Multi-energy transformer power mutual aid system for zero-carbon park

Abstract

The application discloses a kind of multi-energy transformer power mutual aid systems for zero-carbon park, it is related to transformer equipment technical field, comprising the following steps: S1, response demand analysis based on time dimension is carried out;S2, response adaptation degree model is constructed;S3, dispatching sequencing function is constructed, and energy dispatching priority sequence is generated;S4, actual regulating output function is constructed, and actual output power is output;S5, system-level power balance detection is executed, whether to meet the power supply requirement of park is judged;S6, short-time gain or peak clipping processing is executed to single energy by local regulation function.This application constructs whole-process mutual aid mechanism from load behavior modeling, energy adaptation evaluation, dynamic optimal order control to disturbance correction by adopting multi-step coupling control mode.Through the linkage adjustment of optimal order control module and disturbance correction module, millisecond-level response and redundant resource rapid compensation are realized, and system operation stability, energy utilization efficiency are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of transformer equipment technology, specifically to a multi-energy transformer power balance system for zero-carbon industrial parks. Background Technology

[0002] With the introduction of the "dual-carbon target," the construction of zero-carbon industrial parks has become an important development direction for green city energy systems. Against this backdrop, multi-energy power supply systems combining various renewable energy sources (such as photovoltaics and wind power) with energy storage and power grids are being widely deployed. To achieve effective management and dispatch of these energy sources, multi-energy transformer technology is gradually incorporating the control concept of "power balance," which dynamically coordinates the output of various energy sources to achieve supply and demand balance while ensuring stable power supply to the load. However, existing multi-energy balance control systems generally have the following shortcomings:

[0003] On the one hand, most existing technologies rely on static scheduling strategies or simple numerical optimization models, ignoring the essential semantic differences between different energy consumption scenarios. This leads to delayed response and reduced energy efficiency when facing nonlinear disturbances (such as sudden charging surges or extreme weather), which seriously affects system robustness and energy supply continuity.

[0004] On the other hand, traditional scheduling logic lacks complex mechanisms such as "output ratio suppression, redundancy resource elasticity, and disturbance trend response", which can easily lead to single energy overload or competitive dispatch of multiple energy sources, reduce energy use efficiency, and even cause scheduling conflicts and instantaneous voltage fluctuations, thus limiting the large-scale integration and high-density deployment of multi-energy systems in zero-carbon parks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-energy transformer power balance system for zero-carbon industrial parks, thereby resolving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a multi-energy transformer power mutual assistance system for zero-carbon industrial parks, comprising the following steps:

[0008] S1. Conduct a time-based response requirement analysis of the load within the park;

[0009] S2. Based on the analysis results, construct a model to measure the response adaptability of various energy sources under different load intensities;

[0010] S3. Based on the results output by the response adaptability model, construct a scheduling ranking function to generate an energy scheduling priority sequence;

[0011] S4, based on the result of the scheduling sorting function, constructing an actual adjustment output function adapting to real-time fluctuations of the load, and outputting an actual output power;

[0012] S5, based on the actual output power, performing system-level power balance detection to determine whether the power supply requirement of the park is met;

[0013] S6, judging whether there is a rapid mutation in the system, and performing short-time gain or peak clipping processing on a single energy source through a local adjustment function.

[0014] Further optimize the technical solution, in step S1, when responding to demand analysis, a load characteristic function model is constructed, and interval segmentation is performed according to the time dimension;

[0015] The load characteristic function model is as follows:

[0016]

[0017] Among them,

[0018] : represents the load intensity at time t, with the unit of kilowatt;

[0019] : the current real-time power demand of the park;

[0020] : the power load caused by the building temperature control system of the park;

[0021] : additional energy consumption caused by the equipment running state;

[0022] : the response weight of each load item, which is dynamically adjusted through experience setting and operation and maintenance data;

[0023] Based on the model output , the load intensity is segmented into high response level and low response level.

[0024] Further optimize the technical solution, in step S2, the various types of energy including photovoltaic, wind energy, energy storage, and power grid, the response adaptation model is as follows:

[0025]

[0026] Among them,

[0027] : the response adaptation of the i-th type of energy to the load intensity at time t;

[0028] : the available output of the i-th energy source at time t, in kilowatts;

[0029] : the fluctuation rate of the i-th energy source, reflecting its instability;

[0030] : the priority coefficient of the i-th energy source;

[0031] : the fluctuation response suppression coefficient, controlling the negative impact of unstable energy sources on the system;

[0032] The model output will be input as a parameter for subsequent scheduling sorting.

[0033] Further optimize the technical solution, in step S3, the scheduling sorting function is as follows:

[0034]

[0035] Wherein,

[0036] : the final scheduling priority value of the i-th energy source;

[0037] : the current output proportion of the i-th energy source;

[0038] : the balance coefficient, used to avoid excessive output of a single energy source;

[0039] The system sorts according to The value size forms an energy scheduling priority sequence , wherein is the preferred energy source, and the priority decreases.

[0040] Further optimize the technical solution, in step S4, the actual adjustment output function is as follows:

[0041]

[0042] Wherein,

[0043] : the actual output power of the i-th energy source at time t;

[0044] : the redundancy adjustment coefficient of the i-th energy source, setting its ability to participate in compensation scheduling;

[0045] : the overall standby capacity ratio of the system, used to represent the callability of redundant resources;

[0046] The model not only determines the scheduling priority value ; but also uses the redundancy adjustment term to achieve flexible compensation when there is fluctuation or sudden power shortage.

[0047] Further optimization of the technical solution, in step S5, when performing system-level power balance detection, a system power balance equation is constructed, the power flow of all transformer access energy sources is integrated, it is judged whether the power supply requirements of the park are met, and a power gap or surplus signal is outputted;

[0048] The system power balance equation is as follows:

[0049]

[0050] Among them,

[0051] : the current power redundancy value of the system, a positive value indicates surplus, and a negative value indicates power shortage;

[0052] : the total number of access energy sources;

[0053] When , the system will reduce the output of low-priority energy; when , the output proportion of the unsaturated energy will be increased or the energy storage will be activated to ensure system real-time power conservation and reduce the impact on the power grid side.

[0054] Further optimization of the technical solution, in step S6, the local adjustment function is as follows:

[0055]

[0056] Among them,

[0057] : the output value of the i-th energy after disturbance correction;

[0058] : the change rate of the power redundancy value, used to judge the burst degree of the power gap;

[0059] : the transient response adjustment factor of the energy, reflecting its dynamic response ability;

[0060] When the power redundancy is rapidly reduced, that is, , the system will immediately amplify the response value to compensate for short-term power shortage and prevent local abnormal phenomena such as flicker and voltage drop.

[0061] ​Further optimization of the technical scheme, the system is based on the complex and changeable daily operation scene of zero carbon park, constructs a dynamic scene label mechanism, introduces non-numerical scene labels corresponding to energy use behavior, including "high temperature day", "exhibition activities", "night charging", and embeds them into mutual scheduling.

[0062] Further optimization of the technical scheme, in the dynamic scene label mechanism, the system identifies the typical scene of the park operation by comprehensively analyzing historical data and artificially adding time dimension information, and gives a scene label to the current time, which will affect the energy output sequencing strategy:

[0063] Under the label of "high temperature day", the air conditioner load is the main one, and the low fluctuation and high response energy is preferentially called;

[0064] Under the label of "exhibition activities", the overall load mutation is frequent, and the redundancy ratio is preferentially improved;

[0065] Under the label of "night charging", the proportion of wind energy and power grid cooperation is improved.

[0066] Further optimization of the technical scheme, the system further comprises the following functional modules:

[0067] Load image module;

[0068] Energy adaptation module;

[0069] Optimal sequence control module;

[0070] Output adjustment module;

[0071] Power balance module;

[0072] Disturbance correction module.

[0073] Secondly, the application provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the multi-energy transformer power mutual system for zero-carbon park in the first aspect of the application.

[0074] Thirdly, the application provides a computer readable storage medium, which stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the multi-energy transformer power mutual system for zero-carbon park in the first aspect of the application.

[0075] Compared with the prior art, the application provides a multi-energy transformer power mutual system for zero-carbon park, which has the following beneficial effects:

[0076] The multi-energy transformer power mutual aid system for the zero-carbon park adopts a multi-step coupling control mode to construct a full-process mutual aid mechanism from load behavior modeling, energy adaptation evaluation, dynamic optimal sequence regulation to disturbance correction. The system introduces a dynamic scene tag mechanism, so that the dispatching strategy has semantic adaptive capability and can adjust the energy calling logic according to the actual operation scene. Through the linkage adjustment of the optimal sequence regulation module and the disturbance correction module, millisecond-level response and rapid compensation of redundant resources are realized, which significantly improves the system operation stability, energy utilization efficiency and new energy access proportion, and meets the operation requirements of zero-carbon park multi-energy integration, high-frequency fluctuation and high reliability. BRIEF DESCRIPTION OF DRAWINGS

[0077] 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. 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.

[0078] Figure 1 A flowchart of a multi-energy transformer power mutual aid system for a zero-carbon park is provided for the present application.

[0079] Figure 2 A module schematic diagram of a multi-energy transformer power mutual aid system for a zero-carbon park is provided for the present application. DETAILED DESCRIPTION

[0080] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0081] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0082] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. In this specification, "in one embodiment" does not mean the same embodiment, nor does it mean an embodiment that is separate or selectively excluded from other embodiments.

[0083] Embodiment one:

[0084] Reference Figure 1For the first embodiment of the present application, the embodiment provides a multi-energy transformer power mutual aid system for a zero-carbon park, comprising the following steps:

[0085] S1, the time dimension based response demand analysis of the load in the park is carried out.

[0086] In order to realize the efficient mutual aid of multi-energy system in zero-carbon park, the first step is to carry out time dimension based response demand analysis of the load in the park.

[0087] In the existing multi-energy power supply system, load identification and scheduling usually take "real-time power" or "historical average load" as the basic input, and carry out static threshold judgment or linear programming calculation, which is used to guide the start-stop and output sequence of different types of energy (such as photovoltaic, wind power, energy storage and power grid). However, this kind of method often regards the load as a single numerical input, without deeply decomposing its source and characteristics, and lacks fine modeling of complex loads composed of different types of building equipment, temperature control system and running state. Therefore, under the complex scene of multi-energy coupling (such as frequent start-stop of equipment in the park, large environmental temperature difference, and significant change of peak-valley demand), the existing method cannot accurately reflect the load response structure, resulting in deviation of energy scheduling result from actual demand, power supply-demand mismatch, insufficient use of redundant energy, insufficient power supply capacity in critical period and other problems, which affects the power mutual aid scheduling accuracy and system robustness.

[0088] In the present application, the analysis not only considers the basic power demand of the electrical load, but also includes environmental factors (such as the influence of day-night temperature difference on building air conditioning energy), and when carrying out response demand analysis, a load characteristic function model is constructed, and the time dimension is divided into intervals;

[0089] The load characteristic function model is as follows:

[0090]

[0091] Among them,

[0092] : represents the load intensity at time t, unit: kilowatt.

[0093] : the current real-time power demand of the park. It is collected in real time through the total power metering device of the park (such as intelligent electric meter, low-voltage monitoring device), and is usually uploaded to the energy management system (EMS) in minute or second period.

[0094] : the power load caused by the building temperature control system in the park. Through the interface with building automation system (BAS), the instantaneous running power of energy-consuming equipment such as air conditioner and fresh air system is extracted, or it is estimated by multiplying the unit area temperature control load by the current active area.

[0095] : Additional energy consumption caused by the running state of the equipment. By collecting the running state and power data of key power-consuming equipment (such as data centers, industrial machinery, electric vehicle charging piles, etc.), the interface is supported by the electric control module or intelligent socket at the equipment end.

[0096] : The response weight of each load is set by experience and dynamically adjusted by operation and maintenance data. The energy system designer or operation and maintenance engineer sets the initial value in the range of 0.2-0.6 according to long-term power consumption data and scheduling preference experience, and can dynamically correct it through historical data fitting; in some scenarios, it can be automatically adjusted with time. The value range meets the normalization condition of , and each item is initially set in the range of 0.2-0.6, which is dynamically adjusted according to season, time period, and equipment structure.

[0097] Based on the model output , the load intensity is segmented into intervals, divided into high response levels (such as peak load period) and low response levels (such as night maintenance load period). Through the continuous output sequence of the model, the system can divide the all-weather load characteristics into multiple interval segments, such as early morning initial load, noon air conditioning peak, night equipment maintenance load, etc., realizing the "load level labeling" of time semantics and structure information fusion, and greatly improving the understanding of load dynamic behavior.

[0098] S2, based on the analysis results, construct a response adaptation model for measuring the response of various types of energy under different load intensities.

[0099] In a multi-energy coordinated power supply system, the scheduling strategy often relies on the size of the energy output power for direct sorting, such as according to the principle of "the one with the largest current power first" or "the one with the lowest unit energy consumption cost first" to allocate various types of energy. This method has certain advantages in terms of simple structure and lightweight algorithm, but its main defect is the lack of understanding of energy characteristics, especially the lack of consideration of the adaptation relationship between energy output stability and load level. For example, if only the instantaneous output of photovoltaic is high during the noon high-temperature period, it is easy to cause system fluctuations due to cloud cover or environmental disturbances, and even lead to power quality problems such as voltage drop and frequency deviation on the load side.

[0100] Various types of energy, including photovoltaic, wind energy, energy storage, and power grid, have a response adaptation model as follows:

[0101]

[0102] Where,

[0103] : The response fitness of the i-th energy source at time t.

[0104] : The available output of the i-th energy source at time t, in kilowatt. The power data can be read in real time through the output interface of the energy-side inverter or converter, such as photovoltaic inverter, energy storage system PCS, grid metering node, etc. The data is usually updated at a second or minute level.

[0105] : The fluctuation rate of the i-th energy source, reflecting its instability (e.g. high fluctuation rate of photovoltaic in cloudy environment). It is calculated by the ratio of standard deviation to mean value within a sliding time window (e.g. past 5 minutes), reflecting the relative instability of energy output. The value is automatically updated by the internal formula running in the controller. The value range is 0~1, the larger the value, the stronger the volatility. Photovoltaic can reach above 0.6 in cloudy weather, and energy storage system is usually below 0.1.

[0106] : The priority coefficient of the i-th energy source (e.g. photovoltaic is 1, grid is 0.2). It is set by the energy management strategy, artificially assigned according to the cleanliness, cost structure or strategic intention of different energy types, or automatically generated through the strategy library.

[0107] : The fluctuation response suppression coefficient, controlling the negative impact of unstable energy on the system. It is preset according to the fluctuation characteristics of various energy sources during system design, representing the control proportion of tolerance to instability during dispatching, which can be dynamically adjusted according to actual operation. The value range is 0.1 to 1, energy storage can be set to 0.1 due to its small fluctuation, photovoltaic and wind power are usually 0.3~0.7, and can be set to 1 if the system requires high stability.

[0108] Before the actual dispatching starts, the system calculates a set of response fitness based on the current state of various energy sources, forming a one-dimensional index set reflecting the current energy supply reliability and applicability. In addition, through the flexible adjustment of and , the model has good scalability and engineering debugging flexibility, and can quickly adapt to different park scenarios, different seasons, and even different dispatching strategies.

[0109] The output by the model will be used as a parameter input for subsequent dispatching sorting.

[0110] S3, based on the results output by the response fitness model, a dispatching sorting function is constructed to generate an energy dispatching priority sequence. Ensure stable energy supply under the premise of using clean energy first and minimizing dependence on the grid. ​

[0111] In the scheduling control of multi-energy systems, traditional methods mostly use static priority strategy or optimal allocation model based on single factor. For example, a common practice is to preset fixed priority for each type of energy (such as photovoltaic first, energy storage second, and grid last), and to call them in order of priority until the load requirement is met. The above technology has three shortcomings: first, it lacks dynamicity and cannot adjust the sorting logic in time according to the running state; second, it ignores energy supply balance and easily causes some energy to be overloaded for a long time, reducing its service life; third, it does not consider output proportion feedback, leading to the formation of "energy monopoly" in medium and long-term operation, that is, one energy continuously dominates energy supply, and other energies are in idle state, making it difficult to play the energy redundancy and safety regulation capacity of the overall system.

[0112] In the present application, the scheduling sorting function is as follows:

[0113]

[0114] Among them,

[0115] : the final scheduling priority value of the i-th type of energy.

[0116] : the current output proportion of the i-th type of energy. The total output energy of a certain energy from the start time of scheduling to the current time is calculated, and the value is calculated by comparing it with the total energy output in the same period. This value is automatically calculated and updated by EMS or scheduling control platform. This value is always between 0 and 1, indicating the contribution proportion of this energy in the overall energy supply. The proportion of each type of energy can dynamically float when the system is running normally. Typical values are 0.2-0.5 for photovoltaic, and less than 0.3 for low-carbon grid systems.

[0117] : balance coefficient, used to avoid excessive output of a single energy. It is set by system designers according to energy type, redundancy capacity and operation safety requirements, and is used to control the degree of inhibition of "energy output overload" in the scheduling logic. It can also be adjusted adaptively based on historical operation data. The value range is 0.1 to 1. The larger the value, the stronger the inhibition of the energy that has already output too much from participating in the next round of priority scheduling, which is suitable for preventing a single energy from dominating energy supply; for slow-response or high-cost energy (such as the grid), it can be set to 0.8, and for energy storage, it can be set to 0.6.

[0118] The system sorts according to the value of to form a priority sequence of energy scheduling , where is the first choice of energy, and the priority decreases.

[0119] Compared with the existing method of only relying on fixed priority or single index decision, the method can realize the scheduling strategy of "response leading and redundancy balance assisting", and avoid the problems of unbalanced energy use structure or long-term marginalization of specific energy.

[0120] S4, based on the result of the scheduling sorting function, an actual adjustment output function adapting to real-time fluctuations of load is constructed, and actual output power is output. When the preferred energy cannot be output in proportion due to force majeure (such as sudden weakening of light), the energy with the second highest priority can be timely supplemented, and a redundancy control factor is introduced to improve robustness.

[0121] In the output control of a multi-energy system, the traditional method is to take "planned output" or "predicted load demand" as the basis for adjustment, and is supplemented by a simple power balance control strategy to realize the coordinated output of various energies. A typical approach is to set a fixed total power value on the load side, and then allocate the load power among different energies in a static proportion or linear distribution manner. Although this method is simple in logic and low in implementation cost, it has serious practical adaptability problems. On the one hand, this method cannot respond to random fluctuations in energy output, such as sudden drop in photovoltaic power due to sudden change in light conditions, or drastic fluctuations in wind power output due to changes in wind speed, which can easily cause power gaps and cannot respond quickly. On the other hand, the traditional power distribution mechanism lacks dynamic utilization of system redundancy, and cannot effectively mobilize energy storage or other redundant energy in standby state, resulting in insufficient system robustness and easy occurrence of unstable operation problems such as flicker and voltage drop.

[0122] In the present application, the actual adjustment output function is as follows:

[0123]

[0124] Among them,

[0125] : the actual output power of the i-th energy at time t.

[0126] : the redundancy adjustment coefficient of the i-th energy, which sets the ability of participating in compensation scheduling. This parameter is set by the operation and maintenance personnel in the system design or operation strategy configuration stage, and its value can be set according to the response speed, energy storage capacity, charging and discharging rate and other indexes, or can be calibrated offline combined with operation data. The energy storage system has fast response and strong adjustment capacity, is set to 1, and wind power, photovoltaic power and the like are generally set to 0.2, and the power grid is set to 0.3 according to whether it is flow-limited.

[0127] The overall system reserve capacity ratio is used to indicate the availability of redundant resources. It is calculated by comparing the currently available energy capacity in reserve (such as energy storage SOC status and the grid's available capacity limit) with the current... The ratio is calculated. This value is updated in real time by the energy management system and is used to assess the overall regulation potential of the system.

[0128] This model not only determines based on scheduling priority values It can also be used during power fluctuations or sudden power outages. Redundant adjustment terms enable flexible compensation.

[0129] In practical system control, this model can be periodically invoked and calculated by the energy management system (EMS) or transformer integrated controller, and the output will be... The results will be directly transmitted to various energy interfaces (such as energy storage converters, photovoltaic inverters, and grid disconnection modules) as execution-level power setting commands to achieve precise output control. Through this mechanism, the system can not only intelligently allocate power but also have predictable dynamic response capabilities in the face of fluctuating loads.

[0130] This method breaks through the limitations of traditional static power allocation, dynamically linking energy output control with real-time priority, and endowing the system with "fluctuation resistance" through backup capacity parameters, enabling sustainable and stable power supply and response to transient disturbances during operation. Especially in environments with strong photovoltaic and wind power fluctuations, such as zero-carbon industrial parks, this mechanism can ensure that the system maintains a high-response, high-stability, and high-efficiency operating state when facing complex fluctuating load conditions.

[0131] S5. Based on the actual output power, perform system-level power balance detection to determine whether the power supply requirements of the park are met.

[0132] Currently, in multi-energy integrated power supply systems, to achieve a balance between total load and energy supply, most systems employ a simple control logic based on "static balance judgment + threshold triggering." A typical approach involves setting an allowable range for the power supply-demand difference in the energy management system. When the difference between the total output of all energy sources and the load power exceeds the set threshold, the control logic activates backup energy or initiates load-side management (such as power curtailment or peak shaving). While this method has some application value in small and medium-sized microgrid systems, it suffers from two significant drawbacks: firstly, it exhibits response lag. Because the power difference is determined discretely, the system can only trigger adjustment when the deviation reaches a certain fixed value, making it difficult to track rapidly changing load fluctuations in real time. Secondly, it employs a single strategy, failing to differentiate based on the adjustment capabilities or scheduling priorities of different energy sources. It often adopts a "one-size-fits-all" strategy (such as adjusting all outputs upwards or downwards), resulting in inaccurate adjustment effects, low utilization efficiency of redundant resources, and negatively impacting system economy and stability.

[0133] In the present application, when performing system-level power balance detection, a system power balance equation is constructed, all power flows of the transformer accessing energy are integrated, it is judged whether the power supply requirements of the park are met, and a power gap or surplus signal is outputted

[0134] The system power balance equation is as follows:

[0135]

[0136] Wherein,

[0137] The current power redundancy value of the system, a positive value represents surplus, and a negative value represents power shortage.

[0138] The total number of accessed energy types.

[0139] When , it means that the current supply is greater than the demand, and the system can appropriately reduce the output of the low-priority energy (such as the power grid) or reduce the output of part of the high-fluctuation energy to maintain energy efficiency; when , it means that the supply is less than the demand, and the output of the energy (such as energy storage and power grid) that still has adjustment margin needs to be timely improved, or the standby resource is activated to fill the difference, for ensuring the real-time power conservation of the system and reducing the impact on the power grid side.

[0140] S6, since the load characteristics in the zero-carbon park may have burstiness (such as the start of large equipment, the centralized charging of electric vehicles, etc.), it is necessary to judge whether there is a rapid mutation in the system, and a local adjustment function is used to perform short-time gain or peak clipping processing on a single energy.

[0141] In actual operation, especially in the case of a zero-carbon park with diverse load structures and strong burst behavior, there are often a large number of “second-level” disturbance events, such as the instantaneous start of large high-power equipment, the centralized insertion of electric vehicle groups, or the sudden drop of renewable energy causing power surge. Such disturbances have obvious short-term and violent fluctuation characteristics. If the medium and slow scheduling logic is still relied on for response, it will be severely delayed and cannot achieve millisecond-level intervention, resulting in temporary abnormalities in system frequency and voltage, causing terminal equipment tripping, data center fluctuations, and even power event alarms.

[0142] In the present application, the local adjustment function is as follows:

[0143]

[0144] Wherein,

[0145] The output value of the i-th energy after disturbance correction.

[0146] ​ : the rate of change of power redundancy value, used to determine the burst degree of power gap.

[0147] : the transient response adjustment factor of energy, reflecting its dynamic response ability. The parameter is set by the system designer according to the dynamic adjustment ability of the energy, usually based on the response time, power climbing rate and power control accuracy of the energy. It can be obtained by simulation or measurement of the output response time of the energy under historical disturbance conditions. The fastest energy storage system (such as lithium battery, super capacitor) can be set to 1; renewable energy such as wind power and photovoltaic is generally 0.3; the grid input is usually set to 0.3 due to the response lag.

[0148] When the power redundancy is detected to decrease rapidly, i.e. , the system will immediately amplify the response value to compensate for short-term power shortage in time and prevent local abnormal phenomena such as flicker and voltage drop.

[0149] The system will directly transmit this output to the digital control platform inside the transformer to dynamically adjust the power instructions of each energy port (such as energy storage output and grid call). In the actual operation of the control system, this model can be executed every second or higher frequency (such as 200 ms period) to capture the trend of each power redundancy mutation in the system and immediately switch the energy with fast adjustment ability to the emergency response state.

[0150] For example, at a certain moment, if multiple electric vehicles are simultaneously plugged into the charging pile system, causing the of the system to change from positive to negative within a few seconds, and , the system will immediately amplify the of the energy storage system or the wind power converter to avoid fluctuations in the main grid frequency and complete the short-term adjustment of the "first line of defense" before the main dispatching logic intervenes.

[0151] In this embodiment, the system constructs a dynamic scenario label mechanism based on the complex and variable daily operation scenarios of the zero-carbon park, introduces non-numerical scenario labels corresponding to energy use behaviors, including "high temperature day", "exhibition activity", "night charging", and embeds them into mutual dispatching.

[0152] In the dynamic scenario label mechanism, the system identifies the typical scenario in which the park is running by comprehensively analyzing historical data and artificially added time-dimension information, and gives the current time a scenario label, which will affect the energy output sequencing strategy:

[0153] Under the "high temperature day" label, the air conditioning load is dominant, and low fluctuation and high response energy is preferentially called;

[0154] Under the label of "exhibition activities", the overall load mutation is frequent, and the redundancy ratio is preferentially increased;

[0155] Under the label of "night charging", the proportion of wind energy and power grid cooperation is increased.

[0156] Compared with the traditional method, the mechanism has higher response accuracy and energy matching accuracy in the burst load environment, and enhances the perception and adaptation ability of the system to unstructured behavior patterns. By coupling the artificial experience rule with the adaptive label generation mechanism, the method significantly improves the interpretation ability of the power mutual assistance system to the real running situation and the scene robustness of the control strategy, and has good engineering implementability and intelligent scheduling promotion value.

[0157] Embodiment two:

[0158] Reference Figure 2 For the second embodiment of the application, the embodiment provides the following functional modules of the multi-energy transformer power mutual assistance system:

[0159] The load portrait module, corresponding to step S1, is used to obtain the typical electricity consumption behavior data of the park in each period, and to establish a load characteristic function model based on building load, device state and environmental influence. Through the structured modeling of electricity consumption behavior in different periods, the module outputs a unified load intensity index, providing a quantitative basis for subsequent energy supply and demand adaptation.

[0160] The energy adaptation module, corresponding to step S2, is used to analyze the current output capacity, fluctuation characteristics of various types of energy (photovoltaic, wind power, energy storage, power grid) and the matching degree of load level, and to output the response adaptation degree. This module can reflect the adaptability of each type of energy to the park load, supporting the generation of scheduling priority sequence.

[0161] The optimal sequence control module, corresponding to step S3, constructs a scheduling sorting function according to the energy adaptation degree and the energy redundancy ratio, and determines the calling sequence of different energies.

[0162] The output adjustment module, corresponding to step S4, is responsible for calculating the actual output power of each energy according to the previous energy scheduling priority sequence, considering the standby capacity, and introducing a redundancy factor to realize flexible control of energy output. This module ensures that the system has good dynamic scalability and uncertainty defense capability.

[0163] The power balance module, corresponding to step S5, calculates the difference between the total output of all energies in the system and the load demand, realizes the rapid detection of power redundancy or gap, and feeds back the results to the scheduling system to trigger the activation of standby energy or the reduction of part of the energy output, ensuring the overall power conservation.

[0164] The disturbance correction module, corresponding to step S6, tracks the change trend of the power redundancy value in real time, identifies a sudden load disturbance, adjusts the energy port output through a local adjustment function, and realizes millisecond-level interference response. This module is mainly used to ensure the short-time power supply stability of the park in complex power behavior scenarios.

[0165] The system realizes efficient mutual aid and dynamic supply-demand matching of various clean energies in zero-carbon parks through the cooperative work of the above six functional modules, and has strong robustness, adaptability and regulation response capability.

[0166] Embodiment three:

[0167] The embodiment also provides a computer device suitable for the case of the multi-energy transformer power mutual aid system for zero-carbon parks, which comprises a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the multi-energy transformer power mutual aid system for zero-carbon parks as proposed in the above embodiment.

[0168] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to realize the multi-energy transformer power mutual aid system for zero-carbon parks as proposed in the above embodiment.

[0169] The computer device can be a terminal, which comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0170] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0171] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0172] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0173] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the embodiments described above, various steps or methods can be implemented, in part, or in whole, in software, or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, can be employed: a combination of discrete logic circuits having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having logic gates, field programmable gate arrays (FPGA), or others.

[0174] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A multi-energy transformer power balance system for zero-carbon industrial parks, characterized in that, Includes the following steps: S1. Conduct a time-based response requirement analysis of the load within the park; S2. Based on the analysis results, construct a model to measure the response adaptability of various energy sources under different load intensities; The response fit model for various energy sources, including photovoltaic, wind, energy storage, and grid power, is shown below: ； in, The load intensity corresponding to the i-th type of energy at time t Response adaptability; The available output of the i-th type of energy at time t, in kilowatts; The volatility of energy of type i reflects its instability; Priority coefficient of the i-th energy type; : Fluctuation response suppression coefficient, which controls the negative impact of unstable energy on the system; The model outputs This will be used as a parameter input for subsequent scheduling and sorting; S3. Based on the results output by the response adaptability model, construct a scheduling ranking function to generate an energy scheduling priority sequence; The scheduling and sorting function is shown below: ； in, : The final scheduling priority value of the i-th type of energy; : The current output percentage of the i-th energy type; Balance coefficient: used to avoid over-output from a single energy source; The system according to The values ​​are sorted by size to form an energy scheduling priority sequence. ,in It is the preferred energy source, with priority decreasing thereafter; S4. Based on the results of the scheduling and sorting function, construct an actual power output adjustment function that adapts to real-time load fluctuations and outputs the actual output power. S5. Based on the actual output power, perform system-level power balance detection to determine whether the power supply requirements of the park are met; S6. Determine whether there is a rapid change in the system, and perform short-term gain or peak clipping on individual energy sources through local adjustment functions; The local adjustment function is shown below: ； in, : The output value of the i-th type of energy after perturbation correction; The rate of change of power redundancy value is used to determine the suddenness of power shortage. The transient response adjustment factor of energy reflects its dynamic response capability; When a rapid decrease in power redundancy is detected, i.e. At that time, the system will immediately amplify the response value. This is to compensate for short-term power shortages in a timely manner and prevent localized abnormal phenomena such as flickering and voltage drops.

2. The multi-energy transformer power balance system for zero-carbon industrial parks according to claim 1, characterized in that, In step S1, when performing response demand analysis, a load characteristic function model is constructed, and intervals are divided according to the time dimension; The load characteristic function model is shown below: ； in, : Represents the load intensity at time t, in kilowatts; The park's current real-time electricity demand; Power load caused by the building temperature control system in the park; Additional energy consumption caused by equipment operating status; : The response weights for each load component, which are dynamically adjusted based on experience and operational data; Based on model output , load strength The interval is segmented into high response level and low response level.

3. The multi-energy transformer power balance system for zero-carbon industrial parks according to claim 1, characterized in that, In step S4, the actual output force adjustment function is as follows: ； in, : The actual output power of the i-th type of energy at time t; : The redundancy adjustment coefficient of the i-th type of energy, setting its ability to participate in compensation scheduling; The overall system standby capacity ratio indicates the availability of redundant resources. This model not only determines based on scheduling priority values It can also be used during power fluctuations or sudden power outages. Redundant adjustment terms enable flexible compensation.

4. A multi-energy transformer power balance system for zero-carbon industrial parks according to claim 1, characterized in that, In step S5, when performing system-level power balance detection, a system power balance equation is constructed, integrating the power flow of all energy sources connected to the transformer, and determining whether the requirements of the park are met. The power supply requirements are met, and a power gap or surplus signal is output; The power balance equations for the system are as follows: ； in, : The current power redundancy value of the system; a positive value indicates surplus, and a negative value indicates insufficient power. : The total number of energy types connected; when When this happens, the system will reduce the output of low-priority energy sources; when At the same time, the output ratio of unsaturated energy sources can be increased or energy storage can be activated to ensure real-time power conservation of the system and reduce grid-side impact.

5. A multi-energy transformer power balance system for zero-carbon industrial parks according to claim 1, characterized in that, Based on the complex and ever-changing daily operation scenarios of zero-carbon parks, the system constructs a dynamic scenario labeling mechanism, introduces non-numerical scenario labels corresponding to energy consumption behavior, including scenario labels such as "high temperature day", "exhibition activities" and "nighttime charging", and embeds them into mutual assistance scheduling.

6. A multi-energy transformer power balance system for zero-carbon industrial parks according to claim 5, characterized in that, In the aforementioned dynamic scene labeling mechanism, the system identifies typical scenarios in which the park operates by integrating historical data and manually added time-dimensional information, and assigns a scene label to the current moment. This label will affect the energy output ranking strategy. Under the "high temperature day" label, air conditioning is the main load, and low-fluctuation, high-response energy is prioritized; Under the "Exhibition and Event" tag, the overall load fluctuates frequently, so it is advisable to prioritize increasing the redundancy ratio. Under the "Nighttime Charging" label, increase the synergy between wind power and the power grid.

7. A multi-energy transformer power balance system for zero-carbon industrial parks according to claim 1, characterized in that, The system also includes the following functional modules: Load profiling module; Energy adaptation module; Priority control module; Output adjustment module; Power balancing module; Disturbance correction module.

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