Power distribution network source-network-storage collaborative control method and system

Abstract

The application provides a power distribution network source-network-storage collaborative control method and system, relates to the technical field of power distribution networks, and comprises the following steps: collecting multi-dimensional power grid information of the power distribution network, and setting target constraint conditions according to the multi-dimensional power grid information; establishing target optimization indexes, constructing a collaborative control simulation model in cooperation with the target constraint conditions, simulating the collaborative control of the power distribution network through the collaborative control simulation model, and obtaining simulation records; introducing fitness evaluation strategies to analyze a plurality of simulation data groups in the simulation records, and screening to obtain an optimal collaborative control scheme; and performing source-network-storage collaborative control of the power distribution network through the optimal collaborative control scheme. Through the application, the technical problem that the power distribution network is difficult to realize effective coordination of power generation sources, energy storage devices and loads in the prior art can be solved, and the technical effects of improving new energy consumption capacity, reducing operation cost, guaranteeing power grid operation safety and improving system overall stability are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network, and particularly relates to a power distribution network source-grid-storage collaborative control method and system. BACKGROUND

[0002] With the transformation of global energy structure and large-scale access of renewable energy, the complexity and uncertainty of the operation of the power distribution network, which is an important link connecting power sources and terminal users in the power system, have significantly increased.

[0003] At present, the traditional power distribution network mainly relies on single power dispatching and load management mode, which is difficult to effectively coordinate distributed power generation, energy storage devices and user-side load, leading to imbalance between supply and demand, energy waste and increasing operation safety risks. For example, the output of wind power and photovoltaic power generation has strong randomness and volatility, and the traditional control strategy is difficult to respond to these fluctuations in real time, resulting in serious curtailment of wind and light, and waste of a large amount of clean energy.

[0004] In summary, in the prior art, due to the lack of comprehensive utilization of multi-dimensional grid information and scientific multi-objective collaborative optimization method, the power distribution network is difficult to realize effective coordination of power generation sources, energy storage devices and load, which further affects the full utilization of new energy and the economy and safety of system operation. SUMMARY

[0005] The purpose of the present application is to provide a power distribution network source-grid-storage collaborative control method and system to solve the technical problem in the prior art that due to the lack of comprehensive utilization of multi-dimensional grid information and scientific multi-objective collaborative optimization method, the power distribution network is difficult to realize effective coordination of power generation sources, energy storage devices and load, which further affects the full utilization of new energy and the economy and safety of system operation.

[0006] In view of the above problems, the present application provides a power distribution network source-grid-storage collaborative control method and system.

[0007] In a first aspect, the present application provides a power distribution network source-grid-storage collaborative control method, which is realized by a power distribution network source-grid-storage collaborative control system, comprising: collecting multi-dimensional grid information of the power distribution network, and setting target constraint conditions according to the multi-dimensional grid information; establishing target optimization indexes, constructing a collaborative control simulation model in cooperation with the target constraint conditions, and simulating the power distribution network through the collaborative control simulation model to obtain simulation records; introducing an fitness evaluation strategy to analyze a plurality of simulation data groups in the simulation records, and screening to obtain an optimal collaborative control scheme; and performing source-grid-storage collaborative control of the power distribution network through the optimal collaborative control scheme.

[0008] Preferably, the power distribution network source network storage collaborative control method further comprises: setting a power balance constraint according to power information in the multi-dimensional power grid information; setting a device capacity constraint according to device information in the multi-dimensional power grid information; setting a running safety constraint according to running information in the multi-dimensional power grid information; the power balance constraint, the device capacity constraint and the running safety constraint constitute the target constraint condition.

[0009] Preferably, the power distribution network source network storage collaborative control method further comprises: extracting a power generation time sequence in the multi-dimensional power grid information; extracting a power consumption time sequence in the multi-dimensional power grid information; performing space-time alignment processing on the power generation time sequence and the power consumption time sequence to obtain an electric quantity alignment time sequence; performing fast Fourier transform on the electric quantity alignment time sequence to obtain an electric quantity alignment frequency spectrum, and analyzing to obtain a maximum spectral density of the electric quantity alignment frequency spectrum; setting the power balance constraint according to the maximum spectral density.

[0010] Preferably, the power distribution network source network storage collaborative control method further comprises: acquiring any device in the power distribution network, wherein the any device has a physical connection relationship with any line; setting the device capacity constraint by coordinating any output of the any device with any loss of the any line.

[0011] Preferably, the power distribution network source network storage collaborative control method further comprises: reading a predetermined running index, and randomly extracting any one index in the predetermined running index as a target running index; acquiring a predetermined safety threshold of the target running index, and constituting the running safety constraint.

[0012] Preferably, the power distribution network source network storage collaborative control method further comprises: the target optimization index includes running cost, voltage deviation, load variance and new energy proportion, wherein the running cost includes power purchase and sale cost, power supply running and maintenance cost, wind and light abandonment penalty cost, energy storage charging and discharging running cost, dispatching compensation cost and carbon emission cost.

[0013] Preferably, the power distribution network source network storage collaborative control method further comprises: generating an initial collaborative control scheme set based on the principle of tent chaotic algorithm, wherein the initial collaborative control scheme set includes a first initial scheme; simulating the first initial scheme through the collaborative control simulation model to obtain first simulation information; when the first simulation information meets a predetermined simulation condition, establishing a first scheme domain based on the first initial scheme, and adding the first scheme domain to a simulation list; simulating and analyzing the simulation list through the collaborative control simulation model to obtain the simulation record.

[0014] Preferably, the power distribution network source network storage collaborative control method further comprises: obtaining a first neighborhood scheme in the first scheme domain; if second simulation information of the first neighborhood scheme meets the predetermined simulation condition, adding the first neighborhood scheme to a support set, if the second simulation information of the first neighborhood scheme does not meet the predetermined simulation condition, adding the first neighborhood scheme to an opposition set; obtaining a first support rate of the first initial scheme according to the support set and the opposition set; sorting the first initial scheme based on the first support rate to obtain an optimal initial scheme; sequentially simulating a plurality of optimal neighborhood schemes in an optimal scheme domain of the optimal initial scheme to obtain a plurality of simulation data groups, and composing the simulation record.

[0015] Preferably, the power distribution network source network storage collaborative control method further comprises: obtaining a predetermined weight distribution in the fitness evaluation strategy, wherein the predetermined weight distribution refers to the weight distribution of each index in the target optimization index; fitness evaluation of the plurality of simulation data groups is performed in combination with the predetermined weight distribution to obtain a plurality of collaborative control fitness; the optimal neighborhood scheme corresponding to the maximum fitness in the plurality of collaborative control fitness is taken as the optimal collaborative control scheme; wherein, the model conversion mechanism is introduced to convert the collaborative control simulation model into a mixed integer convex programming model; the collaborative control verification scheme of the power distribution network is obtained through the mixed integer convex programming model, wherein the collaborative control verification scheme is used to verify the optimal collaborative control scheme.

[0016] In a second aspect, the application also provides a power distribution network source network storage collaborative control system for executing the power distribution network source network storage collaborative control method as described in the first aspect, comprising: a target constraint condition setting module for collecting multi-dimensional power grid information of a power distribution network, and setting target constraint conditions according to the multi-dimensional power grid information; a collaborative control simulation module for constructing target optimization indexes, constructing a collaborative control simulation model in collaboration with the target constraint conditions, and performing collaborative control simulation on the power distribution network through the collaborative control simulation model to obtain a simulation record; an analysis module for introducing a fitness evaluation strategy to analyze a plurality of simulation data groups in the simulation record and screening to obtain an optimal collaborative control scheme; a source network storage collaborative control module for performing source network storage collaborative control of the power distribution network through the optimal collaborative control scheme.

[0017] The technical solutions provided in the application have at least the following technical effects or advantages: by achieving the technical target of multi-dimensional information fusion and target constraint collaborative optimization of power distribution network source network storage intelligent control, the technical effects of improving new energy consumption capacity, reducing operation cost, ensuring power grid operation safety and improving overall stability of the system are achieved.

[0018] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the description, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0020] Figure 1 The flowchart of the power distribution network source network storage collaborative control method of the present application.

[0021] Figure 2 The structure diagram of the power distribution network source network storage collaborative control system of the present application.

[0022] Explanation of reference signs: target constraint condition setting module 1, collaborative control simulation module 2, analysis module 3, source network storage collaborative control module 4. DETAILED DESCRIPTION

[0023] The present application provides a power distribution network source network storage collaborative control method and system, which solves the technical problem in the prior art that due to the lack of comprehensive utilization of multi-dimensional grid information and scientific multi-objective collaborative optimization method, the power distribution network is difficult to realize effective coordination of power generation source, energy storage device and load, further affecting the full utilization of new energy and the economy and safety of system operation. The technical target of multi-dimensional information fusion and target constraint collaborative optimization of power distribution network source network storage intelligent control is achieved, and the technical effects of improving new energy consumption capacity, reducing operation cost, ensuring power grid operation safety and improving system overall stability are achieved.

[0024] The technical solutions in the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application. In addition, it should be noted that only parts related to the present application are shown in the drawings for convenience of description, not all.

[0025] Embodiment one, please refer to the attached Figure 1 The application provides a power distribution network source network storage collaborative control method, applied to a power distribution network source network storage collaborative control system, and specifically includes the following steps:

[0026] S1: Collecting multi-dimensional power grid information of the power distribution network, and setting target constraint conditions according to the multi-dimensional power grid information.

[0027] Specifically, a plurality of categories of data sets involved in the operation of the power distribution network are obtained. The multi-dimensional power grid information includes power information, device information and operation information. The power information refers to the change data of power generation and power consumption at different times, the device information refers to the type, capacity and state of the generator, transformer, energy storage and the like, and the operation information refers to the voltage, frequency, line load rate and the like reflecting the system health degree. The target constraint conditions are set according to the multi-dimensional power grid information, that is, the different dimension data collected is used to formulate operation limit rules. The target constraint conditions are boundaries that must be met during optimization scheduling, including power balance constraints, device capacity constraints and operation safety constraints.

[0028] S2: Assembling target optimization indexes, constructing a collaborative control simulation model in cooperation with the target constraint conditions, and simulating the power distribution network through the collaborative control simulation model to obtain simulation records.

[0029] Specifically, the target optimization indexes are assembled to determine the key quantitative standards for measuring the pros and cons of the operation of the power distribution network. The target optimization indexes can include operation cost, voltage deviation, load variance and new energy proportion, etc. For example, the operation cost reflects the total cost of purchasing and selling electricity, equipment maintenance, energy storage operation, etc., the voltage deviation represents the difference degree of the system voltage from the rated value, the load variance measures the balance degree of the load distribution in each period, and the new energy proportion represents the proportion of clean energy such as wind power and photovoltaic in total power supply. The collaborative control simulation model is constructed in cooperation with the target constraint conditions, the target optimization indexes and the limit conditions of the power grid operation are combined to form a calculation model that can consider economy, safety and stability at the same time. The target constraint conditions include power balance constraints, device capacity constraints and operation safety constraints, which can ensure operation safety and provide a feasible range for optimization indexes.

[0030] The power distribution network is simulated through the collaborative control simulation model, the power distribution network is virtually operated in the computer by using the collaborative control simulation model, the operation results under different strategy and parameter combinations are tested, for example, the influence of different energy storage charging and discharging schemes and scheduling strategies on operation cost and new energy proportion is simulated in a 24-hour period, all the data generated in the simulation process are systematically saved to obtain simulation records, which are used for subsequent analysis and scheme optimization.

[0031] S3: introducing a fitness evaluation strategy to analyze a plurality of simulation data groups in the simulation record and screen an optimal coordinated control scheme.

[0032] Specifically, a fitness evaluation strategy is introduced to comprehensively evaluate the simulation results of a plurality of operation schemes of the power distribution network. The fitness evaluation strategy refers to scoring each simulation data according to the pre-set target optimization indicators and weights to measure the pros and cons of the operation effect. A plurality of simulation data groups in the simulation record are analyzed, and the saved operation data under different control schemes are calculated and compared one by one. The simulation data groups include key parameters such as operation cost, voltage deviation, load fluctuation, and new energy proportion. The optimal coordinated control scheme is screened, which means that the scheme with the highest comprehensive evaluation, which best meets the operation target and constraint condition, is selected from a plurality of candidate schemes as the final dispatching strategy.

[0033] S4: performing source-network-storage coordinated control of the power distribution network through the optimal coordinated control scheme.

[0034] Specifically, the optimal coordinated control scheme is used to perform source-network-storage coordinated control of the power distribution network, and the control scheme with the highest comprehensive evaluation and best meeting the operation target is applied to the actual operation of the power distribution network to realize coordinated management of power sources, power distribution networks, and energy storage devices. The optimal coordinated control scheme refers to a dispatching strategy that achieves the best comprehensive optimization of economy, stability, and safety after simulation and evaluation, which can reasonably arrange the output of various energy devices and load distribution; the power distribution network refers to a low-voltage and medium-voltage power system that transmits electric energy from the transmission network to the terminal user, including lines, transformers, switch devices, etc.; the source-network-storage coordinated control refers to unified coordinated control of power sources (such as wind power and photovoltaic power) in the power grid, power distribution networks, and energy storage devices (such as battery energy storage systems) to ensure power supply and demand balance, voltage stability, and safe operation of devices.

[0035] Further, the present application further comprises: setting a power balance constraint according to the power information in the multi-dimensional power grid information; setting a device capacity constraint according to the device information in the multi-dimensional power grid information; setting an operation safety constraint according to the operation information in the multi-dimensional power grid information; the power balance constraint, the device capacity constraint, and the operation safety constraint constitute the target constraint condition.

[0036] Specifically, the power balance constraint is set according to the power information in the multi-dimensional power grid information, and the data about the change of power generation and power consumption with time in the power distribution network is used to formulate a limit condition for maintaining relative balance between power generation and power consumption. The power information includes the total output on the power generation side and the total consumption on the load side, and the power balance constraint is used to avoid power generation deficiency or excess.

[0037] According to the device information in the multi-dimensional power grid information, device capacity constraints are set, and by using the rated capacity, actual available capacity and connection line loss data of devices such as generators, transformers and energy storage devices in the power distribution network, the upper limit of the power that each device can output or withstand in operation is formulated. The device information includes device type, capacity, operating state and the like, and the device capacity constraints can prevent overload operation.

[0038] According to the operation information in the multi-dimensional power grid information, operation safety constraints are set, and by using real-time operation state indicators such as voltage, frequency and load rate of the power distribution network, a safety range that is not allowed to be exceeded is set for each key indicator. The operation information is derived from an online monitoring system or historical operation data, and the purpose of the operation safety constraint is to ensure system stability.

[0039] The power balance constraint, the device capacity constraint and the operation safety constraint constitute the target constraint condition, which serves as the basis for subsequent optimal scheduling and control, and ensures that the optimal operating state is realized under the premise of meeting the supply-demand balance, device capacity and safe operation.

[0040] Further, the application also includes: extracting a power generation time sequence in the multi-dimensional power grid information; extracting a power consumption time sequence in the multi-dimensional power grid information; performing spatio-temporal alignment processing on the power generation time sequence and the power consumption time sequence to obtain an aligned power sequence; performing fast Fourier transform on the aligned power sequence to obtain an aligned power spectrum, and analyzing to obtain the maximum spectral density of the aligned power spectrum; and setting the power balance constraint according to the maximum spectral density.

[0041] Specifically, the power generation time sequence in the multi-dimensional power grid information is extracted, and the data related to power generation that changes over time is extracted from the power grid operation data containing multiple dimensions. The power generation time sequence can be the power generation value every hour, or the power generation record every minute. Then, the power consumption time sequence in the multi-dimensional power grid information is extracted, which represents the data related to power consumption that changes over time, reflecting the power consumption demand of users at different time points, and obtaining a power consumption time sequence corresponding to the power generation time sequence.

[0042] Then, the power generation time sequence and the power consumption time sequence are subjected to spatio-temporal alignment processing to obtain an aligned power sequence. By using interpolation, resampling, timestamp calibration and other methods, the power generation time sequence and the power consumption time sequence, which are of different sources and have different time record frequencies or timestamps, are made to have data at the same time nodes, thereby forming a one-to-one corresponding aligned data sequence.

[0043] Subsequently, the power alignment time sequence is subjected to fast Fourier transform to obtain a power alignment spectrum, and the maximum spectral density of the power alignment spectrum is analyzed, i.e., the data in the time domain are converted into the frequency domain by fast Fourier transform to calculate the energy distribution under different frequency components. The maximum spectral density represents that the energy is most concentrated at a certain frequency, i.e., the period characteristic of the most obvious change between power generation and power consumption.

[0044] Finally, a power balance constraint is set according to the maximum spectral density, and a balance condition that must be met by power generation and power consumption is set, for example, if the frequency corresponding to the maximum spectral density reflects that the power consumption during the peak period of each day is 3 MW higher than the power generation, then the power balance constraint requires that the discharge of energy storage or the dispatch of power generation be increased to make up for the gap of 3 MW. Table 1 is a data table of time sequence alignment and spectrum analysis of power generation and power consumption of a power distribution network.

[0045] Table 1: Data table of time sequence alignment and spectrum analysis of power generation and power consumption of a power distribution network

[0046]

[0047] Further, the application further comprises: acquiring any device in the power distribution network, wherein the any device has a physical connection relationship with any line; setting the device capacity constraint in cooperation with any output of the any device and any loss of the any line.

[0048] Specifically, a specific device can be selected randomly or as needed from all devices in the power distribution network, which can be a generator, a transformer, an energy storage device or other power consumption terminals, and any line refers to a power transmission or distribution line directly connected to the any device, and the physical connection relationship means that the any device and the any line are physically connected through wires, busbars or other electrical connection methods.

[0049] The device capacity constraint is set in cooperation with any output of the any device and any loss of the any line, i.e., the maximum available capacity of the device is limited by comprehensively considering the actual output power of the any device and the loss of the any line in the transmission process, wherein the any output represents the electric power that the any device can provide or consume at a certain time. The any loss refers to the part of the electric energy that disappears in the power transmission line due to factors such as resistance and is converted into heat energy, and the device capacity constraint is the upper limit of the available power of the device set in design or operation.

[0050] Further, the application further comprises: reading a predetermined operation index, and randomly extracting any one index in the predetermined operation index as a target operation index; acquiring a predetermined safety threshold of the target operation index, and composing the operation safety constraint.

[0051] Specifically, the predetermined operation indicators are read, and all indicator data are obtained from the pre-set power grid operation state evaluation parameters, which can include voltage level, frequency stability, line load rate, reserve capacity rate, etc., for reflecting the operation health status of the distribution network at different times. Any one of the predetermined operation indicators is randomly extracted, representing the selection of a specific indicator in a random manner in the predetermined operation indicators, denoted as the target operation indicator, for example, the line load rate is randomly selected as the target operation indicator, and its safety is focused on in the subsequent steps.

[0052] The predetermined safety threshold of the target operation indicator is obtained, and the upper limit or lower limit of the safety range of the selected target operation indicator is found. The predetermined safety threshold is determined in the system design or operation procedure, for example, the safety threshold of the line load rate can be 90%, which will increase the risk of overload when exceeded, and constitutes the operation safety constraint, indicating that the predetermined safety threshold is included in the control model as a limiting condition to ensure that the target operation indicator does not exceed the safety range during operation scheduling, thereby ensuring the stable operation of the distribution network.

[0053] Further, the application also includes: the target optimization indicators include operation cost, voltage deviation, load variance and new energy proportion, wherein the operation cost includes purchase and sale of electricity cost, power supply operation and maintenance cost, wind and light abandonment penalty cost, energy storage charging and discharging operation cost, dispatching compensation cost and carbon emission cost.

[0054] Specifically, the target optimization indicators include operation cost, voltage deviation, load variance and new energy proportion. The operation cost refers to various fees generated in the process of maintaining normal operation and power supply service of the power grid, such as the purchase fee required for purchasing power from the upper-level power grid, the income difference after selling electricity to users, etc.; the voltage deviation refers to the difference between the actual voltage value and the rated voltage value of the power grid, which will affect the safety of equipment operation and power supply quality if too large or too small; the load variance refers to the dispersion degree of the electricity load data within a certain period, reflecting the size of load fluctuation, and the larger the variance, the more intense the electricity change; the new energy proportion refers to the proportion of renewable energy such as wind power and photovoltaic in the total power supply, and the higher the proportion, the greater the degree of clean energy utilization.

[0055] The operation cost includes a purchase and sale electricity cost, a power supply operation and maintenance cost, a wind and light abandonment penalty cost, a storage energy charging and discharging operation cost, a dispatch compensation cost, and a carbon emission cost. The purchase and sale electricity cost is the difference between the electricity purchase expenditure and the electricity sale income. The power supply operation and maintenance cost refers to the cost generated by daily maintenance and repair of the power generation equipment, such as lubrication of the fan and replacement of parts. The wind and light abandonment penalty cost refers to the penalty or loss compensation required to be paid due to the waste of wind power or photovoltaic power that cannot be utilized in grid connection. The storage energy charging and discharging operation cost refers to the energy consumed by the storage energy equipment in the charging and discharging process and the converted cost thereof. The dispatch compensation cost refers to the additional cost generated due to adjustment of the power grid operation mode or temporary dispatch of the power supply, such as the fuel cost of starting the standby combustion engine. The carbon emission cost refers to the carbon emission tax required to be paid or the expenditure of purchasing carbon quota due to the generation of carbon dioxide by the coal-fired and gas-fired power generation.

[0056] Further, the application also includes: generating an initial cooperative control scheme set based on the tent chaotic algorithm principle, wherein the initial cooperative control scheme set includes a first initial scheme; simulating the first initial scheme through the cooperative control simulation model to obtain first simulation information; when the first simulation information meets a predetermined simulation condition, establishing a first scheme domain based on the first initial scheme, and adding the first scheme domain to a simulation list; and simulating and analyzing the simulation list through the cooperative control simulation model to obtain the simulation record.

[0057] Specifically, the initial cooperative control scheme set is generated based on the tent chaotic algorithm principle to generate a group of initial control strategies that are uniformly distributed in the search space and have strong coverage. The tent chaotic algorithm is an algorithm that uses the nonlinearity and randomness of chaotic sequences to enhance the optimization search capability, and can avoid falling into local optimum. The initial cooperative control scheme set refers to multiple initial regulation and control schemes involving different devices, lines and operation parameters, which are used for subsequent optimization calculation. The initial cooperative control scheme set includes a first initial scheme. The first initial scheme is a scheme randomly selected from the initial cooperative control scheme set.

[0058] The first initial scheme is simulated through the cooperative control simulation model, and the first initial scheme is input into a calculation model capable of simulating the cooperative operation state of multiple devices of the power distribution network for calculation. The cooperative control simulation model is a mathematical and computer model combining the operation mechanism and dispatch logic of the power system, which can simulate the output change of the device, the load distribution of the line and the system operation index, for example, the influence of wind power output fluctuation on the voltage level of the main transformer can be observed in the simulation. The result is the first simulation information, which includes voltage curve, power balance, device utilization rate and other data.

[0059] When the first simulation information meets the predetermined simulation condition, that is, the simulation result meets the predetermined performance and safety standards, a first scheme domain is established based on the first initial scheme, the first scheme domain refers to a scheme set formed by expanding or fine-tuning the parameters of the first initial scheme within a certain range with the first initial scheme as the center, which can explore more possibilities close to the optimal solution, and the first scheme domain is added to the simulation list, the simulation list is a task queue storing all scheme sets to be simulated, which is used for subsequent batch simulation calculation.

[0060] The simulation list is simulated and analyzed by the cooperative control simulation model, all schemes in the simulation list are input into the cooperative control simulation model one by one for calculation, and the simulation records are counted and compared. The simulation record is a complete saving of the simulation result of each time, which includes the scheme parameters, the running index and the optimization target value.

[0061] Further, the application also includes: obtaining a first neighborhood scheme in the first scheme domain; if the second simulation information of the first neighborhood scheme meets the predetermined simulation condition, adding the first neighborhood scheme to the support set, if the second simulation information of the first neighborhood scheme does not meet the predetermined simulation condition, adding the first neighborhood scheme to the opposition set; obtaining the first support rate of the first initial scheme according to the support set and the opposition set; sorting the first initial scheme based on the first support rate to obtain the optimal initial scheme; sequentially simulating a plurality of optimal neighborhood schemes in the optimal scheme domain of the optimal initial scheme to obtain a plurality of simulation data groups, and composing the simulation record.

[0062] Specifically, a first neighborhood scheme in the first scheme domain is obtained, that is, a scheme is randomly selected in the parameter range set of the first scheme domain. After simulating and calculating the first neighborhood scheme, the second simulation information is obtained. If the second simulation information meets the predetermined simulation condition, the running result meets the predetermined standards such as safety and economy, the first neighborhood scheme is added to the support set, and the support set refers to a scheme set that is considered to have improvement potential and correct direction. If the second simulation information does not meet the predetermined simulation condition, it is added to the opposition set, and the opposition set refers to a scheme set that does not meet the target requirement and needs to be corrected.

[0063] The total number of the first neighborhood schemes in the support set and the opposition set is counted, and the proportion coefficient of the number of the first neighborhood schemes in the support set to the total number is calculated as the first support rate. The support rate reflects the potential of the first initial scheme to extend optimization in the neighborhood search space.

[0064] Based on the first support rate, the first initial scheme is sorted, all initial schemes are ranked from high to low according to the support rate, so as to find the scheme with the most stable performance and the largest optimization space in the neighborhood, and the optimal initial scheme is obtained.

[0065] The neighborhood corresponding to the optimal initial scheme is an optimal scheme domain, and the schemes in the optimal scheme domain are optimal neighborhood schemes. The multiple optimal neighborhood schemes are simulated in turn, and each is calculated by the cooperative control simulation model to obtain multiple simulation data sets. The simulation data sets record different operating costs, voltage deviations, new energy proportions, and other indicators under the neighborhood schemes, and form simulation records for final scheme evaluation and decision-making.

[0066] Further, the application also includes obtaining a predetermined weight distribution in the fitness evaluation strategy, wherein the predetermined weight distribution refers to the weight distribution of each indicator in the target optimization indicator; combining the predetermined weight distribution to perform fitness evaluation on the multiple simulation data sets to obtain multiple cooperative control fitnesses; taking the optimal neighborhood scheme corresponding to the maximum fitness in the multiple cooperative control fitnesses as the optimal cooperative control scheme; wherein it further includes introducing a model transformation mechanism to transform the cooperative control simulation model into a mixed integer convex programming model; obtaining a cooperative control verification scheme of the power distribution network through the mixed integer convex programming model, wherein the cooperative control verification scheme is used to verify the optimal cooperative control scheme.

[0067] Specifically, the predetermined weight distribution in the fitness evaluation strategy is obtained, and the weight of each indicator set in advance is read from the strategy for evaluating the pros and cons of the scheme. The predetermined weight distribution refers to the importance proportion of different indicators to the final evaluation result in the target optimization indicator. The higher the weight of an indicator, the greater the impact on the final calculation;

[0068] Combining the predetermined weight distribution to perform fitness evaluation on the multiple simulation data sets combines the predetermined weight with the simulation results of different schemes to calculate the matching degree of each scheme to the optimization target as the multiple cooperative control fitnesses. The multiple cooperative control fitnesses reflect the comprehensive ability of multiple schemes to meet the optimization target.

[0069] Taking the optimal neighborhood scheme corresponding to the maximum fitness in the multiple cooperative control fitnesses as the optimal cooperative control scheme means selecting the scheme with the highest comprehensive evaluation among all candidate schemes as the final recommended scheme.

[0070] Introducing a model transformation mechanism to transform the cooperative control simulation model into a mixed integer convex programming model transforms the model used for dynamic simulation into a mathematical optimization model, which can handle problems with both integer variables and continuous variables, and maintains convexity to ensure finding a global optimal solution. For example, the on-off state of a device is taken as an integer variable, and the power output is taken as a continuous variable.

[0071] The mixed integer convex programming model is used to obtain a collaborative control verification scheme of the power distribution network, an operation scheme for verification is recalculated and generated by using the optimization model, and it is checked whether the optimal collaborative control scheme is still feasible under actual constraints. If the operation result of the verification scheme is consistent with the simulation result, the implementability is verified.

[0072] To sum up, the power distribution network source network storage collaborative control method provided in the application has the following technical effects: by realizing the technical target of multi-dimensional information fusion and target constraint collaborative optimization of the power distribution network source network storage intelligent control, the technical effects of improving new energy consumption capacity, reducing operation cost, ensuring power grid operation safety and improving overall stability of the system are achieved.

[0073] In the embodiment two, based on the same inventive concept as the power distribution network source network storage collaborative control method in the foregoing embodiments, the application further provides a power distribution network source network storage collaborative control system, please refer to the accompanying drawings Figure 2 , including: a target constraint condition setting module 1, configured to collect multi-dimensional power grid information of the power distribution network, and set target constraint conditions according to the multi-dimensional power grid information; a collaborative control simulation module 2, configured to construct target optimization indexes, construct a collaborative control simulation model in cooperation with the target constraint conditions, and perform collaborative control simulation on the power distribution network through the collaborative control simulation model to obtain simulation records; an analysis module 3, configured to introduce fitness evaluation strategies to analyze a plurality of simulation data groups in the simulation records, and filter to obtain an optimal collaborative control scheme; a source network storage collaborative control module 4, configured to perform source network storage collaborative control of the power distribution network through the optimal collaborative control scheme.

[0074] Further, the power distribution network source network storage collaborative control system is further configured to: set a power balance constraint according to power information in the multi-dimensional power grid information; set a device capacity constraint according to device information in the multi-dimensional power grid information; set an operation safety constraint according to operation information in the multi-dimensional power grid information; the power balance constraint, the device capacity constraint and the operation safety constraint constitute the target constraint condition.

[0075] Further, the power distribution network source network storage collaborative control system is further configured to: extract a power generation time sequence in the multi-dimensional power grid information; extract a power consumption time sequence in the multi-dimensional power grid information; perform space-time alignment processing on the power generation time sequence and the power consumption time sequence to obtain an aligned power time sequence; perform fast Fourier transform on the aligned power time sequence to obtain an aligned power spectrum, and analyze to obtain a maximum spectral density of the aligned power spectrum; set the power balance constraint according to the maximum spectral density.

[0076] Further, the power distribution network source network storage collaborative control system is further used for: acquiring any device in the power distribution network, wherein the any device has a physical connection relationship with any line; coordinating any output of the any device with any loss of the any line, and setting the device capacity constraint.

[0077] Further, the power distribution network source network storage collaborative control system is further used for: reading a predetermined operation index, and randomly extracting any one index in the predetermined operation index as a target operation index; acquiring a predetermined safety threshold of the target operation index, and composing the operation safety constraint.

[0078] Further, the power distribution network source network storage collaborative control system is further used for: the target optimization index includes operation cost, voltage deviation, load variance and new energy proportion, wherein the operation cost includes power purchase and sale cost, power supply operation and maintenance cost, wind and light abandonment penalty cost, energy storage charging and discharging operation cost, dispatching compensation cost and carbon emission cost.

[0079] Further, the power distribution network source network storage collaborative control system is further used for: generating an initial collaborative control scheme set based on the principle of tent chaos algorithm, wherein the initial collaborative control scheme set includes a first initial scheme; simulating the first initial scheme through the collaborative control simulation model to obtain first simulation information; when the first simulation information meets a predetermined simulation condition, establishing a first scheme domain based on the first initial scheme, and adding the first scheme domain to a simulation list; simulating and analyzing the simulation list through the collaborative control simulation model to obtain the simulation record.

[0080] Further, the power distribution network source network storage collaborative control system is further used for: acquiring a first neighborhood scheme in the first scheme domain; if second simulation information of the first neighborhood scheme meets the predetermined simulation condition, adding the first neighborhood scheme to a support set, and if the second simulation information of the first neighborhood scheme does not meet the predetermined simulation condition, adding the first neighborhood scheme to an opposition set; obtaining a first support rate of the first initial scheme according to the support set and the opposition set; sorting the first initial scheme based on the first support rate to obtain an optimal initial scheme; sequentially simulating a plurality of optimal neighborhood schemes in an optimal scheme domain of the optimal initial scheme to obtain a plurality of simulation data groups, and composing the simulation record.

[0081] Further, the power distribution network source-network-storage collaborative control system is further used for: obtaining a predetermined weight distribution in the fitness evaluation strategy, wherein the predetermined weight distribution refers to weight distribution of each index in the target optimization index; performing fitness evaluation on the plurality of simulation data groups in combination with the predetermined weight distribution to obtain a plurality of collaborative control fitness; taking an optimal neighborhood scheme corresponding to a maximum fitness in the plurality of collaborative control fitness as the optimal collaborative control scheme; wherein further comprising: introducing a model conversion mechanism to convert the collaborative control simulation model into a mixed integer convex programming model; obtaining a collaborative control verification scheme of the power distribution network through the mixed integer convex programming model, wherein the collaborative control verification scheme is used for verifying the optimal collaborative control scheme.

[0082] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The power distribution network source-network-storage collaborative control method and specific examples in the first embodiment are also applicable to the power distribution network source-network-storage collaborative control system of the present embodiment. Through the foregoing detailed description of the power distribution network source-network-storage collaborative control method, those skilled in the art can clearly know the power distribution network source-network-storage collaborative control system in the present embodiment. Therefore, for the sake of brevity of the specification, the power distribution network source-network-storage collaborative control system will not be described in detail here.

[0083] The above description of disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0084] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application also intends to include these modifications and variations.

Claims

1. A power distribution network source-grid-storage collaborative control method, characterized in that, The method comprises the following steps: Collecting multi-dimensional power grid information of a power distribution network, and setting target constraint conditions according to the multi-dimensional power grid information; Assembling target optimization indexes, constructing a coordinated control simulation model in cooperation with the target constraint conditions, and performing coordinated control simulation on the power distribution network through the coordinated control simulation model to obtain simulation records; Introducing fitness evaluation strategies to analyze a plurality of simulation data groups in the simulation records, and screening to obtain an optimal coordinated control scheme; Performing source-grid-storage coordinated control of the power distribution network through the optimal coordinated control scheme; The target optimization indexes include operation cost, voltage deviation, load variance, and new energy proportion, wherein the operation cost includes power purchase and sale cost, power supply operation and maintenance cost, wind and light curtailment penalty cost, energy storage charging and discharging operation cost, dispatching compensation cost, and carbon emission cost; Assembling target optimization indexes, constructing a coordinated control simulation model in cooperation with the target constraint conditions, and performing coordinated control simulation on the power distribution network through the coordinated control simulation model to obtain simulation records, comprising: Generating an initial coordinated control scheme set based on the principle of tent chaotic algorithm, wherein the initial coordinated control scheme set includes a first initial scheme; Simulating the first initial scheme through the coordinated control simulation model to obtain first simulation information; When the first simulation information meets predetermined simulation conditions, a first scheme domain is established based on the first initial scheme, and the first scheme domain is added to a simulation list; Simulating and analyzing the simulation list through the coordinated control simulation model to obtain the simulation records; Simulating and analyzing the simulation list through the coordinated control simulation model to obtain the simulation records, comprising: Obtaining a first neighborhood scheme in the first scheme domain; If second simulation information of the first neighborhood scheme meets the predetermined simulation conditions, the first neighborhood scheme is added to a support set, and if the second simulation information of the first neighborhood scheme does not meet the predetermined simulation conditions, the first neighborhood scheme is added to an opposition set; Obtaining a first support rate of the first initial scheme according to the support set and the opposition set; Sorting the first initial scheme based on the first support rate to obtain an optimal initial scheme; Simulating a plurality of optimal neighborhood schemes in an optimal scheme domain of the optimal initial scheme in turn to obtain a plurality of simulation data groups, and assembling the plurality of simulation data groups to form the simulation records.

2. The power grid source-grid-storage collaborative control method according to claim 1, characterized in that, Collecting multi-dimensional power grid information of a power distribution network, and setting target constraint conditions according to the multi-dimensional power grid information, comprising: Setting power balance constraints according to power information in the multi-dimensional power grid information; Setting device capacity constraints according to device information in the multi-dimensional power grid information; Setting operation safety constraints according to operation information in the multi-dimensional power grid information; The power balance constraints, the device capacity constraints, and the operation safety constraints constitute the target constraint conditions.

3. The power grid source-grid-storage collaborative control method according to claim 2, characterized in that, Setting power balance constraints according to power information in the multi-dimensional power grid information, comprising: Extracting generation time series in the multi-dimensional power grid information; Extracting power consumption time series in the multi-dimensional power grid information; The power generation time sequence and the power consumption time sequence are spatio-temporally aligned to obtain a power alignment time sequence; The power alignment time sequence is subjected to fast Fourier transform to obtain a power alignment frequency spectrum, and the maximum spectral density of the power alignment frequency spectrum is analyzed; The maximum spectral density is used to set the power balance constraint.

4. The power grid source-grid-storage collaborative control method according to claim 2, characterized in that, The device capacity constraint is set according to the device information in the multi-dimensional power grid information, including: Any device in the power distribution grid is obtained, wherein the any device has a physical connection relationship with any line; The device capacity constraint is set in cooperation with any output of the any device and any loss of the any line.

5. The power grid source-grid-storage collaborative control method according to claim 2, characterized in that, The operation safety constraint is set according to the operation information in the multi-dimensional power grid information, including: A predetermined operation index is read, and any one index in the predetermined operation index is randomly extracted as a target operation index; A predetermined safety threshold of the target operation index is obtained, and the operation safety constraint is composed.

6. The power grid source-grid-storage collaborative control method according to claim 1, characterized in that, The multiple simulation data groups in the simulation record are analyzed by introducing a fitness evaluation strategy, and the optimal coordinated control scheme is screened, including: A predetermined weight distribution in the fitness evaluation strategy is obtained, wherein the predetermined weight distribution refers to the weight distribution of each index in the target optimization index; The multiple simulation data groups are subjected to fitness evaluation in combination with the predetermined weight distribution to obtain multiple coordinated control fitnesses; The optimal neighborhood scheme corresponding to the maximum fitness in the multiple coordinated control fitnesses is taken as the optimal coordinated control scheme; Further including: The coordinated control simulation model is converted into a mixed integer convex programming model by introducing a model conversion mechanism; The coordinated control verification scheme of the power distribution grid is obtained through the mixed integer convex programming model, wherein the coordinated control verification scheme is used to verify the optimal coordinated control scheme.

7. A power distribution grid source-grid-storage collaborative control system, characterized in that, The steps of the power distribution grid source-grid-storage coordinated control method in any one of claims 1 to 6, including: A target constraint condition setting module is used to collect the multi-dimensional power grid information of the power distribution grid, and set the target constraint condition according to the multi-dimensional power grid information; A coordinated control simulation module is used to construct a target optimization index, construct a coordinated control simulation model in cooperation with the target constraint condition, and simulate the coordinated control of the power distribution grid through the coordinated control simulation model to obtain a simulation record; An analysis module is used to analyze the multiple simulation data groups in the simulation record by introducing a fitness evaluation strategy, and screen the optimal coordinated control scheme; A source-grid-storage coordinated control module is used to perform the source-grid-storage coordinated control of the power distribution grid through the optimal coordinated control scheme.

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