energy output reaches at least 85% of the predicted output, ensuring a certain degree of renewable energy consumption. Energy storage dynamic balance constraint: describes the state of charge (SOC) change relationship of the energy storage system at adjacent time. SOC t+1 , SOC t are the state of charge at t+1 and t, η 充 , η 放 are the charging and discharging efficiency, P 充,t , P 放,t are the charging and discharging power at t, Δt is the time interval. At the same time, SOC needs to satisfy SOC∈[0.2,0.9], to avoid overcharging or over-discharging of the energy storage system. Charging and discharging power constraint: charging and discharging power ≤ P 额定 , to ensure that the charging and discharging power of the energy storage system does not exceed its rated power, ensuring the safety of the equipment. Voltage stability constraint: 0.95≤V i≤1.05, which limits the node voltage within a reasonable range and ensures the voltage stability of the power system. Through the multi-objective optimization model, the economic cost, renewable energy consumption, and voltage deviation are considered simultaneously, which can balance and coordinate between different objectives and achieve the comprehensive optimization of the distribution network topology. It not only ensures the economy of the system, but also improves the utilization rate of renewable energy and maintains the stability of the voltage. The objective function includes the goal of maximizing the renewable energy consumption rate and sets the renewable energy consumption constraint, which encourages the optimization results to increase the access and use of renewable energy, reduce the phenomenon of curtailment, improve energy efficiency, and promote sustainable development of energy. The voltage deviation penalty objective and voltage stability constraint ensure that the system voltage fluctuates within a reasonable range and improves the voltage stability of the power system. The energy storage dynamic balance constraint and charge / discharge power constraint ensure the stable operation of the energy storage system, further enhancing the reliability and stability of the system. The minimum economic cost objective encourages the reasonable selection of lines, energy storage devices, and operation and maintenance strategies in the distribution network topology configuration, reduces the construction and operation cost, and improves the economic benefit of the power system. The model considers multiple factors and constraints, which can adapt to different power system operation scenarios and demands. By adjusting the weight of the objective function, the priority of each objective can be flexibly adjusted according to the actual situation, making the optimization results more consistent with the specific application scenario.

[0030] Specifically, the acquisition method of renewable energy predicted power involves various technologies and methods, which are explained in detail from the aspects of data collection, prediction model construction, and prediction process, etc. First, data collection. Historical operation data: Collect historical power generation data of renewable energy power generation equipment (such as photovoltaic power stations, wind farms), including power generation in different time periods (hours, days, months, etc.). These data can reflect the long-term trends and periodic changes of renewable energy power generation. For example, photovoltaic power generation has higher power generation capacity during the day, and different power generation characteristics in different seasons due to differences in sunshine time; the power generation of wind farms is related to the seasonal changes of wind speed. Meteorological data: Meteorological conditions have a significant impact on renewable energy power generation. For photovoltaic power generation, solar radiation intensity, sunshine duration, temperature, cloud cover, etc. are needed; for wind power, wind speed, wind direction, air pressure, etc. are needed. Meteorological data can be obtained through meteorological stations, satellite remote sensing, etc. Accurate meteorological data helps to more accurately predict the power generation of renewable energy. Equipment state data: It is also important to understand the running state and performance parameters of the power generation equipment. For example, the conversion efficiency of photovoltaic components, the wear and tear of wind turbine blades, etc. will affect the power generation capacity. By monitoring the real-time state data of the equipment, the prediction model can be corrected to improve the accuracy of the prediction. Second, prediction model construction. Based on the physical principles of renewable energy power generation, the model is constructed. For photovoltaic power generation, according to the solar radiation model, the electrical characteristics of photovoltaic components, etc., the power generation in different meteorological conditions is calculated. For wind power, the wind speed-power curve is used, combined with aerodynamics, to convert wind speed into power generation. The physical model needs to have a detailed understanding of the power generation equipment and meteorological conditions, and is suitable for the prediction of new equipment or specific scenarios. Finally, implement the prediction process. Clean, missing value processing and outlier detection are performed on the collected historical operation data, meteorological data and equipment state data. Ensure the quality and integrity of the data to improve the accuracy of the prediction model. Divide the preprocessed data into training set and test set, use the training set to train the prediction model, adjust the parameters of the model, so that the model can better fit the historical data. Use the test set to verify the trained model, evaluate the prediction performance of the model. According to the verification result, optimize the model, such as adjusting the model structure, parameters, etc., until the model reaches a satisfactory prediction accuracy. After the model training and optimization are completed, real-time meteorological data and equipment state data are input into the model for real-time prediction of renewable energy power generation. At the same time, continuously collect new data to update and optimize the model to adapt to the changes in the characteristics of renewable energy power generation.

[0031] In the embodiments of the present application, optionally, the method further comprises: if the error between the renewable energy predicted output and the renewable energy actual output is greater than a preset error, calculating a reward value according to the reward function corresponding to the power distribution network topology multi-objective optimization model and the multi-source operation data, and correcting and optimizing the power distribution network topology multi-objective optimization model according to the reward value.

[0032] In this embodiment, when the error between the renewable energy predicted output and the renewable energy actual output is greater than a preset error, for example, the prediction error ΔP RE > 10%, the model correction and optimization process is triggered. The preset error is a pre-set threshold for measuring the acceptable deviation range between the predicted output and the actual output. Due to the intermittency and uncertainty of renewable energy (such as photovoltaic and wind power), there will inevitably be errors between the predicted output and the actual output, but when the error exceeds a certain limit, it may affect the stable operation and optimization effect of the power distribution network, so the model needs to be corrected. After triggering the correction and optimization process, a reward value is calculated according to the reward function corresponding to the power distribution network topology multi-objective optimization model and the multi-source operation data. The reward function is an index system for evaluating the performance or optimization effect of the model, which combines multi-source operation data (such as real-time photovoltaic / wind power output data, energy storage state data, user load demand data, and power grid topology structure, etc.) to quantitatively evaluate the performance of the current power distribution network topology configuration scheme in multiple objectives (such as economic cost, renewable energy consumption rate, voltage deviation, etc.), and obtain a reward value. The power distribution network topology multi-objective optimization model is corrected and optimized according to the calculated reward value. The reward value reflects the performance of the current model in actual operation. If the reward value is low, it means that the model performs poorly in some aspects (such as economy, renewable energy consumption, or voltage stability, etc.), and the parameters or structure of the model need to be adjusted to improve the performance of the model, so that it can better adapt to the actual operation situation. Thus, by introducing the error judgment and model correction and optimization mechanism based on the reward function, the model can be adjusted in time according to the actual error of the renewable energy output. This makes the model better adapt to the intermittency and uncertainty of renewable energy, improves the prediction and optimization ability of the model to the actual operation situation, and reduces the operation problems of the power distribution network caused by prediction errors.

[0033] Step 103, solving the power distribution network topology multi-objective optimization model according to the multi-source operation data to obtain a power distribution network topology configuration scheme.

[0034] In the embodiments of the present application, the obtained multi-source operation data is substituted into the constructed power distribution network topology multi-objective optimization model, and a suitable optimization algorithm is used to solve the model. The optimization algorithm will find the solution that makes the objective function optimal (or approximately optimal) under the premise of meeting the constraint condition, and the power distribution network topology configuration scheme corresponding to the solution is the optimized result. The scheme comprehensively considers various factors and aims to realize the reasonable configuration of the power distribution network topology and improve the operation efficiency and reliability of the power system.

[0035] In the embodiments of the present application, the power distribution network topology multi-objective optimization model is solved according to the multi-source operation data to obtain a power distribution network topology configuration scheme, including: every time window length T, a mixed integer second-order cone programming solver is called to solve the power distribution network topology multi-objective optimization model by using newly obtained multi-source operation data to obtain a switch state, energy storage scheduling, and line switching scheme in the next time period as the power distribution network topology configuration scheme.

[0036] In this embodiment, the model solving process is triggered every time window length T. This timed solving method is to adapt to the dynamic characteristics of the power system. The operation data in the power system, such as renewable energy output and user load demand, are constantly changing over time. By setting a fixed time window length T, the distribution network topology can be optimized regularly according to the latest system state to ensure that the distribution network is always in an optimized operating state. Each time the model is solved, the newly acquired multi-source operation data is called. The multi-source operation data covers real-time photovoltaic / wind power output data, energy storage state data, user load demand data, and power grid topology structure information. The newly acquired data can more accurately reflect the actual operating conditions of the current power system, and inputting it into the distribution network topology multi-objective optimization model can make the solving results of the model more in line with actual demand, improving the effectiveness and reliability of the optimization scheme. The mixed integer second-order cone programming solver is used to solve the distribution network topology multi-objective optimization model. Mixed integer second-order cone programming (MISOCP) is a powerful mathematical programming method that combines the advantages of mixed integer programming and second-order cone programming. In the distribution network topology optimization problem, integer variables such as switch state (usually discrete 0-1 variables representing the opening and closing of switches) and continuous variables (such as voltage and power) are often involved, and the objective function and constraints may have a second-order cone form. The mixed integer second-order cone programming solver can effectively handle complex optimization problems involving integer and continuous variables, finding the optimal solution that meets the multi-objective requirements and constraints. The switch state, energy storage scheduling, and line switching scheme obtained after solving the next period are used as the distribution network topology configuration scheme. The switch state determines the connection or disconnection of each part of the power grid, affecting the power transmission path; the energy storage scheduling involves the charging and discharging strategy of the energy storage device, which can balance power supply and demand; and the line switching scheme determines which lines are put into operation or taken out of operation to optimize the structure and performance of the power grid. These schemes together constitute the distribution network topology configuration scheme, which is used to guide the operation of the power system in the next period. The embodiment of the present application solves the model according to the new data at regular intervals, so that the distribution network can respond to dynamic factors such as fluctuations in renewable energy output and changes in user load demand in a timely manner, maintaining efficient operation of the system. For example, when renewable energy output suddenly increases, by adjusting the switch state and energy storage scheduling, these energy sources can be better absorbed, avoiding the phenomenon of wind and light abandonment. And using the latest multi-source operation data, the actual state of the power system can be more accurately described, so that the solving results of the optimization model are closer to the optimal solution. This helps to improve the efficiency of the distribution network, reduce network losses, and reduce operating costs. The optimized distribution network topology configuration scheme can better adapt to the access and fluctuations of renewable energy, effectively transport the power generated by renewable energy to the load center through reasonable energy storage scheduling and line adjustment, improve the proportion of renewable energy consumption, and promote sustainable energy development.

[0037] Step 104, stress simulation test is performed on the power distribution network topology configuration scheme, and the power distribution network topology of the power system is optimized and configured according to the power distribution network topology configuration scheme passing the stress simulation test.

[0038] In the embodiments of the present application, the obtained power distribution network topology configuration scheme needs to be verified under various extreme or complex operating conditions to ensure its reliability and stability in actual operation. Stress simulation test is to simulate these extreme conditions, such as high load, equipment failure, etc., to test the power distribution network topology configuration scheme. Only the scheme passing the stress simulation test is considered feasible, and the power distribution network topology of the power system is optimized and configured according to these schemes passing the test, which can ensure that the power system can cope with various complex situations in actual operation and improve the overall performance of the power system.

[0039] In the embodiments of the present application, the stress simulation test on the power distribution network topology configuration scheme comprises: designing a stress test scene in which the photovoltaic / wind power output is reduced to a preset output and the user load demand is increased to a preset demand; performing stress simulation test on the power distribution network topology configuration scheme through the set stress test scene to determine the frequency deviation and the transient voltage recovery time; if the frequency deviation and the transient voltage recovery time both meet the stress test conditions, it is determined that the power distribution network topology configuration scheme passes the stress simulation test. Wherein, the frequency deviation meeting the preset condition means that Δf max = max |f t - 50Hz | ≤ 0.2Hz, wherein f t represents the frequency value of the power system at time t; the transient voltage recovery time meeting the preset condition means that the transient voltage recovery time to 0.9pu is ≤ 1 second.

[0040] In this embodiment, a pressure test scenario is designed to reduce photovoltaic / wind power output to a preset output, for example, a 50% reduction in output. In actual operation, renewable energy output is significantly affected by natural factors such as weather, and may be lower than expected. By simulating this scenario, the ability of the distribution network topology configuration scheme to respond to insufficient renewable energy supply can be tested, for example, whether it will cause local power shortages, voltage drops, and other problems. In addition, a pressure test scenario is designed to increase user load demand to a preset demand, for example, a 30% increase in peak load. User load is volatile and uncertain, and in peak periods or special circumstances, load demand may increase significantly. This scenario is used to evaluate the performance of the distribution network topology configuration scheme when the load exceeds expectations, such as whether it will cause line overload, voltage out-of-limit, and other situations. Under the set pressure test scenario, the distribution network topology configuration scheme is subjected to stress simulation testing. Through professional simulation software or tools, the operation state of the power system under these extreme scenarios is simulated, focusing on two key indicators: frequency deviation: determine the frequency deviation of the power system during simulation. Frequency is one of the important indicators of stable operation of the power system, and its stability reflects the balance between power generation and load. Transient voltage recovery time: determine the transient voltage recovery time of the system after disturbance. Transient voltage recovery time reflects the ability and speed of the system to recover to normal voltage after experiencing load mutation, power output change, and other disturbances. If both the frequency deviation and the transient voltage recovery time meet the pressure test conditions, it is determined that the distribution network topology configuration scheme passes the stress simulation test. This means that the scheme can maintain the stable operation of the power system in actual operation, facing similar extreme scenarios, and has good reliability and adaptability. Frequency deviation preset condition: Δf max = max |f t - 50Hz | ≤ 0.2Hz, where f t represents the frequency value of the power system at time t. This condition specifies that in the pressure test scenario, the maximum deviation of the system frequency from the rated frequency (50Hz) should not exceed 0.2Hz to ensure the stability of the system frequency. Transient voltage recovery time preset condition: the time for transient voltage to recover to 0.9pu ≤ 1 second. This condition requires that after disturbance, the voltage can recover to 90% (0.9pu) of the rated voltage within 1 second, ensuring that the voltage quickly recovers to an acceptable range and maintains the normal operation of the system. Through the above stress simulation test scheme, the performance of the distribution network topology configuration scheme under extreme scenarios can be comprehensively and deeply evaluated, providing a reliable basis for the practical application of the scheme and ensuring that the distribution network can operate stably and efficiently under various complex conditions.

[0041] In the embodiments of the present application, optionally, the method further comprises: acquiring the adjustable load of each user in the power system and the regular load corresponding to each user, calculating the adjustable load ratio of each user according to the adjustable load and the regular load; calculating the response potential of each user in the power system according to the adjustable load and the cost coefficient of each user in the power system; and formulating differentiated incentive measures for each user according to the adjustable load ratio and the response potential of each user.

[0042] In this embodiment, first, the response potential is calculated, which reflects the adjustable load ratio of user i, that is, the proportion of the load that user i can adjust according to the demand of the power system to the base load (regular load). The calculation formula is as follows: P DR,i P 基荷,i represents the base load power of user i. This formula quantifies the adjustable load ratio of the user by the ratio of the adjustable load to the base load, and the value range is between 0 and 0.3, which means that the adjustable load of the user accounts for at most 30% of the base load. Second, the response cost is calculated, which is the cost generated by the user adjusting the load, represented by the response potential, which comprehensively considers the adjustable load power under different cost coefficients. The calculation formula is as follows: This formula shows that the response cost is proportional to the cost coefficient of the user and the square of the adjustable load power. Different users have different cost coefficients, which reflects the difficulty and economic cost difference of different users adjusting the load. According to the adjustable load ratio and the response potential of each user, differentiated incentive measures are formulated for each user. For users with high adjustable load ratio and large response potential, more attractive incentives can be provided, such as higher electricity price subsidies, priority electricity right, etc., to encourage them to actively adjust the load when the power system needs, such as peak load shifting and participating in demand response. For users with low adjustable load ratio and small response potential, relatively simple incentive measures can be provided, such as a small amount of electricity discount or integral reward. Further, APP notification is carried out to push the response request through the mobile terminal. By identifying and utilizing the adjustable load of the user, the power system can reduce the electricity demand during the load peak and increase the electricity consumption during the load valley, thereby improving the flexibility and adaptability of the system. Differentiated incentive measures can encourage more users to participate in load adjustment, further enhance the adjustment capacity of the system, and effectively cope with the intermittency and uncertainty of renewable energy generation.

[0043] Further, it can also include: updating the prediction model parameters (such as the LSTM network weights of photovoltaic output prediction) every month, adjusting the DR incentive mechanism according to user feedback, automatically switching to the backup topology if a line fault is detected, and starting the energy storage black start function.

[0044] By applying the technical solution of the embodiment, the economic cost, renewable energy consumption and voltage stability are balanced through the power distribution network topology multi-objective optimization model, the model adaptability is improved by combining the model correction based on error feedback; the optimization configuration scheme is obtained by using multi-source operation data to solve the model at regular intervals, and the complex problem is handled by means of a mixed integer second-order cone programming solver; the reliability of the scheme is verified by carrying out pressure simulation test; the user adjustable load is modeled, the differentiated incentive is formulated according to the response potential and cost, the user adjusts the load combined with the dynamic electricity price and APP notification, and finally the flexible, stable and economic operation of the power system is realized, the renewable energy consumption capacity is improved, and the user participation and system reliability are enhanced.

[0045] The following conclusions can be drawn through the test: by means of sliding window updating and prediction error modeling, dynamically adjusting the topology configuration, adapting to the renewable energy output fluctuation, re-optimizing based on the latest data every 15 minutes, ensuring that the scheme always fits the actual operation state, and improving the system flexibility; compared with the traditional method, the line, energy storage and operation and maintenance costs are reduced, the consumption rate is improved, the wind and light curtailment is reduced, the "double carbon" target is supported, and the voltage stability is high; there are strict safety constraints: voltage range and frequency deviation double protection to prevent overload or collapse, considering the worst prediction error scenario to ensure the feasibility of the scheme under extreme conditions; integrated with demand response (DR) and dynamic electricity price incentive; the algorithm used has high efficiency and intelligence, the mixed integer second-order cone programming (MI-SOCP) solving speed is improved by 40% compared with the traditional method, the strategy is dynamically adjusted when the prediction error is out of limit, the manual intervention is reduced, and the response speed is improved.

[0046] The embodiment of the application further provides a computer device, which can be a personal computer, a server, a network device, etc. The computer device comprises a bus, a processor, a memory and a communication interface, and can further comprise an input / output interface and a display device. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store location information. The network interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the steps in the method embodiments.

[0047] Those skilled in the art can understand that the structure of the computer device described above is only part of the structure related to the scheme of the application, and does not constitute a limitation on the computer device to which the scheme of the application is applied. The specific computer device can include more or fewer components, or combine certain components, or have a different component arrangement.

[0048] In an embodiment, a computer readable storage medium is provided, which can be non-volatile or volatile, and has stored thereon a computer program, which, when executed by a processor, implements the steps of any of the above method embodiments.

[0049] In an embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any of the above method embodiments.

[0050] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0051] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0052] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0053] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A self-adaptive power distribution network topology optimization configuration method for a new power system, characterized in that, The method comprises: acquiring multi-source operation data of a power system; constructing a power distribution network topology multi-objective optimization model, and determining an objective function and a constraint condition of the power distribution network topology multi-objective optimization model; solving the power distribution network topology multi-objective optimization model according to the multi-source operation data to obtain a power distribution network topology configuration scheme; performing stress simulation test on the power distribution network topology configuration scheme, and performing power distribution network topology optimization configuration on the power system according to the power distribution network topology configuration scheme that passes the stress simulation test. 2.The method of claim 1, wherein, The multi-source operation data comprises real-time photovoltaic / wind power output data, energy storage state data, user load demand data and power grid topology structure. 3.The method of claim 2, wherein, The method of acquiring multi-source operation data of a power system comprises: setting a collection time window length T, and rolling updating the multi-source operation data according to the time window length T; based on the 3σ principle, regarding the multi-source operation data exceeding μ±3σ as abnormal data, and filling the abnormal data by interpolation, wherein μ represents the mean value of the multi-source operation data, and σ represents the standard deviation of the multi-source operation data. 4.The method of claim 1, wherein, The objective function of the power distribution network topology multi-objective optimization model is represented as: min(αC 经济 +β(1-η 消纳 )+γΔV max ), where C 经济 denotes the economic cost, C 经济 =C 线路 +C 储能 +C 运维 ,C 线路 represents the line construction cost, C 储能 represents the energy storage system cost, C 运维 represents the operation and maintenance cost, η 消纳 represents the renewable energy consumption rate, which is the ratio of the actual renewable energy output ∑P RE,实际 to the predicted renewable energy output ∑P RE,预测 , the voltage deviation penalty ΔV max = max |V i -1.0|, V i represents the voltage at different nodes in the power system, and α, β, γ represent the economic cost weight, the renewable energy consumption weight, and the voltage deviation penalty weight, respectively.

5. The adaptive power distribution network topology optimization configuration method for new-type power system according to claim 4, characterized in that, The constraint condition of the power distribution network topology multi-objective optimization model is represented as: Renewable energy consumption constraint:∑P RE,实际 ≥ 0.85 ·∑P RE,预测 ; Energy storage dynamic balance constraint: SOC t+1 , SOC t respectively represent the state of charge at t+1 and t time, η 充 , η 放 respectively represent the charging efficiency, discharging efficiency, P 充,t , P 放,t respectively represent the charging power, discharging power at t time, Δt represents the time interval, SOC ∈ [0.2, 0.9], SOC represents the state of charge at any time, and the charging and discharging power ≤ P 额定 ; Voltage stability constraint: 0.95 < V < 1.

05. i ≤1.

05.

6. The adaptive power distribution network topology optimization configuration method for new-type power systems according to claim 5, characterized in that, The method further comprises: if the error between the predicted output of the renewable energy and the actual output of the renewable energy is greater than a preset error, calculating a reward value according to the reward function corresponding to the power distribution network topology multi-objective optimization model and the multi-source operation data, and modifying and optimizing the power distribution network topology multi-objective optimization model according to the reward value.

7. The adaptive power system oriented power distribution network topology optimization configuration method according to claim 2, characterized in that, Solving the power distribution network topology multi-objective optimization model according to the multi-source operation data to obtain a power distribution network topology configuration scheme comprises: every time interval of the time window length T, calling a mixed integer second-order cone programming solver to solve the power distribution network topology multi-objective optimization model by using newly acquired multi-source operation data to obtain a switch state, energy storage scheduling and line switching scheme in the next time period as the power distribution network topology configuration scheme. 8.The method of claim 1, wherein, Performing stress simulation test on the power distribution network topology configuration scheme comprises: designing a stress test scenario in which the photovoltaic / wind power output is reduced to a preset output and the user load demand is increased to a preset demand; performing stress simulation test on the power distribution network topology configuration scheme through the set stress test scenario, and determining frequency deviation and transient voltage recovery time; if both the frequency deviation and the transient voltage recovery time meet the stress test condition, it is determined that the power distribution network topology configuration scheme passes the stress simulation test.

9. The adaptive power distribution network topology optimization configuration method for a new power system according to claim 8, characterized in that: The frequency deviation satisfying the preset condition refers to Δf max = max |f t - 50 Hz | ≤ 0.2 Hz, wherein f t represents the frequency value of the power system at the time t. the transient voltage recovery time meeting the preset condition means that the time for the transient voltage to recover to 0.9pu is less than or equal to 1 second.

10. The adaptive power distribution network topology optimization configuration method for new-type power system according to any one of claims 1 to 9, characterized in that, The method further comprises: acquiring adjustable loads of users in the power system and corresponding regular loads of the users, and calculating adjustable load proportions of the users according to the adjustable loads and the regular loads; calculating response potentials of the users in the power system according to the adjustable loads and cost coefficients of the users in the power system; formulating differentiated incentive measures for the users according to the adjustable load proportions and the response potentials of the users.

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