An industrial park-based energy management method and system

Abstract

This application relates to an energy management method and system for industrial parks, belonging to the field of energy management technology. The method includes: acquiring low-price replenishment periods and subsequent photovoltaic periods; dividing the subsequent photovoltaic periods into unit electricity consumption periods based on preset unit electricity consumption durations, and acquiring a park work set based on these unit electricity consumption periods; acquiring environmental forecast information within each unit electricity consumption period, and determining the predicted power generation based on the environmental forecast information, and determining the predicted power consumption based on the park work set; calculating the predicted power storage capacity based on the predicted power generation and predicted power consumption, and analyzing the predicted power storage capacity in each subsequent photovoltaic period to determine the instantaneous power storage capacity; calculating the required power storage capacity based on preset full-load power storage capacity and the maximum instantaneous power storage capacity, and replenishing the energy storage batteries according to the required power storage capacity during the low-price replenishment period. This application has the function of improving the energy management effect within the industrial park.

Description

Technical Field

[0001] This application relates to the field of energy management technology, and in particular to an energy management method and system based on industrial parks. Background Technology

[0002] With the widespread application of distributed photovoltaic (PV) power generation in industrial parks, improving the self-consumption rate of PV power generation, reducing dependence on the power grid, and lowering energy costs have become important issues for energy management in these parks. To address the intermittency and volatility of PV power generation, industrial parks are now commonly equipped with battery energy storage systems to achieve the dual goals of peak shaving and valley filling, and green electricity consumption.

[0003] Currently, most typical energy storage dispatch methods in industrial parks are based on fixed threshold strategies. That is, the system usually prioritizes using off-peak electricity prices at night to replenish energy storage batteries in order to obtain peak-valley price difference revenue. At the same time, in order to avoid the phenomenon of "curtailment" caused by excess electricity generated by photovoltaic power generation during the daytime of the next day, the dispatch system will set a reserved capacity threshold. That is, during the nighttime charging phase, the batteries are not fully charged, but a certain proportion of capacity space is reserved to store the portion of photovoltaic power generation that exceeds the immediate load for the next day.

[0004] Among the aforementioned technologies, photovoltaic (PV) power output is significantly affected by weather conditions, with actual power generation varying considerably across different weather types. Fixed thresholds cannot dynamically adapt to weather changes. When the next day is cloudy / rainy and PV power generation is low, the reserved storage space may not be fully utilized, resulting in some energy storage capacity not being used to absorb off-peak electricity at night, leading to wasted storage capacity and reduced peak-valley arbitrage profits. Conversely, when the next day is sunny and PV power generation is high, the fixed reserved space may not be sufficient to fully absorb the excess PV power, still posing a risk of curtailment. Therefore, the current energy management performance in the park is poor and there is room for improvement. Summary of the Invention

[0005] To improve energy management within industrial parks, this application provides an energy management method and system based on industrial parks.

[0006] Firstly, this application provides an energy management method based on industrial parks, employing the following technical solution: An energy management method based on industrial parks includes: To secure low-price supplementary periods and subsequent photovoltaic periods; The subsequent photovoltaic period is divided according to the preset unit electricity consumption duration to determine the unit electricity consumption period, and the park operation set is obtained according to the unit electricity consumption period; Environmental forecast information is obtained during the unit's electricity consumption period, and the predicted power generation is determined based on the environmental forecast information. The predicted electricity consumption is also determined based on the park's work set. The predicted energy storage capacity is determined by calculating the predicted power generation and predicted power consumption, and the instantaneous energy storage capacity is determined by analyzing the predicted energy storage capacity in each subsequent photovoltaic period. The required storage capacity is determined by calculating the preset full-load storage capacity and the maximum instantaneous storage capacity, and the energy storage battery is replenished according to the required storage capacity during the low-price replenishment period.

[0007] Optionally, the steps for determining the predicted electricity consumption based on the park's work set include: The required tasks are determined based on the park's task set, and attention windows are constructed based on the preset attention duration. Within the monitoring window, obtain the individual electricity consumption based on the work sets of each park; The park operation set containing only the required operation tasks is defined as the included operation set, and the benchmark operation set is determined in the included operation set. The electricity consumption of the individual units in the benchmark operation set is defined as the benchmark electricity consumption. Demand tasks that are not within the included task set are defined as external tasks, and the independent power consumption of external tasks is determined by analyzing the power consumption of each individual unit within the attention window. The predicted electricity consumption is determined by calculation based on the baseline electricity consumption and all independent electricity consumption.

[0008] Optionally, the steps for determining the baseline job set within the contained job set include: The number of tasks within the included task set is determined by counting the required tasks within that set. Define the set of jobs corresponding to the largest number of embedded tasks as the candidate job set, and determine the number of tasks that appear in the attention window based on each required job task in the candidate job set. The frequency of set tasks is determined by calculating the number of occurrences of each task in the candidate task set, and the candidate task set corresponding to the minimum set task frequency is determined as the baseline task set.

[0009] Optionally, after the frequency of the aggregated tasks is determined, the energy management method based on the industrial park also includes: Determine if there exist at least two sets of candidate jobs with the same and smallest number of task frequencies; If there are no at least two candidate job sets with the same minimum set task frequency, then the candidate job set corresponding to the minimum set task frequency is determined as the baseline job set. If there are at least two candidate job sets with the same minimum set task frequency, then the candidate job set corresponding to the minimum set task frequency is defined as the backup job set. The external tasks are randomly combined within the candidate task set to construct external task combinations, and the frequency of combined tasks is determined based on the external task combinations. The number of external tasks is determined by counting the external tasks in the combination of external tasks, and the important weight parameters corresponding to the number of external tasks are determined according to the preset important matching relationship. The appropriate matching coefficient is determined by calculating all the important weight parameters and the corresponding combination task frequency, and the candidate task set corresponding to the largest appropriate matching coefficient is determined as the baseline task set.

[0010] Optionally, the steps to determine the independent power consumption of external tasks by analyzing the power consumption of each individual unit within the monitoring window include: The electricity consumption of each park's work set within the attention window is combined to define the combination that can determine the electricity consumption of external work tasks as the associated combination, and the electricity consumption of the associated combination is defined as the local electricity consumption. The local similar range is determined by calculating the local electricity consumption and the preset allowable deviation, and the local similar range is determined by counting the local electricity consumption within the local similar range. The local similar range corresponding to the largest similar internal quantity is defined as the local representative range, and the independent electricity consumption is determined based on the local electricity consumption of each local representative range.

[0011] Optionally, the step of calculating and determining the independent electricity consumption based on the electricity consumption of each local area within the local representative range includes: The number of items within the associated group is determined by counting the park operation sets corresponding to the local electricity consumption. The number of tasks contained within the park operation set corresponding to the local electricity consumption is determined, and this number of tasks is defined as the number of local units. The reliability parameters of the data are determined by calculating the number of related internal units and the number of all local units. A simulated electricity consumption is randomly generated within a local representative range, and the simulation deviation is determined based on the simulated electricity consumption and the local electricity consumption. The simulation reliability is determined by calculating all simulation deviations and data reliability parameters, and the simulation power consumption corresponding to the highest simulation reliability is determined as the independent power consumption.

[0012] Optionally, the steps for replenishing the energy storage battery based on the required stored capacity during periods of low price replenishment include: Get the remaining battery level; The required replenishment amount is determined by calculating the difference between the remaining stored power and the required stored power, and the required replenishment duration is determined according to the preset replenishment matching relationship. Based on the demand-based replenishment duration, a simulated replenishment period is randomly constructed within the low-price replenishment period, and the local electricity consumption is determined based on the simulated replenishment period within the monitoring window. The simulated replenishment period corresponding to the minimum local power consumption is defined as the replenishment operation period, and the energy storage battery replenishment operation begins at the beginning of the replenishment operation period.

[0013] Secondly, this application provides an energy management system based on an industrial park, which adopts the following technical solution: An energy management system based on an industrial park includes: The acquisition module is used to acquire low-price supplementary periods and subsequent photovoltaic periods; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The processing module divides the subsequent photovoltaic period into units based on the preset unit electricity consumption duration to determine the unit electricity consumption period, and obtains the park operation set based on the unit electricity consumption period; The acquisition module obtains environmental forecast information during the unit electricity consumption period, and the processing module determines the predicted power generation based on the environmental forecast information and the predicted electricity consumption based on the park's work set. The processing module calculates the predicted power storage capacity based on the predicted power generation and predicted power consumption, and analyzes the predicted power storage capacity in each subsequent photovoltaic period to determine the instantaneous power storage capacity. The processing module calculates the required storage capacity based on the preset full-load storage capacity and the maximum instantaneous storage capacity, and replenishes the energy storage battery according to the required storage capacity during the low-price replenishment period.

[0014] In summary, this application includes at least one of the following beneficial technical effects: Before carrying out energy storage operations, the required amount of low-cost energy storage can be determined based on the tasks to be performed in the coming period and the forecast of photovoltaic power generation, thereby improving the energy management effect in the park. In the process of forecasting electricity consumption, appropriate data can be selected for analysis based on the frequency of each task in historical data, thereby improving the accuracy of data analysis. Attached Figure Description

[0015] Figure 1 This is a flowchart of an energy management approach based on industrial parks.

[0016] Figure 2 This is a module flowchart based on energy management methods for industrial parks. Detailed Implementation

[0017] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0018] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0019] This application discloses an energy management method based on industrial parks, referring to... Figure 1 The methodology for energy management based on industrial parks includes the following steps: Step S100: Obtain the low-price supplementary period and subsequent photovoltaic periods.

[0020] The low-price replenishment period is the time period during which electricity can be replenished at a low unit price, such as 0:00-8:00 every day; the subsequent photovoltaic period is the time when photovoltaic charging can be carried out after the low-price replenishment period, such as 8:00-16:00. The specific time will be set by the staff according to the local environmental conditions.

[0021] Step S101: Divide the subsequent photovoltaic time period into units based on the preset unit electricity consumption duration to determine the unit electricity consumption period, and obtain the park operation set based on the unit electricity consumption period.

[0022] The unit electricity consumption duration is a fixed duration set by the staff, such as 1 hour. The unit electricity consumption duration can be used to divide the subsequent photovoltaic period into unit electricity consumption periods of equal width. The beginning of the first unit electricity consumption period coincides with the beginning of the subsequent photovoltaic period, and the end of the last unit electricity consumption period coincides with the end of the subsequent photovoltaic period. The park operation set is the set of tasks that can be carried out in the industrial park within the unit electricity consumption period, such as the operation of Plant A. It can be obtained and determined by the operation tasks to be performed the next day that are entered in advance by the staff.

[0023] Step S102: Obtain environmental forecast information during the unit's electricity consumption period, determine the predicted power generation based on the environmental forecast information, and determine the predicted electricity consumption based on the park's work set.

[0024] Environmental prediction information refers to meteorological forecast data obtained through weather forecasting algorithms for a unit electricity consumption period, including solar radiation intensity, temperature, cloud cover, humidity, wind speed, etc.; predicted power generation refers to the amount of electricity that the photovoltaic system in the park can generate within a unit electricity consumption period under the environmental prediction information, calculated by a set prediction algorithm. Specifically, this can be achieved through steps such as data acquisition, feature processing, model prediction, and result output. This power generation prediction method is a conventional technical means for those skilled in the art and will not be elaborated here; predicted power consumption is the amount of electricity consumed when the industrial park executes a set of park operations, as detailed in steps S200-S204.

[0025] Step S103: Calculate the predicted power storage capacity based on the predicted power generation and predicted power consumption, and analyze the predicted power storage capacity in each subsequent photovoltaic period to determine the instantaneous power storage capacity.

[0026] Predicted energy storage capacity refers to the extra energy generated in a single unit of electricity consumption, determined by subtracting predicted energy consumption from predicted power generation. Instantaneous energy storage capacity refers to the amount of energy stored in the battery during each unit of electricity consumption. The instantaneous energy storage capacity in the first unit of electricity consumption is its own predicted energy storage capacity, the instantaneous energy storage capacity in the second unit of electricity consumption is the sum of its own predicted energy storage capacity and the predicted energy storage capacity in the first unit of electricity consumption, and so on, with the instantaneous energy storage capacity in the last unit of electricity consumption being the sum of all predicted energy storage capacities.

[0027] Step S104: Calculate the required storage capacity based on the preset full-load storage capacity and the maximum instantaneous storage capacity, and replenish the energy storage battery according to the required storage capacity during the low-price replenishment period.

[0028] The full-load storage capacity is the maximum charging capacity of the energy storage battery set by the staff, such as 100%. However, a margin is usually set to prevent the occurrence of light curtailment. This parameter can be set to 98%. By subtracting the maximum instantaneous storage capacity from the full-load storage capacity, you can obtain the amount of electricity that needs to be purchased at a low price to maintain the required storage capacity. At this time, the energy storage battery can be replenished to the required storage capacity during the low-price replenishment period.

[0029] The steps for determining forecasted electricity consumption based on the park's work set include: Step S200: Determine the required tasks based on the park's task set, and construct a focus window based on the preset focus duration.

[0030] The demand tasks are the execution tasks of the park's work set. The park's work set is formed by combining one or more demand tasks. The attention duration is a duration value set by the staff that can reflect the historical electricity consumption of the industrial park. By constructing the attention time window, it is convenient to collect, acquire, process and analyze the data within the attention time window.

[0031] Step S201: Obtain the individual power consumption based on the work sets of each park within the attention window.

[0032] Individual power consumption refers to the actual power consumed by each park work group within the monitoring time window under a unit power consumption duration.

[0033] Step S202: Define the park operation set that only contains the required operation tasks as the contained operation set, determine the benchmark operation set in the contained operation set, and define the individual power consumption of the benchmark operation set as the benchmark power consumption.

[0034] Define an embedded work set to identify and define different work sets in the park; the benchmark work set is an embedded work set among all embedded work sets, which can be randomly determined or determined by the method of steps S300-S302; define a benchmark electricity consumption to identify and distinguish the electricity consumption of individual units in the benchmark work set. When a single benchmark work set exists multiple times, the average electricity consumption of each unit can be calculated to determine the benchmark electricity consumption.

[0035] Step S203: Define the required tasks that are not in the contained task set as external tasks, and analyze the individual power consumption of each unit within the attention window to determine the independent power consumption of the external tasks.

[0036] External tasks are defined to identify and distinguish required tasks that are not included in the contained task set. This facilitates the analysis of the power consumption required by these required tasks. Independent power consumption refers to the power consumption required to execute a single external task. For the specific determination method, please refer to steps S600-S604.

[0037] Step S204: Calculate and determine the predicted electricity consumption based on the baseline electricity consumption and all independent electricity consumption.

[0038] At this point, by adding the baseline electricity consumption to all independent electricity consumption, the electricity consumption situation can be predicted, i.e., the electricity consumption can be predicted.

[0039] The steps for determining the baseline job set from the contained job set include: Step S300: Count the required tasks within the included job set to determine the number of included tasks.

[0040] The number of contained tasks is the number of required tasks that are within the contained job set.

[0041] Step S301: Define the set of contained jobs corresponding to the largest number of contained tasks as the candidate job set, and determine the number of tasks appearing in the attention window based on each required job task in the candidate job set.

[0042] Define a set of candidate jobs to analyze the most relevant task scenarios, and mark them for subsequent analysis; the number of tasks appearing is the number of single-requirement job tasks appearing within the focus window.

[0043] Step S302: Calculate the frequency of each task in the candidate job set based on the number of occurrences of each task, and determine the candidate job set corresponding to the minimum frequency of each task as the baseline job set.

[0044] The set task frequency is a parameter value that reflects the overall frequency of task occurrence in the set. It is determined by averaging the occurrences of all tasks and dividing by a preset fixed calculation parameter. The set task frequency with the lowest frequency indicates that the data for the corresponding task is difficult to obtain and there is little reference data. Therefore, the corresponding candidate task set is defined as the baseline task set. Tasks with little data are defined as external tasks that need to be analyzed separately, thereby improving the accuracy of data analysis.

[0045] Once the frequency of aggregated tasks is determined, energy management methods based on industrial parks also include: Step S400: Determine whether there exist at least two sets of candidate jobs with the same and smallest task frequency.

[0046] The purpose of this judgment is to determine whether there are multiple alternative job sets that meet the requirements, so as to determine the unique baseline job set.

[0047] Step S4001: If there are no at least two candidate job sets with the same minimum set task frequency, then the candidate job set corresponding to the minimum set task frequency is determined as the baseline job set.

[0048] When there are no at least two candidate job sets with the same and smallest task frequency, it means that there is only one candidate job set that meets the requirements, so it can be defined as the baseline job set.

[0049] Step S4002: If there are at least two candidate job sets with the same and smallest set task frequency, then the candidate job set corresponding to the smallest set task frequency is defined as the backup job set.

[0050] When there are at least two candidate job sets with the same and smallest task frequency, it indicates that there are multiple candidate job sets that meet the requirements. In this case, they are defined as candidate job sets for identification, which facilitates subsequent analysis.

[0051] Step S401: Randomly combine each external task in the candidate task set to construct an external task combination, and determine the frequency of the combined task based on the external task combination.

[0052] External task combinations refer to data combinations of varying quantity that contain only external work tasks. The combination task frequency is the frequency value of the data of external task combinations appearing within the same park work set within the attention window.

[0053] Step S402: Count the external tasks in the external task combination to determine the number of external contents, and determine the important weight parameters corresponding to the number of external contents according to the preset important matching relationship.

[0054] The external content quantity refers to the number of external tasks within the external task combination; the importance weight parameter is the parameter value that reflects the importance of the corresponding external task combination. The larger the external content quantity, the larger the corresponding importance weight parameter. The important matching relationship between the two is determined in advance by the staff based on the actual situation.

[0055] Step S403: Calculate the appropriate matching coefficient based on all important weight parameters and the corresponding combination task frequency, and determine the candidate task set corresponding to the largest appropriate matching coefficient as the baseline task set.

[0056] The appropriate matching coefficient can be determined by multiplying the important weight parameters of all external task combinations by the corresponding combination task frequency and then adding them all together. The largest appropriate matching coefficient indicates that the power consumption of the other external tasks under the selected candidate task set can be analyzed well. Therefore, the corresponding candidate task set can be defined as the benchmark task set.

[0057] The steps for determining the independent power consumption of external tasks by analyzing the power consumption of each individual unit within the monitoring window include: Step S500: Combine the work sets of each park within the attention window to define the combination of power consumption that can determine the external work tasks as the associated combination, and define the power consumption of the associated combination as the local power consumption.

[0058] Among them, the combination of electricity consumption for external tasks can be determined by comprehensively analyzing various data to determine the combination of electricity consumption for a single external task. This can be understood as solving a specific unknown x by multiple linear equations. As long as the solution to x is satisfied and the solution to x is unique, the corresponding set of equations is the combination that meets the requirements, that is, the associated combination; at this time, the local electricity consumption is the solution to x.

[0059] Step S501: Calculate the local similar range based on the local power consumption and the preset allowable deviation, and count the local power consumption within the local similar range to determine the number of similar units.

[0060] The permissible deviation is the maximum difference allowed when two electricity consumption data are considered to be relatively close, as set by the staff. The local similarity range can be determined by adding and subtracting the permissible deviation from the local electricity consumption. The number of local similarities is the number of local electricity consumptions within the local similarity range.

[0061] Step S502: Define the local similar range corresponding to the largest similar internal quantity as the local representative range, and calculate the independent electricity consumption based on the local electricity consumption of each local representative range.

[0062] Define a local representative range to determine the concentrated range of local power consumption. That is, this range best represents the power consumption of the external operation task. Therefore, the independent power consumption can be determined based on the data within this range. This data can be obtained by calculating the average value or by calculating through steps S600-S604.

[0063] The steps for determining independent electricity consumption based on the electricity consumption of each local area within the local representative range include: Step S600: Count the park operation set in the associated combination corresponding to the local power consumption to determine the quantity within the association.

[0064] The associated internal quantity is the number of park operation set data used to calculate the current local electricity consumption.

[0065] Step S601: Determine the number of tasks contained in the park operation set in the associated combination corresponding to the local power consumption, and define the number of tasks contained in the park as the number of local units.

[0066] The number of local units is determined by the number of tasks contained in the currently selected park work set, which facilitates subsequent analysis. The method for determining the number of tasks contained is the same as in step S300.

[0067] Step S602: Calculate and determine the data reliability parameters based on the associated internal quantity and the quantity of all local units.

[0068] The data reliability parameter is a parameter value that reflects the reliability of the data. It is obtained by averaging the number of all local units to obtain the average number. Then, the average number and the associated internal number are quantified and unified according to the same preset parameter. Finally, the data reliability parameter is obtained by dividing the unified average number by the associated internal number.

[0069] Step S603: Randomly generate a simulated power consumption within the local representative range, and determine the simulation deviation based on the simulated power consumption and the local power consumption.

[0070] By randomly constructing a simulated electricity consumption model, the simulation analysis of various parameters within a local representative range can be achieved; the simulation deviation is the difference between the simulated electricity consumption and the local electricity consumption, and this difference is an absolute value.

[0071] Step S604: Calculate the simulation reliability based on all simulation deviations and data reliability parameters, and determine the simulation power consumption corresponding to the highest simulation reliability as the independent power consumption.

[0072] The simulation reliability can be obtained by dividing the data reliability parameter by the corresponding simulation deviation and then adding them all together. The higher the simulation reliability, the more accurate the determined simulation power consumption data. Therefore, the simulation power consumption corresponding to the highest simulation reliability can be determined as the independent power consumption.

[0073] The steps for replenishing energy storage batteries based on demand during periods of low prices include: Step S700: Obtain the remaining battery power.

[0074] The remaining stored energy refers to the remaining energy of the energy storage battery when entering a low-price replenishment period.

[0075] Step S701: Calculate the difference between the remaining stored power and the required stored power to determine the required replenishment amount, and determine the required replenishment duration corresponding to the required replenishment amount according to the preset replenishment matching relationship.

[0076] The required replenishment amount is the amount of electricity to be purchased, which is determined by subtracting the remaining stored electricity from the required stored electricity; the required replenishment duration is the charging time required for the energy storage battery to replenish the required replenishment amount, and the specific replenishment matching relationship is determined by the staff in advance through multiple tests.

[0077] Step S702: Based on the demand replenishment duration, randomly construct a simulated replenishment period within the low-price replenishment period, and determine the local power consumption within the attention window based on the simulated replenishment period.

[0078] The simulated replenishment period is a time period within the low-price replenishment period with a width equal to the demand replenishment duration. The local electricity consumption is the average electricity consumption within the time window of interest, with each day repeating 24 hours.

[0079] Step S703: Define the simulated replenishment period corresponding to the minimum local power consumption as the replenishment operation period, and start the energy storage battery replenishment operation at the beginning of the replenishment operation period.

[0080] The minimum local power consumption indicates that the corresponding simulated replenishment period has a small power load, which is convenient for charging and replenishment operations. Therefore, it is defined as the replenishment operation period for identification and distinction. At this time, the energy storage battery replenishment operation is started at the beginning of the replenishment operation period to ensure that the energy storage battery has a good replenishment effect.

[0081] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide an energy management system for industrial parks, comprising: The acquisition module is used to acquire low-price supplementary periods and subsequent photovoltaic periods; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The processing module divides the subsequent photovoltaic period into units based on the preset unit electricity consumption duration to determine the unit electricity consumption period, and obtains the park operation set based on the unit electricity consumption period; The acquisition module obtains environmental forecast information during the unit electricity consumption period, and the processing module determines the predicted power generation based on the environmental forecast information and the predicted electricity consumption based on the park's work set. The processing module calculates the predicted power storage capacity based on the predicted power generation and predicted power consumption, and analyzes the predicted power storage capacity in each subsequent photovoltaic period to determine the instantaneous power storage capacity. The processing module calculates the required storage capacity based on the preset full-load storage capacity and the maximum instantaneous storage capacity, and replenishes the energy storage battery according to the required storage capacity during the low-price replenishment period. The predicted electricity consumption determination module is used to determine the predicted electricity consumption. The baseline job set determination module is used to determine the baseline job set; The alternative job set filtering module is used to filter multiple alternative job sets that meet the requirements. The independent power consumption determination module is used to determine the independent power consumption. An independent power consumption precision module is used to accurately measure independent power consumption. The battery replenishment operation control module is used to control the specific replenishment operation of the energy storage battery.

[0082] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

Claims

1. An industrial park-based energy management method, characterized by, include: To secure low-price supplementary periods and subsequent photovoltaic periods; The subsequent photovoltaic period is divided according to the preset unit electricity consumption duration to determine the unit electricity consumption period, and the park operation set is obtained according to the unit electricity consumption period; Environmental forecast information is obtained during the unit's electricity consumption period, and the predicted power generation is determined based on the environmental forecast information. The predicted electricity consumption is also determined based on the park's work set. The predicted energy storage capacity is determined by calculating the predicted power generation and predicted power consumption, and the instantaneous energy storage capacity is determined by analyzing the predicted energy storage capacity in each subsequent photovoltaic period. The required storage capacity is determined by calculating the preset full-load storage capacity and the maximum instantaneous storage capacity, and the energy storage battery is replenished according to the required storage capacity during the low-price replenishment period.

2. The industrial park-based energy management method according to claim 1, wherein, The steps for determining forecasted electricity consumption based on the park's work set include: The required tasks are determined based on the park's task set, and attention windows are constructed based on the preset attention duration. Within the monitoring window, obtain the individual electricity consumption based on the work sets of each park; The park operation set containing only the required operation tasks is defined as the included operation set, and the benchmark operation set is determined in the included operation set. The electricity consumption of the individual units in the benchmark operation set is defined as the benchmark electricity consumption. Demand tasks that are not within the included task set are defined as external tasks, and the independent power consumption of external tasks is determined by analyzing the power consumption of each individual unit within the attention window. The predicted electricity consumption is determined by calculation based on the baseline electricity consumption and all independent electricity consumption.

3. The energy management method based on industrial parks according to claim 2, characterized in that, The steps for determining the baseline job set from the contained job set include: The number of tasks within the included task set is determined by counting the required tasks within that set. Define the set of jobs corresponding to the largest number of embedded tasks as the candidate job set, and determine the number of tasks that appear in the attention window based on each required job task in the candidate job set. The frequency of set tasks is determined by calculating the number of occurrences of each task in the candidate task set, and the candidate task set corresponding to the minimum set task frequency is determined as the baseline task set.

4. The energy management method based on industrial parks according to claim 3, characterized in that, Once the frequency of aggregated tasks is determined, energy management methods based on industrial parks also include: Determine if there exist at least two sets of candidate jobs with the same and smallest number of task frequencies; If there are no two candidate job sets with the same minimum set task frequency, then the candidate job set corresponding to the minimum set task frequency is determined as the baseline job set. If there are at least two candidate job sets with the same minimum set task frequency, then the candidate job set corresponding to the minimum set task frequency is defined as the backup job set. The external tasks are randomly combined within the candidate task set to construct external task combinations, and the frequency of combined tasks is determined based on the external task combinations. The number of external tasks is determined by counting the external tasks in the combination of external tasks, and the important weight parameters corresponding to the number of external tasks are determined according to the preset important matching relationship. The appropriate matching coefficient is determined by calculating all the important weight parameters and the corresponding combination task frequency, and the candidate task set corresponding to the largest appropriate matching coefficient is determined as the baseline task set.

5. The energy management method based on industrial parks according to claim 2, characterized in that, The steps for determining the independent power consumption of external tasks by analyzing the power consumption of each individual unit within the monitoring window include: The electricity consumption of each park's work set within the attention window is combined to define the combination that can determine the electricity consumption of external work tasks as the associated combination, and the electricity consumption of the associated combination is defined as the local electricity consumption. The local similar range is determined by calculating the local electricity consumption and the preset allowable deviation, and the local similar range is determined by counting the local electricity consumption within the local similar range. The local similar range corresponding to the largest similar internal quantity is defined as the local representative range, and the independent electricity consumption is determined based on the local electricity consumption of each local representative range.

6. The energy management method based on industrial parks according to claim 5, characterized in that, The steps for determining independent electricity consumption based on the electricity consumption of each local area within the local representative range include: The number of items within the associated group is determined by counting the park operation sets corresponding to the local electricity consumption. The number of tasks contained within the park operation set corresponding to the local electricity consumption is determined, and this number of tasks is defined as the number of local units. The reliability parameters of the data are determined by calculating the number of related internal units and the number of all local units. A simulated electricity consumption is randomly generated within a local representative range, and the simulation deviation is determined based on the simulated electricity consumption and the local electricity consumption. The simulation reliability is determined by calculating all simulation deviations and data reliability parameters, and the simulation power consumption corresponding to the highest simulation reliability is determined as the independent power consumption.

7. The energy management method based on industrial parks according to claim 1, characterized in that, The steps for replenishing energy storage batteries based on demand during periods of low prices include: Get the remaining battery level; The required replenishment amount is determined by calculating the difference between the remaining stored power and the required stored power, and the required replenishment duration is determined according to the preset replenishment matching relationship. Based on the demand-based replenishment duration, a simulated replenishment period is randomly constructed within the low-price replenishment period, and the local electricity consumption is determined based on the simulated replenishment period within the monitoring window. The simulated replenishment period corresponding to the minimum local power consumption is defined as the replenishment operation period, and the energy storage battery replenishment operation begins at the beginning of the replenishment operation period.

8. An energy management system based on an industrial park, used to implement the energy management method based on an industrial park as described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire low-price supplementary periods and subsequent photovoltaic periods; The processing module, connected to the acquisition and judgment modules, is used for information storage and processing; The processing module divides the subsequent photovoltaic period into units based on the preset unit electricity consumption duration to determine the unit electricity consumption period, and obtains the park operation set based on the unit electricity consumption period; The acquisition module obtains environmental forecast information during the unit electricity consumption period, and the processing module determines the predicted power generation based on the environmental forecast information and the predicted electricity consumption based on the park's work set. The processing module calculates the predicted power storage capacity based on the predicted power generation and predicted power consumption, and analyzes the predicted power storage capacity in each subsequent photovoltaic period to determine the instantaneous power storage capacity. The processing module calculates the required storage capacity based on the preset full-load storage capacity and the maximum instantaneous storage capacity, and replenishes the energy storage battery according to the required storage capacity during the low-price replenishment period.

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