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

Abstract

The invention discloses a zero-carbon park-oriented multi-energy transformer power mutual aid system, and relates to the technical field of transformer equipment, and the system comprises the following steps: S1, carrying out response demand analysis based on a time dimension; s2, constructing a response adaptation degree model; s3, constructing a scheduling sorting function, and generating an energy scheduling priority sequence; s4, constructing an actual adjustment output function, and outputting actual output power; s5, executing system-level power balance detection, and judging whether the power supply requirement of the park is met or not; and S6, performing short-time gain or peak clipping processing on the single energy through the local adjustment function. According to the method, a full-process mutual aid mechanism from load behavior modeling, energy adaptation evaluation, dynamic optimal sequence regulation and control to disturbance correction is constructed by adopting a multi-step coupling control mode. Through linkage adjustment of the optimal sequence regulation and control module and the disturbance correction module, millisecond-level response and redundant resource rapid compensation are realized, and the system operation stability and the energy utilization efficiency are remarkably 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 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.

[0012] 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;

[0013] 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.

[0014] To further optimize this technical solution, in step S1, when performing response demand analysis, a load characteristic function model is constructed, and intervals are divided according to the time dimension;

[0015] The load characteristic function model is shown below:

[0016]

[0017] in,

[0018] : Represents the load intensity at time t, in kilowatts;

[0019] The park's current real-time electricity demand;

[0020] Power load caused by the building temperature control system in the park;

[0021] Additional energy consumption caused by equipment operating status;

[0022] : The response weights for each load component, which are dynamically adjusted based on experience and operational data;

[0023] Based on model output , load strength The interval is segmented into high response level and low response level.

[0024] To further optimize this technical solution, in step S2, various energy sources, including photovoltaic, wind, energy storage, and power grid, are represented by the following response adaptation model:

[0025]

[0026] in,

[0027] The load intensity corresponding to the i-th type of energy at time t Response adaptability;

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

[0029] The volatility of energy of type i reflects its instability;

[0030] Priority coefficient of the i-th energy type;

[0031] : Fluctuation response suppression coefficient, which controls the negative impact of unstable energy on the system;

[0032] The model outputs This will be used as a parameter input for subsequent scheduling and sorting.

[0033] To further optimize this technical solution, the scheduling and sorting function in step S3 is as follows:

[0034]

[0035] in,

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

[0037] : The current output percentage of the i-th energy type;

[0038] Balance coefficient: used to avoid over-output from a single energy source;

[0039] 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.

[0040] To further optimize this technical solution, in step S4, the actual output force adjustment function is as follows:

[0041]

[0042] in,

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

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

[0045] The overall system standby capacity ratio indicates the availability of redundant resources.

[0046] 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.

[0047] To further optimize this technical solution, 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;

[0048] The power balance equations for the system are as follows:

[0049]

[0050] in,

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

[0052] : The total number of energy types connected;

[0053] 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.

[0054] To further optimize this technical solution, in step S6, the local adjustment function is as follows:

[0055]

[0056] in,

[0057] : The output value of the i-th type of energy after perturbation correction;

[0058] The rate of change of power redundancy value is used to determine the suddenness of power shortage.

[0059] The transient response adjustment factor of energy reflects its dynamic response capability;

[0060] 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.

[0061] To further optimize this technical solution, the system constructs a dynamic scene tagging mechanism based on the complex and ever-changing daily operation scenarios of zero-carbon parks. It introduces non-numerical scene tags corresponding to energy consumption behavior, including scene tags such as "high temperature day", "exhibition activities" and "nighttime charging", and embeds them into the mutual assistance dispatch.

[0062] To further optimize this technical solution, the dynamic scene labeling mechanism identifies typical scenarios in the park's operation 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.

[0063] On "high-temperature days," air conditioning is the primary load, and low-fluctuation, high-response energy sources are prioritized.

[0064] Under the "Exhibition and Event" tag, the overall load fluctuates frequently, so it is advisable to prioritize increasing the redundancy ratio.

[0065] Under the "Nighttime Charging" label, increase the synergy between wind power and the power grid.

[0066] To further optimize this technical solution, the system also includes the following functional modules:

[0067] Load profiling module;

[0068] Energy adaptation module;

[0069] Priority control module;

[0070] Output adjustment module;

[0071] Power balancing module;

[0072] Disturbance correction module.

[0073] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a multi-energy transformer power mutual assistance system for zero-carbon parks as described in the first aspect of the present invention.

[0074] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a multi-energy transformer power mutual assistance system for zero-carbon parks as described in the first aspect of the present invention.

[0075] Compared with existing technologies, this invention provides a multi-energy transformer power mutual assistance system for zero-carbon industrial parks, which has the following beneficial effects:

[0076] This multi-energy transformer power mutual assistance system for zero-carbon industrial parks employs a multi-step coupled control approach to construct a full-process mutual assistance mechanism, encompassing load behavior modeling, energy matching assessment, dynamic priority control, and disturbance correction. The system introduces a dynamic scenario labeling mechanism, enabling the scheduling strategy to possess semantic adaptive capabilities and adjust energy dispatch logic according to actual operating scenarios. Through the coordinated adjustment of the priority control module and the disturbance correction module, millisecond-level response and rapid compensation of redundant resources are achieved, significantly improving system operational stability, energy utilization efficiency, and the proportion of new energy access, thus meeting the operational requirements of zero-carbon industrial parks for multi-energy integration, high-frequency fluctuations, and high reliability. Attached Figure Description

[0077] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 This is a schematic diagram of a multi-energy transformer power mutual assistance system for zero-carbon industrial parks proposed in this invention.

[0079] Figure 2 This is a schematic diagram of a multi-energy transformer power mutual assistance system for zero-carbon industrial parks proposed in this invention. Detailed Implementation

[0080] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0081] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0082] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0083] Example 1:

[0084] Reference Figure 1This is the first embodiment of the present invention, which provides a multi-energy transformer power mutual assistance system for zero-carbon industrial parks, including the following steps:

[0085] S1. Conduct a time-based response requirement analysis of the load within the park.

[0086] To achieve efficient mutual support among multiple energy systems in a zero-carbon industrial park, the first step is to conduct a time-based response demand analysis of the loads within the park.

[0087] In existing multi-energy power supply systems, load identification and scheduling typically use "real-time power" or "historical average load" as basic inputs, performing static threshold judgments or linear programming calculations to guide the start-up, shutdown, and output sequence of different types of energy (such as photovoltaic, wind power, energy storage, and the power grid). However, these methods often treat the load as a single numerical input, failing to deeply decompose its source and characteristics, and lacking detailed modeling of composite loads composed of different types of building equipment, temperature control systems, and operating states. Therefore, in complex multi-energy coupled scenarios (such as frequent equipment start-ups and shutdowns within a park, large environmental temperature differences, and significant peak-valley demand variations), existing methods cannot accurately reflect the load response structure, leading to energy scheduling results deviating from actual demand. This results in problems such as power supply-demand mismatch, insufficient utilization of redundant energy, or insufficient power supply capacity during critical periods, affecting the accuracy of power mutual assistance scheduling and system robustness.

[0088] In this invention, the analysis not only considers the basic power demand of the electrical load, but also includes environmental factors (such as the impact of diurnal temperature difference on building air conditioning energy consumption). When conducting response demand analysis, a load characteristic function model is constructed and the interval is divided according to the time dimension.

[0089] The load characteristic function model is shown below:

[0090]

[0091] in,

[0092] : Represents the load intensity at time t, in kilowatts.

[0093] Real-time electricity demand in the park. This data is collected in real time by the park's main electricity metering devices (such as smart meters and low-voltage monitoring devices) and is typically uploaded to the Energy Management System (EMS) on a minute-by-minute or second-by-second basis.

[0094] Power load caused by the building temperature control system in the park. It is obtained by interfacing with the building automation system (BAS) to extract the instantaneous operating power of energy-consuming equipment such as air conditioning and fresh air systems, or by multiplying the temperature control load per unit area by the currently used area.

[0095] Additional energy consumption caused by equipment operating status. This is achieved by collecting operating status and power data from key electrical equipment (such as data centers, industrial machinery, and electric vehicle charging stations), with interface support provided by the equipment's electrical control module or smart socket.

[0096] This represents the response weight for each load component. The weights are set empirically and dynamically adjusted based on operational data. They are set by energy system designers or operations engineers based on long-term electricity consumption data and scheduling preferences, and can be dynamically corrected through historical data fitting; in some scenarios, they can be set to adjust automatically over time. The value range meets the following requirements. The normalization conditions are such that each term has a constant initial value in the range of 0.2–0.6, which is dynamically fine-tuned according to the season, time period, and equipment structure.

[0097] Based on model output , load strength The system segments load into high-response levels (e.g., peak load periods) and low-response levels (e.g., nighttime maintenance load periods). Through the continuous output sequence of this model, the system can divide the all-weather load characteristics into multiple segments, such as the initial load in the morning, the midday high-temperature air conditioning peak, and the nighttime equipment maintenance load. This achieves "load level labeling" that integrates temporal semantics and structural information, greatly improving the understanding of dynamic load behavior.

[0098] S2. Based on the analysis results, construct a model to measure the response adaptability of various energy sources under different load intensities.

[0099] In multi-energy coordinated power supply systems, scheduling strategies often rely on directly prioritizing energy output power, such as allocating various energy sources according to the principles of "prioritizing the energy with the highest current power" or "prioritizing the energy with the lowest unit energy consumption cost." While these methods offer advantages in terms of simple structure and lightweight algorithms, their main drawback lies in insufficient understanding of energy characteristics, particularly the lack of adequate consideration of the compatibility between energy output stability and load levels. For example, prioritizing photovoltaic power generation solely based on its high instantaneous output during the midday heat can easily lead to system fluctuations due to cloud cover or environmental disturbances, and may even cause power quality problems such as voltage drops and frequency shifts on the load side.

[0100] The response fit model for various energy sources, including photovoltaic, wind, energy storage, and grid power, is shown below:

[0101]

[0102] in,

[0103] The load intensity corresponding to the i-th type of energy at time t Response adaptability.

[0104] The available power output of the i-th type of energy at time t, expressed in kilowatts. Power data is read in real-time through the output interface of energy-side inverters or converters, such as photovoltaic inverters, PCS energy storage systems, and grid metering nodes. Data is typically updated on a second- or minute-by-minute basis.

[0105] The i-th type of energy volatility reflects its instability (e.g., high volatility of photovoltaic power in cloudy environments). It is calculated by the ratio of the standard deviation to the mean of energy output within a sliding time window (e.g., the past 5 minutes), reflecting the relative instability of energy output. This value is automatically updated by the controller's internal operating formula. The value ranges from 0 to 1, with a larger value indicating stronger volatility. Photovoltaic power can reach above 0.6 in cloudy weather, while energy storage systems are typically below 0.1.

[0106] : Priority coefficient for the i-th energy type (e.g., 1 for photovoltaic and 0.2 for grid). This is set by the energy management strategy and can be manually assigned based on the cleanliness, cost structure, or strategic intent of different energy types, or can be automatically generated from a strategy library.

[0107] : Fluctuation response suppression coefficient, controlling the negative impact of unstable energy sources on the system. Preset during system design based on the fluctuation characteristics of various energy sources, it represents the control ratio for the instability tolerance during dispatching. It can be dynamically adjusted according to actual operation. The value range is 0.1 to 1. For energy storage, due to its low volatility, it can be set to 0.1; for photovoltaic and wind power, it is typically 0.3 to 0.7; if the system requires high stability, it can be set to 1.

[0108] Before the actual scheduling is initiated, the system calculates a set of response adaptability based on the current state of various energy sources. This forms a one-dimensional index set reflecting the current reliability and applicability of energy supply. Furthermore, through... and With its flexible adjustments, the model has good scalability and engineering debugging flexibility, and can be quickly adapted to different park scenarios, different seasons, and even different scheduling strategies.

[0109] The model outputs This will be used as a parameter input for subsequent scheduling and sorting.

[0110] S3. Based on the results output by the response adaptability model, construct a scheduling ranking function to generate an energy scheduling priority sequence. Prioritize the use of clean energy while ensuring a stable energy supply and minimizing dependence on the power grid.

[0111] In the scheduling and control of multi-energy systems, traditional methods often employ static priority strategies or single-factor optimal allocation models. For example, a common practice is to pre-set fixed priorities for various energy sources (e.g., photovoltaic first, energy storage second, and grid last), and then call them sequentially according to priority during operation until the load requirements are met. These techniques have three main shortcomings: first, they lack dynamism and cannot adjust the sequencing logic in a timely manner based on the operating status; second, they neglect energy supply balance, easily leading to long-term overload operation of some energy sources and reducing their service life; and third, they do not consider output ratio feedback, which can easily lead to "energy monopoly" in the medium to long term, where one energy source continuously dominates the energy supply while other energy sources remain idle, making it difficult to leverage the overall system's energy redundancy and safety regulation capabilities.

[0112] In this invention, the scheduling and sorting function is as follows:

[0113]

[0114] in,

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

[0116] : The current output proportion of energy type i. This is calculated by accumulating the total output energy of a specific energy source from the start of dispatch to the current time, and then comparing it to the total system energy output during the same period. This value is automatically calculated and updated by the EMS or dispatch control platform. This value always ranges from 0 to 1, representing the contribution proportion of this energy source to the overall energy supply. During normal system operation, the proportion of each energy type can dynamically fluctuate; typical values ​​include photovoltaic (PV) at 0.2-0.5%, and in low-carbon grid systems, it can be less than 0.3.

[0117] Balance coefficient: Used to prevent excessive output from a single energy source. It is set by system designers based on energy type, redundancy capacity, and operational safety requirements, and is used to regulate the degree of suppression of "energy output overload" in the scheduling logic. It can also be adaptively adjusted based on historical operating data. The value ranges from 0.1 to 1. A larger value indicates a stronger suppression of energy that has already been over-output from participating in the next round of priority scheduling, suitable for preventing a single energy source from dominating power supply; for energy sources with slow response or high costs (such as the power grid), it can be set to 0.8, and for energy storage, it can be set to 0.6.

[0118] 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.

[0119] Compared to existing methods that rely solely on fixed priorities or single indicators for decision-making, this approach enables a scheduling strategy that is "response-driven and redundancy-balanced," thus avoiding the problems of imbalanced energy use structure or the long-term marginalization of specific energy sources.

[0120] 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. Ensure that when the preferred energy source cannot output proportionally due to force majeure (such as a sudden decrease in sunlight), the next highest priority energy source can promptly fill the gap, and introduce a redundancy control factor to improve robustness.

[0121] In the output control of multi-energy systems, traditional methods often rely on "planned output" or "predicted load demand" as the basis for adjustment, supplemented by simple power balance control strategies to achieve coordinated output of various energy sources. A typical approach is to set a fixed total power value on the load side and then distribute the load power among different energy sources according to a static ratio or linear allocation method. While this method is logically simple and has low implementation costs, it suffers from serious practical adaptability problems. On the one hand, this method cannot cope with random fluctuations in energy output, such as sudden changes in sunlight conditions causing a momentary drop in photovoltaic power, or changes in wind speed causing drastic fluctuations in wind power output, easily leading to system power gaps and an inability to respond quickly. On the other hand, traditional power allocation mechanisms lack dynamic utilization of system redundancy capabilities and cannot effectively mobilize energy storage or other redundant energy sources in standby mode, resulting in insufficient system robustness and susceptibility to operational instability problems such as flickering and voltage drops.

[0122] In this invention, the actual output force adjustment function is as follows:

[0123]

[0124] in,

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

[0126] : The redundancy adjustment coefficient for the i-th type of energy, defining its ability to participate in compensation scheduling. This parameter is set by operations and maintenance personnel during the system design or operation strategy configuration phase. Its value can be set based on indicators such as energy response speed, energy storage capacity, and charge / discharge rate, or it can be calibrated offline using operational data. Energy storage systems have fast response and strong adjustment capabilities. Set to 1; for fluctuating energy sources such as wind power and photovoltaics, it is generally set to 0.2; and for the power grid, it is set to 0.3 depending on whether current is 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 this invention, 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 to determine whether the requirements of the park are met. The power supply requirements are met, and a power gap or surplus signal is output;

[0134] The power balance equations for the system are as follows:

[0135]

[0136] in,

[0137] : The current power redundancy value of the system; a positive value indicates surplus, and a negative value indicates insufficient power.

[0138] : The total number of energy types connected;

[0139] when When the current supply exceeds demand, the system can appropriately reduce the output of low-priority energy sources (such as the power grid) or reduce the output of some highly volatile energy sources to maintain energy efficiency; when When this occurs, it indicates that supply is insufficient to meet demand, and it is necessary to promptly increase the output of energy sources with remaining adjustment capacity (such as energy storage and the power grid), or activate backup resources to fill the gap, in order to ensure real-time power conservation of the system and reduce grid-side impact.

[0140] S6. Since the load characteristics in the zero-carbon park may be sudden (such as the start-up of large equipment, centralized charging of electric vehicles, etc.), it is necessary to determine whether there are rapid changes in the system, and to perform short-term gain or peak shaving processing on individual energy sources through local adjustment functions.

[0141] In actual operation, especially in scenarios like zero-carbon industrial parks where load structures are diverse and behaviors are highly unpredictable, there are often numerous "second-level" disturbance events. These include the instantaneous startup of large, high-power equipment, concentrated charging of electric vehicles, or sudden power fluctuations caused by a sudden drop in renewable energy. These disturbances are characterized by significant short-term and drastic fluctuations. If the response relies on medium- or slow-speed scheduling logic, it often results in severe lag and fails to achieve millisecond-level intervention. This leads to brief anomalies in system frequency and voltage, causing terminal equipment to trip, data center fluctuations, and even power event alarms.

[0142] In this invention, the local adjustment function is as follows:

[0143]

[0144] in,

[0145] : The output value of the i-th type of energy after perturbation correction.

[0146] The rate of change of power redundancy is used to determine the suddenness of power shortage.

[0147] The transient response adjustment factor of an energy source reflects its dynamic response capability. This parameter is set by the system designer based on the energy source's dynamic adjustment capability, typically evaluated comprehensively based on indicators such as energy response time, power ramp-up rate, and power control accuracy. It can be obtained through simulation or actual measurement of the energy source's output response time under historical disturbance conditions. The fastest-responding energy storage systems (such as lithium batteries and supercapacitors) can be set to 1; renewable energy sources such as wind power and photovoltaics are generally set to 0.3; grid input, due to its response lag, is usually set to 0.3.

[0148] 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.

[0149] The system will transmit this output directly to the digital control platform inside the transformer, dynamically adjusting the power commands corresponding to each energy port (such as energy storage output and grid access). In the actual operation of the control system, this model can be executed every second or higher (such as a 200ms cycle) to capture the trend of every power redundancy change in the system and immediately switch energy sources with rapid adjustment capabilities to emergency response status.

[0150] For example, if multiple electric vehicles simultaneously plug into the charging station system at a certain moment, it causes the system to... It changes from positive to negative within seconds, and At this point, the system will immediately amplify the energy storage system or wind power converter. This is to avoid frequency fluctuations in the main power grid and to complete short-term adjustments of the "first line of defense" before the main dispatch logic intervenes.

[0151] In this embodiment, the system constructs a dynamic scene labeling mechanism based on the complex and ever-changing daily operation scenarios of the zero-carbon park, introduces non-numerical scene labels corresponding to energy consumption behavior, including scene labels such as "high temperature day", "exhibition activities" and "nighttime charging", and embeds them into the mutual assistance scheduling.

[0152] 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.

[0153] On "high-temperature days," air conditioning is the primary load, and low-fluctuation, high-response energy sources are prioritized.

[0154] Under the "Exhibition and Event" tag, the overall load fluctuates frequently, so it is advisable to prioritize increasing the redundancy ratio.

[0155] Under the "Nighttime Charging" label, increase the synergy between wind power and the power grid.

[0156] Compared to traditional methods, this mechanism exhibits higher response accuracy and energy matching precision in sudden load environments, while enhancing the system's perception and adaptation to unstructured behavioral patterns. By coupling human-based empirical rules with an adaptive label generation mechanism, this method significantly improves the power mutual assistance system's ability to interpret real-world operating scenarios and the scenario robustness of its control strategies, demonstrating good engineering feasibility and potential for widespread application in intelligent scheduling.

[0157] Example 2:

[0158] Reference Figure 2 This is the second embodiment of the present invention, which provides the following functional modules of a multi-energy transformer power mutual assistance system:

[0159] The load profiling module, corresponding to step S1, is used to acquire typical electricity consumption behavior data for different time periods in the park and establish a load characteristic function model based on building load, equipment status, and environmental impact. Through structured modeling of electricity consumption behavior at different time periods, this 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 matching degree between the current output capacity, fluctuation characteristics, and load level of various energy sources (photovoltaics, wind power, energy storage, and power grid) and output the response adaptation degree. This module can reflect the adaptability of each energy source to the park's load and support the generation of scheduling priority sequences.

[0161] The priority control module, corresponding to step S3, constructs a scheduling sorting function based on information such as energy adaptability and energy redundancy ratio to determine the order in which different energy sources are called.

[0162] The output regulation module, corresponding to step S4, is responsible for calculating the actual output power of each energy source based on the priority sequence of preceding energy dispatches, taking into account reserve capacity, and introducing a redundancy factor to achieve flexible control of energy output. This module ensures that the system has good dynamic scalability and uncertainty defense capabilities.

[0163] The power balancing module, corresponding to step S5, calculates the difference between the total output of all energy sources in the system and the load demand, enabling rapid detection of power redundancy or shortage, and feeds the results back to the scheduling system to trigger the activation of backup energy or the reduction of some energy output, ensuring overall power conservation.

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

[0165] The system works in concert through the above six functional modules to achieve efficient mutual support and dynamic supply and demand matching of various clean energy sources in the zero-carbon park, and has strong robustness, adaptability and control response capabilities.

[0166] Example 3:

[0167] This embodiment also provides a computer device applicable to a multi-energy transformer power exchange system for zero-carbon parks, including 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 a multi-energy transformer power exchange system for zero-carbon parks as proposed in the above embodiment.

[0168] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a multi-energy transformer power balance system for zero-carbon parks as proposed in the above embodiments.

[0169] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[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 various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0174] 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 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; S3. Based on the results output by the response adaptability model, construct a scheduling ranking function to generate an energy scheduling priority sequence; 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.

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 S2, various energy sources, including photovoltaic, wind, energy storage, and power grid, are represented by the following response adaptation model: ； 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.

4. A multi-energy transformer power balance system for zero-carbon industrial parks according to claim 1, characterized in that, In step S3, the scheduling and sorting function is as follows: ； 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.

5. A 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.

6. 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.

7. A multi-energy transformer power balance system for zero-carbon industrial parks according to claim 1, characterized in that, In step S6, the local adjustment function is as follows: ； 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.

8. 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.

9. A multi-energy transformer power balance system for zero-carbon industrial parks according to claim 8, 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.

10. 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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