Industrial park source network load storage, insurance and supply method, system and device and storage medium

Abstract

The invention discloses an industrial park source network load storage, insurance and supply method, system and device and a storage medium, and the method comprises the steps: collecting the multi-source data of a power system, and recognizing the operation mode of the power system based on the multi-source data; based on risk constraints of different operation modes of the power system, taking minimization of economic cost as a target function, and establishing a source network load storage regulation resource planning model; and dynamically adjusting a planning strategy generated by the source network load storage adjustment resource planning model according to the actually occurring extreme weather event and the real-time operation state of the power system so as to optimize the source network load storage resource configuration. According to a risk constraint optimization mechanism, insufficient standby capacity and voltage out-of-limit risks are avoided, seamless connection of risk dynamic constraint and optimization planning is realized through a collaborative planning framework, the new energy consumption capability can be improved, the operation cost is reduced, and the adaptability of a power grid to high-proportion new energy access is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, and in particular to an industrial park source-grid-load-storage-protection-supply method, system, device and storage medium. BACKGROUND

[0002] With the continuous increase of the penetration rate of wind power, photovoltaic and other new energy, the power system is facing multiple uncertainty challenges. Extreme weather such as typhoon and blizzard can easily cause sudden drop of new energy output. The traditional deterministic planning method formulates generation plan based on a single prediction scenario. In the face of extreme situations, it is difficult to quantify the risks of power shortage, power curtailment and voltage out-of-limit, and the system safety cannot be guaranteed. Under normal circumstances, it may cause waste of resources and affect system economy. Due to the unified constraint system for normal and special scenarios, the economy under normal conditions is damaged or the safety risk under extreme events is increased. Fixed safety margin leads to poor economy under normal period and insufficient defense capability under extreme weather. From risk identification to planning execution, it takes a long time, which is difficult to meet the second-level regulation and control demand of new energy power grid.

[0003] In addition, the existing method adopts a "prediction-planning" decoupling architecture and does not establish a dynamic risk response mechanism, which cannot balance economy and safety. Therefore, a new planning method with flexible constraints and efficient optimization is needed to cope with the complex uncertainty of high-proportion new energy power grid. SUMMARY

[0004] In view of the above existing problems, the present application is proposed. Therefore, the present application provides an industrial park source-grid-load-storage-protection-supply method, system, device and storage medium to solve the problems of inflexible planning strategy and long time-consuming decision planning of the existing method.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] In a first aspect, the embodiments of the present application provide an industrial park source-grid-load-storage-protection-supply method, comprising:

[0007] Collecting multi-source data of the power system, and identifying the operation mode of the power system based on the multi-source data;

[0008] Based on the risk constraints of different operation modes of the power system, a source-grid-load-storage regulation resource planning model is established with the minimum economic cost as the objective function;

[0009] According to the actual extreme weather event and the real-time operation state of the power system, the planning strategy generated by the source-grid-load-storage regulation resource planning model is dynamically adjusted to optimize the source-grid-load-storage resource configuration.

[0010] As a preferred scheme of the industrial park source network load storage supply method, the method comprises the following steps of:

[0011] Based on the multi-source data, key features of the power system operation are extracted, and the key features are combined into a numerical vector according to a predetermined order as a feature vector of the current scene;

[0012] The similarity between the feature vector of the current scene and the feature vector of the historical scene is calculated;

[0013] If no red warning is issued or the similarity of all historical records is lower than the first threshold value, it is determined that the current power system is in a normal mode, and no special response is required;

[0014] If the current power system is in a red warning state and there is at least one historical record with a similarity not lower than the first threshold value, it is determined that the current power system is in a special mode, and a special response mechanism is started.

[0015] As a preferred scheme of the industrial park source network load storage supply method, the multi-source data comprises real-time weather data, equipment information and real-time state of the power grid.

[0016] As a preferred scheme of the industrial park source network load storage supply method, the method comprises the following steps of:

[0017] The source network load storage adjustment resource planning model takes the power system power shortage constraint, power abandonment constraint and voltage constraint as constraint conditions, and takes the minimization of the total investment construction cost and the total operation and maintenance cost of the system as the objective function;

[0018] When the power system is in a normal mode, the standby capacity of the power system is at least 1.1 times the peak load, the power abandonment rate is not more than 15%, and the voltage deviation is controlled within ±5%;

[0019] When the power system is in a special mode, the standby capacity of the power system is at least 1.8 times the peak load, the power abandonment rate is not more than 5%, and the voltage deviation is controlled within ±2%.

[0020] The beneficial effects of the preferred technical scheme are that the intelligent decision driven by scene recognition and risk dynamic constraint provides a planning paradigm with safety and economy for the power system, and promotes energy structure transformation and power grid resilience improvement.

[0021] As a preferred scheme of the industrial park source network load storage supply method, the method further comprises the following step of:

[0022] When the power system is in the normal mode, power supply safety is placed in the first priority, economy is placed in the second priority, and equipment life is placed in the third priority;

[0023] When the power system is in the special mode, power supply safety is placed in the first priority, equipment life is placed in the second priority, and economy is placed in the third priority.

[0024] As a preferred scheme of the industrial park source network load storage supply method provided by the application, wherein: the dynamic adjustment of the planning strategy generated by the source network load storage adjustment resource planning model according to the actual occurrence of the extreme weather event and the real-time operation feedback information of the power system comprises:

[0025] Based on the physical law of the power system and the operation characteristics of the equipment, a dynamic planning strategy model is constructed according to the multi-dimensional constraint combined with the source network load storage adjustment resource planning strategy, and the dynamic planning strategy model optimizes the resource configuration and operation mode in real time according to the actual weather event and the operation state of the power system;

[0026] The multi-dimensional constraint comprises branch flow constraint, node power balance constraint, controllable photovoltaic constraint, reactive power compensation equipment constraint, network reconstruction constraint and energy storage constraint.

[0027] The beneficial effect of the preferred technical scheme is that the risk dynamic constraint is seamlessly connected with the optimization planning, the new energy consumption capacity is improved, the operation cost is reduced, and the adaptability of the power grid to high proportion of new energy access is enhanced.

[0028] In a second aspect, the application provides an industrial park source network load storage supply system, comprising:

[0029] An operation mode recognition module is configured to collect multi-source data of the power system and recognize the operation mode of the power system based on the multi-source data;

[0030] A model establishment module is configured to establish a source network load storage adjustment resource planning model based on the risk constraint of different operation modes of the power system, and take the minimization of economic cost as an objective function;

[0031] An optimization module is configured to dynamically adjust the planning strategy generated by the source network load storage adjustment resource planning model according to the actual occurrence of the extreme weather event and the real-time operation feedback information of the power system, so as to optimize the source network load storage resource configuration.

[0032] As a preferred scheme of the industrial park source network load storage supply system provided by the application, wherein: the operation mode recognition module is further configured to:

[0033] extracting a key feature of power system operation based on the multi-source data, and combining the key feature into a numerical vector according to a predetermined order as a feature vector of a current scenario;

[0034] calculating a similarity between the feature vector of the current scenario and a feature vector of a historical scenario;

[0035] if no red alert is issued or the similarity of all historical records is lower than a first threshold value, determining that the current power system is in a normal mode and no special response is needed;

[0036] if the current power system is in a red alert state and there is at least one historical record with a similarity not lower than the first threshold value, determining that the current power system is in a special mode and starting a special response mechanism.

[0037] In a third aspect, the present application provides an electronic device, comprising:

[0038] a memory and a processor;

[0039] the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so as to realize the steps of the industrial park source network load storage security supply method.

[0040] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor to realize the steps of the industrial park source network load storage security supply method.

[0041] Compared with the prior art, the present application has the following beneficial effects: the present application performs scene recognition through a scene matching algorithm, avoids the risk of insufficient backup capacity and voltage limit through a risk constraint optimization mechanism, and realizes seamless connection between dynamic risk constraint and optimization planning through a collaborative planning framework, which can improve new energy consumption capacity, reduce operation cost, and enhance the adaptability of power grid to high proportion of new energy access. The present application provides a planning paradigm with safety and economy for power system through scene recognition and intelligent decision driven by dynamic risk constraint, which can promote energy structure transformation and power grid resilience improvement. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0043] Figure 1A method flowchart of an industrial park source network load storage supply method according to an embodiment of the present application is shown in the figure.

[0044] Figure 2 An industrial park power distribution network 39 node system topology diagram of an industrial park source network load storage supply method according to an embodiment of the present application is shown in the figure.

[0045] Figure 3 A system voltage level diagram before planning of an industrial park source network load storage supply method according to an embodiment of the present application is shown in the figure.

[0046] Figure 4 A system voltage level diagram after planning of an industrial park source network load storage supply method according to an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0047] In order to make the above objectives, characteristics and advantages of the present application more apparent, obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0048] Embodiment 1, refer to Figure 1 According to an embodiment of the present application, the embodiment provides an industrial park source network load storage supply method, comprising:

[0049] S100: Collecting multi-source data of the power system, and identifying the operation mode of the power system based on the multi-source data;

[0050] S200: Based on the risk constraints of different operation modes of the power system, establishing a source network load storage regulation resource planning model with the minimum economic cost as the objective function;

[0051] S300: According to the actual extreme weather event and the real-time operation state of the power system, dynamically adjusting the planning strategy generated by the source network load storage regulation resource planning model to optimize the configuration of the source network load storage resource.

[0052] It should be noted that the traditional deterministic planning method formulates the power generation plan based on a single prediction scenario, and it is difficult to quantify the risk of power shortage, power curtailment and voltage overrun in the face of extreme conditions, and the system security is difficult to guarantee; and under normal conditions, it may cause waste of resources and affect the economic efficiency of the system; due to the unified constraint system for normal and special scenarios, the economic efficiency under normal conditions is damaged or the safety risk under extreme events is increased. The fixed safety margin leads to poor economic efficiency in normal period and insufficient defense capability under extreme weather. It takes a long time from risk identification to planning execution, which is difficult to meet the second-level regulation and control demand of new energy power grid. The existing method adopts a decoupled architecture of "prediction-planning", and does not establish a dynamic risk response mechanism, which cannot balance the economy and safety. The present application breaks through the dependence of traditional planning method on conventional scene, realizes the coordinated optimization of safety and economy by constructing the decision mechanism of "scene identification-dynamic constraint". Based on multi-source data, the normal mode or special mode is output, and the dynamic constraint is divided into normal constraint and special constraint, different constraint thresholds are used for each risk constraint, and the probability of each risk occurrence is minimized; the injected risk data is corrected in real time, and a dynamic planning strategy model of source-grid-load-storage adjustment resources considering extreme weather is established.

[0053] In the embodiment of the present application, the multi-source data of the power system collected in step S100 includes real-time weather data, equipment information and real-time state of the power grid.

[0054] Specifically, in the embodiment of the present application, the real-time weather data in step S100 includes the warning level of typhoon, snowstorm or high temperature weather; the real-time state of the power grid includes the load rate of the key line and the SOC of the energy storage.

[0055] It should be noted that the multi-source data needs to be preprocessed before identifying the operation mode of the power system in step S100.

[0056] In the embodiment of the present application, the operation mode of the power system is identified based on the multi-source data in step S100, which includes:

[0057] Based on the multi-source data, the key features of the operation of the power system are extracted, and the key features are combined into a numerical vector according to a predetermined order as a feature vector of the current scene;

[0058] The similarity of the feature vector of the current scene and the feature vector of the historical scene is calculated;

[0059] If there is no red warning issued or the similarity of all historical records is lower than the first threshold, it is determined that the current power system is in a normal mode and no special response is needed;

[0060] If the current power system is in a red warning state and there is at least one historical record with a similarity not lower than the first threshold, it is determined that the current power system is in a special mode, and a special response mechanism is started.

[0061] It should be noted that in this embodiment, the first threshold can be set to 0.85.

[0062] In an optional embodiment, the specific steps for scene recognition using a scene matching algorithm include:

[0063] Input real-time weather warning levels, historical extreme event database, and real-time power grid status data;

[0064] Features such as typhoon distance, wind speed gradient, and load fluctuation rate are constructed; the similarity between the current scene feature vector and each record in the historical event database is calculated; the historical event database consists of feature vectors and related information of power scenarios related to typhoons in the past, which may include typhoon paths and generator output drop records in the past 5 years.

[0065] Check for outliers and missing values ​​in the data. Outliers can be corrected using interpolation; missing values ​​can be filled based on the data distribution and correlation, such as using the average value of the preceding and following time points to fill missing load data. Since the dimensions and numerical ranges of features such as typhoon distance, wind speed gradient, and load fluctuation rate may vary significantly, the data needs to be normalized to map it to the [0,1] interval in order to eliminate the impact of such differences on similarity calculation.

[0066] The pre-processed data such as typhoon distance, wind speed gradient, and load fluctuation rate are combined into a feature vector in a certain order and used as the feature vector of the current scene.

[0067] The Euclidean distance method is used to calculate the similarity between the current scene feature vector and each record in the historical event database. Let the current scene feature vector be x, and a record's feature vector in the historical event database be y. The Euclidean distance is expressed as:

[0068]

[0069] Where n is the dimension of the feature vector.

[0070] Similarity can be defined as s = 1 / (1 + d(x,y)). The closer the similarity value is to 1, the more similar the two scenes are.

[0071] In this embodiment of the invention, step S200, based on the risk constraints of different operating modes of the power system, establishes a source-grid-load-storage regulation resource planning model with the objective function of minimizing economic cost. The source-grid-load-storage regulation resource planning model takes the power shortage constraint, power curtailment constraint and voltage constraint of the power system as constraints, and minimizes the total investment construction cost and total operation and maintenance cost of the system as objective functions.

[0072] When the power system is in conventional mode, the reserve capacity of the power system should be at least 1.1 times the peak load, the curtailment rate should not exceed 15%, and the voltage deviation should be controlled within ±5%.

[0073] When the power system is in a special mode, the reserve capacity of the power system shall be at least 1.8 times the peak load, the curtailment rate shall not exceed 5%, and the voltage deviation shall be controlled within ±2%.

[0074] Furthermore, the objective function is expressed as:

[0075] min(C INV +C OM )

[0076] Among them, C INV C represents the total investment and construction cost of the system. OM This represents the total system maintenance cost.

[0077] It should be noted that the reserve capacity multiple is set based on the maximum load during historical extreme events. The reserve capacity design references the most severe load conditions in historical data to ensure that the system can maintain stable operation when facing similar extreme events. The curtailment rate threshold is set based on the regulation capacity of renewable energy power plants and the upper limit of grid absorption. The voltage constraint is a limitation at the per-unit voltage value. The selected threshold considers various factors such as power supply security and economy in different scenarios. Other parameter values ​​may be feasible under specific circumstances, but their impact on the entire system needs to be reassessed to ensure that load demand is met without incurring additional risks or costs.

[0078] In this embodiment of the invention, step S200 further includes: planning source-grid-load-storage regulation resources according to the priority of the operating mode;

[0079] When the power system is in conventional mode, power supply security is given the first priority, economy is given the second priority, and equipment lifespan is given the third priority.

[0080] When the power system is in a special mode, power supply security is given the first priority, equipment lifespan is given the second priority, and economy is given the third priority.

[0081] It should be noted that, given the frequent occurrence of extreme weather events, one of the core objectives of the power generation, grid, load, and storage regulation resource planning model is to ensure the stability of power supply. By rationally planning power generation, grid, load, and storage resources, the power system can still meet user power demands and reduce the duration and scope of power outages during extreme weather events. Therefore, in addition to the constraints mentioned earlier, such as power shortage and curtailment constraints, it is necessary to further consider system network constraints and resource characteristic constraints to establish a dynamic adjustment model for the planning strategy. This model allows for timely adjustments to the planning strategy based on the actual occurrence of extreme weather events and feedback from the power system's operation.

[0082] In this embodiment of the invention, step S300, which involves dynamically adjusting the planning strategy generated by the source-grid-load-storage regulation resource planning model based on actual extreme weather events and real-time operation feedback information of the power system, includes:

[0083] Based on the physical laws of the power system and the operating characteristics of the equipment, a dynamic planning strategy model is constructed by combining multi-dimensional constraints with the resource planning strategy of source-grid-load-storage regulation. The dynamic planning strategy model optimizes resource allocation and operation mode in real time according to actual weather events and the operating status of the power system.

[0084] Multidimensional constraints include branch power flow constraints, node power balance constraints, controllable photovoltaic constraints, reactive power compensation equipment constraints, network reconfiguration constraints, and energy storage constraints.

[0085] Specifically, the polar coordinate form of the branch power flow constraint can be expressed as:

[0086]

[0087] Where ρ,k∈{a,b,c} represents phase, U i U j Indicates the node voltage amplitude. and Let ρ and k represent the mutual coupling conductance and susceptance of phase ρ and phase k of the three-phase line ij, respectively. If ρ = k, then and Let p represent the phase conductance and susceptance of the three-phase line ij, respectively. This represents the phase angle difference between phase ρ and phase k at node i; This represents the phase angle difference between the ρ phase of node i and the k phase of node j.

[0088] Specifically, considering the active and reactive power outputs of the planned equipment at the nodes, the node power balance constraint can be expressed as:

[0089]

[0090] Where n represents the total number of nodes; ΔP i ρ , These represent the adjustable active and reactive power values ​​of the controllable load connected to node i, respectively. and These represent the ρ-phase active and reactive loads at node i, respectively. and These represent the active and reactive power outputs of photovoltaic systems, respectively. and These represent the charging and discharging power of the energy stored at node i, respectively. and These represent the reactive power compensation power of the parallel capacitor and reactor at node i, respectively. This indicates the reactive power compensation of the SVC.

[0091] Specifically, a local control strategy is applied to the distributed photovoltaic converter to ensure that its active and reactive power meet the following constraints:

[0092]

[0093] Among them, S PV P represents the configuration capacity of distributed photovoltaic power; PV Q represents photovoltaic output. PV This represents the reactive power flowing into the system; The power factor angle.

[0094] Furthermore, parallel capacitors / reactors are used for reactive power compensation through switching, and the amount of reactive power compensation is a discrete variable:

[0095]

[0096] in, and These represent the number of parallel capacitors and reactors in phase ρ at node i, respectively; and These represent the capacities of a single group of parallel capacitors and reactors to be planned, respectively. and These represent the switching states of the parallel capacitor and reactor at time t in phase ρ at node i, respectively, and are binary variables.

[0097] In addition, the actual system needs to reserve some reactive power reserve capacity, and the reactive power compensation equipment can only be put into operation after construction. Therefore, the following constraints apply:

[0098]

[0099] Among them, Q res This is the reactive power reserve factor. For the maximum reactive load, the capacitive reactive power reserve capacity should be 7% to 8% of the reactive load; 0-1 variable. These represent the planned states of the capacitor and reactor at node i in phase ρ (1 represents yes).

[0100] Furthermore, similar to the aforementioned route type planning, based on the branch power flow equations, considering n rc If there are several alternative routes, then the branch power flow equations are changed to n branch +n rc There are 1, and whether each line uses a decision variable R is determined by the decision variable R. ij Decision, R ijThis is a binary variable (equal to 1 indicates the line is in use), therefore the branch power flow constraints... Replace with This can represent the power flow constraints with network reconfiguration:

[0101]

[0102] The constraints are radial topological constraints, where Φ l Let n represent the set of all routes. b and n s These represent the total number of nodes and the number of root nodes in the power distribution system, respectively.

[0103] Furthermore, energy storage batteries are restricted from being charged and discharged simultaneously, and cannot be put into operation before planning.

[0104]

[0105] Among them, 0-1 variables These represent the charging and discharging states of energy storage, respectively. When the energy storage battery is charging... =1, otherwise =1; This indicates the planning state of energy storage in phase ρ at node i.

[0106] Excessive charge / discharge state transitions can significantly reduce the lifespan of energy storage. To improve its economic efficiency, the number of charge / discharge state transitions needs to be limited, as shown in the following formula.

[0107]

[0108] in, It is an auxiliary variable characterizing the changes in the charging and discharging state of energy storage during the planning cycle.

[0109]

[0110] SOC min N batt P rated，batt ≤E(t)≤SOC max N batt P rated，batt

[0111]

[0112] Among them, E(t), P ch (t), P dis Δt and Δt represent the remaining energy (kWh), charging / discharging power (kW), and time interval (h) of the energy storage battery at time t, respectively, and δ and η. ch η disThese represent the self-discharge rate and charge / discharge efficiency of the energy storage battery per hour, respectively; N batt S indicates the number of energy storage batteries; batt，unit It refers to the capacity of a single energy storage unit; SOC min and SOC max These represent the minimum and maximum values ​​for energy storage and percentage of electricity, respectively.

[0113] It should be noted that this invention uses algorithms for scenario identification, and the risk constraint optimization mechanism effectively avoids the risks of insufficient reserve capacity and voltage exceeding limits. The collaborative planning framework achieves seamless integration of dynamic risk constraints and optimization planning, significantly improving the renewable energy absorption capacity, reducing operating costs, and enhancing the grid's adaptability to high-proportion renewable energy access. Through intelligent decision-making driven by scenario identification and dynamic risk constraints, it provides the power system with a planning paradigm that combines safety and economy, promoting energy structure transformation and grid resilience improvement.

[0114] Example 2: The above example is an illustrative scheme of a power generation, grid, load, and storage guarantee method for industrial parks. It should be noted that the technical solution of this power generation, grid, load, and storage guarantee system for industrial parks belongs to the same concept as the technical solution of the aforementioned power generation, grid, load, and storage guarantee method for industrial parks. Details not described in detail in this example can be found in the description of the aforementioned technical solution of the power generation, grid, load, and storage guarantee method for industrial parks.

[0115] This embodiment describes a power supply system for an industrial park, comprising:

[0116] The operation mode recognition module is used to collect multi-source data from the power system and identify the current operation mode of the power system based on the multi-source data.

[0117] The model building module is used to preset risk constraints for different operating modes and establish a resource planning model for source-grid-load-storage regulation with the objective function of minimizing economic costs.

[0118] The optimization module is used to dynamically adjust the planning strategy generated by the source-grid-load-storage regulation resource planning model based on actual extreme weather events and real-time operation feedback information of the power system, so as to optimize the allocation of source-grid-load-storage resources.

[0119] In this embodiment of the invention, the running pattern recognition module is further used for:

[0120] Key features of power system operation are extracted from multi-source data and combined into a numerical vector according to a predetermined order, which serves as the feature vector for the current scenario.

[0121] Calculate the similarity between the feature vector of the current scene and the feature vector of the historical scene;

[0122] If no red alert is issued or the similarity of all historical records is below the first threshold, the current power system is determined to be in normal mode and no special response is required.

[0123] If the current power system is under a red alert and there is at least one historical record with a similarity not lower than the first threshold, then the current power system is determined to be in a special mode, and a special response mechanism is activated.

[0124] This embodiment also provides an electronic device applicable to the power supply guarantee method for industrial parks, including:

[0125] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the power generation, grid, load, and energy storage supply guarantee method for industrial parks as proposed in the above embodiments.

[0126] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for ensuring the supply of power, grid, load and energy in industrial parks as proposed in the above embodiments.

[0127] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for ensuring the supply of power, grid, load and energy in industrial parks proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0128] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0129] Example 3, referring to Figures 2 to 4 This is one embodiment of the present invention. This embodiment uses a 39-node power distribution network system in an industrial park for testing and experimentation to verify the effectiveness of the present invention.

[0130] The industrial park's 39-node power distribution network system, such as Figure 2As shown, the system's base power is 1 MVA and the base voltage is 10 kV; the industrial park's resource regulation planning scheme is shown in Table 1:

[0131] Table 1. Resource Regulation Planning Scheme for Industrial Parks

[0132]

[0133] Calculate the original voltage level and the power flow results after adjusting resource planning, combined with Table 1 and Figure 3 as well as Figure 4 By comparing the voltage levels of each node in the system before and after the planning, it can be seen that adjusting resource planning can effectively reduce the voltage deviation of the system and improve the safety of the industrial park's power distribution system, while considering supply requirements. This invention uses a scenario matching algorithm for scenario identification, avoids the risks of insufficient reserve capacity and voltage exceeding limits based on a risk constraint optimization mechanism, and achieves seamless integration of dynamic risk constraints and optimization planning through a collaborative planning framework. This can improve the renewable energy absorption capacity, reduce operating costs, and enhance the grid's adaptability to high-proportion renewable energy access. Through scenario identification and risk dynamic constraint-driven intelligent decision-making, this invention provides a planning paradigm for the power system that combines safety and economy, and can promote energy structure transformation and grid resilience improvement.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for ensuring supply through power generation, grid, load, and energy storage in industrial parks, characterized in that: include: Collect multi-source data from the power system and identify the power system operation mode based on the multi-source data; Based on the risk constraints of different power system operation modes, and with minimizing economic cost as the objective function, a resource planning model for source-grid-load-storage regulation is established. Based on actual extreme weather events and the real-time operating status of the power system, the planning strategy generated by the source-grid-load-storage regulation resource planning model is dynamically adjusted to optimize the allocation of source-grid-load-storage resources.

2. The industrial park source-grid-load-storage supply guarantee method as described in claim 1, characterized in that, The identification of power system operation modes based on the multi-source data includes: Based on the multi-source data, key features of power system operation are extracted, and the key features are combined into a numerical vector according to a predetermined order, which serves as the feature vector of the current scenario. Calculate the similarity between the feature vector of the current scene and the feature vector of the historical scene; If no red alert is issued or the similarity of all historical records is below the first threshold, the current power system is determined to be in normal mode and no special response is required. If the current power system is under a red alert and there is at least one historical record with a similarity not lower than the first threshold, then the current power system is determined to be in a special mode, and a special response mechanism is activated.

3. The industrial park source-grid-load-storage supply guarantee method as described in claim 2, characterized in that, The multi-source data includes: real-time meteorological data, equipment information, and real-time power grid status.

4. The industrial park source-grid-load-storage supply guarantee method as described in claim 3, characterized in that, Based on the risk constraints of different power system operation modes, and with minimizing economic costs as the objective function, a source-grid-load-storage regulation resource planning model is established. The source-grid-load-storage regulation resource planning model takes the power system's power shortage constraints, power curtailment constraints, and voltage constraints as constraints, and aims to minimize the total system investment construction cost and total operation and maintenance cost as the objective function. When the power system is in conventional mode, the reserve capacity of the power system should be at least 1.1 times the peak load, the curtailment rate should not exceed 15%, and the voltage deviation should be controlled within ±5%. When the power system is in a special mode, the reserve capacity of the power system shall be at least 1.8 times the peak load, the curtailment rate shall not exceed 5%, and the voltage deviation shall be controlled within ±2%.

5. The industrial park source-grid-load-storage supply guarantee method as described in claim 4, characterized in that, Also includes: Resource allocation for source-grid-load-storage regulation is planned according to the priority of operation mode; When the power system is in conventional mode, power supply security is given the first priority, economy is given the second priority, and equipment lifespan is given the third priority. When the power system is in a special mode, power supply security is given the first priority, equipment lifespan is given the second priority, and economy is given the third priority.

6. The industrial park source-grid-load-storage supply guarantee method as described in claim 5, characterized in that, Based on actual extreme weather events and real-time operational feedback information from the power system, the planning strategy generated by the source-grid-load-storage regulation resource planning model is dynamically adjusted, including: Based on the physical laws of the power system and the operating characteristics of the equipment, a dynamic planning strategy model is constructed according to multi-dimensional constraints and the resource planning strategy of source-grid-load-storage regulation. The dynamic planning strategy model optimizes resource allocation and operation mode in real time according to actual weather events and the operating status of the power system. Multidimensional constraints include branch power flow constraints, node power balance constraints, controllable photovoltaic constraints, reactive power compensation equipment constraints, network reconfiguration constraints, and energy storage constraints.

7. A power supply system for industrial parks, characterized in that, include: The operation mode recognition module is used to collect multi-source data of the power system and identify the operation mode of the power system based on the multi-source data; The model building module is used to establish a source-grid-load-storage regulation resource planning model based on risk constraints of different power system operation modes, with the objective function of minimizing economic costs. The optimization module is used to dynamically adjust the planning strategy generated by the source-grid-load-storage regulation resource planning model based on actual extreme weather events and real-time operation feedback information of the power system, so as to optimize the allocation of source-grid-load-storage resources.

8. The industrial park power generation, grid, load, storage, and supply guarantee system as described in claim 7, characterized in that, The operating mode recognition module is also used for: Based on the multi-source data, key features of power system operation are extracted, and the key features are combined into a numerical vector according to a predetermined order, which serves as the feature vector of the current scenario. Calculate the similarity between the feature vector of the current scene and the feature vector of the historical scene; If no red alert is issued or the similarity of all historical records is below the first threshold, the current power system is determined to be in normal mode and no special response is required. If the current power system is under a red alert and there is at least one historical record with a similarity not lower than the first threshold, then the current power system is determined to be in a special mode, and a special response mechanism is activated.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the industrial park source-grid-load-storage supply guarantee method according to any one of claims 1 to 6.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the industrial park source-grid-load-storage supply guarantee method according to any one of claims 1 to 6.

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