Multi-source energy storage system regulation and control method based on campus energy low-carbon target

Abstract

The invention relates to the field of energy storage system regulation and control, and particularly discloses a multi-source energy storage system regulation and control method based on a campus energy low-carbon target, and the method comprises the steps: dividing a campus into a plurality of power utilization units, and predicting the comprehensive use rate interval of each unit in a current time period based on historical energy consumption and meteorological data; the actual comprehensive self-use rate of each unit is monitored in real time, low abnormity is identified, reasons including prediction deviation, power generation access abnormity, optical storage and discharge abnormity or equipment failure are analyzed, and regulation intervention or early warning is correspondingly executed; and based on the self-use rate of the current time period and the predicted value of the next time period, identifying a power generation surplus unit, determining a surplus power transmission receiving end in combination with the inter-unit distance and the carbon emission reduction potential, and generating a dynamic cooperative scheduling instruction. According to the invention, real-time monitoring, abnormal intelligent diagnosis and cross-unit cooperative scheduling of the campus green energy consumption state are realized, and the self-generation and self-use rate of the green energy and the overall emission reduction benefit are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of energy storage system regulation, and relates to a multi-source energy storage system regulation method based on a low-carbon target of campus energy. BACKGROUND

[0002] With the acceleration of global climate change and energy transformation, low-carbon campus construction has become one of the important measures to achieve the "double carbon" target. As an energy-intensive place, power consumption is the main source of carbon emissions in the campus. In order to achieve the low-carbon target, green and clean energy such as photovoltaic power generation and wind power generation is generally introduced into the campus to gradually replace traditional grid power supply and reduce carbon emissions. However, in the actual application and deployment of green energy in the campus, there are still many deficiencies, which affect the maximization of emission reduction effect.

[0003] Currently, the multi-source energy storage system in the campus mainly faces the following problems in operation: first, the system cannot real-time monitor and identify the abnormal state of "high production capacity but low utilization rate" of green energy, resulting in waste of renewable energy and failure to fully exert its emission reduction potential; second, there are significant differences in power demand and green energy production capacity between different building units in the campus, and part of the buildings have excess power generation but cannot be timely consumed, while part of the buildings still need to rely on grid power supply due to insufficient power generation, lacking effective cross-building energy collaborative scheduling mechanism, which restricts the overall utilization rate of green energy.

[0004] In the prior art, although there are energy storage regulation methods based on electricity price and power consumption peak as described in CN117060462A, and solar energy storage system energy regulation schemes based on machine learning and virtual power plant as proposed in CN120073709A, these methods mainly focus on the optimization of a single system or the response based on market electricity price, and fail to provide a real-time monitoring, abnormal diagnosis and dynamic collaborative energy storage regulation method for the characteristics of large differences in energy consumption between building units, uneven green energy consumption, and clear low-carbon target orientation in the campus scenario, so it is difficult to achieve the overall optimal operation of the multi-source energy storage system in the campus under the low-carbon target.

[0005] Therefore, there is an urgent need for a campus multi-source energy storage system regulation method that can real-time monitor the green energy consumption state of each building unit, intelligently diagnose abnormal reasons, and dynamically schedule surplus power based on the low-carbon target, in order to improve the self-generation and self-consumption rate of green energy and maximize the emission reduction benefit. SUMMARY

[0006] In view of the above problems, the present application provides a multi-source energy storage system regulation method based on the low-carbon target of campus energy, and the specific technical scheme is as follows: a multi-source energy storage system regulation method based on the low-carbon target of campus energy, comprising the following steps: step S1, dividing the campus into a plurality of power consumption units according to buildings, predicting the total power consumption range and photovoltaic power generation range of each power consumption unit in the current period based on historical energy consumption data and meteorological data, and combining the existing power of the light storage system to predict the comprehensive self-use rate interval of each power consumption unit.

[0007] Step S2, monitoring the actual comprehensive self-use rate of each power consumption unit, and determining whether there is an abnormally low abnormality, if not, then go to step S4, otherwise execute step S3.

[0008] Step S3, analyze the cause of the abnormally low abnormality: if it is a prediction error, then remove the abnormality and enter step S4, if it is a power generation access or light storage discharge abnormality, then execute the regulation intervention, and correct the comprehensive self-use rate to enter step S4, if it is a device fault, then mark the power consumption unit and issue a warning.

[0009] Step S4, summarize the comprehensive self-use rate of each power consumption unit and the remaining power of the light storage system in the current period, combine the predicted value of the comprehensive self-use rate in the next period, identify the power generation surplus power consumption unit, and determine the receiving end of the remaining power transmission according to the distance between the power consumption units and the carbon emission reduction potential, and generate dynamic cooperative regulation instructions.

[0010] Compared with the prior art, the multi-source energy storage system regulation method based on the low-carbon target of campus energy has the following beneficial effects: 1. The present application can quickly identify the abnormal state of high green energy production but low utilization rate by predicting the comprehensive self-use rate interval of each power consumption unit and monitoring the actual self-use rate in real time, and automatically analyze the abnormal reason, realize the closed-loop management from monitoring to diagnosis, and avoid the waste of green energy.

[0011] 2. The present application can not only automatically correct the prediction deviation, but also can issue a warning and isolate the device fault, so as to avoid the influence of the fault power consumption unit on the overall scheduling and improve the reliability and operation efficiency of the system.

[0012] 3. The present application identifies the power generation surplus unit through double conditions, and dynamically determines the remaining power transmission target based on distance and carbon emission reduction potential, realizes the complementary and optimized distribution of electric energy between buildings, avoids the problem of coexistence of local excess and local shortage, and improves the overall energy efficiency and emission reduction effect of the campus micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description only some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0014] Figure 1 The method flowchart of the present application.

[0015] Figure 2 The composition diagram of the building independent power system of the present application.

[0016] Figure 3 The flowchart of analyzing the reason of low abnormality. DETAILED DESCRIPTION

[0017] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object of the present application, the following will be described in detail in combination with the drawings and preferred embodiments, the specific implementation, structure, features and effects of a multi-source energy storage system regulation method based on the low-carbon target of campus energy according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0019] The specific scheme of the multi-source energy storage system regulation method based on the low-carbon target of campus energy provided by the present application will be specifically described below in combination with the drawings.

[0020] Please refer to Figure 1 and Figure 2 The multi-source energy storage system regulation method based on the low-carbon target of campus energy provided by the present application includes the following steps: step S1, dividing the campus into several power consumption units according to the building, predicting the total power consumption range and photovoltaic power generation range of each power consumption unit in the current period based on historical energy consumption data and meteorological data, combining the existing power of the light storage system, and predicting the comprehensive self-use rate interval of each power consumption unit.

[0021] In one embodiment of the present application, in order to realize the low-carbon scheduling and management of campus energy, it is necessary to reasonably divide the power consumption structure in the campus. By dividing the campus into multiple power consumption units according to the building, the energy use and power generation of each unit can be more finely monitored and managed, laying a foundation for subsequent energy storage regulation and collaborative scheduling.

[0022] Considering that the power consumption behaviors of the buildings in the campus are influenced by factors such as course arrangement and work and rest rules, and present certain periodicity and predictability, and meanwhile, the photovoltaic power generation is greatly influenced by meteorological conditions, and the power generation capacity fluctuates, therefore, before dispatching, the total power consumption capacity of each power consumption unit in the current period and the photovoltaic power generation capacity need to be predicted, and the self-sufficient capacity of each unit is comprehensively evaluated, that is, the comprehensive self-use rate, in combination with the energy storage state of the light storage system.

[0023] To further clarify the division basis of the power consumption units, and ensure that the division result is consistent with the spatial layout of the campus and is convenient for subsequent energy dispatching management, the embodiment of the present application proposes to divide the power consumption units based on a spatial clustering method. This method can comprehensively consider the spatial distance and functional association between buildings, and buildings with similar locations or functions are classified into the same power consumption unit, so as to ensure the dispatching efficiency while reducing the energy transmission loss.

[0024] Based on this, in a preferred embodiment of the present application, the method for dividing the power consumption units comprises: marking the location information of each building in the campus map, and dividing the campus into a plurality of power consumption units according to the spatial distribution of the buildings based on a spatial clustering method, each power consumption unit containing one or more buildings.

[0025] In an embodiment of the present application, the roofs of the buildings in the campus are provided with photovoltaic panels, the photovoltaic power generation adopts a self-generation and self-use mode, and the excess power is stored in the light storage system. Each building is configured with an independent power system, including a photovoltaic power generation system, a grid access system, a control system and a light storage system. The power supply of the building is derived from photovoltaic power generation and grid power purchase; the photovoltaic power generation system and the grid access system are connected to the control system through a switching module, the photovoltaic power generation power can be used for instant power supply or stored in the light storage system, and the light storage system is provided with a charge-discharge switching module. The independent power systems of the buildings are connected to each other to form a campus micro-grid.

[0026] It should be noted that although the campus photovoltaic power generation provides part of the power, the total power generation capacity is insufficient to cover all the power consumption demand of the campus, therefore, the photovoltaic power generation capacity is entirely consumed within the campus, including instant use and temporary storage in the light storage system, and no excess power is connected to the grid.

[0027] It should also be noted that the power consumption priority of the campus micro-grid is: preferentially using real-time photovoltaic power generation, secondly using the stored power of the light storage system, and finally purchasing power from the grid.

[0028] Preferably, in an embodiment of the present application, the method for predicting the total power consumption range of the power consumption unit in the current period comprises: extracting the pre-stored historical energy consumption data from the database, obtaining the load curve of each day of the week in the historical period of the power consumption unit, and the load curve takes time as the abscissa and total power consumption as the ordinate.

[0029] Screening the historical dates of the same typical day type as the current day, and aggregating the corresponding load curves to obtain a plurality of historical load curves corresponding to the current day.

[0030] Segmenting each historical load curve with a set time window, and for each curve segment, subtracting the total power consumption corresponding to the two endpoints to obtain the historical total power consumption corresponding to each time period.

[0031] Identifying the time period corresponding to the current time period in each historical load curve, and extracting the historical total power consumption of each curve in the time period to form a historical total power consumption dataset for the current time period.

[0032] Based on the historical total power consumption dataset, a numerical interval is constructed according to a pre-set confidence level as the range of the total power consumption of the power consumption unit in the current time period.

[0033] In one specific embodiment, the historical period can be set as a time range of half a month, one month or three months away from the current date. Generally speaking, the closer the historical period is to the current time, the higher the reference value of the historical energy consumption data used.

[0034] It should be understood that, due to the relatively fixed course arrangement in the campus and the strong regularity of the work and rest of teachers and students, the campus power consumption behavior presents certain periodic characteristics. Based on this rule, the use of historical energy consumption data to predict the total power consumption has high reliability.

[0035] It should be noted that the setting of the confidence level is mainly based on the statistical distribution characteristics of the historical data and the actual demand of the system for prediction reliability, and its purpose is to quantify the uncertainty range of the prediction result, and provide a reasonable fluctuation interval for subsequent energy storage scheduling and abnormality judgment. In one specific embodiment, a 95% confidence level is selected.

[0036] Preferably, in one embodiment of the present application, the method for predicting the range of photovoltaic power generation of the power consumption unit in the current time period comprises: D1: extracting the pre-stored historical photovoltaic power generation log of the power consumption unit from the database, obtaining the power generation of each historical photovoltaic power generation of the power consumption unit and the corresponding meteorological data, the meteorological data including solar irradiance, ambient temperature, wind speed and ambient humidity.

[0037] D2: based on the predicted meteorological data of the current time period, matching the historical photovoltaic power generation records consistent with the current time period power generation condition.

[0038] It should be noted that the power generation condition consistent means that the values of each sub-item of the meteorological data are the same within a set small error range.

[0039] If the matching is successful, the number of historical photovoltaic power generation records consistent with the power generation condition is counted, and step D3 is performed.

[0040] If the matching is unsuccessful, step D4 is performed.

[0041] D3: If the number is one, the photovoltaic power generation amount in the historical photovoltaic power generation record is taken as the predicted value of the photovoltaic power generation amount of the power consumption unit in the current period, and the fluctuation range of the photovoltaic power generation amount of the power consumption unit in the current period is determined according to a preset rule.

[0042] It should be noted that when the historical photovoltaic power generation record with consistent or similar power generation conditions is only one time, the determination of the photovoltaic power generation range will be directly generated according to the preset rule due to the lack of sufficient data for statistical analysis. As an example, the range is determined according to an allowable relative error of ±5%.

[0043] If the number is multiple, the photovoltaic power generation amount in each historical photovoltaic power generation record is used to construct a numerical interval according to a preset confidence level, as the photovoltaic power generation range of the power consumption unit in the current period.

[0044] In one specific embodiment, the numerical interval of the photovoltaic power generation amount in the historical photovoltaic power generation record is constructed according to a confidence level of 95%.

[0045] D4: The similarity of each historical photovoltaic power generation record to the power generation condition in the current period is evaluated.

[0046] As an example, the similarity of each historical photovoltaic power generation record to the power generation condition in the current period is evaluated, and the specific method is as follows: the meteorological data corresponding to each historical photovoltaic power generation record is compared with the meteorological data in the current period item by item, and the meteorological data sub-items with numerical deviation are identified.

[0047] The influence weight of each sub-item on the photovoltaic power generation amount is obtained from the database.

[0048] For each historical photovoltaic power generation record, the product of the deviation of each sub-item and the corresponding influence weight is calculated, and the products of all sub-items are added, and the reciprocal of the addition result is taken to obtain the similarity of the historical record to the power generation condition in the current period.

[0049] The historical photovoltaic power generation records with a similarity greater than a set threshold are screened as the historical records with similar power generation conditions, the number of which is counted, and then the photovoltaic power generation range of the power consumption unit in the current period is determined according to the method described in step D3.

[0050] It should be noted that the influence weight of each sub-item of meteorological data on photovoltaic power generation is set based on the physical mechanism of photovoltaic power generation, statistical correlation analysis of historical data, and actual operation experience, for quantifying the influence degree of different meteorological factors on power generation, thereby supporting similarity calculation and prediction when the meteorological conditions are not completely matched. As an example, the influence weight of solar irradiance is 0.60, the influence weight of ambient temperature is 0.25, the influence weight of wind speed is 0.10, and the influence weight of ambient humidity is 0.05.

[0051] It should be noted that the setting of the similarity threshold is based on balancing the reliability of historical data reference and the prediction coverage, which determines which historical records can still be used for photovoltaic power generation prediction when the meteorological conditions are not completely matched. The threshold needs to ensure that the selected records can reflect the current power generation working condition characteristics and have sufficient statistical representativeness. As an example, the similarity distribution interval of all historical records and the current period power generation working condition is obtained, and the 80% quantile of the interval is taken as the similarity threshold.

[0052] Preferably, in an embodiment of the present application, the method for predicting the comprehensive self-use rate interval of the power consumption unit comprises: obtaining the existing power of the light storage system in the power consumption unit, taking the upper and lower limits of the total power consumption range and the photovoltaic power generation range of the power consumption unit, and combining a comprehensive self-use rate calculation model to calculate the minimum value and the maximum value of the comprehensive self-use rate.

[0053] It should be noted that the existing power of the light storage system refers to the storage power of the light storage system at the end of the last period.

[0054] According to the minimum value and the maximum value, the comprehensive self-use rate interval of the power consumption unit is determined.

[0055] The comprehensive self-use rate calculation model is the ratio between the cumulative value of the photovoltaic power generation and the existing power of the light storage system and the total power consumption.

[0056] As an example, the method for calculating the minimum value and the maximum value of the comprehensive self-use rate is: inputting the upper limit of the total power consumption range of the power consumption unit and the lower limit of the photovoltaic power generation range into the comprehensive self-use rate calculation model to calculate the minimum value of the comprehensive self-use rate.

[0057] The lower limit of the total power consumption range of the power consumption unit and the upper limit of the photovoltaic power generation range are inputted into the comprehensive self-use rate calculation model to calculate the maximum value of the comprehensive self-use rate.

[0058] It should be noted that the existing power of the light storage system in the comprehensive self-use rate calculation model is approximately the dischargeable power of the light storage system in the current period.

[0059] After the division of the electricity using units and the comprehensive self-use rate prediction are completed, the system enters a real-time monitoring and abnormal diagnosis stage, so as to timely find and handle abnormal situations in energy use, and guarantee the stable operation of the campus energy system and the realization of the low-carbon target.

[0060] In step S2, the actual comprehensive self-use rate of each electricity using unit is monitored, and it is judged whether there is a low abnormality. If there is no abnormality, it is transferred to step S4, otherwise step S3 is executed.

[0061] In the operation process of the campus energy system, the actual comprehensive self-use rate of each electricity using unit may deviate from the prediction interval due to various factors, such as sudden increase of electricity load, unexpected photovoltaic power generation, abnormal discharge of energy storage system or equipment failure, etc. In order to timely identify these abnormal situations and take corresponding measures, the system needs to monitor the actual comprehensive self-use rate of each electricity using unit in real time, and compare it with the prediction interval to judge whether there is a low abnormality, i.e. the actual self-use rate is lower than the lower limit of the prediction interval.

[0062] In order to accurately judge whether there is a low abnormality, the actual comprehensive self-use rate of each electricity using unit in the current period needs to be calculated first. By comparing the actual comprehensive self-use rate with the prediction interval, the units with low energy use efficiency or high external power purchase dependence can be quickly identified, providing a basis for subsequent abnormal diagnosis and regulation.

[0063] Based on this, in a preferred embodiment of the present application, the method for judging whether there is a low abnormality comprises: calculating the ratio of the difference between the total electricity consumption of each electricity using unit in the current period and the electricity purchased from the power grid to the total electricity consumption, to obtain the actual comprehensive self-use rate of each electricity using unit.

[0064] If the actual comprehensive self-use rate of the electricity using unit is lower than the lower limit of its comprehensive self-use rate interval, it is determined that there is a low abnormality, otherwise it is determined that there is no abnormality.

[0065] In step S3, the cause of the low abnormality is analyzed: if it is a prediction error, the abnormality is removed and step S4 is entered, if it is a power generation access or photovoltaic storage discharge abnormality, regulation intervention is performed, and after the comprehensive self-use rate is corrected, step S4 is entered, if it is an equipment failure, the electricity using unit is marked and a warning is given.

[0066] When the system determines that there is a low abnormality in a certain electricity using unit, the cause of the abnormality needs to be analyzed in depth, so as to take appropriate handling measures. The causes of the abnormality mainly include three categories: prediction error, operation abnormality (including power generation access abnormality and photovoltaic storage discharge abnormality) and equipment failure. According to different causes, the system will execute different processing logic to restore the normal operation of the unit or isolate the fault unit, so as to guarantee the overall efficiency and stability of the campus energy system.

[0067] To systematically handle the abnormal reasons, the embodiment of the present application designs a classification processing mechanism based on the diagnosis results. If the abnormality is caused by prediction error, it is considered as normal fluctuation, the abnormality mark is removed, and it continues to participate in subsequent scheduling; if it is a running abnormality, the system operation is optimized through regulation and intervention, and the comprehensive self-use rate is corrected to reflect the actual state after intervention; if it is a device fault, the unit is marked as a fault state and a warning is issued to avoid its participation in subsequent energy scheduling until the fault is eliminated.

[0068] Based on this, referring to Figure 3 In a preferred embodiment of the present application, the method for analyzing the low abnormality reason comprises: S31: judging whether the actual total power consumption in the current period exceeds the upper limit of the prediction range and the actual comprehensive self-use rate is located in the comprehensive self-use rate interval recalculated according to the actual total power consumption.

[0069] If yes, it is determined that the total power consumption prediction deviation, and the abnormality mark is removed.

[0070] If no, step S32 is executed.

[0071] S32: obtaining the actual photovoltaic power generation in the current period, and judging whether the following conditions are met at the same time: (1) the actual photovoltaic power generation is lower than the lower limit of the prediction range.

[0072] (2) the actual meteorological data in the current period is worse than the predicted meteorological data.

[0073] (3) the actual comprehensive self-use rate is located in the comprehensive self-use rate interval recalculated according to the actual photovoltaic power generation.

[0074] If all the conditions are met, it is determined that the photovoltaic power generation prediction deviation, and the abnormality mark is removed.

[0075] Otherwise, step S33 is executed.

[0076] S33: calculating the photovoltaic self-use power in the current period, which is the total power consumption in the current period minus the power purchase from the power grid and the discharge power of the light storage system.

[0077] Based on the ratio of the photovoltaic self-use power to the actual photovoltaic power generation in the current period, the photovoltaic power generation self-use rate is calculated.

[0078] If the photovoltaic power generation self-use rate is lower than a preset threshold, it is preliminarily determined that the power generation access is abnormal, and the regulation and intervention are executed.

[0079] It should be noted that the regulation and intervention refers to the dynamic optimization and adjustment of the operation parameters and control strategies of the photovoltaic power generation device and the light storage system. As an example, the regulation and intervention includes adjusting the maximum power point tracking strategy of the photovoltaic inverter, modifying the charge and discharge power setting value of the energy storage unit in the light storage system, etc.

[0080] If the actual comprehensive self-use rate is increased after the regulatory intervention, it is verified as abnormal power generation access, and the median of the comprehensive self-use rate interval is taken as the actual comprehensive self-use rate of the power consumption unit in the current period. Otherwise, step S34 is performed.

[0081] S34: Calculate the deviation between the discharge amount of the light storage system and the existing electric quantity of the light storage system in the current period to obtain a discharge deviation value.

[0082] If the discharge deviation value is greater than a preset deviation threshold, it is preliminarily determined that the light storage discharge is abnormal, the regulatory intervention is performed and verified, and if the verification is yes, the actual comprehensive self-use rate of the power consumption unit in the current period is revalued. Otherwise, step S35 is performed.

[0083] S35: If the determinations of steps S31 to S34 are all not established, it is determined that the equipment is faulty, the corresponding power consumption unit is marked and a warning is issued.

[0084] It should be noted that the power consumption unit determined to be faulty does not participate in the energy collaborative scheduling described in step S4.

[0085] It should be noted that the process of steps S31-S35 is a diagnostic path of the main or typical reason, and in the actual system, multiple checks can be performed in parallel, or a more complex comprehensive judgment logic can be set.

[0086] After the abnormality processing is completed, the system enters the energy collaborative scheduling phase. At this time, each power consumption unit (except the faulty unit) is in a normal or corrected state, and its comprehensive self-use rate and energy storage state can be used as the basis for scheduling decisions to support the excess power transmission from the power generation surplus unit to the power shortage unit, thereby improving the overall energy self-sufficiency rate and low carbon level of the campus.

[0087] Step S4: The comprehensive self-use rate of each power consumption unit in the current period and the remaining electric quantity of the light storage system are summarized, combined with the predicted value of the comprehensive self-use rate in the next period, the power generation surplus power consumption unit is identified, and the receiving end of the excess power transmission is determined according to the distance between the power consumption units and the carbon emission reduction potential to generate dynamic collaborative control instructions.

[0088] In order to realize the optimal scheduling and low carbon target of campus energy, the system needs to perform energy collaboration between units to transmit the remaining electric energy of the power generation surplus unit to the power shortage unit to reduce the dependence on external power purchase. Therefore, the system first needs to identify the units that have power generation surplus potential in the current period and the next period, and then determine the optimal receiving end unit according to factors such as distance and carbon emission reduction potential, and finally generate collaborative control instructions to guide the implementation of excess power transmission.

[0089] In order to accurately identify the power generation surplus power consumption unit, the current actual self-use rate and the predicted self-use rate of the next period need to be considered comprehensively. Only the surplus of the current period may be a temporary phenomenon, and the combination of the next period prediction can ensure the continuity of the surplus and the stability of the scheduling. By setting the surplus critical self-use rate as the judgment threshold, the units that truly have surplus power output capability can be screened out, and reliable input is provided for subsequent transmission decision.

[0090] Based on this, in one preferred embodiment of the present application, the method for identifying the power generation surplus power consumption unit comprises: aggregating the comprehensive self-use rate of each power consumption unit in the current period and the remaining power of the light storage system.

[0091] The comprehensive self-use rate interval of each power consumption unit in the next period is predicted, and the median of the interval is taken as the comprehensive self-use rate prediction value.

[0092] For each power consumption unit, if both the comprehensive self-use rate in the current period and the comprehensive self-use rate prediction value in the next period are greater than the preset surplus critical self-use rate, the power consumption unit is identified as a power generation surplus power consumption unit.

[0093] It should be noted that by predicting the total power consumption range and the photovoltaic power generation range of each power consumption unit in the next period, and combining the remaining power of the light storage system, the comprehensive self-use rate interval of each power consumption unit in the next period is further predicted.

[0094] It should be further pointed out that the purpose of setting double conditions to judge the power generation surplus in the present application is to ensure the continuity of the surplus power transmission and avoid frequent switching.

[0095] After identifying the power generation surplus unit, the target receiving end of the surplus power transmission needs to be further determined. The selection of the receiving end needs to consider the loss caused by the transmission distance and the carbon emission reduction demand of the receiving end unit, so as to realize the goal of maximizing the overall carbon emission reduction benefit of the campus under the condition of minimum transmission loss.

[0096] Based on this, in one preferred embodiment of the present application, the method for determining the surplus power transmission receiving end of the power generation surplus power consumption unit comprises: taking the power generation surplus power consumption unit as the center, obtaining the distance between the unit and each surrounding power consumption unit, and sorting according to the distance from near to far, and determining the distance priority factor of each surrounding power consumption unit according to the corresponding relationship between the preset sorting serial number and the distance priority factor.

[0097] The carbon emission reduction potential of each surrounding power consumption unit is evaluated, and sorted from high to low according to the carbon emission reduction potential, and the carbon emission reduction potential priority factor of each surrounding power consumption unit is determined according to the corresponding relationship between the preset sorting serial number and the carbon emission reduction potential priority factor.

[0098] The distance priority factor and the carbon emission reduction potential priority factor of each peripheral power consumption unit are multiplied by preset distance weight and carbon emission reduction potential weight respectively, and a comprehensive priority factor of each peripheral power consumption unit is calculated by weighted summation.

[0099] The peripheral power consumption unit with the highest comprehensive priority factor is selected as the surplus power transmission receiving end of the power generation surplus power consumption unit.

[0100] It should be noted that the smaller the serial number, the closer the distance, and the closer the distance, the greater the distance priority factor.

[0101] It should be noted that the greater the carbon emission reduction potential, the greater the carbon emission reduction potential priority factor.

[0102] It should be noted that the distance weight and the carbon emission reduction potential weight are set according to the campus energy low-carbon target. Because the unit green electricity emission reduction benefit of different buildings is much greater than the power transmission loss caused by the distance in the campus, the carbon emission reduction potential weight is higher than the distance weight. As an example, the carbon emission reduction potential weight is 0.7, and the distance weight is 0.3.

[0103] It should be noted that if there are multiple power generation surplus power consumption units, when determining the receiving end of the surplus power transmission of each unit, the comprehensive self-use rate of each unit in the current period will be sorted from high to low, and the analysis and decision will be made in this order. It should also be noted that when multiple power generation surplus power consumption units select the same power consumption unit as the receiving end of the surplus power transmission, the system will allocate the transmission power in proportion or according to the priority order based on the remaining power of each surplus unit, the transmission line capacity and the receivable capacity of the receiving end.

[0104] To further clarify the evaluation method of carbon emission reduction potential, the embodiment of the present application proposes a quantitative evaluation model based on the cumulative comprehensive self-use rate of power consumption units. The lower the cumulative comprehensive self-use rate, the higher the dependence of the unit on external power purchase, and the greater the carbon emission reduction potential achieved by receiving green electricity. By comparing the cumulative comprehensive self-use rate of each unit with the average level, the emission reduction potential can be quantitatively evaluated to support the calculation of the priority factor and the selection of the receiving end.

[0105] Based on this, in a preferred embodiment of the present application, the method for evaluating the carbon emission reduction potential of each peripheral power consumption unit comprises: calculating the sum of the comprehensive self-use rate of each peripheral power consumption unit in the current period and the predicted value of the comprehensive self-use rate in the next period as the cumulative comprehensive self-use rate of the power consumption unit.

[0106] The average value of the cumulative comprehensive self-use rate of all peripheral power consumption units is calculated.

[0107] The average value is divided by the cumulative comprehensive self-use rate of each peripheral power consumption unit to obtain a quantitative evaluation value of the carbon emission reduction potential of each peripheral power consumption unit.

[0108] In an embodiment of the present application, after the receiving end selection is completed, the system will generate specific dynamic collaborative regulation instructions according to the remaining power of the power generation surplus unit, the transmission line capacity and the receiving capacity of the receiving end, including transmission power, transmission period and other parameters, and issue them to the control system of the corresponding unit for execution, so as to realize efficient and low-carbon collaborative scheduling of campus energy.

[0109] In summary, the present application divides the campus into multiple power consumption units, predicts the comprehensive self-use rate interval of each unit in the current period based on historical energy consumption and meteorological data; monitors the actual comprehensive self-use rate of each unit in real time, identifies abnormal low self-use rate and analyzes the reasons, including prediction deviation, abnormal power generation access, abnormal light storage discharge or equipment failure, and performs corresponding regulation intervention or early warning; based on the current period self-use rate and the predicted value of the next period, identifies the power generation surplus unit, determines the surplus power transmission receiving end combined with the distance between units and the carbon emission reduction potential, and generates dynamic collaborative scheduling instructions. The present application realizes real-time monitoring of campus green energy consumption status, intelligent diagnosis of abnormality and cross-unit collaborative scheduling, effectively improves the self-generation and self-use rate of green energy and the overall emission reduction benefit.

[0110] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0111] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

Claims

1. A method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, characterized in that, Includes the following steps: S1. Divide the campus into several power consumption units according to buildings. Based on historical energy consumption data and meteorological data, predict the total power consumption range and photovoltaic power generation range of each power consumption unit in the current period. Combined with the existing power of the photovoltaic and energy storage system, predict the comprehensive self-consumption rate range of each power consumption unit. S2. Monitor the actual comprehensive self-consumption rate of each power consumption unit and determine whether there is an abnormally low rate. If there is no abnormality, proceed to S4; otherwise, execute S3. S3. Analyze the cause of the abnormally low value: If it is a prediction error, then resolve the abnormality and proceed to S4; if it is a power generation access or photovoltaic-storage discharge abnormality, then perform regulation intervention, and after correcting the comprehensive self-consumption rate, proceed to S4; if it is an equipment failure, then mark the corresponding power application unit and issue a warning. S4. Summarize the comprehensive self-consumption rate of each power consumption unit and the remaining power of the photovoltaic and energy storage system in the current period, combine the comprehensive self-consumption rate forecast for the next period, identify the power consumption units with surplus power generation, and determine the receiving end of the remaining power transmission based on the distance between power consumption units and carbon emission reduction potential, and generate dynamic coordinated control instructions.

2. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: Methods for dividing electrical units include: Mark the location information of each building on the campus map. Based on the spatial clustering method, divide the campus into several power consumption units according to the spatial distribution of the buildings. Each power consumption unit contains one or more buildings.

3. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: Methods for predicting the total electricity consumption range of an electricity unit in the current time period include: Extract pre-stored historical energy consumption data from the database to obtain the load curve of the power consumption unit for each day of the week within the historical period. The load curve is plotted with time on the horizontal axis and total electricity consumption on the vertical axis. Filter historical dates that belong to the same typical day type as the current day, summarize their corresponding load curves, and obtain multiple historical load curves corresponding to the current day; The historical load curves are divided into segments by setting a time window. For each curve segment, the total electricity consumption corresponding to its two ends is subtracted to obtain the historical total electricity consumption for each time period. Identify the time periods corresponding to the current time period in each historical load curve, and extract the historical total electricity consumption of each curve in that time period to form the historical total electricity consumption dataset for the current time period; Based on the historical total electricity consumption dataset, a numerical range is constructed according to a preset confidence level, which serves as the range of total electricity consumption for the electricity-consuming unit in the current time period.

4. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: Methods for predicting the range of photovoltaic power generation by a power-consuming unit in the current period include: D1: Extract the pre-stored historical photovoltaic power generation logs of the power consumption unit from the database, obtain the power generation of each historical photovoltaic power generation of the power consumption unit and its corresponding meteorological data, the meteorological data including solar irradiance, ambient temperature, wind speed and ambient humidity; D2: Based on the meteorological data predicted for the current period, match historical photovoltaic power generation records that are consistent with the power generation conditions of the current period; If the match is successful, count the number of historical photovoltaic power generation records that match the power generation conditions, and proceed to step D3; If the match fails, proceed to step D4; D3: If the quantity is once, the photovoltaic power generation in the historical photovoltaic power generation record of that time shall be used as the predicted value of the photovoltaic power generation of the power consumption unit in the current period, and the fluctuation range of the photovoltaic power generation of the power consumption unit in the current period shall be determined according to the preset rules. If the quantity is multiple times, a numerical range is constructed based on the photovoltaic power generation in each historical photovoltaic power generation record, according to a preset confidence level, as the range of photovoltaic power generation of the power-consuming unit in the current time period; D4: Assess the similarity between historical photovoltaic power generation records and current power generation conditions; Historical photovoltaic power generation records with a similarity greater than a set threshold are selected as historical records of similar power generation conditions. After counting the number of times they are selected, the range of photovoltaic power generation of the power-consuming unit in the current time period is determined according to the method described in step D3.

5. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: Methods for predicting the overall self-consumption rate range of electricity consumption units include: Obtain the existing electricity of the photovoltaic and energy storage system in the power consumption unit, take the upper and lower limits of the total electricity consumption range of the power consumption unit and the photovoltaic power generation range, and combine the comprehensive self-consumption rate calculation model to calculate the minimum and maximum values ​​of the comprehensive self-consumption rate. Based on the minimum and maximum values, determine the comprehensive self-consumption rate range of the power consumption unit; The comprehensive self-consumption rate calculation model is the ratio between the cumulative value of photovoltaic power generation and the existing electricity of the photovoltaic-storage system and the total electricity consumption.

6. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: The methods for determining whether there is an abnormally low level include: The actual comprehensive self-consumption rate of each power-consuming unit is obtained by calculating the ratio of the difference between the total electricity consumption of each power-consuming unit and the electricity purchased from the grid in the current period to the total electricity consumption. If the actual comprehensive self-consumption rate of an electricity consumption unit is lower than the lower limit of its comprehensive self-consumption rate range, it is determined that there is an abnormality due to low self-consumption rate; otherwise, it is determined that there is no abnormality.

7. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: Methods for analyzing the causes of abnormally low values ​​include: S31: Determine whether the actual total electricity consumption in the current period exceeds the upper limit of the predicted range, and whether the actual comprehensive self-consumption rate is within the comprehensive self-consumption rate range recalculated based on the actual total electricity consumption; If so, it is determined to be a deviation in the total electricity consumption prediction, and the abnormality mark is removed; If not, proceed to step S32; S32: Obtain the actual photovoltaic power generation for the current time period and determine whether the following conditions are met simultaneously: (1) The actual photovoltaic power generation is lower than the lower limit of the predicted range; (2) The measured meteorological data for the current period is worse than the predicted meteorological data; (3) The actual comprehensive self-consumption rate is within the range of comprehensive self-consumption rate recalculated based on the actual photovoltaic power generation; If all conditions are met, it is determined to be a photovoltaic power generation prediction deviation, and the abnormality mark is removed; Otherwise, proceed to step S33; S33: Calculate the self-consumption of photovoltaic power in the current period. Its value is the total electricity consumption in the current period minus the electricity purchased from the grid and the discharge of the photovoltaic-storage system. The photovoltaic self-consumption rate is calculated based on the ratio of the photovoltaic self-consumption to the actual photovoltaic power generation in the current period. If the photovoltaic power generation self-consumption rate is lower than a preset threshold, it is initially determined to be an abnormal power generation connection, and control intervention is implemented. Determine whether the actual comprehensive self-consumption rate increases after the regulation intervention. If so, verify that the power generation access is abnormal and take the median of the comprehensive self-consumption rate range as the actual comprehensive self-consumption rate of the power consumption unit in the current period. Otherwise, proceed to step S34. S34: Calculate the deviation between the current discharge amount of the photovoltaic-storage system and the current electricity in the photovoltaic-storage system to obtain the discharge deviation value; If the discharge deviation value is greater than the preset deviation threshold, it is initially determined to be an abnormal photovoltaic-storage discharge. Control intervention is performed and verified. If the verification is successful, the actual comprehensive self-consumption rate of the power consumption unit in the current time period is reassigned. Otherwise, step S35 is executed. S35: If the determinations in steps S31 to S34 are all invalid, the equipment is determined to be faulty, the corresponding power consumption unit is marked and an early warning is issued.

8. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: The method for identifying surplus power generation units includes: Summarize the overall self-consumption rate of each power-consuming unit and the remaining power of the photovoltaic and energy storage system during the current period; Predict the range of comprehensive self-consumption rate for each electricity consumption unit in the next time period, and use the median of the range as the predicted value of comprehensive self-consumption rate; For each power consumption unit, if both its overall self-consumption rate for the current period and the predicted overall self-consumption rate for the next period are greater than the preset surplus critical self-consumption rate, then the power consumption unit is identified as a power generation surplus consumption unit.

9. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 1, is characterized in that: The method for determining the receiving end of surplus power transmission from the power generation surplus consumption unit includes: Taking the power generation surplus unit as the center, the distance between the unit and the surrounding power consumption units is obtained, and they are sorted from near to far. According to the preset correspondence between the sorting number and the distance priority factor, the distance priority factor of each surrounding power consumption unit is determined. Assess the carbon reduction potential of each surrounding power consumption unit and rank them from high to low according to their carbon reduction potential. Based on the pre-set correspondence between the ranking number and the priority factor of carbon reduction potential, determine the priority factor of carbon reduction potential for each surrounding power consumption unit. Based on the distance priority factor and carbon emission reduction potential priority factor of each surrounding power consumption unit, the comprehensive priority factor of each surrounding power consumption unit is calculated by multiplying it by the preset distance weight and carbon emission reduction potential weight, respectively, and then summing the results. The surrounding power-consuming unit with the highest comprehensive priority factor is selected as the receiving end of the surplus power transmission for the power generation surplus unit.

10. The method for regulating a multi-source energy storage system based on the goal of low-carbon energy on campus, as described in claim 9, is characterized in that: The methods for assessing the carbon emission reduction potential of surrounding electricity-consuming units include: The sum of the comprehensive self-consumption rate of each surrounding power consumption unit in the current period and the predicted comprehensive self-consumption rate in the next period is used as the cumulative comprehensive self-consumption rate of that power consumption unit. Calculate the average cumulative self-consumption rate of all surrounding power-consuming units; Divide the average value by the cumulative comprehensive self-consumption rate of each surrounding power consumption unit to obtain a quantitative assessment value of the carbon emission reduction potential of each surrounding power consumption unit.

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