Power distribution network source network storage cooperative control method and system

Abstract

The invention provides a power distribution network source network storage cooperative control method and system, and relates to the technical field of power distribution networks, and the method comprises the steps: collecting multi-dimensional power grid information of a power distribution network, and setting a target constraint condition according to the multi-dimensional power grid information; establishing a target optimization index and a cooperative control simulation model in cooperation with the target constraint condition, and performing cooperative control simulation on the power distribution network through the cooperative control simulation model to obtain a simulation record; a fitness evaluation strategy is introduced to analyze a plurality of simulation data sets in the simulation records, and an optimal cooperative control scheme is obtained through screening; and performing source network storage cooperative control of the power distribution network through the optimal cooperative control scheme. According to the invention, the technical problem that the power distribution network is difficult to realize effective coordination of the power generation source, the energy storage equipment and the load in the prior art can be solved, and the technical effects of improving the new energy consumption capability, reducing the operation cost, guaranteeing the operation safety of the power grid and improving the overall stability of the system are achieved.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a method and system for coordinated control of power generation, grid and storage in power distribution networks. Background Technology

[0002] With the transformation of the global energy structure and the large-scale integration of renewable energy, the complexity and uncertainty of the distribution network, as a crucial link connecting power sources and end users in the power system, have increased significantly.

[0003] Currently, traditional power distribution networks mainly rely on a single power dispatch and load management model, which makes it difficult to effectively coordinate distributed generation, energy storage devices, and user-side loads. This leads to supply-demand imbalances, energy waste, and increased operational safety risks. For example, the output of wind and solar power is highly random and volatile, and traditional control strategies cannot respond to these fluctuations in real time, resulting in severe wind and solar curtailment and a significant waste of clean energy.

[0004] In summary, existing technologies suffer from a lack of comprehensive utilization of multi-dimensional power grid information and scientific multi-objective collaborative optimization methods, which makes it difficult for distribution networks to effectively coordinate power generation, energy storage devices, and loads. This further affects the full utilization of new energy sources and the economic efficiency and safety of system operation. Summary of the Invention

[0005] The purpose of this application is to provide a power generation, grid, and energy storage coordinated control method and system for distribution networks, in order to solve the technical problems in the existing technology where the lack of comprehensive utilization of multi-dimensional grid information and scientific multi-objective coordinated optimization methods makes it difficult for distribution networks to achieve effective coordination of power generation, energy storage equipment, and loads, which further affects the full utilization of new energy and the economy and safety of system operation.

[0006] In view of the above problems, this application provides a method and system for coordinated control of power generation, grid and storage in distribution networks.

[0007] Firstly, this application provides a distribution network source-grid-storage coordinated control method, implemented through a distribution network source-grid-storage coordinated control system, comprising: collecting multi-dimensional grid information of the distribution network and setting target constraints based on the multi-dimensional grid information; constructing target optimization indices, coordinating the target constraints to build a coordinated control simulation model, and performing coordinated control simulation on the distribution network through the coordinated control simulation model to obtain simulation records; introducing a fitness evaluation strategy to analyze multiple simulation data groups in the simulation records and selecting the optimal coordinated control scheme; and performing source-grid-storage coordinated control of the distribution network through the optimal coordinated control scheme.

[0008] Preferably, the power grid-source-grid-storage coordinated control method further includes: setting power balance constraints based on the power information in the multi-dimensional power grid information; setting equipment capacity constraints based on the equipment information in the multi-dimensional power grid information; setting operation safety constraints based on the operation information in the multi-dimensional power grid information; the power balance constraints, the equipment capacity constraints, and the operation safety constraints constitute the target constraint conditions.

[0009] Preferably, the power grid source-grid-storage coordinated control method further includes: extracting the power generation time series from the multi-dimensional power grid information; extracting the power consumption time series from the multi-dimensional power grid information; performing spatiotemporal alignment processing on the power generation time series and the power consumption time series to obtain a power-aligned time series; performing a fast Fourier transform on the power-aligned time series to obtain a power-aligned spectrum, and analyzing to obtain the maximum spectral density of the power-aligned spectrum; and setting the power balance constraint based on the maximum spectral density.

[0010] Preferably, the power distribution network source-grid-storage coordinated control method further includes: acquiring any device in the power distribution network, wherein the arbitrary device has a physical connection with any line; and coordinating the arbitrary output of the arbitrary device with the arbitrary loss of the arbitrary line to set the device capacity constraint.

[0011] Preferably, the power distribution network source-grid-storage coordinated control method further includes: reading predetermined operating indicators, randomly extracting any one of the predetermined operating indicators and recording it as a target operating indicator; obtaining a predetermined safety threshold of the target operating indicator and forming the operating safety constraint.

[0012] Preferably, the distribution network source-grid-storage coordinated control method further includes: the target optimization indicators include operating cost, voltage deviation, load variance and the proportion of new energy, wherein the operating cost includes electricity purchase and sale cost, power source operation and maintenance cost, wind and solar curtailment penalty cost, energy storage charging and discharging operation cost, dispatch compensation cost and carbon emission cost.

[0013] Preferably, the power grid-source-grid-storage coordinated control method further includes: generating an initial coordinated control scheme set based on the TENT chaotic algorithm principle, wherein the initial coordinated control scheme set includes a first initial scheme; simulating the first initial scheme using the coordinated control simulation model to obtain first simulation information; when the first simulation information meets predetermined simulation conditions, establishing a first scheme domain based on the first initial scheme and adding the first scheme domain to the simulation list; and performing simulation analysis on the simulation list using the coordinated control simulation model to obtain the simulation record.

[0014] Preferably, the power distribution network source-grid-storage coordinated control method further includes: obtaining a first neighboring scheme in the first scheme domain; if the second simulation information of the first neighboring scheme meets the predetermined simulation conditions, then adding the first neighboring scheme to the support set; if the second simulation information of the first neighboring scheme does not meet the predetermined simulation conditions, then adding the first neighboring scheme to the opposition set; obtaining a first support rate of the first initial scheme based on the support set and the opposition set; sorting the first initial scheme based on the first support rate to obtain the optimal initial scheme; and simulating multiple optimal neighboring schemes in the optimal scheme domain of the optimal initial scheme in sequence to obtain the multiple simulation data groups, and forming the simulation record.

[0015] Preferably, the distribution network source-grid-storage coordinated control method further includes: obtaining a predetermined weight allocation in the fitness evaluation strategy, wherein the predetermined weight allocation refers to the weight allocation of each index in the target optimization index; performing fitness evaluation on the multiple simulated data groups in combination with the predetermined weight allocation to obtain multiple coordinated control fitnesss; taking the optimal neighborhood scheme corresponding to the maximum fitness among the multiple coordinated control fitnesss as the optimal coordinated control scheme; further including: introducing a model transformation mechanism to transform the coordinated control simulation model into a mixed integer convex programming model; obtaining a coordinated control verification scheme for the distribution network through the mixed integer convex programming model, wherein the coordinated control verification scheme is used to verify the optimal coordinated control scheme.

[0016] Secondly, this application also provides a distribution network source-grid-storage coordinated control system for executing the distribution network source-grid-storage coordinated control method as described in the first aspect, comprising: a target constraint setting module for collecting multi-dimensional grid information of the distribution network and setting target constraints based on the multi-dimensional grid information; a coordinated control simulation module for constructing target optimization indices, constructing a coordinated control simulation model in conjunction with the target constraints, and performing coordinated control simulation on the distribution network through the coordinated control simulation model to obtain simulation records; an analysis module for introducing a fitness evaluation strategy to analyze multiple simulation data groups in the simulation records and select the optimal coordinated control scheme; and a source-grid-storage coordinated control module for performing source-grid-storage coordinated control of the distribution network through the optimal coordinated control scheme.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by realizing the technical goal of intelligent control of power grid source, grid and storage in distribution network through multi-dimensional information fusion and target constraint synergistic optimization, it achieves the technical effects of improving the capacity for new energy consumption, reducing operating costs, ensuring the safety of power grid operation and improving the overall stability of the system.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the power generation, grid, and storage coordinated control method for the distribution network in this application.

[0021] Figure 2 This is a schematic diagram of the structure of the power grid-source-grid-storage coordinated control system of this application.

[0022] Figure labeling: Target constraint setting module 1, Cooperative control simulation module 2, Analysis module 3, Source-grid-storage cooperative control module 4. Detailed Implementation

[0023] This application provides a distribution network source-grid-storage coordinated control method and system, solving the technical problem in existing technologies where the lack of comprehensive utilization of multi-dimensional grid information and scientific multi-objective coordinated optimization methods makes it difficult for distribution networks to effectively coordinate power generation, energy storage devices, and loads, further affecting the full utilization of new energy and the economic efficiency and safety of system operation. The application achieves the technical goal of intelligent control of distribution network source-grid-storage through multi-dimensional information fusion and objective-constrained coordinated optimization, thereby improving the capacity for new energy absorption, reducing operating costs, ensuring grid operation safety, and enhancing the overall stability of the system.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a power generation, grid, and energy storage coordinated control method for distribution networks, which is applied to a power generation, grid, and energy storage coordinated control system for distribution networks. Specifically, it includes the following steps: S1: Collect multi-dimensional power grid information of the distribution network and set target constraints based on the multi-dimensional power grid information.

[0026] Specifically, this involves acquiring various types of datasets related to distribution network operation. Multidimensional grid information includes power consumption information, equipment information, and operational information. Power consumption information refers to the changes in power generation and consumption at different times; equipment information refers to the type, capacity, and status of equipment such as generators, transformers, and energy storage; and operational information refers to indicators reflecting the system's health, such as voltage, frequency, and line load rate. Based on this multidimensional grid information, target constraints are set, that is, operational restriction rules are formulated using the collected data from different dimensions. Target constraints are boundaries that must be met during optimized scheduling, including power balance constraints, equipment capacity constraints, and operational safety constraints.

[0027] S2: Establish target optimization indicators, construct a collaborative control simulation model in conjunction with the target constraints, and perform collaborative control simulation on the distribution network through the collaborative control simulation model to obtain simulation records.

[0028] Specifically, target optimization indicators are established to determine key quantitative standards for measuring the performance of the distribution network. These indicators may include operating costs, voltage deviation, load variance, and the proportion of renewable energy sources. For example, operating costs reflect the total cost of electricity purchase and sale, equipment maintenance, and energy storage operation; voltage deviation indicates the degree of difference between the system voltage and its rated value; load variance measures the balance of load distribution across different time periods; and the proportion of renewable energy sources represents the proportion of clean energy such as wind power and solar power in the total power supply. A collaborative control simulation model is constructed using coordinated target constraints, combining the target optimization indicators with the constraints of grid operation to form a computational model that simultaneously considers economy, safety, and stability. Target constraints include power balance constraints, equipment capacity constraints, and operational safety constraints, ensuring operational safety while providing a feasible range for the optimization indicators.

[0029] The distribution network is simulated through a collaborative control simulation model. The distribution network is then virtually run in a computer using the collaborative control simulation model. The operating results are tested under different strategies and parameter combinations. For example, the impact of different energy storage charging and discharging schemes and scheduling strategies on operating costs and the proportion of new energy sources is simulated within a 24-hour cycle. All data generated during the simulation process is systematically saved to obtain simulation records for subsequent analysis and scheme optimization.

[0030] S3: Introduce an fitness evaluation strategy to analyze multiple sets of simulation data in the simulation record and select the optimal cooperative control scheme.

[0031] Specifically, an fitness evaluation strategy is introduced to comprehensively evaluate the simulation results of multiple operation schemes for the distribution network. The fitness evaluation strategy involves scoring each set of simulation data based on preset target optimization indicators and weights to measure its operational effectiveness. Multiple sets of simulation data in the simulation records are analyzed, and the operational data under different control schemes are calculated and compared one by one. The simulation data sets include key parameters such as operating costs, voltage deviation, load fluctuations, and the proportion of renewable energy. Selecting the optimal coordinated control scheme means choosing the scheme with the highest comprehensive evaluation and best meeting the operational objectives and constraints from among many candidate schemes as the final scheduling strategy.

[0032] S4: Perform source-grid-storage coordinated control of the distribution network through the optimal coordinated control scheme.

[0033] Specifically, the optimal collaborative control scheme for power generation, distribution network, and energy storage involves applying the selected control scheme with the highest comprehensive evaluation and best meeting operational objectives to the actual operation of the distribution network, thereby achieving collaborative management of power generation, distribution network, and energy storage devices. The optimal collaborative control scheme refers to a scheduling strategy that achieves the best overall balance of economy, stability, and security through simulation and evaluation, enabling the rational allocation of output and load across various energy devices. The distribution network refers to the low- and medium-voltage power systems that transmit electricity from the transmission network to end users, including lines, transformers, and switchgear. The collaborative control of power generation, distribution network, and energy storage refers to the unified and coordinated control of power generation (such as wind power and photovoltaics), distribution network, and energy storage devices (such as battery energy storage systems) within the power grid to ensure power supply and demand balance, voltage stability, and safe equipment operation.

[0034] Furthermore, this application also includes: setting power balance constraints based on the power information in the multidimensional power grid information; setting equipment capacity constraints based on the equipment information in the multidimensional power grid information; setting operation safety constraints based on the operation information in the multidimensional power grid information; the power balance constraints, the equipment capacity constraints, and the operation safety constraints constitute the target constraint conditions.

[0035] Specifically, power balance constraints are set based on the power information in the multidimensional power grid information. By using data on the changes in power generation and consumption over time in the distribution network, constraints are formulated to maintain a relative balance between power generation and consumption. The power information includes the total output of the generation side and the total consumption of the load side. The role of power balance constraints is to avoid insufficient or excessive power generation.

[0036] Based on the equipment information in the multidimensional power grid information, equipment capacity constraints are set. Using the rated capacity, actual available capacity and loss data of equipment such as generators, transformers and energy storage devices in the distribution network, the upper limit of the power that each device can output or withstand during operation is determined. The equipment information includes equipment type, capacity, operating status, etc. Equipment capacity constraints can prevent overload operation.

[0037] Operational safety constraints are set based on the operational information in the multidimensional power grid information. Real-time operational status indicators of the distribution network, such as voltage, frequency, and load rate, are used to set a safe range that cannot be exceeded for each key indicator. The operational information comes from the online monitoring system or historical operational data. The purpose of the operational safety constraints is to ensure system stability.

[0038] Power balance constraints, equipment capacity constraints, and operational safety constraints constitute the target constraints, which serve as the basis for subsequent optimization scheduling and control, ensuring that the optimal operating state is achieved under the premise of satisfying supply and demand balance, equipment capacity, and safe operation.

[0039] Furthermore, this application also includes: extracting the power generation time series from the multidimensional power grid information; extracting the power consumption time series from the multidimensional power grid information; performing spatiotemporal alignment processing on the power generation time series and the power consumption time series to obtain a power-aligned time series; performing a fast Fourier transform on the power-aligned time series to obtain a power-aligned spectrum, and analyzing to obtain the maximum spectral density of the power-aligned spectrum; and setting the power balance constraint based on the maximum spectral density.

[0040] Specifically, the generation time series is extracted from the multi-dimensional power grid information. This involves extracting time-varying data related to power generation from the multi-dimensional power grid operation data. The generation time series can be the power generation value per hour per day or per minute. Next, the electricity consumption time series is extracted from the multi-dimensional power grid information. This means extracting all time-varying data related to electricity consumption, reflecting the electricity demand of users at different points in time, resulting in an electricity consumption time series corresponding to the generation time series.

[0041] Then, the power generation time series and power consumption time series are spatiotemporally aligned to obtain the power-aligned time series. By interpolating, resampling, and timestamp calibration, the power generation time series and power consumption time series are aligned to have data at the same time node, thus forming a one-to-one corresponding aligned data sequence.

[0042] Subsequently, the energy-aligned time series was obtained by Fast Fourier Transform (FFT) to generate the energy-aligned spectrum, and the maximum spectral density of the energy-aligned spectrum was analyzed. This involves converting the time-domain data into the frequency domain using FFT to calculate the energy distribution at different frequency components. The maximum spectral density indicates the highest energy concentration at a given frequency, representing the most pronounced periodic characteristic of changes in power generation and consumption.

[0043] Finally, power balance constraints are set based on the maximum spectral density, defining the balance conditions that power generation and consumption must meet. For example, if the frequency corresponding to the maximum spectral density reflects that the peak daily power consumption is 3 MW higher than the power generation, then the power balance constraint requires increased energy storage discharge or dispatched power generation during that period to make up for the 3 MW shortfall. Table 1 shows the time-series alignment and spectrum analysis data of power generation and consumption in the distribution network.

[0044] Table 1: Time-series alignment and spectrum analysis data of power generation and consumption in the distribution network

[0045] Furthermore, this application also includes: acquiring any device in the power distribution network, wherein the arbitrary device has a physical connection with any line; and setting the device capacity constraint in coordination with the arbitrary output of the arbitrary device and the arbitrary loss of the arbitrary line.

[0046] Specifically, among all the equipment in the distribution network, a specific device can be selected randomly or as needed. This device could be a generator, transformer, energy storage device, or other power terminal. An arbitrary line refers to a transmission or distribution line that is directly connected to any device. Having a physical connection means that any device and any line are physically connected through conductors, busbars, or other electrical connection methods.

[0047] By coordinating the arbitrary output of any device with the arbitrary losses of any line, a device capacity constraint is set. This constraint limits the maximum usable capacity of the device by comprehensively considering the actual output power of any device and the losses during transmission. Arbitrary output refers to the electrical power that any device can provide or consume at a given moment. Arbitrary loss refers to the portion of electrical energy lost in the transmission line due to factors such as resistance, which is converted into heat. The device capacity constraint sets an upper limit on the usable power of the device during design or operation.

[0048] Furthermore, this application also includes: reading predetermined operating indicators, randomly extracting any one of the predetermined operating indicators and recording it as a target operating indicator; obtaining a predetermined safety threshold of the target operating indicator and forming the operating safety constraint.

[0049] Specifically, the process involves reading predetermined operational indicators, obtaining all indicator data from pre-set power grid operation status evaluation parameters, which may include voltage levels, frequency stability, line load factor, and reserve capacity rate, to reflect the operational health status of the distribution network at different times. Randomly selecting any one of the predetermined operational indicators means selecting a specific indicator randomly from the predetermined indicators, denoted as the target operational indicator. For example, if the line load factor is randomly selected as the target operational indicator, its safety will be a key focus in subsequent steps.

[0050] Obtain the predetermined safety threshold for the target operating index, find the upper or lower limit of the safety range set in advance for the selected target operating index. The predetermined safety threshold is determined in the system design or operation procedure. For example, the safety threshold for line load rate may be 90%. Exceeding this value will increase the risk of overload and form an operating safety constraint. This means that the predetermined safety threshold is incorporated into the control model as a limiting condition to ensure that the target operating index does not exceed the safety range during operation and scheduling, thereby ensuring the stable operation of the distribution network.

[0051] Furthermore, this application also includes: the target optimization indicators include operating costs, voltage deviation, load variance and the proportion of new energy, wherein the operating costs include electricity purchase and sale costs, power supply operation and maintenance costs, wind and solar curtailment penalty costs, energy storage charging and discharging operation costs, dispatch compensation costs and carbon emission costs.

[0052] Specifically, the target optimization indicators include operating costs, voltage deviation, load variance, and the proportion of renewable energy. Operating costs refer to various expenses incurred by the power grid in maintaining normal operation and providing power supply services, such as the daily electricity purchase cost from the upstream grid and the revenue difference after selling electricity to users; voltage deviation refers to the degree of difference between the actual voltage value and the rated voltage value of the power grid, and excessive or insufficient deviation will affect the safe operation of equipment and the quality of power supply; load variance refers to the dispersion of electricity load data within a certain period, reflecting the magnitude of load fluctuations, and the larger the variance, the more drastic the changes in electricity consumption; the proportion of renewable energy refers to the proportion of renewable energy such as wind power and photovoltaics in the total power supply, and the higher the proportion, the greater the degree of clean energy utilization.

[0053] Operating costs include electricity purchase and sale costs, power source operation and maintenance costs, wind and solar curtailment penalty costs, energy storage charging and discharging operation costs, dispatch compensation costs, and carbon emission costs. Electricity purchase and sale costs are the difference between electricity purchase expenditures and electricity sales revenue; power source operation and maintenance costs refer to the expenses incurred in the daily maintenance and repair of power generation equipment, such as wind turbine lubrication and parts replacement; wind and solar curtailment penalty costs refer to the fines or compensation for losses incurred due to the waste of wind or solar power caused by failure to connect to the grid; energy storage charging and discharging operation costs refer to the energy consumed by energy storage equipment during charging and discharging and its equivalent costs; dispatch compensation costs refer to the additional costs incurred due to adjusting the grid operation mode or temporarily dispatching power sources, such as the fuel cost for starting standby gas turbines; and carbon emission costs refer to the carbon emission tax or carbon allowance expenditures required for generating carbon dioxide through coal-fired or gas-fired power generation.

[0054] Furthermore, this application also includes: generating an initial cooperative control scheme set based on the principle of the TENT chaos algorithm, wherein the initial cooperative control scheme set includes a first initial scheme; simulating the first initial scheme using the cooperative control simulation model to obtain first simulation information; when the first simulation information meets predetermined simulation conditions, establishing a first scheme domain based on the first initial scheme and adding the first scheme domain to the simulation list; and performing simulation analysis on the simulation list using the cooperative control simulation model to obtain the simulation record.

[0055] Specifically, an initial cooperative control scheme set is generated based on the principles of the Tent chaotic algorithm, producing a set of initial control strategies that are relatively evenly distributed and have strong coverage in the search space. The Tent chaotic algorithm is an algorithm that utilizes the nonlinear and stochastic characteristics of chaotic sequences to enhance optimization search capabilities and avoid getting trapped in local optima. The initial cooperative control scheme set refers to multiple initial control schemes involving different equipment, lines, and operating parameters, used for subsequent optimization calculations. The initial cooperative control scheme set includes a first initial scheme, which is a scheme randomly selected from the initial cooperative control scheme set.

[0056] The first initial scheme is simulated using a collaborative control simulation model. This initial scheme is input into a computational model capable of simulating the collaborative operation of multiple devices in a power distribution network. The collaborative control simulation model is a mathematical and computer model that combines power system operating mechanisms and dispatching logic. It can simulate changes in equipment output, line load distribution, and system operating indicators. For example, the impact of wind power output fluctuations on the main transformer voltage level can be observed in the simulation. The results obtained constitute the first simulation information, including voltage curves, power balance, and equipment utilization data.

[0057] When the first simulation information meets the predetermined simulation conditions, that is, when the simulation results meet the pre-set performance and safety standards, a first scheme domain is established based on the first initial scheme. The first scheme domain refers to the set of schemes 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. It can explore more possibilities that are close to the optimal solution. The first scheme domain is added to the simulation list. The simulation list is a task queue that stores all the schemes to be simulated and is used for subsequent batch simulation calculations.

[0058] The simulation list is analyzed using a collaborative control simulation model. Each scheme in the list is input into the model for calculation, and the simulation records are statistically analyzed and compared. The simulation records are a complete record of each simulation result, including scheme parameters, operating indicators, and optimization target values.

[0059] Furthermore, this application also includes: obtaining a first neighboring scheme in the first scheme domain; if the second simulation information of the first neighboring scheme meets the predetermined simulation conditions, then adding the first neighboring scheme to the support set; if the second simulation information of the first neighboring scheme does not meet the predetermined simulation conditions, then adding the first neighboring scheme to the opposition set; obtaining a first support rate of the first initial scheme based on 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 multiple optimal neighboring schemes in the optimal scheme domain of the optimal initial scheme to obtain the multiple simulation data groups, and forming the simulation record.

[0060] Specifically, the first neighboring scheme in the first scheme domain is obtained, that is, a scheme is randomly selected from the parameter range set of the first scheme domain. After simulation calculation on the first neighboring scheme, second simulation information is obtained. If the second simulation information meets the predetermined simulation conditions and its running results meet the predetermined standards such as safety and economy, then the first neighboring scheme is added to the support set, which refers to the set of schemes considered to have improvement potential and correct direction. If the second simulation information does not meet the predetermined simulation conditions, then it is added to the opposition set, which refers to the set of schemes that do not meet the target requirements and whose direction needs to be corrected.

[0061] The total number of solutions in the first neighborhood of both the support and opposition sets is counted, and the ratio of the number of solutions in the first neighborhood of the support set to the total number is calculated as the first support rate. The support rate reflects the potential for the first initial solution to extend and optimize in the surrounding search space.

[0062] The first initial solution is ranked based on the first support rate. All initial solutions are ranked from high to low according to their support rates, so as to find the solution with the most stable performance and the largest optimization space in the neighborhood and obtain the optimal initial solution.

[0063] The neighborhood corresponding to the optimal initial scheme is called the optimal scheme domain, and the schemes in the optimal scheme domain are called optimal neighborhood schemes. Multiple optimal neighborhood schemes are simulated sequentially, and each is calculated through a collaborative control simulation model to obtain multiple sets of simulation data. The simulation data sets record different operating costs, voltage deviations, and renewable energy ratios under different neighborhood schemes, and are used to form simulation records for the final evaluation and decision-making of the schemes.

[0064] Furthermore, this application also includes: obtaining a predetermined weight allocation in the fitness evaluation strategy, wherein the predetermined weight allocation refers to the weight allocation of each index in the target optimization index; performing fitness evaluation on the multiple simulated data groups in combination with the predetermined weight allocation to obtain multiple cooperative control fitnesss; taking the optimal neighborhood scheme corresponding to the maximum fitness among the multiple cooperative control fitnesss as the optimal cooperative control scheme; further comprising: 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 for the distribution network through the mixed integer convex programming model, wherein the cooperative control verification scheme is used to verify the optimal cooperative control scheme.

[0065] Specifically, the predetermined weight allocation in the fitness evaluation strategy is obtained by retrieving the pre-set weights of each indicator from the strategy used to evaluate the merits of the solutions. The predetermined weight allocation refers to the proportion of importance of different indicators to the final evaluation result among the target optimization indicators; indicators with higher weights have a greater impact on the final calculation. Fitness assessments are performed on multiple simulated data sets using predetermined weight allocations. By combining these predetermined weights with the simulation results of different schemes, the degree of matching of each scheme to the optimization objective is calculated, serving as the fitness of multiple collaborative control schemes. The fitness of these collaborative control schemes reflects the comprehensive ability of multiple schemes to meet the optimization objective.

[0066] The optimal neighborhood solution corresponding to the maximum fitness among multiple cooperative control fitnesss is taken as the optimal cooperative control solution, that is, the solution with the highest comprehensive evaluation among all candidate solutions is selected as the final recommended solution.

[0067] The introduction of a model transformation mechanism transforms the collaborative control simulation model into a mixed integer convex programming model, converting the model used for dynamic simulation into a mathematical optimization model. This model can handle problems with both integer and continuous variables while maintaining convexity to ensure the finding of the global optimum. For example, the switching state of the device can be treated as an integer variable, while the power output can be treated as a continuous variable.

[0068] The coordinated control verification scheme of the distribution network is obtained by using a mixed integer convex programming model. An operation scheme for verification is recalculated and generated using an optimization model. The feasibility of the optimal coordinated control scheme under actual constraints is checked. If the operation results of the verification scheme are consistent with the simulation results, its feasibility is verified.

[0069] In summary, the distribution network source-grid-storage coordinated control method provided in this application has the following technical effects: by realizing the technical goal of intelligent control of distribution network source-grid-storage through multi-dimensional information fusion and target constraint coordinated optimization, it achieves the technical effects of improving the renewable energy absorption capacity, reducing operating costs, ensuring grid operation safety, and improving the overall system stability.

[0070] Example 2: Based on the same inventive concept as the distribution network source-grid-storage coordinated control method in the foregoing examples, this application also provides a distribution network source-grid-storage coordinated control system. Please refer to the appendix. Figure 2 The system includes: a target constraint setting module 1, used to collect multi-dimensional grid information of the distribution network and set target constraints based on the multi-dimensional grid information; a cooperative control simulation module 2, used to construct target optimization indicators, construct a cooperative control simulation model in conjunction with the target constraints, and perform cooperative control simulation on the distribution network through the cooperative control simulation model to obtain simulation records; an analysis module 3, used to introduce a fitness evaluation strategy to analyze multiple simulation data groups in the simulation records and select the optimal cooperative control scheme; and a source-grid-storage cooperative control module 4, used to perform source-grid-storage cooperative control of the distribution network through the optimal cooperative control scheme.

[0071] Furthermore, the power grid-source-grid-storage coordinated control system is also used to: set power balance constraints based on the power information in the multi-dimensional power grid information; set equipment capacity constraints based on the equipment information in the multi-dimensional power grid information; and set operation safety constraints based on the operation information in the multi-dimensional power grid information; the power balance constraints, the equipment capacity constraints, and the operation safety constraints constitute the target constraint conditions.

[0072] Furthermore, the power grid-source-grid-storage coordinated control system is also used for: extracting the power generation time series from the multi-dimensional power grid information; extracting the power consumption time series from the multi-dimensional power grid information; performing spatiotemporal alignment processing on the power generation time series and the power consumption time series to obtain a power-aligned time series; performing a fast Fourier transform on the power-aligned time series to obtain a power-aligned spectrum, and analyzing to obtain the maximum spectral density of the power-aligned spectrum; and setting the power balance constraint based on the maximum spectral density.

[0073] Furthermore, the power distribution network source-grid-storage coordinated control system is also used to: acquire any device in the power distribution network, wherein the arbitrary device has a physical connection with any line; coordinate the arbitrary output of the arbitrary device with the arbitrary loss of the arbitrary line, and set the capacity constraint of the device.

[0074] Furthermore, the power distribution network source-grid-storage coordinated control system is also used to: read predetermined operating indicators, and randomly extract any one of the predetermined operating indicators as the target operating indicator; obtain the predetermined safety threshold of the target operating indicator, and form the operating safety constraint.

[0075] Furthermore, the power grid-source-grid-storage coordinated control system is also used for: the target optimization indicators include operating cost, voltage deviation, load variance and the proportion of new energy, wherein the operating cost includes electricity purchase and sale cost, power source operation and maintenance cost, wind and solar curtailment penalty cost, energy storage charging and discharging operation cost, dispatch compensation cost and carbon emission cost.

[0076] Furthermore, the power grid-source-grid-storage coordinated control system is also used for: generating an initial coordinated control scheme set based on the TENT chaotic algorithm principle, wherein the initial coordinated control scheme set includes a first initial scheme; simulating the first initial scheme using the coordinated control simulation model to obtain first simulation information; when the first simulation information meets predetermined simulation conditions, establishing a first scheme domain based on the first initial scheme and adding the first scheme domain to the simulation list; and performing simulation analysis on the simulation list using the coordinated control simulation model to obtain the simulation record.

[0077] Furthermore, the power distribution network source-grid-storage coordinated control system is also used to: obtain a first neighboring scheme in the first scheme domain; if the second simulation information of the first neighboring scheme meets the predetermined simulation conditions, add the first neighboring scheme to the support set; if the second simulation information of the first neighboring scheme does not meet the predetermined simulation conditions, add the first neighboring scheme to the opposition set; obtain a first support rate of the first initial scheme based on the support set and the opposition set; sort the first initial scheme based on the first support rate to obtain the optimal initial scheme; and sequentially simulate multiple optimal neighboring schemes in the optimal scheme domain of the optimal initial scheme to obtain the multiple simulation data groups and form the simulation record.

[0078] Furthermore, the power distribution network source-grid-storage coordinated control system is also used for: obtaining a predetermined weight allocation in the fitness evaluation strategy, wherein the predetermined weight allocation refers to the weight allocation of each index in the target optimization index; performing fitness evaluation on the multiple simulated data groups in combination with the predetermined weight allocation to obtain multiple coordinated control fitnesss; taking the optimal neighborhood scheme corresponding to the maximum fitness among the multiple coordinated control fitnesss as the optimal coordinated control scheme; wherein, it further includes: introducing a model transformation mechanism to transform the coordinated control simulation model into a mixed integer convex programming model; obtaining a coordinated control verification scheme for the power distribution network through the mixed integer convex programming model, wherein the coordinated control verification scheme is used to verify the optimal coordinated control scheme.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The distribution network source-grid-storage coordinated control method and specific examples in the aforementioned embodiment one are also applicable to the distribution network source-grid-storage coordinated control system of this embodiment. Through the foregoing detailed description of the distribution network source-grid-storage coordinated control method, those skilled in the art can clearly understand the distribution network source-grid-storage coordinated control system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0081] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for coordinated control of power generation, grid, and energy storage in a distribution network, characterized in that, include: Collect multi-dimensional power grid information of the distribution network and set target constraints based on the multi-dimensional power grid information; Develop target optimization indicators, construct a collaborative control simulation model in conjunction with the target constraints, and perform collaborative control simulation on the distribution network through the collaborative control simulation model to obtain simulation records; An fitness evaluation strategy is introduced to analyze multiple sets of simulation data in the simulation record and select the optimal cooperative control scheme. The source-grid-storage coordinated control of the distribution network is carried out through the optimal coordinated control scheme.

2. The power grid-source-storage coordinated control method according to claim 1, characterized in that, Collect multi-dimensional power grid information of the distribution network, and set target constraints based on the multi-dimensional power grid information, including: Power balance constraints are set based on the power information in the multidimensional power grid information; Set equipment capacity constraints based on the equipment information in the multidimensional power grid information; Set operational safety constraints based on the operational information in the multidimensional power grid information; The power balance constraint, the equipment capacity constraint, and the operational safety constraint constitute the target constraint conditions.

3. The power grid-source-storage coordinated control method according to claim 2, characterized in that, Based on the power information in the multidimensional power grid information, power balance constraints are set, including: Extract the power generation time sequence from the multidimensional power grid information; Extract the electricity consumption time sequence from the multidimensional power grid information; The power generation time series and the power consumption time series are spatiotemporally aligned to obtain the power-aligned time series. The charge alignment time series is obtained by fast Fourier transform to obtain the charge alignment spectrum, and the maximum spectral density of the charge alignment spectrum is obtained by analysis. The power balance constraint is set according to the maximum spectral density.

4. The power grid-source-storage coordinated control method according to claim 2, characterized in that, Setting equipment capacity constraints based on equipment information in the multidimensional power grid information includes: Obtain any device in the power distribution network, wherein the arbitrary device has a physical connection with any line; By coordinating the arbitrary output of any device with the arbitrary loss of any line, the capacity constraint of the device is set.

5. The power grid-source-storage coordinated control method according to claim 2, characterized in that, Based on the operational information in the multidimensional power grid information, operational safety constraints are set, including: Read the predetermined operating indicators and randomly extract any one of the predetermined operating indicators, and record it as the target operating indicator; Obtain the predetermined safety threshold of the target operating indicator and form the operating safety constraint.

6. The power grid-source-storage coordinated control method according to claim 1, characterized in that, The target optimization indicators include operating costs, voltage deviation, load variance, and the proportion of new energy sources. The operating costs include electricity purchase and sale costs, power supply operation and maintenance costs, wind and solar curtailment penalty costs, energy storage charging and discharging operation costs, dispatch compensation costs, and carbon emission costs.

7. The power grid-source-storage coordinated control method according to claim 6, characterized in that, A target optimization index is established, and a collaborative control simulation model is constructed in conjunction with the target constraints. The collaborative control simulation model is then used to simulate the collaborative control of the distribution network, yielding simulation records, including: An initial cooperative control scheme set is generated based on the principle of the TENT chaos algorithm, wherein the initial cooperative control scheme set includes a first initial scheme; The first initial scheme is simulated using the cooperative control simulation model to obtain first simulation information; When the first simulation information meets the predetermined simulation conditions, a first scheme domain is established based on the first initial scheme, and the first scheme domain is added to the simulation list; The simulation list is analyzed using the collaborative control simulation model to obtain the simulation record.

8. The power grid-source-storage coordinated control method according to claim 7, characterized in that, The simulation list is analyzed using the aforementioned collaborative control simulation model to obtain the simulation records, including: Obtain the first neighboring scheme in the first scheme domain; If the second simulation information of the first neighborhood scheme meets the predetermined simulation conditions, the first neighborhood scheme is added to the support set; if the second simulation information of the first neighborhood scheme does not meet the predetermined simulation conditions, the first neighborhood scheme is added to the opposition set. The first support rate of the first initial scheme is obtained based on the support set and the opposition set. The first initial scheme is sorted based on the first support rate to obtain the optimal initial scheme; Multiple optimal neighborhood schemes in the optimal scheme domain of the optimal initial scheme are simulated sequentially to obtain multiple simulation data sets, which are then used to form the simulation record.

9. The power grid-source-storage coordinated control method for distribution networks according to claim 8, characterized in that, An fitness evaluation strategy is introduced to analyze multiple sets of simulation data in the simulation records and to select the optimal cooperative control scheme, including: Obtain the predetermined weight allocation in the fitness evaluation strategy, wherein the predetermined weight allocation refers to the weight allocation of each indicator in the target optimization index; The fitness of the multiple simulated data sets is evaluated by combining the predetermined weight allocation to obtain multiple cooperative control fitnesss; The optimal neighborhood scheme corresponding to the maximum fitness among the multiple cooperative control fitnesss is taken as the optimal cooperative control scheme. This also includes: A model transformation mechanism is introduced to transform the collaborative control simulation model into a hybrid integer convex programming model; The cooperative control verification scheme of the power distribution network is obtained through the hybrid integer convex programming model, wherein the cooperative control verification scheme is used to verify the optimal cooperative control scheme.

10. A power distribution network source-grid-storage coordinated control system, characterized in that, The steps for implementing the distribution network source-grid-storage coordinated control method according to any one of claims 1 to 9 include: The target constraint setting module is used to collect multi-dimensional power grid information of the distribution network and set target constraints based on the multi-dimensional power grid information. The collaborative control simulation module is used to construct target optimization indicators, coordinate the target constraints to build a collaborative control simulation model, and perform collaborative control simulation on the distribution network through the collaborative control simulation model to obtain simulation records. The analysis module is used to introduce an fitness evaluation strategy to analyze multiple sets of simulation data in the simulation record and select the optimal cooperative control scheme. The source-grid-storage coordinated control module is used to perform source-grid-storage coordinated control of the distribution network through the optimal coordinated control scheme.

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