Green low-carbon park energy distribution optimization method and system

Abstract

This application provides a method and system for optimizing energy allocation in green and low-carbon industrial parks, relating to the field of energy allocation technology. The method includes: calculating the net load power for future periods based on the predicted photovoltaic output and load demand data for each zone; determining the power adjustability margin and baseline carbon emission intensity for each zone based on the net load power, the adjustable capacity of energy storage devices, and the adjustability potential of flexible loads; solving for the green electricity allocation priority for each zone, with the goal of minimizing the overall carbon emissions of the park, combined with the flexible load response level and baseline carbon emission intensity; and when the real-time power balance state of a power supply unit is detected to exceed a predetermined fluctuation range, coordinating the switching sequence of flexible loads and the actual charging and discharging power of energy storage based on the corresponding power adjustability margin and green electricity allocation priority. This application, through low-carbon-oriented optimization decision-making and closed-loop collaborative control, can achieve coordinated regulation of source, load, and storage resources, thereby improving the level of green electricity consumption and low-carbon operation efficiency.

Description

Technical Field

[0001] This application relates to the field of energy distribution technology, and more specifically, to a method and system for optimizing energy distribution in green and low-carbon industrial parks. Background Technology

[0002] With the increasing penetration rate of distributed photovoltaic power in the park, and the increasing abundance of adjustable resources such as load-side energy storage and flexible loads, energy distribution is showing multi-energy complementarity characteristics. The core objective of park energy management is to rationally arrange photovoltaic output, energy storage charging and discharging, flexible load regulation and grid interaction while meeting the energy needs of each zone, so as to minimize the overall carbon emissions of the park.

[0003] In related technologies, existing energy allocation methods for industrial parks generally suffer from a disconnect between optimized scheduling and real-time control. On the one hand, the optimization layer often bases its global scheduling plan on day-ahead forecast data, but fails to incorporate differences in carbon emission intensity and flexible load response capabilities as priority weights into the optimization objectives, resulting in a lack of low-carbon orientation in green electricity allocation. On the other hand, the real-time control layer relies solely on simple threshold judgments based on power deviations, employing fixed-sequence load switching or single energy storage responses. It neither utilizes the green electricity allocation priorities obtained through pre-optimization to guide the adjustment sequence nor coordinates allocation based on the actual adjustability margins of energy storage and flexible loads. This leads to adjustments deviating from the overall low-carbon optimization objectives, resulting in insufficient local consumption of green electricity and increased carbon emissions. Therefore, how to achieve coordinated regulation of source, load, and storage resources to improve green electricity consumption and low-carbon operation efficiency has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for optimizing energy allocation in green and low-carbon industrial parks, which can achieve coordinated regulation of energy sources, loads, and storage resources, thereby improving the level of green electricity consumption and the efficiency of low-carbon operation.

[0005] Firstly, this application provides a method for optimizing energy distribution in green and low-carbon industrial parks, including: Obtain photovoltaic power output forecast and load demand forecast for each zone in the park for future periods, as well as adjustable capacity data of energy storage devices and adjustable potential data of flexible loads in each zone; Based on the photovoltaic power output forecast data and load demand forecast data, the net load power of each zone in the future period is calculated, and then based on the net load power, the adjustable capacity data and the adjustable potential data, the power adjustability margin and baseline carbon emission intensity of each zone in the future period are determined. With the goal of minimizing the total carbon emissions of the park, the green electricity allocation priority of each zone in the future time period is obtained by optimizing the solution based on the flexible load response level of each zone and the benchmark carbon emission intensity. When the real-time power balance of any power supply unit in each zone is detected to exceed the predetermined fluctuation range, the switching sequence of flexible loads and the actual charging and discharging power of the energy storage device in that power supply unit are adjusted in a coordinated manner according to the power adjustable margin and the green electricity allocation priority of the corresponding zone.

[0006] Preferably, calculating the net load power of each zone in the future period based on the photovoltaic power output forecast data and load demand forecast data specifically includes: For each zone within the park, the photovoltaic power output data and the load demand data for each zone in future time periods are aligned in a time series. For each partition and each time segment, the net load power of each time segment is calculated based on the difference between the predicted load demand data and the predicted photovoltaic output data, and then the net load power of each partition in the future period is calculated.

[0007] Preferably, determining the power adjustability margin and baseline carbon emission intensity of each zone in the future period based on the net load power, the adjustable capacity data, and the adjustable potential data specifically includes: Based on the adjustable capacity data, determine the adjustable power range of the energy storage device in each zone during the future time period; Based on the adjustable potential data, the adjustable power range of the flexible load in each zone during the future time period is calculated; Based on the adjustable power range of the energy storage device and the adjustable power range of the flexible load, the power adjustability margin of each zone in the future time period is calculated. The regional power grid baseline emission factor for each zone is determined based on historical power grid operation data. The baseline carbon emission intensity for each zone in the future period is determined based on the regional grid baseline emission factor for each zone and the net load power of the corresponding zone.

[0008] Preferably, the flexible load response level is determined in the following manner: Collect the response speed parameters, adjustable capacity parameters, and adjustment cost parameters of each flexible load in each zone; The response speed parameter, adjustable capacity parameter, and adjustment cost parameter are normalized respectively, and then weighted and summed according to preset weights to obtain the comprehensive score of each flexible load. Based on the comparison between the comprehensive score and the preset level mapping table, the response level of the flexible load in each zone is determined. The response level is used to specify the order of call for different flexible loads during the adjustment process.

[0009] Preferably, with the goal of minimizing the overall carbon emissions of the park, the green electricity allocation priority for each zone in the future time period is optimized and solved based on the flexible load response level of each zone and the baseline carbon emission intensity. Specifically, this includes: With the goal of minimizing the total carbon emissions of the park, an optimization model is constructed using the baseline carbon emission intensity of each zone as the optimization weight. The flexible load response level is used as the adjustment sequence constraint of the optimization model to specify the order in which different flexible loads are invoked during the adjustment process; The constraints of the optimization model are set, including power balance constraints for each zone, operation constraints of energy storage devices, and adjustment capability constraints of flexible loads. The optimization model is solved to obtain the green electricity allocation priority for each zone in future time periods.

[0010] Preferably, when the real-time power balance state of any power supply unit in each zone is detected to exceed the predetermined fluctuation range, the switching sequence of flexible loads and the actual charging and discharging power of the energy storage device within that power supply unit are adjusted in a coordinated manner according to the power adjustability margin of the corresponding zone and the green electricity allocation priority. Specifically, this includes: Real-time monitoring of the power balance status of each power supply unit to determine whether it exceeds the preset fluctuation range; For power supply units that exceed the predetermined fluctuation range, identify the current power deviation type, which includes power deficit state and power surplus state; The adjustment priority order of the flexible load and the energy storage device is determined according to the green electricity allocation priority corresponding to the power supply unit and the response level of each flexible load in the zone; If there is a power deficit, the energy storage discharge power will be increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, the flexible load will be cut off according to the power adjustable margin. If there is a power surplus, the energy storage charging power will be increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, flexible load will be put into operation according to the power adjustable margin. During the adjustment process, the power balance status is continuously fed back until it returns to the fluctuation range.

[0011] Preferably, the predetermined fluctuation range is determined in advance based on the fluctuation characteristics of the historical power balance state of each power supply unit.

[0012] Preferably, obtaining the photovoltaic power output forecast data for each zone within the park for future periods specifically includes: Collect historical power output data of photovoltaic power stations in each zone and numerical weather forecast data for the corresponding time periods; The historical power output data and the numerical weather forecast data are input into a pre-trained photovoltaic power prediction model to obtain the photovoltaic power output data for each region in the future period.

[0013] Preferably, the load forecast demand data is obtained by using a forecasting model based on historical load data, date type data, and meteorological factor data of each zone.

[0014] Secondly, this application provides a green and low-carbon industrial park energy distribution optimization system, comprising: The acquisition module is used to acquire photovoltaic power output forecast data and load demand forecast data for each zone in the park in the future time period, as well as the adjustable capacity data of energy storage devices and the adjustable potential data of flexible loads in each zone. The calculation module is used to calculate the net load power of each zone in the future period based on the photovoltaic power forecast output data and the load forecast demand data, and then determine the power adjustability margin and baseline carbon emission intensity of each zone in the future period based on the net load power, the adjustable capacity data and the adjustable potential data. The optimization module is used to optimize and solve the green electricity allocation priority of each zone in the future time period with the goal of minimizing the total carbon emissions of the park, based on the flexible load response level of each zone and the benchmark carbon emission intensity. The control module is used to coordinately adjust the switching sequence of flexible loads and the actual charging and discharging power of energy storage devices in the power supply unit when the real-time power balance state of any power supply unit in each zone exceeds the predetermined fluctuation range, based on the power adjustable margin of the corresponding zone and the green electricity allocation priority.

[0015] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described green and low-carbon park energy allocation optimization method.

[0016] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned green and low-carbon park energy allocation optimization method.

[0017] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application deeply embeds low-carbon goals into the entire process of energy allocation in the industrial park by constructing a hierarchical collaborative control mechanism that links optimized decision-making with real-time control. First, a baseline carbon emission intensity is determined based on net load power and the regional power grid's baseline emission factor, quantifying the carbon emission level as an optimization weight. Simultaneously, power adjustability margin is calculated based on the adjustable capacity of energy storage and the adjustable potential of flexible loads, providing a quantitative decision boundary for low-carbon optimization. Second, with the goal of minimizing the overall carbon emissions of the industrial park, an optimization model is constructed using the baseline carbon emission intensity as the optimization weight and the flexible load response level as the adjustment sequence constraint. This model solves for the green electricity allocation priority of each zone, upgrading green electricity allocation from fixed rules to differentiated dynamic decision-making based on carbon emission intensity. Then, during real-time operation, the optimized green electricity allocation priority and the flexible load response level are used together as the adjustment priority. Energy storage is prioritized to smooth out instantaneous fluctuations, and when energy storage is insufficient, flexible loads are switched in an orderly manner according to the response level based on the power adjustability margin, achieving deep coupling between optimized decision-making and real-time control. Finally, closed-loop feedback continuously monitors the power balance state until it returns to the fluctuation range, ensuring adaptive regulation and operational stability. In summary, this application achieves a significant improvement in the efficient coordination of source, load, and storage resources and the level of green electricity consumption through the synergistic effect of low-carbon-oriented optimization decision-making, hierarchical collaborative real-time control, and closed-loop feedback adaptive adjustment. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the working application of a green and low-carbon park energy distribution optimization system according to some embodiments of this application; Figure 2 This is an exemplary flowchart of a green and low-carbon park energy allocation optimization method according to some embodiments of this application; Figure 3 This is a flowchart illustrating the process of determining the priority of green electricity allocation according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a green and low-carbon park energy distribution optimization system according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a method for optimizing energy distribution in green and low-carbon industrial parks, according to some embodiments of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] refer to Figure 1This figure is a flowchart illustrating the working application of the green and low-carbon park energy distribution optimization system according to some embodiments of this application. In the green and low-carbon park, data acquisition devices 110 deployed in each zone collect real-time operating data such as the actual output of photovoltaic power, the actual power of the load, the state of charge and charging / discharging power of the energy storage device, and the operating status of the flexible load. The data is then aggregated and transmitted to the server 120. The server 120 uses locally stored historical data and numerical weather forecast information to perform photovoltaic forecasting and load forecasting, and obtains the photovoltaic power forecasting output data and load forecasting demand data for each zone in the future period. At the same time, based on the real-time data uploaded by the data acquisition devices 110, the adjustable capacity of the energy storage device and the adjustable potential of the flexible load are calculated. Based on this, server 120 calculates the net load power of each zone, thereby determining the power adjustability margin and baseline carbon emission intensity. Then, with the goal of minimizing the overall carbon emissions of the park, it optimizes the green electricity allocation priority of each zone based on the flexible load response level and the baseline carbon emission intensity. When the real-time power balance of any power supply unit is detected to exceed the predetermined fluctuation range, server 120 generates a coordinated control command based on the power adjustability margin and green electricity allocation priority of the corresponding zone, and sends it to execution device 130. Execution device 130 coordinates the switching sequence of flexible loads within the power supply unit and the actual charging and discharging power of the energy storage device according to the command until the power balance returns to the fluctuation range. At the same time, server 120 pushes the optimized allocation results and real-time control status to visualization terminal 140 for display, allowing park management personnel to monitor and intervene.

[0021] Among them, the data acquisition device 110 may include smart meters, sensors, battery management systems and building automation systems; the server 120 may be a locally deployed edge computing server or cloud server; the execution device 130 may be, but is not limited to, an energy storage converter, a flexible load controller, or a switchable switch; and the visualization terminal 140 may be a park central control screen or a mobile monitoring platform.

[0022] refer to Figure 2 The figure is an exemplary flowchart of a green and low-carbon park energy allocation optimization method according to some embodiments of this application. The green and low-carbon park energy allocation optimization method mainly includes the following steps: In step 101, the projected photovoltaic output data and load forecast demand data for each zone in the park for future periods are obtained, as well as the adjustable capacity data of energy storage devices and the adjustable potential data of flexible loads in each zone.

[0023] Before implementing this method, it is preferable to first divide the target park into multiple zones. This is because different regions have significant differences in energy consumption characteristics, photovoltaic resource endowment, load type, and distribution of adjustable resources. If a unified scheduling strategy is adopted, it will be difficult to achieve accurate supply and demand matching and differentiated low-carbon optimization. At the same time, zone management can be physically divided according to the distribution network topology, functional area boundaries, and metering points of independent power supply units. This allows each zone to have a relatively independent energy consumption monitoring system, controllable resources, and power balance boundaries, thereby providing a refined decision-making unit basis for subsequent zone net load calculation, differentiated assessment of carbon emission intensity, and green electricity priority allocation.

[0024] In this application, the energy storage device refers to an electrochemical energy storage device deployed within each zone for storing and releasing electrical energy; flexible load refers to a load device whose power consumption can be flexibly adjusted according to dispatch instructions, including air conditioning systems, electric vehicle charging piles, and interruptible production equipment, used to provide power regulation capabilities for the zone to support green electricity consumption and power balance.

[0025] In some embodiments, obtaining the photovoltaic power output forecast and load demand forecast for each zone within the park for future periods can be achieved in the following ways: For photovoltaic (PV) power output prediction data, historical power output data of PV power plants in each region and historical numerical weather forecast data for the corresponding time period are collected. The historical power output data and numerical weather forecast data are aligned with the same time resolution to construct a training sample set. Based on the training sample set, a PV power prediction model is pre-trained using a deep learning network or physical method. In a specific embodiment, the PV power prediction model uses a three-layer LSTM network. The input is historical power output data and numerical weather forecast data for the past 24 hours, and the output is the predicted power output data for each time segment of the next 24 hours. The numerical weather forecast data for the future time period is input into the PV power prediction model, and the output is the PV predicted power output data for each region at each time segment of the future time period. The future time period is set to 24 hours, 48 ​​hours or other predetermined periods according to the optimization scheduling requirements, and the time segment resolution is set to 15 minutes, 30 minutes or 1 hour. For load forecast demand data, historical load data of each zone is collected, and date type data and meteorological factor data for the corresponding time period are obtained simultaneously. The date type data includes classification labels for weekdays, weekends, and holidays, and the meteorological factor data includes at least one of temperature, humidity, and weather type. A load forecast model is constructed based on the collected data. The load forecast model can use any algorithm among time series analysis, support vector regression, or neural networks. The date type data and meteorological forecast data for the future time period are input into the load forecast model, and the load forecast demand data for each zone at each time section in the future time period are output, so that the forecast results are aligned with the photovoltaic power output forecast data in the time dimension.

[0026] In some embodiments, obtaining adjustable capacity data of energy storage devices and adjustable potential data of flexible loads within each zone can be achieved in the following manner: For the adjustable capacity data of energy storage devices, the battery management system of each zone's energy storage system collects the current state of charge (SOC), maximum allowable charging power, maximum allowable discharging power, and rated capacity parameters in real time. Based on the preset safe operating limits of SOC, and combined with the current SOC and rated capacity, the available discharge capacity and available recharge capacity of the energy storage device at the start of a future time period are calculated, and the upward and downward adjustable power ranges within each future time segment are further estimated. Specifically, let the current SOC be SOC_cur, the lower limit of safe operation be SOC_min, the upper limit of safe operation be SOC_max, and the rated capacity be E_rate. Then, the current discharge capacity is (SOC_cur - SOC_min) × E_rate, and the current rechargeable capacity is (SOC_max - SOC_cur) × E_rate. For a single time segment, the current dischargeable capacity is divided by the duration of the time segment to obtain the average dischargeable power within that time segment. The smaller of this value and the maximum allowable discharge power is taken as the upper limit of the upward adjustable power range, and the lower limit is zero. The current chargeable capacity is divided by the duration of the time segment to obtain the average chargeable power within that time segment. The smaller of this value and the maximum allowable charging power is taken as the upper limit of the downward adjustable power range, and the lower limit is zero. The above calculation results together constitute the adjustable capacity data of the energy storage device for each time segment in the future period.

[0027] For the adjustable potential data of flexible loads, real-time operating power, equipment status, and environmental parameters of flexible loads are collected through the energy management system or building automation system in each zone. The flexible loads include at least one of air conditioning systems, lighting systems, electric vehicle charging piles, and interruptible production equipment. Based on a pre-built evaluation model of the adjustable potential of flexible loads, combined with the response characteristics, adjustment rate, user comfort constraints, and cost function of each flexible load, the upward adjustable power range and downward adjustable power range of flexible loads in each time segment of the future period are calculated. The upward adjustable power range represents the ability of flexible loads to increase electricity consumption, and the downward adjustable power range represents the ability of flexible loads to reduce electricity consumption. The upward and downward adjustable power ranges are then output as the adjustable potential data of flexible loads. In practical applications, based on a pre-built assessment model of the flexible load adjustment potential, and combining the response characteristics, adjustment rate, user comfort constraints, and cost functions of each flexible load, the upward and downward adjustable power ranges of the flexible loads in each time segment of the future period can be calculated in the following way: First, classify and model the flexible loads according to their type. For air conditioning temperature control loads, obtain the current indoor temperature, predicted outdoor temperature, set temperature, and air conditioning operating power through the building energy management system. Based on a pre-built equivalent thermal parameter model, this model equates the building's thermal dynamic characteristics to a resistive-capacitive network. The system takes indoor temperature, outdoor temperature, and air conditioning cooling power as inputs and indoor temperature changes as outputs. It incorporates user comfort constraints, such as setting the indoor temperature adjustment range to 24°C to 28°C for summer cooling conditions. It calculates the upper limit of power reduction that the air conditioning load can achieve in future time segments under the premise of meeting this comfort constraint, forming a downward adjustable power range. At the same time, under the premise of meeting the lower comfort limit, it calculates the upper limit of the increase in cooling power that the air conditioning load can achieve, forming an upward adjustable power range. The upward adjustment is also subject to adjustment rate constraints, such as setting the adjustment rate to no more than 10% of the rated power per minute. For electric vehicle charging piles, the charging pile management system obtains the current charging power, remaining battery capacity, user-set expected off-grid time, and target capacity of each charging pile. Based on the constraint that users must meet the target capacity before the expected off-grid time, the potential for delayed charging or reduced charging power in future time segments is calculated, yielding the downward adjustable power range. Based on the charging pile's rated power limit, the battery's maximum allowable charging power, and grid connection capacity constraints, the potential for increased charging power is calculated, yielding the upward adjustable power range. For interruptible production equipment, the production management system obtains the equipment's operating status, planned tasks, and allowed interruption time windows. Combined with an adjustment cost function—which quantifies the losses caused by production interruption or the compensation costs payable to users—the interruptible power capacity in each time segment is determined. Typically, interruptible production equipment can only provide downward adjustment capability (load shedding), and its upward adjustable power range is set to zero. If standby equipment exists, the upward adjustable range is calculated separately based on the standby equipment's rated power and start-up time constraints. Finally, the upward adjustable power ranges of all flexible loads in each zone are superimposed to obtain the total upward adjustable power range of that zone; the downward adjustable power ranges of all flexible loads are superimposed to obtain the total downward adjustable power range of that zone, which serves as the output of the adjustable potential data of the flexible loads.

[0028] In step 102, based on the photovoltaic power output forecast data and load demand forecast data, the net load power of each zone in the future period is calculated, and then the power adjustability margin and baseline carbon emission intensity of each zone in the future period are determined according to the net load power, the adjustable capacity data and the adjustable potential data.

[0029] In some embodiments, calculating the net load power of each zone in the future period based on the photovoltaic power output forecast data and load demand forecast data can be achieved by the following steps: For each zone within the park, the photovoltaic power output data and the load demand data for each zone in future time periods are aligned in a time series. For each partition and each time segment, the net load power of each time segment is calculated based on the difference between the predicted load demand data and the predicted photovoltaic output data, and then the net load power of each partition in the future period is calculated.

[0030] The net load power in this application is a technical indicator that reflects the net electricity demand or net power generation surplus after the load demand and photovoltaic output of each zone are offset in the future period.

[0031] In practical application, firstly, for each zone within the park, the start and end times of the future period are determined. Then, the time segment resolution is determined based on the sampling interval of the photovoltaic power output data and the load demand forecast data. This resolution is typically set to 15 minutes, 30 minutes, or one hour. Next, the photovoltaic power output and load demand forecast values ​​for each zone within the future period are read from the forecast data storage unit. The two data sequences are then arranged according to the same time segment label, ensuring that each time segment simultaneously contains both the photovoltaic power output and load demand forecast values ​​for that zone. This results in a time-registered data pair. Then, for each time segment within the future period of the partition, the predicted load demand and predicted photovoltaic output values ​​for that time segment are read from the aligned data pairs. The predicted load demand value is subtracted from the predicted photovoltaic output value, and the difference is the net load power for that time segment. When the difference is positive, it indicates that there is a power deficit in the partition at that time segment, requiring the purchase of electricity from the external grid or the use of energy storage devices and flexible loads within the partition to supplement the power. When the difference is negative, it indicates that there is a power surplus in the partition at that time segment, with photovoltaic power generation exceeding local load demand, requiring the backfeeding of electricity to the external grid or the absorption of excess electricity by energy storage devices within the partition. The above difference calculation operation is performed on all time segments one by one to obtain the net load power of each time segment. These net load powers are then arranged in chronological order to form the net load power sequence for the partition in the future period, and this sequence is used as a quantitative indicator to characterize the energy deficit or surplus status of each partition in the future period.

[0032] In some embodiments, determining the power adjustability margin and baseline carbon emission intensity of each zone in the future period based on the net load power, the adjustable capacity data, and the adjustable potential data can be achieved using the following steps: Based on the adjustable capacity data, determine the adjustable power range of the energy storage device in each zone during the future time period; Based on the adjustable potential data, the adjustable power range of the flexible load in each zone during the future time period is calculated; Based on the adjustable power range of the energy storage device and the adjustable power range of the flexible load, the power adjustability margin of each zone in the future time period is calculated. The regional power grid baseline emission factor for each zone is determined based on historical power grid operation data. The baseline carbon emission intensity for each zone in the future period is determined based on the regional grid baseline emission factor for each zone and the net load power of the corresponding zone.

[0033] In practical application, firstly, the adjustable power range of the energy storage devices in each zone is determined based on the adjustable capacity data of the energy storage devices in the future period. The adjustable capacity data includes the maximum allowable discharge power, maximum allowable charging power, current available discharge capacity, and current available charging capacity of each energy storage system at each time segment in the future period. For each zone at each time segment, an energy storage power constraint calculation method is adopted: the maximum allowable discharge power is compared with the power value corresponding to a unit time period of the current available discharge capacity, and the smaller value is taken as the upper limit of the adjustable power of energy storage in that zone at that time segment. This upper limit, together with zero, constitutes the upper limit of the adjustable power range of energy storage. The maximum allowable charging power is compared with the power value corresponding to a unit time period of the current available charging capacity, and the smaller value is taken as the lower limit of the adjustable power of energy storage in that zone at that time segment. This upper limit, together with zero, constitutes the lower limit of the adjustable power range of energy storage. The above calculation is repeated for all zones and all time segments to obtain the upper and lower adjustable power ranges of the energy storage devices in each zone in the future period. Secondly, based on the adjustable potential data of flexible loads, the adjustable power range of flexible loads in each zone during future periods is calculated. This adjustable potential data includes the upper limit of increaseable power consumption and the upper limit of decreaseable power consumption for each flexible load in each zone, pre-assessed based on its response characteristics, adjustment rate, and user comfort constraints, for each time segment in the future period. For each zone at each time segment, the upper limit of increaseable power consumption of all flexible loads in that zone is summed using an overlay method to obtain the upper limit of upward adjustable power consumption of the flexible load in that zone at that time segment. This upper limit, together with the zero value, constitutes the upper limit of upward adjustable power consumption of the flexible load. Similarly, the upper limit of decreaseable power consumption of all flexible loads in that zone is summed to obtain the upper limit of downward adjustable power consumption of the flexible load in that zone at that time segment. This upper limit, together with the zero value, constitutes the lower limit of downward adjustable power consumption of the flexible load. This summation operation is repeated for all zones and all time segments to obtain the upper and lower adjustable power ranges of the flexible load in each zone during future periods. Next, the power adjustability margin of each zone in future periods is calculated based on the adjustable power range of the energy storage device and the adjustable power range of the flexible load. For each zone at each time segment, the upper limit of the upward adjustable power range of the energy storage device and the upper limit of the upward adjustable power range of the flexible load are added to obtain the comprehensive upward adjustable power upper limit value. This upper limit value and the zero value together constitute the comprehensive upward adjustable power range. The upper limit of the downward adjustable power range of the energy storage device and the upper limit of the downward adjustable power range of the flexible load are added to obtain the comprehensive downward adjustable power upper limit value. This upper limit value and the zero value together constitute the comprehensive downward adjustable power range. The comprehensive upward adjustable power range and the comprehensive downward adjustable power range are used as the power adjustability margin of the zone at that time segment. The power adjustability margin is used to characterize the comprehensive adjustment capability of the zone to cope with net load power fluctuations. The above calculation is repeated for each zone at each time segment to obtain the power adjustability margin of each zone in future periods. Then, based on historical power grid operation data, the regional power grid baseline emission factor for each zone is determined. Historical operation data of the regional power grid for the previous calendar year can be obtained from the power grid dispatching department or the power trading center. This data includes the on-grid electricity of various types of generating units in the region and their corresponding fuel types and carbon emission coefficients. Then, using the known calculation method of the regional power grid average emission factor, the total carbon emissions of all generating units in the region are divided by the total on-grid electricity to obtain the regional power grid baseline emission factor for each zone. If there are differences in the emission factors of the power grid nodes connected to each zone, the node emission factors are calculated separately based on the historical carbon emission data of the power supply nodes to which each zone belongs. The regional power grid baseline emission factor is a characteristic parameter that reflects the average carbon emission level corresponding to a unit power supply of the regional power grid. Finally, based on the regional grid baseline emission factor and the corresponding net load power of each zone, the baseline carbon emission intensity of each zone in the future period is determined. For each zone at each time segment, the net load power value of that time segment is multiplied by the regional grid baseline emission factor of that zone, and the product is used as the baseline carbon emission intensity of that zone at that time segment. When the net load power is positive, the baseline carbon emission intensity is positive, indicating the amount of carbon emissions generated by the zone from drawing power from the grid at that time segment. When the net load power is negative, the baseline carbon emission intensity is negative, indicating that the zone can offset carbon emissions from other regions by feeding back power to the grid. The above multiplication operation is repeated for all zones and all time segments to obtain the baseline carbon emission intensity of each zone in the future period. The baseline carbon emission intensity is an indicator that measures the level of carbon emissions per unit time generated by each zone from drawing power from the grid due to net load power in the future period.

[0034] In step 103, with the goal of minimizing the total carbon emissions of the park, the green electricity allocation priority of each zone in the future time period is optimized and solved based on the flexible load response level of each zone and the baseline carbon emission intensity.

[0035] In some embodiments, the flexible load response level may be achieved by the following steps: Collect the response speed parameters, adjustable capacity parameters, and adjustment cost parameters of each flexible load in each zone; The response speed parameter, adjustable capacity parameter, and adjustment cost parameter are normalized respectively, and then weighted and summed according to preset weights to obtain the comprehensive score of each flexible load. Based on the comparison between the comprehensive score and the preset level mapping table, the response level of the flexible load in each zone is determined. The response level is used to specify the order of call for different flexible loads during the adjustment process.

[0036] The flexible load response level in this application is a comprehensive indicator used to specify the priority order of different flexible loads during power regulation. In specific applications, the response speed parameter, adjustable capacity parameter, and regulation cost parameter of each type of flexible load or each adjustable device can be collected from the energy management system of each zone. Among them, the response speed parameter refers to the time required from receiving the regulation command to the actual power change reaching the expected value; the adjustable capacity parameter refers to the maximum power regulation that the load can provide; and the regulation cost parameter refers to the economic cost or user comfort loss caused by unit power regulation. Specifically, the response speed parameter, adjustable capacity parameter, and regulation cost parameter can be normalized first, then weighted and summed according to the pre-set weights, and then the summed value can be compared with the pre-set level mapping table to obtain the flexible load response level of the zone. The level mapping table can be set in advance based on historical data, and the weights can also be set in advance based on historical data. No specific limitations are made here.

[0037] In some embodiments, reference Figure 3 As shown in the figure, this is a flowchart illustrating the process of determining the green electricity allocation priority in some embodiments of this application. In this embodiment, with the goal of minimizing the overall carbon emissions of the park, the green electricity allocation priority for each zone in the future time period can be optimized and solved according to the flexible load response level of each zone and the baseline carbon emission intensity. This can be achieved through the following steps: In step 1031, the objective function is to minimize the total carbon emissions of the park as the objective function, and the baseline carbon emission intensity of each zone is used as the optimization weight to construct an optimization model. In step 1032, the flexible load response level is used as the adjustment sequence constraint of the optimization model to specify the order of call for different flexible loads during the adjustment process; In step 1033, the constraints of the optimization model are set, including the power balance constraints of each zone, the operation constraints of the energy storage device, and the adjustment capability constraints of the flexible load. In step 1034, the optimization model is solved to obtain the green electricity allocation priority of each partition in the future time period.

[0038] In this application, power balance constraints refer to the equations that must be satisfied between photovoltaic output, energy storage charging and discharging, grid interaction, and load demand within each zone; the operational constraints of energy storage devices refer to the safety and physical limitations that their state of charge, charging and discharging power, and energy balance must meet; the regulation capacity constraints of flexible loads refer to the fact that their regulation power must not exceed the adjustable power range and the order of call must comply with the response level requirements; and the green electricity allocation priority is a decision indicator used to guide the order in which each zone obtains green electricity allocation.

[0039] In practical application, firstly, taking the minimum total carbon emissions of the park as the objective function, and using the baseline carbon emission intensity of each zone as the optimization weight, the optimization model can be constructed in the following way: Determine the optimization objective of minimizing the total total carbon emissions of the park. For each zone in the future period, multiply the baseline carbon emission intensity of the zone by the power taken from the grid by the zone to obtain the carbon emissions of the zone at that time point. Add up the carbon emissions of all zones in the park at all time points to obtain the total total carbon emissions of the park. Then, with the goal of minimizing this total, use the baseline carbon emission intensity of each zone as the weight coefficient of the power taken from the grid by each zone in the optimization model. That is, the higher the baseline carbon emission intensity of a zone, the greater its power taken from the grid contributes to the objective function. In the optimization, it is more inclined to allocate green electricity to such zones to reduce their power taken from the grid. After constructing the mathematical expression of the objective function, the objective function is used as the core optimization direction of the optimization model. Finally, the constructed objective function is used as the evaluation criterion for evaluating the merits of the scheme in the subsequent solution. Secondly, the flexible load response level is used as the adjustment sequence constraint of the optimization model to specify the calling order of different flexible loads during the adjustment process. This can be achieved in the following way: the flexible load response level within each zone is used as the adjustment sequence constraint in the optimization model. The specific implementation of this constraint is as follows: when the optimization model needs to call flexible loads for power adjustment, the loads must be selected sequentially in descending order of response level. That is, the flexible load with the highest response level is called first. If its adjustment capacity is insufficient, the next highest level load is called, and so on. Cross-level calling is not allowed. In the optimization model, this sequence constraint is implemented by introducing 0-1 variables and sequence constraints. For example, let the flexible loads sorted by response level from high to low be 1, 2, ..., n, and their corresponding calling state variables be x1, x2, ..., xn. n Where xᵢ is a 0-1 variable, the adjustment sequence constraint can be expressed as x1≥x2≥ …≥x n This means that a low-response-level load must not be invoked until a high-response-level load has been fully invoked. Then, the constraints of the optimization model are set. These constraints include power balance constraints for each zone, operational constraints for energy storage devices, and constraints on the adjustment capability of flexible loads. These constraints can be implemented in the following way: Three types of constraints are set. The first type is the power balance constraint for each zone, meaning that at each time point, the predicted photovoltaic output plus the energy storage discharge power plus the power drawn from the grid for each zone must equal the load demand for that zone plus the energy storage charging power plus the flexible load adjustment power. This constraint ensures energy conservation. The second type is the operational constraint for energy storage devices, including ensuring that the state of charge of the energy storage devices at each time point must be within a preset safe range. Between the upper limit and the lower limit of safety, the charging and discharging power of the energy storage device cannot exceed its maximum allowable charging and discharging power, and there is an energy balance relationship between the change in state of charge and the charging and discharging power between adjacent time segments; the third type is the adjustment capability constraint of flexible loads, including that the actual adjustment power of each flexible load in each time segment cannot exceed its upward or downward adjustable power range, and the calling order must meet the adjustment sequence constraint. After writing the above three types of constraints into the optimization model in the form of mathematical inequalities, a complete feasible region is formed, and finally the set constraints are used as the boundary of the solution space when solving the optimization model; Finally, the optimization model is solved to obtain the green electricity allocation priority of each zone in the future time period. This can be achieved in the following way: First, the constructed objective function, adjustment sequence constraints, and set constraints are combined to form a complete optimization model. This optimization model belongs to mixed integer linear programming or linear programming problems. It is solved using optimization solvers known in the field, such as the simplex method or interior point method combined with the branch and bound method to handle integer variables. During the solution process, the optimization model outputs the power taken from the grid, the energy storage charging and discharging power, and the adjustment power of each flexible load in each time section of the future time period for each zone. Based on the relationship between the power drawn from the grid and the predicted output of photovoltaic power in each zone, the allocation ratio of green electricity among the zones is deduced: the total photovoltaic output is preferentially allocated to the zones with the largest power drawn from the grid. The larger the power drawn, the higher the priority of green electricity allocation for that zone. For example, if the total predicted output of photovoltaic power in the park is P_PV_total and the power drawn from the grid by each zone is P_grid_i, then the zones are sorted from largest to smallest according to P_grid_i. The zone that ranks higher in the sorting receives a higher priority for green electricity allocation. Then, the green electricity allocation order of each zone in the solution results is sorted from highest to lowest to obtain a list of green electricity allocation priorities for each zone in the future time period. Finally, the green electricity allocation priority is used as the decision basis for determining the adjustment order of each zone in subsequent real-time coordinated control.

[0040] In step 104, when the real-time power balance status of any power supply unit in each partition is detected to exceed the predetermined fluctuation range, the switching sequence of flexible loads and the actual charging and discharging power of the energy storage device in the power supply unit are adjusted in a coordinated manner according to the power adjustable margin of the corresponding partition and the green electricity allocation priority.

[0041] The power balance status in this application is an operational indicator used to determine whether the power supply unit needs power regulation.

[0042] In actual operation, photovoltaic power output is intermittent and fluctuating due to weather changes, and load demand also changes in real time with user behavior. Optimized scheduling schemes based on forecast data cannot fully cover various random disturbances in actual operation. If such power deviations are not addressed, power imbalances will occur within the power supply units of a given zone, leading to safety risks such as grid frequency fluctuations, voltage overruns, and even equipment protection activation. Therefore, it is essential to continuously monitor the real-time power balance status of power supply units to promptly detect abnormal states deviating from the expected operating range. When the real-time power balance status of any power supply unit exceeds the preset fluctuation range, it indicates that the unit is in an abnormal operating condition of power deficit or surplus. At this point, based on the pre-calculated power adjustability margin of the zone, it is possible to accurately determine the upward or downward adjustment capacity that energy storage devices and flexible loads within the zone can provide, avoiding adjustment commands exceeding actual capacity. Simultaneously, based on the green electricity allocation priority obtained from the optimized solution, it is ensured that green electricity consumption or power support for high-carbon emission intensity zones is prioritized during the adjustment process, continuing the overall low-carbon optimization goal. Through the aforementioned coordinated adjustment mechanism, on the one hand, the rapid response of energy storage devices is prioritized to smooth out instantaneous power fluctuations, leveraging their advantages of fast response speed and zero carbon emissions in regulation. On the other hand, when the energy storage regulation capacity is insufficient, flexible loads are switched on and off in an orderly manner according to the response level. This ensures the rapid recovery of system power balance and minimizes the impact of regulation behavior on user comfort and production activities. Thus, while ensuring the safe and stable power supply of the park, it achieves the efficient utilization of green power resources and the continuous optimization of the overall carbon emissions of the park.

[0043] In some embodiments, when the real-time power balance state of any power supply unit in each partition is detected to exceed a predetermined fluctuation range, the following steps can be used to coordinately adjust the switching sequence of flexible loads and the actual charging and discharging power of the energy storage device according to the power adjustability margin of the corresponding partition and the green electricity allocation priority: Real-time monitoring of the power balance status of each power supply unit to determine whether it exceeds the preset fluctuation range; For power supply units that exceed the predetermined fluctuation range, identify the current power deviation type, which includes power deficit state and power surplus state; The adjustment priority order of the flexible load and the energy storage device is determined according to the green electricity allocation priority corresponding to the power supply unit and the response level of each flexible load in the zone; If there is a power deficit, the energy storage discharge power will be increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, the flexible load will be cut off according to the power adjustable margin. If there is a power surplus, the energy storage charging power will be increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, flexible load will be put into operation according to the power adjustable margin. During the adjustment process, the power balance status is continuously fed back until it returns to the fluctuation range.

[0044] The predetermined fluctuation range in this application is a threshold pre-set based on the fluctuation characteristics of the historical power balance state of each power supply unit, which can be determined by the standard deviation multiple method of historical operating data or the engineering experience threshold method.

[0045] Compared to existing technologies, which suffer from disordered energy storage and flexible load dispatching, and a disconnect between regulation behavior and optimization objectives, this solution addresses the technical problems of unclear priority of regulation resources and mismatch between response strategies and low-carbon scheduling in real-time control by constructing a hierarchical collaborative regulation mechanism. Specifically, existing technologies typically employ single energy storage response or fixed-sequence load switching, lacking dynamic identification of power deficit and surplus states, and failing to embed the pre-optimized green electricity allocation priority and flexible load response level into the real-time control logic, causing the regulation process to deviate from the overall low-carbon optimization objective. This solution first monitors the power balance status of the power supply unit in real time and identifies the type of deviation. Based on this, it prioritizes energy storage devices to quickly smooth out instantaneous fluctuations, utilizing their millisecond-level response speed and zero-carbon emission characteristics to achieve precise regulation. When the energy storage regulation capacity is insufficient, it orderly puts on or cuts off flexible loads according to the power adjustment margin, based on the priority of green electricity allocation and the response level of flexible loads. This ensures that high-carbon emission intensity zones receive priority power support, while high-response loads participate in regulation first. This mechanism realizes real-time closed-loop linkage between source, load, and storage. Under the premise of ensuring rapid recovery of power balance, it reduces the frequency of deep charging and discharging of energy storage, extends the service life of energy storage, and strictly limits the regulation of flexible loads within the low-carbon optimization framework, effectively improving the local consumption rate of green electricity, reducing curtailment of solar power, and ultimately achieving the dual goals of power supply security and minimizing total carbon emissions in the park.

[0046] In practical applications, firstly, the power balance status of each power supply unit is monitored in real time to determine whether it exceeds the preset fluctuation range. This can be achieved in the following way: Real-time data is collected from each power supply unit in each zone through the park's energy management system. The collected data includes the actual output power of the photovoltaic inverter in the power supply unit, the actual charging and discharging power of the energy storage system, the actual load power of each feeder outlet, and the actual exchange power of the grid interaction point. The collected real-time data is substituted into the power balance equation to calculate the real-time power deviation value of the power supply unit. The absolute value of the deviation value is compared with the preset upper limit of the fluctuation range. When the absolute value of the deviation value is less than or equal to the upper limit value, the power balance status is determined to be normal. When the absolute value of the deviation value is greater than the upper limit value, it is determined to exceed the preset fluctuation range. The above monitoring and judgment operation is continuously performed on all power supply units, and the power supply units marked as exceeding the preset fluctuation range in the judgment results are used as the target objects for subsequent adjustment. Secondly, for power supply units that exceed the predetermined fluctuation range, the current power deviation type is identified. The power deviation type includes power deficit state and power surplus state, which can be achieved in the following way: For power supply units marked as exceeding the predetermined fluctuation range, the sign of their real-time power deviation value is further analyzed. If the real-time power deviation value is positive, it means that the actual load power of the power supply unit is greater than the sum of the actual power generation power and the energy storage discharge power, that is, it is in a power deficit state, and it is necessary to increase power generation or reduce the load; if the real-time power deviation value is negative, it means that the sum of the actual power generation power and the energy storage discharge power of the power supply unit is greater than the actual load power, that is, it is in a power surplus state, and it is necessary to reduce power generation or increase the load. The identified power deficit state or power surplus state is taken as the power deviation type of the power supply unit. Furthermore, determining the adjustment priority order of the flexible loads and the energy storage device based on the green electricity allocation priority corresponding to the power supply unit and the response level of each flexible load within the zone can be achieved in the following way: Obtain the green electricity allocation priority of the zone to which the power supply unit belongs and the response level of each flexible load within the zone. Set the energy storage device as the first priority adjustment resource, based on the fact that the energy storage device has a fast response speed, high adjustment accuracy, and does not generate carbon emissions. Sort the flexible loads in descending order of response level as adjustment resources after the energy storage device. Further, if the green electricity allocation priority of the zone to which the power supply unit belongs is high, the restriction on the adjustment range of the flexible load can be appropriately relaxed during the adjustment process to prioritize the power balance restoration of the zone; if the green electricity allocation priority is low, the energy storage device is called first to minimize the intervention on the flexible loads. Then, the call order of the energy storage device and the flexible load is summarized to form a complete adjustment priority order. Then, if there is a power deficit, the energy storage discharge power is increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, the flexible load is cut off according to the power adjustable margin. This can be achieved in the following way: When the identification result is a power deficit, a discharge command is first issued to the energy storage device according to the adjustment priority order to increase the energy storage discharge power to supplement the power gap of the power supply unit. During the adjustment process, the real-time power deviation value is continuously monitored. If the power deviation value still exceeds the predetermined fluctuation range after the energy storage device reaches the maximum discharge power, the flexible load is cut off in order of flexible load response level from high to low according to the power adjustable margin of the power supply unit's zone. That is, the load power consumption is reduced to eliminate the power deficit. During the cut-off process, the power balance status is re-monitored after each level of load is cut off until the power deviation value falls back to the predetermined fluctuation range, or all adjustable resources have been used up. Subsequently, if the power surplus state is reached, the energy storage charging power is increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, the flexible load is then put into operation according to the power adjustable margin. This can be achieved in the following way: When the identification result is a power surplus state, a charging command is first issued to the energy storage device according to the adjustment priority order to increase the energy storage charging power to absorb the excess power of the power supply unit. During the adjustment process, the real-time power deviation value is continuously monitored. If the power deviation value still exceeds the predetermined fluctuation range after the energy storage device reaches the maximum charging power, the flexible load is put into operation in the order of flexible load response level from high to low according to the power adjustable margin of the partition to which the power supply unit belongs. That is, the load power consumption is increased to absorb the power surplus. During the operation process, the power balance state is re-monitored after each level of load is put into operation until the power deviation value falls back to the predetermined fluctuation range, or all adjustable resources have been used up. Ultimately, during the adjustment process, a closed-loop feedback control method is adopted. The real-time power deviation value after each adjustment action is used as a feedback signal to continuously determine whether the current power balance state has been restored to the predetermined fluctuation range. If it has not been restored, the next round of adjustment command is executed based on the current remaining adjustable power margin, and the judgment is repeated. If it has been restored, the current adjustment state is maintained, the adjustment operation is stopped, and the power supply unit is marked as normal operation. Through the above continuous feedback mechanism, it is ensured that the power balance state of the power supply unit is always maintained within the safe operating range, and the power fluctuation during the adjustment process is controlled to a minimum.

[0047] On the other hand, in some embodiments, this application provides a green and low-carbon park energy distribution optimization system, with reference to Figure 4 The figure is a schematic diagram of the structure of a green and low-carbon park energy allocation optimization system 400 according to some embodiments of this application. The green and low-carbon park energy allocation optimization system 400 includes: an acquisition module 401, a calculation module 402, an optimization module 403, and a control module 404, which are described below: The acquisition module 401 is used to acquire photovoltaic power output forecast data and load demand forecast data for each zone in the park in the future time period, as well as the adjustable capacity data of energy storage devices and the adjustable potential data of flexible loads in each zone. The calculation module 402 is used to calculate the net load power of each zone in the future period based on the photovoltaic power forecast output data and the load forecast demand data, and then determine the power adjustability margin and baseline carbon emission intensity of each zone in the future period based on the net load power, the adjustable capacity data and the adjustable potential data. The optimization module 403 is used to optimize and solve the green electricity allocation priority of each zone in the future time period with the goal of minimizing the total carbon emissions of the park as a whole, based on the flexible load response level of each zone and the benchmark carbon emission intensity. The control module 404 is used to coordinately adjust the switching sequence of flexible loads and the actual charging and discharging power of energy storage devices in the power supply unit when the real-time power balance state of any power supply unit in each zone exceeds the predetermined fluctuation range, based on the power adjustable margin of the corresponding zone and the green electricity allocation priority.

[0048] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described green and low-carbon park energy allocation optimization method.

[0049] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a green and low-carbon park energy allocation optimization method according to some embodiments of this application. The green and low-carbon park energy allocation optimization method in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0050] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0051] The communication bus 502 can be used to transmit information between the aforementioned components.

[0052] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0053] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. In the above embodiment, the energy allocation optimization method for green and low-carbon industrial parks can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0054] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0055] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0056] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0057] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned green and low-carbon park energy allocation optimization method.

[0058] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0059] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing energy allocation in a green and low-carbon industrial park, characterized in that, Includes the following steps: Obtain photovoltaic power output forecast and load demand forecast for each zone in the park in the future, as well as adjustable capacity data of energy storage devices and adjustable potential data of flexible loads in each zone; Based on the photovoltaic power output forecast data and load demand forecast data, the net load power of each zone in the future period is calculated, and then based on the net load power, the adjustable capacity data and the adjustable potential data, the power adjustability margin and baseline carbon emission intensity of each zone in the future period are determined. With the goal of minimizing the total carbon emissions of the park, the green electricity allocation priority of each zone in the future time period is obtained by optimizing the solution based on the flexible load response level of each zone and the benchmark carbon emission intensity. When the real-time power balance of any power supply unit in each zone is detected to exceed the predetermined fluctuation range, the switching sequence of flexible loads and the actual charging and discharging power of the energy storage device in that power supply unit are adjusted in a coordinated manner according to the power adjustable margin and the green electricity allocation priority of the corresponding zone.

2. The method as described in claim 1, characterized in that, Based on the photovoltaic power output forecast data and load demand forecast data, the calculation of the net load power of each zone in the future period specifically includes: For each zone within the park, the photovoltaic power output data and the load demand data for each zone in future time periods are aligned in a time series. For each partition and each time segment, the net load power of each time segment is calculated based on the difference between the predicted load demand data and the predicted photovoltaic output data, and then the net load power of each partition in the future period is calculated.

3. The method as described in claim 1, characterized in that, Determining the power adjustability margin and baseline carbon emission intensity for each zone in the future time period based on the net load power, the adjustable capacity data, and the adjustable potential data specifically includes: Based on the adjustable capacity data, determine the adjustable power range of the energy storage device in each zone during the future time period; Based on the adjustable potential data, the adjustable power range of the flexible load in each zone during the future time period is calculated; Based on the adjustable power range of the energy storage device and the adjustable power range of the flexible load, the power adjustability margin of each zone in the future time period is calculated. The regional power grid baseline emission factor for each zone is determined based on historical power grid operation data. The baseline carbon emission intensity for each zone in the future period is determined based on the regional grid baseline emission factor for each zone and the net load power of the corresponding zone.

4. The method as described in claim 1, characterized in that, The flexible load response level is determined in the following manner: Collect the response speed parameters, adjustable capacity parameters, and adjustment cost parameters of each flexible load in each zone; The response speed parameter, adjustable capacity parameter, and adjustment cost parameter are normalized respectively, and then weighted and summed according to preset weights to obtain the comprehensive score of each flexible load. Based on the comparison between the comprehensive score and the preset level mapping table, the response level of the flexible load in each zone is determined. The response level is used to specify the order of call for different flexible loads during the adjustment process.

5. The method as described in claim 1, characterized in that, With the goal of minimizing the overall carbon emissions of the park, and based on the flexible load response level of each zone and the aforementioned baseline carbon emission intensity, the green electricity allocation priority for each zone in the future time period is optimized and solved, specifically including: With the goal of minimizing the total carbon emissions of the park, an optimization model is constructed using the baseline carbon emission intensity of each zone as the optimization weight. The flexible load response level is used as the adjustment sequence constraint of the optimization model to specify the order in which different flexible loads are invoked during the adjustment process; The constraints of the optimization model are set, including power balance constraints for each zone, operation constraints of energy storage devices, and adjustment capability constraints of flexible loads. The optimization model is solved to obtain the green electricity allocation priority for each zone in future time periods.

6. The method as described in claim 1, characterized in that, When the real-time power balance of any power supply unit in any zone is detected to exceed the predetermined fluctuation range, the switching sequence of flexible loads and the actual charging and discharging power of the energy storage device within that power supply unit are adjusted in a coordinated manner according to the power adjustability margin and green electricity allocation priority of the corresponding zone. Specifically, this includes: Real-time monitoring of the power balance status of each power supply unit to determine whether it exceeds the preset fluctuation range; For power supply units that exceed the predetermined fluctuation range, identify the current power deviation type, which includes power deficit state and power surplus state; The adjustment priority order of the flexible load and the energy storage device is determined according to the green electricity allocation priority corresponding to the power supply unit and the response level of each flexible load in the zone; If there is a power deficit, the energy storage discharge power will be increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, the flexible load will be cut off according to the power adjustable margin. If there is a power surplus, the energy storage charging power will be increased first according to the adjustment priority order. If the energy storage adjustment capacity is insufficient, flexible load will be put into operation according to the power adjustable margin. During the adjustment process, the power balance status is continuously fed back until it returns to the fluctuation range.

7. The method as described in claim 1, characterized in that, The predetermined fluctuation range is determined in advance based on the fluctuation characteristics of the historical power balance state of each power supply unit.

8. The method as described in claim 1, characterized in that, Obtaining the photovoltaic power output forecast data for each zone within the park for future periods specifically includes: Collect historical power output data of photovoltaic power stations in each zone and numerical weather forecast data for the corresponding time periods; The historical power output data and the numerical weather forecast data are input into a pre-trained photovoltaic power prediction model to obtain the photovoltaic power output data for each region in the future period.

9. The method as described in claim 1, characterized in that, Based on historical load data, date type data, and meteorological factor data of each zone, the predicted load demand data is obtained through a prediction model.

10. A green and low-carbon industrial park energy distribution optimization system, characterized in that, include: The acquisition module is used to acquire photovoltaic power output forecast data and load demand forecast data for each zone in the park in the future time period, as well as the adjustable capacity data of energy storage devices and the adjustable potential data of flexible loads in each zone. The calculation module is used to calculate the net load power of each zone in the future period based on the photovoltaic power forecast output data and the load forecast demand data, and then determine the power adjustability margin and baseline carbon emission intensity of each zone in the future period based on the net load power, the adjustable capacity data and the adjustable potential data. The optimization module is used to optimize and solve the green electricity allocation priority of each zone in the future time period with the goal of minimizing the total carbon emissions of the park, based on the flexible load response level of each zone and the benchmark carbon emission intensity. The control module is used to coordinately adjust the switching sequence of flexible loads and the actual charging and discharging power of energy storage devices in the power supply unit when the real-time power balance state of any power supply unit in each zone exceeds the predetermined fluctuation range, based on the power adjustable margin of the corresponding zone and the green electricity allocation priority.

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