Source-network-load-storage collaborative planning method, system, device and medium

Abstract

The invention relates to the technical field of power system source-grid-load-storage collaborative planning, and discloses a source-grid-load-storage collaborative planning method, system and device and a medium, and the method comprises the steps: obtaining the structure, operation and economic parameters of a power system; dividing extreme scenes and establishing a frequency response model; setting a frequency security constraint based on the extreme scene; a three-layer optimization planning model is constructed, the three-layer optimization planning model comprises an objective function and constraint conditions, and the third layer comprises frequency security constraints; the model is solved to determine a planning scheme. According to the method, the minimization change point detection technology is utilized, artificial truncation of the wind-light load output features in sample selection is avoided, and the extreme scene is accurately extracted. The constructed frequency response model comprises nine frequency modulation resources and comprehensively reflects the frequency modulation capability of the novel power system. According to the provided three-layer iterative planning method, the construction investment is properly increased, so that the frequency safety of a power supply, a power transmission line, energy storage and a demand side response scheme in an extreme scene is ensured, and the frequency instability risk and the economic loss are avoided.

Description

Technical Field

[0001] The present application relates to the technical field of power system source-grid-load-storage collaborative planning, and in particular to a source-grid-load-storage collaborative planning method, system, equipment and medium. Background Art

[0002] Traditional power system planning methods use a post-verification approach to verify the frequency security of the resulting plan. However, the planning model does not include frequency security constraints, making it impossible to theoretically guarantee that the plan meets frequency security requirements. When a plan fails to meet frequency security requirements, the question of how to rationally allocate frequency regulation resources to enhance the system's frequency regulation capabilities becomes a pressing issue.

[0003] While optimization planning methods based on typical scenarios can reduce computational burdens, they also dilute the impact of more severe extreme scenarios. Frequency security issues often depend not on typical scenarios that represent the system's operating state most of the time, but on a small number of outlier extreme scenarios.

[0004] Traditional outlier detection methods for extreme scenarios typically rely on pre-defined time periods (such as days, weeks, or months) for detection and calculation, determining whether anomalies exist within a specific time period (e.g., a day, week, or month). This fixed time period division may not match the actual anomaly occurrence period, resulting in the anomaly signal being averaged or dispersed, making it impossible to effectively detect. Furthermore, identifying extreme scenarios based on subjectively set thresholds or parameters can lead to inaccurate or inconsistent results when used with different data sets.

[0005] In summary, existing source-grid-load-storage solutions focus on typical scenarios and ignore extreme scenarios. The selected scenarios are divided into time scales according to natural cycles such as "days". When applied to problems with weak periodicity, the effect is average. When considering frequency security issues, the power system frequency regulation resources, including load demand-side response and ultra-high voltage direct current, have not yet been systematically and comprehensively analyzed and modeled. Summary of the Invention

[0006] In view of the above existing problems, this application is proposed.

[0007] Therefore, the present application provides a method, system, equipment and medium for collaborative planning of source, grid, load and storage, which can solve the complexity and uncertainty problems faced in the collaborative planning of source, grid, load and storage in energy systems.

[0008] To solve the above technical problems, this application provides the following technical solutions:

[0009] In a first aspect, the present application provides a method for collaborative planning of source, grid, load and storage, including:

[0010] Acquiring a first structural parameter, a second operating parameter, and a third economic parameter of a target power system;

[0011] Dividing extreme scenarios according to the first structural parameter and the second operating parameter;

[0012] establishing a first frequency response model according to the first structural parameter, the second operating parameter, and the third economic parameter;

[0013] Establishing a frequency safety constraint based on the first frequency response model under the extreme scenario;

[0014] Establishing a three-layer optimization planning model, each of the three-layer optimization planning models includes an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes the frequency safety constraint;

[0015] The three-layer optimization planning model is solved and the planning scheme is determined based on the solution results.

[0016] As a preferred solution of the source-grid-load-storage collaborative planning method described in this application, the division of extreme scenarios includes:

[0017] Acquire a time series of three-dimensional wind and solar load output data according to the first structural parameter and the second operating parameter;

[0018] Establish a cost function based on the target change point;

[0019] The cost function is used to solve the cost of each sub-time period divided according to the target change point;

[0020] The sub-time period in which the cost is higher than the preset value is recorded as an abnormal time period, and an extreme scenario is determined according to the abnormal time period.

[0021] As a preferred solution of the source-grid-load-storage collaborative planning method described in this application, wherein: the establishing of frequency security constraints based on the first frequency response model under the extreme scenario includes:

[0022] The frequency safety constraints include a frequency drop minimum value constraint and a frequency drop rate constraint;

[0023] The frequency drop rate constraint is that when there is a disturbance, the target power system frequency change rate does not exceed the maximum allowable change rate;

[0024] The lowest value of the frequency drop does not exceed the maximum allowed frequency.

[0025] As a preferred solution of the source-grid-load-storage collaborative planning method described in this application, wherein: the establishment of the three-layer optimization planning model includes a first-layer optimization planning model;

[0026] The first-tier optimization planning model takes the construction costs of thermal power, wind power, photovoltaic power, energy storage, transmission line construction costs, load demand-side response setting costs, and the operation and maintenance costs of all types of power sources in typical scenarios and extreme scenarios as the minimum sum as the objective function;

[0027] The constraints of the first-level optimization planning model include construction capacity constraints and timing constraints.

[0028] As a preferred solution of the source-grid-load-storage collaborative planning method described in this application, wherein: the establishment of the three-layer optimization planning model also includes a second-layer optimization planning model;

[0029] The second-level optimization planning model takes the minimization of operation and maintenance costs under typical scenarios as the objective function;

[0030] The typical scenario does not involve energy abandonment or load shedding;

[0031] The constraints of the second-level optimization planning model include power balance constraints, annual power balance constraints, total reserve constraints, spinning reserve constraints, transmission line transmission capacity constraints, unit start-up and shutdown time constraints, unit output limits, unit climbing and descending constraints, and energy storage charging and discharging constraints.

[0032] As a preferred solution of the source-grid-load-storage collaborative planning method described in this application, wherein: the establishment of the three-layer optimization planning model also includes a third-layer optimization planning model;

[0033] The third-level optimization planning model takes the minimization of operation and maintenance costs under extreme scenarios as the objective function;

[0034] The extreme scenarios described above may lead to energy curtailment, load shedding, and demand-side response actions;

[0035] Compared with the constraints of the second-layer optimization planning model, the constraints of the third-layer optimization planning model have an additional frequency safety constraint.

[0036] As a preferred solution of the source-grid-load-storage collaborative planning method described in this application, the constraints of the first-layer optimization planning model also include:

[0037] Constrained by the cut function of the second-level typical scenario running sub-problem feedback;

[0038] Constrained by the cut function of the third-level typical scenario running sub-problem feedback.

[0039] The benefit of this preferred solution is that it can more accurately consider the system's operating characteristics in different scenarios, thereby improving the overall efficiency and reliability of source-grid-load-storage coordinated planning. Especially when faced with complex and changing power system demands, the introduction of cut function constraints can more effectively balance energy supply and demand, reduce energy waste, and ensure safe and stable system operation.

[0040] In a second aspect, the present application provides a source-grid-load-storage collaborative planning system, comprising:

[0041] a data acquisition module, configured to acquire a first structural parameter, a second operating parameter, and a third economic parameter of a target power system;

[0042] a division module, configured to divide extreme scenarios according to the first structural parameter and the second operating parameter;

[0043] A first model building module, configured to build a first frequency response model according to the first structural parameter, the second operating parameter, and the third economic parameter;

[0044] A constraint establishing module, configured to establish a frequency safety constraint on the first frequency response model under the extreme scenario;

[0045] A second model building module is used to establish a three-layer optimization planning model, wherein the three-layer optimization planning models each include an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes the frequency safety constraint;

[0046] The solution module is used to solve the three-layer optimization planning model and determine the planning scheme based on the solution results.

[0047] In a third aspect, the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0048] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when the computer program is executed by a processor.

[0049] Compared with the prior art, the beneficial effects of the present application are as follows: the present application proposes a source-grid-load-storage collaborative planning method, obtains the first structural parameter, second operating parameter and third economic parameter of the target power system; divides the extreme scenarios according to the first structural parameter and the second operating parameter; establishes a first frequency response model according to the first structural parameter, the second operating parameter and the third economic parameter; establishes frequency security constraints based on the first frequency response model under extreme scenarios; establishes a three-layer optimization planning model, each of which includes an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes frequency security constraints; solves the three-layer optimization planning model, and determines the planning scheme based on the solution results. The present application adopts cost minimization change point detection and adaptively intercepts abnormal time periods, effectively avoiding the artificial truncation of wind, solar and load output characteristics during the sample selection process, and can more accurately extract wind, solar and load extreme scenarios. The multi-machine system frequency response model constructed by the present application covers 9 frequency regulation resources, including thermal power, hydropower, pumped storage, nuclear power, wind power, photovoltaic, energy storage, load demand side response, and ultra-high voltage direct current, and comprehensively reflects the frequency regulation capabilities of the new power system's multiple frequency regulation resources. The three-layer iterative source-grid-load-storage coordinated planning method proposed in this application can ensure the frequency safety of the power source construction, transmission line construction, energy storage construction, and demand-side response setting schemes in extreme scenarios by increasing a small amount of construction investment costs. The frequency change rate and the minimum frequency drop value are both within the required limits, avoiding the possible risk of frequency instability in the future and the potential economic losses it brings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 A flow chart of a method for collaborative planning of source, grid, load and storage provided in one embodiment of the present application.

[0052] Figure 2 Schematic diagram of the selection results of extreme scenarios in four seasons for an adaptive time period of a source-grid-load-storage collaborative planning method provided in one embodiment of the present application, (a) is spring, (b) is summer, (c) is autumn, and (d) is winter.

[0053] Figure 3 A schematic diagram of a per-unit value curve of wind-solar-load time-series output for an extreme autumn scenario in a source-grid-load-storage collaborative planning method provided in one embodiment of the present application.

[0054] Figure 4A schematic diagram of an original multi-machine system frequency response model containing nine types of multivariate frequency modulation resources for a source-grid-load-storage collaborative planning method provided in one embodiment of the present application.

[0055] Figure 5 A schematic diagram of an equivalent multi-machine system frequency response model for a source-grid-load-storage collaborative planning method provided in one embodiment of the present application.

[0056] Figure 6 This is a diagram of the internal structure of an electronic device for a source-grid-load-storage collaborative planning method provided in one embodiment of the present application. DETAILED DESCRIPTION

[0057] To make the above-mentioned purposes, features, and advantages of this application more clearly understood, the following detailed description of the specific embodiments of this application is given in conjunction with the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of this application.

[0058] Example 1, with reference to Figures 1-6 , which is the first embodiment of the present application, provides a source-grid-load-storage collaborative planning method, including:

[0059] There are some problems in existing related technologies, such as poor information exchange between the various elements of source, grid, load and storage, resulting in low planning efficiency; or lack of intelligent decision-making support in the planning process, resulting in suboptimal planning schemes.

[0060] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to explain in detail how to implement the source-grid-load-storage collaborative planning method.

[0061] Figure 1 A method flow chart of a source-grid-load-storage collaborative planning method is shown, including:

[0062] S101, obtaining a first structural parameter, a second operating parameter, and a third economic parameter of a target power system;

[0063] In the embodiment of the present application, the construction capacity of thermal power, wind power, photovoltaic power, and energy storage, the construction location of the transmission line, and the candidate set of load demand side response amount are obtained as the first structural parameters.

[0064] In an embodiment of the present application, technical parameters such as the unit climbing rate, maximum and minimum output, frequency regulation standby coefficient, frequency safety limit, wind and solar load time series output data, and frequency regulation parameters such as thermal power, hydropower, pumped storage, nuclear power, wind power, photovoltaics, energy storage, load demand side response, inertia or virtual inertia of ultra-high voltage direct current, droop coefficient, and frequency regulation dead zone are obtained as the second operating parameters.

[0065] In the embodiment of the present application, economic parameters such as unit construction cost, operation and maintenance cost, load shedding penalty cost, and energy abandonment cost are obtained as the third economic parameter.

[0066] In an optional embodiment, the method for obtaining the aforementioned structural and operating parameters can use built-in sensors to collect real-time operational data from the power grid, power sources, loads, and energy storage devices, including but not limited to voltage, current, power, electricity, and temperature. This data is then processed and analyzed using a data analysis algorithm to obtain the required planning parameters. This method ensures data accuracy and real-time performance, improving the efficiency and accuracy of the source-grid-load-storage collaborative planning system.

[0067] In an optional embodiment, economic parameters can be obtained through market surveys and statistics. For example, the unit construction cost can be estimated by referring to the construction cost of similar projects; the operation and maintenance cost can be predicted based on factors such as the type, scale and service life of the equipment; the load shedding penalty cost and energy abandonment cost can be calculated based on the price mechanism and policy regulations of the electricity market.

[0068] It should be noted that this method can comprehensively consider various economic factors, making the planning scheme more in line with actual conditions and economic efficiency requirements.

[0069] It should be noted that obtaining the first structural parameters, second operating parameters, and third economic parameters of the target power system can provide comprehensive and accurate data support for subsequent system planning. The first structural parameters reflect the physical composition of the power system and are the basis for formulating planning schemes; the second operating parameters reflect the actual operating status of the power system and help evaluate the stability and reliability of the system; the third economic parameters are related to the economic feasibility of the planning scheme, ensuring the economic rationality of the planning results in actual application. By comprehensively considering these three types of parameters, the source-grid-load-storage collaborative planning system can formulate more scientific, reasonable, and economic planning schemes to meet the application needs of different scenarios.

[0070] S102, dividing extreme scenarios according to the first structural parameter and the second operating parameter;

[0071] In an optional embodiment, the first structural parameter and the second operating parameter include data during normal operation and data during abnormal operation. In order to achieve source-grid-load-storage coordinated planning of the target power system, this application only needs to consider the data during abnormal operation, that is, it is necessary to divide the scenarios into abnormal time periods.

[0072] It should be noted that extreme scenarios are relatively rare in power system operations but have the potential to have significant impacts, such as sudden load surges caused by extreme weather conditions or widespread power outages due to equipment failures. By classifying and considering these extreme scenarios, the source-grid-load-storage collaborative planning system can more comprehensively assess the stability and reliability of the power system, thereby developing planning solutions that are more adaptable to extreme conditions. This emphasis on extreme scenarios helps enhance the resilience of the power system, ensuring that basic power supply capabilities and system stability can be maintained even under extreme conditions.

[0073] In the embodiment of the present application, dividing extreme scenarios includes:

[0074] Obtaining a time series of three-dimensional wind and solar load output data according to the first structural parameter and the second operating parameter;

[0075] Establish a cost function based on the target change point;

[0076] The cost function is used to solve the cost of each sub-time period divided according to the target change point;

[0077] The sub-time period with a cost higher than a preset value is recorded as an abnormal time period, and the extreme scenario is determined based on the abnormal time period.

[0078] Specifically, based on the idea of dynamic programming and cost function minimization, given a set of time series {y1,y2,...,y n},y i is the three-dimensional output data of wind and solar loads, and the position of a set of change points {τ1,τ2,...,τ n}, so that according to the change point τ i The total cost of the divided sub-intervals is the smallest, as shown in formula (1), which means that the data in each segment have similar characteristics.

[0079]

[0080] Where: is the cost function of the i-th subinterval, which can be taken as an indicator describing data fitting, such as the residual sum of squares; β is the penalty term coefficient, which is used to balance the model complexity (number of change points) and goodness of fit; k is the number of change points.

[0081] It should be noted that the core of outlier time segment detection lies in identifying subintervals where the cost C is significantly higher than a preset value, indicating that the interval may be abnormal. Change points are completely dependent on the characteristics of the time series. By identifying these change points, the time series is divided into multiple stable subintervals, thereby discovering anomalous time segments, which are considered extreme scenarios of adaptive time segments.

[0082] For example, based on the historical data of wind and solar load in a certain actual area, the time granularity is 1 hour, and reconstruction is performed for the future planning level year. Due to the seasonal fluctuation of wind and solar load data, the wind and solar load data in each quarter are extracted and modeled separately, β is set to 5-10, and k is obtained through optimization. When the time span between change points is within 120 hours, the time span between the change points is considered to be an abnormal time period. The results are shown in the attached figure. Figure 2 Using this method, 7 extreme scenes in spring, 6 extreme scenes in summer, 3 extreme scenes in autumn, and 1 extreme scene in winter were extracted. Taking an extreme scene in autumn as an example, the attached Figure 3 The corresponding wind, solar and load output is displayed. It can be seen that the total duration of this extreme scenario is 107 hours, including about 60 hours of no light and large fluctuations in wind power output. The fluctuation period of wind power output exceeds the 24-hour period selected for traditional daily samples.

[0083] It should be noted that dividing extreme scenarios according to the first structural parameter and the second operating parameter can more accurately simulate and predict the operation of the power system under different extreme conditions. Through the detailed division of extreme scenarios, the system can identify specific time periods that may have a significant impact on the stability of the power grid, allowing planners to take targeted measures to optimize resource allocation and enhance the resilience of the power grid. In addition, this extreme scenario-based planning method can also help improve the collaborative efficiency of each link of the source, grid, load and storage, ensuring that in the event of extreme weather or sudden load changes, the system can respond quickly, maintain the balance between supply and demand, and ensure the safety and reliability of power supply.

[0084] S103, establishing a first frequency response model according to the first structural parameter, the second operating parameter, and the third economic parameter;

[0085] It should be noted that in order to achieve the source-grid-load-storage coordinated planning of this application, it is necessary to consider the safety and stability of the power system after coordinated planning. Therefore, it is necessary to select several different parameters to verify the safety and stability of the power system after coordinated planning. These parameters can be key indicators such as frequency, voltage, and power flow distribution. In order to achieve the final coordinated planning, this application selects frequency as the verification parameter.

[0086] In an optional embodiment, frequency can be selected as the verification parameter by establishing a frequency response model or by using equipment to monitor the frequency changes of the power system in real time. However, real-time monitoring requires excessive resources. Therefore, this application chooses to establish a frequency response model to simulate the frequency response characteristics of the power system under different scenarios, and then analyze the stability of the power system after the coordinated planning of source, grid, load and storage.

[0087] It should be noted that the first frequency response model can comprehensively consider the impact of each link of source, network, load and storage, and evaluate the frequency recovery of the power system under extreme weather or sudden load changes, and whether additional control measures are needed to maintain frequency stability.

[0088] In an optional embodiment, different frequency response models may be established according to different frequency regulation resources in the target power system.

[0089] In the embodiment of the present application, the basis for studying frequency security issues is the frequency response model of various frequency regulation resources. The present invention takes into account 9 kinds of multivariate frequency regulation resources. Thermal power units adopt a low-order frequency response model; nuclear power units are similar, but they differ from thermal power units in the settings of parameters such as the regulation coefficient and dead zone; hydropower and pumped storage use an SFR model with water hammer effect; wind power can provide virtual inertial response through multiple types of frequency regulation controllers or virtual synchronization technology, and use load shedding to provide primary frequency regulation response; photovoltaic power does not provide inertial response, and uses load shedding to participate in primary frequency regulation response; energy storage uses virtual synchronization technology to participate in primary frequency regulation; demand-side response resources are integrated by load aggregators, and primary frequency regulation is achieved by droop control; traditional loads themselves participate in frequency response through damping; UHVDC can participate in primary frequency regulation through emergency support control. The original multi-machine system obtained is shown in the attached figure. Figure 4 shown.

[0090] The power system considered in this application is a multi-machine system. There are a large number of primary frequency regulation links in the multi-machine system, and the frequency response parameters of various units are different. The frequency value of this complex multi-machine system is generally obtained through simulation methods, but it is only for specific system states and cannot be directly applied to power system optimization problems with variable operating states. In order to simplify the multi-machine system and ensure accuracy, the original multi-machine model is further converted into an equivalent multi-machine system frequency response model based on the capacity ratio method, as shown in the attached figure. Figure 5 As shown in the figure, F add 、T add 、R add 、H add They are all system equivalent parameters, expressed as formulas (2)-(5). The specific derivation process is not given here.

[0091]

[0092] Where: R gis the regulation coefficient of thermal power unit; R h is the permanent decline rate of the hydropower unit; R c is the comprehensive decline rate of the hydropower unit; K g is the gain coefficient of the thermal power unit; D w 、D s 、D es 、D dc 、D d are the virtual droop coefficients of wind power, photovoltaic power, energy storage, UHVDC, and demand-side response resource loads respectively; T g 、T h 、T w 、T s 、T es 、T dc 、T d They are the primary frequency regulation time constants of thermal / nuclear power, hydro / pumped storage, wind power, photovoltaic, energy storage, UHVDC, and demand-side response resource loads; the system inertia is jointly provided by thermal / nuclear power, hydro / pumped storage, and wind power, H i is the inertia constant of thermal / nuclear power and water / pumped storage, H j is the virtual inertia constant of wind power; the frequency regulation capability of the entire system is related to the start and stop status of conventional units and whether other units participate in frequency regulation decision-making, v g 、v h are the start and stop states of thermal / nuclear power and water / pumped storage units, respectively, u w 、u s 、u es are the decision variables for whether wind power, photovoltaic power and energy storage participate in primary frequency regulation; P g,max 、P h,max They are the installed capacity, i.e. the maximum output, of thermal / nuclear power, and hydro / pumped storage units; are the predicted output values of wind power and photovoltaic power respectively; p es,max 、p dc,max 、p d,N They are energy storage, UHVDC rated capacity, and optimized demand-side response capacity; S sys is the system baseline capacity, generally taken as the total system startup capacity; d b Represents the load of node b.

[0093] It should be noted that establishing a first frequency response model based on the first structural parameter, the second operating parameter, and the third economic parameter can improve the accuracy and efficiency of the source-grid-load-storage coordinated planning system. By establishing the first frequency response model, the system can more accurately simulate and analyze the impact of different parameters on grid frequency, thereby optimizing resource allocation, reducing energy waste, and ensuring stable grid operation.

[0094] S104, establishing a frequency safety constraint based on the first frequency response model under an extreme scenario;

[0095] It should be noted that after establishing the first frequency response model, it is necessary to obtain the frequency variation of the target power system according to the first frequency response model of the specific target power system, and finally establish the frequency constraint.

[0096] In an optional embodiment, the frequency security constraint established based on the first frequency response model can include multiple sub-constraints, such as a minimum frequency constraint, a maximum frequency deviation constraint, and a frequency recovery rate constraint, to ensure that the power system frequency remains within a safe range in extreme scenarios. These sub-constraints can be flexibly set and adjusted based on the specific characteristics and operational requirements of the power system, thereby improving the adaptability and practicality of the source-grid-load-storage collaborative planning system.

[0097] In an embodiment of the present application, establishing a frequency safety constraint based on a first frequency response model in an extreme scenario includes:

[0098] Frequency safety constraints include frequency drop minimum value constraint and frequency drop rate constraint;

[0099] The frequency drop rate constraint is that when there is a disturbance, the target power system frequency change rate does not exceed the maximum allowable change rate;

[0100] The lowest value of the frequency drop does not exceed the maximum value of the maximum allowable frequency.

[0101] Specifically, two frequency safety constraints are constructed: the minimum frequency drop value and the frequency drop rate of the equivalent multi-machine system under extreme scenarios. L Under this condition, the system frequency response process can be expressed by the first-order swing equation, as shown in formula (6).

[0102]

[0103] 1. Frequency change rate constraint

[0104] The system frequency change rate after the disturbance should not exceed the maximum allowable change rate As shown in formula (7).

[0105]

[0106] 2. Frequency drop minimum value constraint

[0107] Based on the attached Figure 5 Write the frequency domain transfer function and convert it into a time domain expression through the inverse Laplace transform. The lowest value of the frequency drop can be obtained by taking its derivative, as shown in Equation (8). The specific derivation process is not given here. The corresponding parameters in Equation (8) are shown in Equation (9).

[0108]

[0109] The minimum value of the frequency drop should not exceed the maximum allowable frequency Δf max , that is, Δf nadir ≤Δf max , and transform it to get formula (10).

[0110]

[0111] Note that G(H add ,R add ,F add ,T add ) is a highly nonlinear function whose magnitude is affected by the equivalent frequency response parameters. The frequency safety constraint can be linearized by the piecewise linearization (PWL) method based on data mining. The PWL method is relatively common, and the specific derivation process is not given here.

[0112] It should be noted that establishing frequency security constraints based on the first frequency response model for extreme scenarios can ensure that frequency stability in extreme scenarios is fully considered in the coordinated planning of sources, grids, loads, and storage, thereby improving the reliability and security of the power system. By accurately modeling and analyzing the first frequency response model, the frequency response characteristics of the power system under extreme conditions can be more accurately predicted and evaluated. Consequently, appropriate measures can be taken during the planning phase to optimize resource allocation, prevent frequency drops beyond safe ranges, and ensure the stable operation of the power system.

[0113] S105, establishing a three-layer optimization planning model, each of which includes an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes a frequency safety constraint;

[0114] It should be noted that the planning optimization model is designed to achieve the optimal allocation of power generation, grid, load, and storage resources to improve the overall efficiency and stability of the power system. This model comprehensively considers the characteristics and constraints of different resources and, through rational planning, enables these resources to work together to meet the needs of the power system.

[0115] In an optional embodiment, the planning optimization model can be established using advanced algorithms and techniques. For example, optimization algorithms such as genetic algorithms and particle swarm optimization can be used, combined with the actual conditions of the power system, to solve the planning optimization model and obtain the optimal planning solution. Specifically, when using a genetic algorithm, the optimal solution can be searched in the solution space by simulating natural selection and genetic mechanisms. The algorithm begins with a set of initial solutions and, through operations such as selection, crossover, and mutation, gradually iterates and optimizes until a solution that satisfies the constraints and has the optimal objective function value is found.

[0116] In an alternative embodiment, a particle swarm algorithm simulates the foraging behavior of a flock of birds, treating each possible solution as a bird in the search space and continuously adjusting the particle's speed and position to gradually approach the optimal solution. The application of these algorithms can significantly improve the efficiency and accuracy of planning optimization models, providing a more scientific and reasonable solution for coordinated planning of power generation, grid, load, and storage.

[0117] In an optional embodiment, the planning optimization model can be further enhanced in accuracy and efficiency by incorporating artificial intelligence and big data technologies. By mining and analyzing large amounts of power system data, potential patterns and trends can be discovered, providing more accurate and comprehensive information support for the planning optimization model. For example, a linear or nonlinear regression model can be established, leveraging historical and real-time data to predict power load trends, providing more accurate load forecasting results for the planning optimization model.

[0118] However, in the present embodiment, a three-layer optimization planning model, including an objective function and constraints, was established instead of the aforementioned method. This is because it better accommodates the complexity and diversity of source-grid-load-storage collaborative planning. By defining the objective function and constraints, this model accurately describes the various factors and objectives in the planning process, thereby ensuring the accuracy and feasibility of the planning results. Furthermore, this model offers high flexibility and scalability, allowing it to be adjusted and optimized based on different planning requirements and scenarios to meet the diverse demands of practical applications.

[0119] In the embodiment of the present application, establishing a three-layer optimization planning model includes a first-layer optimization planning model;

[0120] The first-level optimization planning model takes the construction costs of thermal power, wind power, photovoltaic power, energy storage, transmission line construction costs, load demand-side response setting costs, and the operation and maintenance costs of all types of power sources in typical scenarios and extreme scenarios as the minimum sum as the objective function;

[0121] The constraints of the first-level optimization planning model include construction capacity constraints and timing constraints.

[0122] In the embodiment of the present application, the constraints of the first-level optimization planning model also include:

[0123] Constrained by the cut function of the second-level typical scenario running sub-problem feedback;

[0124] Constrained by the cut function of the third-level typical scenario running sub-problem feedback.

[0125] In the embodiment of the present application, establishing a three-layer optimization planning model also includes a second-layer optimization planning model;

[0126] The second-level optimization planning model takes the minimum operation and maintenance cost under typical scenarios as the objective function;

[0127] In typical scenarios, no energy abandonment or load shedding is required;

[0128] The constraints of the second-level optimization planning model include power balance constraints, annual power balance constraints, total reserve constraints, rotating reserve constraints, transmission line transmission capacity constraints, unit start-up and shutdown time constraints, unit output limits, unit climbing and descending constraints, and energy storage charging and discharging constraints.

[0129] In the embodiment of the present application, establishing a three-layer optimization planning model further includes a third-layer optimization planning model;

[0130] The third-level optimization planning model takes the minimization of operation and maintenance costs under extreme scenarios as its objective function;

[0131] The existence of extreme scenarios can lead to energy curtailment, load shedding, and demand-side response actions;

[0132] Compared with the constraints of the second-level optimization planning model, the constraints of the third-level optimization planning model have an additional frequency safety constraint.

[0133] Specifically, the first-level optimization planning model is constructed with the goal of minimizing the sum of the construction costs of thermal power, wind power, photovoltaic power, and energy storage, the construction costs of transmission lines, the cost of setting the load demand side response quantity, the operation and maintenance costs of all types of power sources in typical scenarios (taken as 0 in the first iteration), and the operation and maintenance costs in extreme scenarios (taken as 0 in the first iteration), as shown in Equation (11).

[0134] minF1=C1+C2+C3+C4+F2+F3 (11)

[0135] Among them, the construction costs of thermal power, wind power and photovoltaic power are:

[0136]

[0137] The construction cost of energy storage is:

[0138]

[0139] The construction cost of the transmission line is:

[0140]

[0141] The setup cost of demand-side response is:

[0142]

[0143] Where: x g 、x w 、x s 、x es 、x l 、x d are the decision variables for construction of thermal power, wind power, photovoltaic power, energy storage, transmission line, and demand-side response configuration, 1 for construction and 0 for non-construction; c g 、c w 、c s 、c es 、c l are unit capacity / line investment cost; c d is the fixed annual compensation price per unit of demand-side response capacity; γ g , γ w , γ s , γ es , γ l are the discount rates for thermal power, wind power, photovoltaic power, energy storage, and transmission lines respectively; Y g 、Y w 、Y s 、Y es 、Y l These are the lifespans of thermal power, wind power, photovoltaic power, energy storage, and transmission lines.

[0144] The constraints of the first-level optimization planning model include construction capacity, timing constraints, and cut function constraints fed back by the second-level typical scenario operation sub-problem, such as formula (17), and cut function constraints fed back by the third-level extreme scenario operation sub-problem, such as formula (20).

[0145] With the goal of minimizing the operation and maintenance costs (including startup costs and fuel costs) under typical scenarios, the optimization planning model of the second-level typical scenario operation sub-problem is constructed, as shown in Equation (16).

[0146]

[0147] Where: ρ is the probability of a typical time series scene, which can be obtained by traditional clustering methods; y g,ρ is the startup action indicator variable of thermal power unit g in scenario ρ, is the corresponding startup cost; y h,ρ,dis、y h,ρ,ch are the discharge and charging action indicator variables of the pumped storage unit h in scenario ρ, is the corresponding action cost; is the unit operation and maintenance cost of thermal / nuclear power, hydropower / pumped storage, energy storage, wind power, and photovoltaic power, p i,t is the output of corresponding thermal / nuclear power, hydropower / pumped storage, energy storage, wind power, and photovoltaic power at time t; is the unit power purchase cost of UHVDC, p dc,t is the corresponding UHVDC transmission power at time t.

[0148] The second-level optimization planning model implicitly assumes that typical scenarios do not involve energy curtailment or load shedding. Constraints include power balance (node power balance), annual power balance, total reserve, spinning reserve, transmission line capacity, unit start-up and shutdown time, unit output limits, unit ramp-up and downhill constraints, and energy storage charging and discharging constraints. Under the premise of the first-level investment decision, the second-level typical scenario operation subproblem can be considered as a function O1(x) of the investment decision variable x. Introducing the slack variable α1 forms a cut function that is fed back to the first level, as shown in Equation (17).

[0149] O1(x)=minF2≤α1 (17)

[0150] Where: O(x) can be expanded by Taylor to express its value in x k The nearby expressions are:

[0151] O1(x)=O1(x k )+(xx k )δ k (18)

[0152] Where: δ k is the sensitivity coefficient of the kth iteration.

[0153] With the goal of minimizing the operation and maintenance costs (including startup costs, fuel costs, load shedding costs, energy abandonment costs, and demand-side response call costs) under extreme scenarios, an optimization planning model for the third-level extreme scenario operation subproblem is constructed, as shown in Equation (19).

[0154]

[0155] Where: is the unit energy curtailment cost of wind power and photovoltaic i, is the predicted output of wind power and photovoltaic i at time t; is the unit load shedding cost, is the load shedding amount at time t; is the unit demand-side response call cost, is the demand side response at time t.

[0156] The implicit premise of the third-level optimization planning model is that extreme scenarios may lead to energy curtailment, load shedding, and demand-side response actions. The constraints of the third-level optimization planning model are the same as those of the second-level optimization planning model: power balance constraints (node power balance) constraints, spinning reserve constraints, transmission line transmission capacity constraints, unit start-up and shutdown time constraints, unit output limits, unit ramp-up and downhill constraints, and energy storage charging and discharging constraints. The difference is that the constraints of the third-level optimization planning model must especially consider frequency security constraints, including formulas (7) and (10). Under the premise of the first-level investment decision, the third-level extreme scenario operation sub-problem can be regarded as a function O2(x) of the investment decision variable x. Introducing the slack variable α2 can form a cut function that is fed back to the first level, as shown in formula (20).

[0157] O2(x)=minF3≤α2 (20)

[0158] It should be noted that establishing a three-tiered optimization planning model comprehensively considers the synergistic relationship between power sources, the grid, loads, and energy storage, ensuring an optimal balance between system economics, reliability, and safety in every aspect of investment decision-making, daily scheduling, and response to extreme scenarios. This layer-by-layer optimization effectively reduces system operating costs, improves energy efficiency, and enhances the system's ability to respond to emergencies, providing a scientific basis and decision-making support for the coordinated planning of sources, grids, loads, and storage.

[0159] S106, solving the three-layer optimization planning model and determining a planning scheme based on the solution results.

[0160] In this embodiment, the first, second, and third optimization planning models are iteratively solved until the first-level optimization plan and the two optimization plans before and after the first-level optimization plan remain unchanged. The planning results are output for the construction capacity of thermal power, wind power, photovoltaic power, and energy storage, the location of transmission line construction, and the load demand-side response.

[0161] In summary, the present application proposes a source-grid-load-storage collaborative planning method, which obtains the first structural parameter, the second operating parameter and the third economic parameter of the target power system; divides the extreme scenarios according to the first structural parameter and the second operating parameter; establishes a first frequency response model according to the first structural parameter, the second operating parameter and the third economic parameter; establishes frequency security constraints based on the first frequency response model under extreme scenarios; establishes a three-layer optimization planning model, and the three-layer optimization planning model includes an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes frequency security constraints; solves the three-layer optimization planning model, and determines the planning scheme according to the solution results. The present application adopts cost minimization change point detection and adaptively intercepts abnormal time periods, effectively avoiding the artificial truncation of wind, solar and load output characteristics during the sample selection process, and can more accurately extract wind, solar and load extreme scenarios. The multi-machine system frequency response model constructed in the present application covers 9 frequency regulation resources, including thermal power, hydropower, pumped storage, nuclear power, wind power, photovoltaic, energy storage, load demand side response, and ultra-high voltage direct current, which fully reflects the frequency regulation capabilities of the new power system's multiple frequency regulation resources. The three-layer iterative source-grid-load-storage coordinated planning method proposed in this application can ensure the frequency safety of the power source construction, transmission line construction, energy storage construction, and demand-side response setting schemes in extreme scenarios by increasing a small amount of construction investment costs. The frequency change rate and the minimum frequency drop value are both within the required limits, avoiding the possible risk of frequency instability in the future and the potential economic losses it brings.

[0162] In Example 2, in a preferred embodiment, still using a specific region as an example, the planning target is a 500kV backbone grid. 220kV power sources are directly equivalent to the 500kV backbone grid based on their respective nodes. The horizontal grid has 59 lines, 22,580MW of thermal power generation, 11,062MW of hydropower generation, 4,150MW of energy storage, 10,800MW of wind power, and 31,000MW of photovoltaic power. There are 35 planned lines to be selected in the annual plan, with a unit construction cost of 6,000,000 yuan / km. There are 20,027MW of thermal power units to be selected, with a unit construction cost of 3,500,000 yuan / MW, 3,000MW of pumped storage units to be selected, with a unit construction cost of 6,000,000 yuan / MW, 2,350MW of energy storage units to be selected, with a unit construction cost of 2,400,000 yuan / MW, a total wind power capacity of 1,500MW, with a unit construction cost of 5,000,000 yuan / MW, a total photovoltaic capacity of 60,000MW, with a unit construction cost of 4,000,000 yuan / MW, a total reserve ratio requirement of 15%, a rotating reserve ratio requirement of 5%, a unit cost of new energy operation and maintenance of 80 yuan / MWh, a unit load shedding cost of 25,000 yuan / MWh, a unit energy abandonment cost of 500 yuan / MWh, an allowable frequency change rate of 0.5Hz / s, and an allowable frequency drop value of 0.2Hz.

[0163] Table 1 compares the planning results of considering and not considering frequency safety in extreme scenarios.

[0164] Table 1: Indicators of planning schemes that consider frequency safety in extreme scenarios

[0165]

[0166]

[0167] As shown in Table 1, before adding extreme frequency safety constraints, annual investment and construction costs were 2.17 billion yuan, annual load shedding costs and annual energy curtailment costs were both zero, annual conventional unit operating costs were 11.8 billion yuan, and annual renewable energy operating costs were 170 million yuan, for a total annual cost of 14.14 billion yuan. However, after considering extreme frequency safety constraints, annual investment and construction costs increased by 330 million yuan to 2.5 billion yuan, and energy curtailment costs incurred 48 million yuan. However, annual conventional unit operating costs decreased to 11.6 billion yuan, and the total annual cost increased to 14.32 billion yuan.

[0168] Further analysis shows that, without considering frequency safety in extreme scenarios, in the extracted extreme scenarios of summer and autumn afternoons, hydropower units are less frequently operated. Due to the need to provide spinning reserve, more thermal power units are operated than hydropower units. During these periods, the system's inertia is low, making frequency safety a more prominent issue. However, the highest number of units operated during winter nights is due to the higher loads during these times. After considering frequency safety in extreme scenarios, previously inactive units are now operated, and the number of units operated increases. For example, during summer afternoons, three more thermal power units are operated, while the number of hydropower units operated increases by 11. This improves the system's inertia and frequency regulation capabilities, ensuring that the frequency change rate and minimum frequency value do not exceed the specified limits when subjected to high-power DC disturbances, ensuring safe and stable system operation.

[0169] Example 3: This embodiment also provides a source-grid-load-storage collaborative planning system, including:

[0170] a data acquisition module, configured to acquire a first structural parameter, a second operating parameter, and a third economic parameter of a target power system;

[0171] a division module, configured to divide extreme scenarios according to the first structural parameter and the second operating parameter;

[0172] A first model building module is used to build a first frequency response model according to the first structural parameter, the second operating parameter and the third economic parameter;

[0173] A constraint establishment module, used to establish frequency safety constraints based on the first frequency response model under extreme scenarios;

[0174] The second model building module is used to establish a three-layer optimization planning model, each of which includes an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes a frequency safety constraint;

[0175] The solution module is used to solve the three-layer optimization planning model and determine the planning scheme based on the solution results.

[0176] The above-mentioned unit modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0177] This embodiment also provides an electronic device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The electronic device includes a processor, memory, communication interface, display screen and input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a source-grid-load-storage collaborative planning method is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the electronic device housing, or an external keyboard, touchpad or mouse.

[0178] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0179] Acquiring a first structural parameter, a second operating parameter, and a third economic parameter of a target power system;

[0180] Dividing extreme scenarios according to the first structural parameter and the second operating parameter;

[0181] establishing a first frequency response model according to the first structural parameter, the second operating parameter, and the third economic parameter;

[0182] Establish frequency safety constraints based on the first frequency response model under extreme scenarios;

[0183] Establish a three-layer optimization planning model, each of which includes an objective function and a set of constraints. The constraint set in the third-layer optimization planning model includes a frequency safety constraint.

[0184] The three-layer optimization planning model is solved and the planning scheme is determined based on the solution results.

[0185] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and all of these should be included in the scope of the claims of the present application.

[0186] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0187] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0188] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0190] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0191] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A source-grid-load-storage collaborative planning method, characterized in that: include: Acquiring a first structural parameter, a second operating parameter, and a third economic parameter of a target power system; Dividing extreme scenarios according to the first structural parameter and the second operating parameter; establishing a first frequency response model according to the first structural parameter, the second operating parameter, and the third economic parameter; Establishing a frequency safety constraint based on the first frequency response model under the extreme scenario; Establishing a three-layer optimization planning model, each of the three-layer optimization planning models includes an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes the frequency safety constraint; The three-layer optimization planning model is solved and the planning scheme is determined based on the solution results.

2. The source-grid-load-storage collaborative planning method according to claim 1, characterized in that: The extreme scenarios include: Acquire a time series of three-dimensional wind and solar load output data according to the first structural parameter and the second operating parameter; Establish a cost function based on the target change point; The cost function is used to solve the cost of each sub-time period divided according to the target change point; The sub-time period in which the cost is higher than the preset value is recorded as an abnormal time period, and an extreme scenario is determined according to the abnormal time period.

3. The source-grid-load-storage collaborative planning method according to claim 2, characterized in that: The establishing of the frequency safety constraint based on the first frequency response model in the extreme scenario includes: The frequency safety constraints include a frequency drop minimum value constraint and a frequency drop rate constraint; The frequency drop rate constraint is that when there is a disturbance, the target power system frequency change rate does not exceed the maximum allowable change rate; The lowest value of the frequency drop does not exceed the maximum allowed frequency.

4. A source-grid-load-storage collaborative planning method according to claim 3, characterized in that: The establishment of the three-layer optimization planning model includes a first-layer optimization planning model; The first-tier optimization planning model takes the construction costs of thermal power, wind power, photovoltaic power, energy storage, transmission line construction costs, load demand-side response setting costs, and the operation and maintenance costs of all types of power sources in typical scenarios and extreme scenarios as the minimum sum as the objective function; The constraints of the first-level optimization planning model include construction capacity constraints and timing constraints.

5. The source-grid-load-storage collaborative planning method according to claim 4, characterized in that: The establishment of the three-layer optimization planning model also includes a second-layer optimization planning model; The second-level optimization planning model takes the minimization of operation and maintenance costs under typical scenarios as the objective function; The typical scenario does not involve energy abandonment or load shedding; The constraints of the second-level optimization planning model include power balance constraints, annual power balance constraints, total reserve constraints, spinning reserve constraints, transmission line transmission capacity constraints, unit start-up and shutdown time constraints, unit output limits, unit climbing and descending constraints, and energy storage charging and discharging constraints.

6. A source-grid-load-storage collaborative planning method according to claim 5, characterized in that: The said establishing of the three-layer optimization planning model also includes a third-layer optimization planning model; The third-level optimization planning model takes the minimization of operation and maintenance costs under extreme scenarios as the objective function; The extreme scenarios described above may lead to energy curtailment, load shedding, and demand-side response actions; Compared with the constraints of the second-layer optimization planning model, the constraints of the third-layer optimization planning model have an additional frequency safety constraint.

7. The source-grid-load-storage collaborative planning method according to claim 6, characterized in that: The constraints of the first-level optimization planning model also include: Constrained by the cut function of the second-level typical scenario running sub-problem feedback; Constrained by the cut function of the third-level typical scenario running sub-problem feedback.

8. A source-grid-load-storage collaborative planning system, applying the method according to any one of claims 1 to 7, characterized in that: include: a data acquisition module, configured to acquire a first structural parameter, a second operating parameter, and a third economic parameter of a target power system; a division module, configured to divide extreme scenarios according to the first structural parameter and the second operating parameter; A first model building module, configured to build a first frequency response model according to the first structural parameter, the second operating parameter, and the third economic parameter; A constraint establishing module, configured to establish a frequency safety constraint on the first frequency response model under the extreme scenario; A second model building module is used to establish a three-layer optimization planning model, wherein the three-layer optimization planning models each include an objective function and a set of constraints, wherein the set of constraints in the third-layer optimization planning model includes the frequency safety constraint; The solution module is used to solve the three-layer optimization planning model and determine the planning scheme based on the solution results.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a source-grid-load-storage collaborative planning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a source-grid-load-storage collaborative planning method according to any one of claims 1 to 7 are implemented.

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