Photovoltaic energy storage coordination control system

Abstract

The application provides a photovoltaic energy storage coordinated control system, and relates to the technical field of electric power energy storage, comprising: a data acquisition module, which is used for acquiring acquisition data of power generation equipment, energy storage equipment and load monitoring equipment in real time, and generating initial data according to the time sequence characteristics of data acquisition; a load analysis module, which is used for processing and analyzing the initial data to generate load-energy storage-power generation analysis data; a prediction analysis module, which is used for analyzing real-time power grid load data and historical load data in the initial data, predicting load change trend and generating a load prediction curve; a regulation and control module, which is used for regulating and controlling the energy storage equipment and the power generation equipment based on the load-energy storage-power generation analysis data and the load prediction curve, and combining a preset regulation and control strategy. The application can monitor and predict ahead of time the load data in the power grid, and improve the regulation and control efficiency and regulation and control effect of the regulation and control system on the power generation equipment and the energy storage equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power energy storage, in particular to a photovoltaic energy storage coordinated control system. BACKGROUND

[0002] In recent years, renewable energy such as photovoltaic and wind power has rapidly emerged in the field of microgrids. Among them, photovoltaic power generation is widely used in residential, commercial and industrial fields due to its characteristics of no noise, low maintenance cost and strong environmental adaptability.

[0003] At present, compared with the traditional centralized power generation system, photovoltaic power generation stations are mostly distributed around the power equipment to generate electricity nearby. The energy generated is generally sufficient to meet the needs of the surrounding users. If the extra energy is not utilized, it will be wasted. Therefore, in order to improve the utilization rate of photovoltaic station power and reduce power waste, the photovoltaic power generation station is connected to the main grid in parallel and off-grid, and real-time coordinated control of the power generation system and the energy storage system according to the load state of the power grid has become a hot topic in the industry. However, the current photovoltaic energy storage coordination system has a single coordination strategy, and most of the coordination strategies depend on real-time monitoring data, and the regulation and control efficiency is low, with strong hysteresis, which is difficult to meet the real-time and accurate regulation and control target.

[0004] Therefore, the present application provides a photovoltaic energy storage coordinated control system. SUMMARY

[0005] The present application provides a photovoltaic energy storage coordinated control system for real-time monitoring and advance prediction of the load in the power grid, while real-time monitoring and regulation of the operating state of each power generation equipment and each energy storage equipment, improving the regulation and control efficiency and effect of the power generation equipment and the energy storage equipment.

[0006] The present application provides a photovoltaic energy storage coordinated control system, comprising:

[0007] A data acquisition module is used to acquire the acquisition data of the power generation equipment, the energy storage equipment and the load monitoring equipment in real time, and to generate initial data according to the time sequence characteristics of data acquisition;

[0008] A load analysis module is used to process and analyze the initial data to generate load-energy storage-power generation analysis data;

[0009] A prediction analysis module is used to analyze the real-time power grid load data and historical load data in the initial data, predict the load change trend and generate a load prediction curve;

[0010] A regulation and control module is used to regulate and control the energy storage equipment and the power generation equipment based on the load-energy storage-power generation analysis data and the load prediction curve, and combined with a preset regulation and control strategy.

[0011] Preferably, the data acquisition module comprises:

[0012] A power generation data acquisition submodule is configured to acquire power generation data of a power generation plan and power generation equipment in real time.

[0013] An energy storage data acquisition submodule is configured to acquire operation data of energy storage equipment in real time and generate energy storage data.

[0014] A load data acquisition submodule is configured to monitor a load state of a power grid based on a load monitoring device in real time and generate load data.

[0015] The power generation data, the energy storage data and the load data constitute acquisition data.

[0016] Preferably, the data acquisition module further comprises:

[0017] A preprocessing submodule is configured to preprocess the power generation data, the energy storage data and the load data to obtain preprocessed data.

[0018] A time sequence feature extraction submodule is configured to extract time sequence features of the preprocessed data to generate time sequence data.

[0019] An initial data generation submodule is configured to analyze and process the time sequence data to obtain initial data.

[0020] Preferably, the load analysis module comprises:

[0021] A capacity acquisition submodule is configured to parse the initial data to obtain power generation capacity and energy storage capacity and generate capacity data.

[0022] A comparison submodule is configured to acquire load data at the same time as the capacity data from the initial data and perform comparative analysis, and simultaneously establish a mapping relationship between the capacity data and the load data to obtain load-energy storage-power generation analysis data.

[0023] Preferably, the prediction analysis module comprises:

[0024] A real-time data extraction submodule is configured to extract load real-time data from the initial data.

[0025] A feature extraction submodule is configured to extract features of the load real-time data to obtain load feature information.

[0026] A historical data screening submodule is configured to screen load historical data with a matching degree greater than a first preset degree from a load historical database based on the load feature information.

[0027] The prediction analysis submodule is configured to analyze the load real-time data and load historical data, determine a load change trend, and generate a load prediction curve.

[0028] Preferably, the prediction analysis module further comprises:

[0029] The real-time numerical analysis submodule is configured to analyze the load real-time data and corresponding sampling time, and calculate a load real-time numerical change rate.

[0030] The increment analysis submodule is configured to calculate an increment of load data between each adjacent sampling point in the preset time period, and obtain an adjacent increment change rate.

[0031] The time period division submodule is configured to divide the preset time period into a plurality of sampling periods.

[0032] The period analysis submodule is configured to analyze the load real-time data in each sampling period, and obtain a period numerical change rate and a period increment change rate.

[0033] The numerical feature extraction submodule is configured to perform feature analysis on all load real-time numerical change rates and period numerical change rates in the preset time period, and obtain a load numerical feature.

[0034] The increment feature extraction submodule is configured to perform feature analysis on all adjacent increment change rates and period increment change rates in the preset time period, and obtain a load increment feature.

[0035] The first historical data acquisition submodule is configured to obtain a time feature based on a time length of the preset time period and data sampling time in the preset time period, and select first historical data from a load historical database.

[0036] The second historical data acquisition submodule is configured to select second historical data from the load historical database based on the load numerical feature and the load increment feature.

[0037] The data processing submodule is configured to process the load real-time data by a preset function, and obtain a load real-time curve.

[0038] The prediction value calculation submodule is configured to obtain a first prediction value and a second prediction value based on the load real-time curve, and in combination with the first historical data and the second historical data.

[0039] The increment prediction submodule is configured to obtain a load prediction increment of a future corresponding time based on the first prediction value and the second prediction value.

[0040] The trend prediction submodule is configured to combine a first trend of the first historical data and a second trend of the second historical data to predict a predicted trend of the load;

[0041] The curve generation submodule is configured to generate a load prediction curve based on the time sequence feature of the future time, the corresponding load prediction increment, and the predicted trend.

[0042] Preferably, the regulation module comprises:

[0043] The first analysis submodule is configured to compare and analyze the load data and the power generation data at the same time to obtain a first analysis result.

[0044] The strategy matching submodule is configured to take a first regulation strategy when the first analysis result meets a first preset condition.

[0045] The second analysis submodule is configured to compare and analyze the load data, the power generation data, and the energy storage data to obtain a second analysis result when the first analysis result does not meet the first preset condition.

[0046] The second regulation strategy is taken when the second analysis result meets a second preset condition.

[0047] The third regulation strategy is taken when the second analysis result does not meet the second preset condition.

[0048] The high-low peak analysis submodule is configured to obtain a load peak period corresponding to the load data and a power generation peak period corresponding to the power generation based on the load-energy storage-power generation analysis data, in combination with the historical load data and the obtained power generation plan, and to obtain an association relationship between the energy storage data and the load peak period and the power generation peak period.

[0049] The comprehensive regulation submodule is configured to generate a strategy adjustment instruction based on the association relationship, to adjust the first regulation strategy, the second regulation strategy, and the third regulation strategy, and to generate a comprehensive regulation strategy.

[0050] The instruction generation submodule is configured to select a corresponding regulation instruction in an instruction database based on the comprehensive regulation strategy.

[0051] The instruction verification submodule is configured to perform control verification on the regulation instruction, to store the regulation instruction that passes the verification in an instruction pool, to select a replacement instruction corresponding to the regulation instruction that fails the verification in the instruction database and to perform verification on the replacement instruction, and to store the replacement instruction that passes the verification in the instruction pool.

[0052] The instruction sending submodule is configured to send the regulation instruction and the replacement instruction in the instruction pool to a corresponding target device and to perform regulation.

[0053] The regulation data acquisition submodule is configured to acquire process data in a regulation process and regulation data after the regulation ends in real time, and compare the regulation data with a target regulation effect corresponding to the comprehensive regulation instruction to obtain a regulation effect analysis table for output and display.

[0054] Preferably, the instruction generation submodule comprises:

[0055] The power generation data set generation unit is configured to parse the load-energy storage-power generation analysis data to obtain operation data of each distributed photovoltaic power station, and construct a power station power generation data set A=(Xi, i=1, 2, …, n), wherein Xi represents data of the ith power station.

[0056] The standard data acquisition unit is configured to acquire a standard operation data set B corresponding to the power station power generation data set based on a preset power generation plan.

[0057] The state determination unit is configured to perform state analysis on the load-energy storage-power generation analysis data to obtain an operation state change table about real-time changes in load state, energy storage state and power generation state.

[0058] The deviation calculation unit is configured to calculate, based on the power station power generation data set and the standard operation data set and in combination with the operation state change table, frequency deviation and power deviation on both sides of a corresponding network point switch of each power station by using a preset data processing model to generate a deviation data set C=(Δfi, Δpi).

[0059] The on-grid / off-grid data statistical unit is configured to compare the deviation data set with a preset on-grid / off-grid condition to obtain an on-grid / off-grid data set D=(D1, D2).

[0060]

[0061]

[0062]

[0063] Wherein, Xij represents the parameter value of the jth parameter under the ith station; Bij represents the standard data value corresponding to the jth parameter under the ith station in the standard operation data group; gamma i represents the preset threshold value under the preset grid-connected condition corresponding to the ith station; delta i represents the preset threshold value under the off-grid threshold condition of the ith station; Delta fi represents the frequency deviation of the ith station; fi1 represents the steady-state frequency of the ith station; fi2 represents the peak value frequency average of the ith station; alpha i represents the frequency adjustment coefficient of the ith station; Delta pi represents the power deviation of the ith station; pi1 represents the steady-state power of the ith station; pi2 represents the peak value power average of the ith station; beta i represents the power adjustment coefficient of the ith station; mu ij represents the parameter weight of the jth parameter under the ith station; D1 represents the grid-connected power generation station data group satisfying ; D2 represents the off-grid power generation station data group existing satisfying ;

[0064] The grid-connected and off-grid control unit is used for obtaining the control port information of the grid-connected station and the off-grid station based on the grid-connected and off-grid data group and the preset tie line power, and screening the grid-connected control instruction corresponding to the grid-connected station and the off-grid control instruction corresponding to the off-grid station in the instruction database respectively.

[0065] The instruction adjustment unit is used for adjusting the grid-connected control instruction and the off-grid control instruction based on the comprehensive regulation strategy, and generating the grid-connected and off-grid regulation instruction.

[0066] The implementation principle and beneficial effects of the present application are as follows: the data acquisition module can accurately acquire the acquisition data of the power generation equipment, energy storage equipment and load monitoring equipment, generate initial data, and improve the accuracy and comprehensiveness of data acquisition; then the load analysis module can process and analyze the power generation, energy storage and load data; at the same time, the real-time power grid load data and historical load prediction are analyzed and processed by the prediction analysis module, the future change trend of the load is predicted, and the load prediction curve is generated, realizing the advanced prediction of the load; and then the energy storage and power generation equipment are regulated and controlled by the regulation module, reducing the influence of load fluctuation on the stability of the power grid, and improving the regulation effect of the regulation system. BRIEF DESCRIPTION OF DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative effort.

[0068] Figure 1is a framework schematic diagram of a photovoltaic energy storage coordinated control system method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0069] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the present application will be described below in conjunction with the accompanying drawings in the present application. Obviously, the described embodiments are only some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0070] The present application provides a photovoltaic energy storage coordinated control system. Figure 1 The present application provides a photovoltaic energy storage coordinated control system.

[0071] Figure 1 is a framework schematic diagram of a photovoltaic energy storage coordinated control system provided by an embodiment of the present application.

[0072] As shown in Figure 1 , the photovoltaic energy storage coordinated control system provided by an embodiment of the present application comprises:

[0073] a data acquisition module, configured to acquire collection data of power generation equipment, energy storage equipment, and load monitoring equipment in real time, and generate initial data according to time sequence characteristics of data acquisition;

[0074] a load analysis module, configured to process and analyze the initial data to generate load-energy storage-power generation analysis data;

[0075] a prediction analysis module, configured to analyze real-time power grid load data and historical load data in the initial data, predict load change trend, and generate a load prediction curve;

[0076] a regulation and control module, configured to regulate and control the energy storage equipment and the power generation equipment based on the load-energy storage-power generation analysis data and the load prediction curve, and in combination with a preset regulation and control strategy.

[0077] In this embodiment, the power generation equipment refers to equipment used for power generation in each photovoltaic power generation station.

[0078] In this embodiment, the energy storage equipment refers to equipment in an energy storage system, including a battery pack, a super capacitor, an inverter, and an energy storage stack.

[0079] In this embodiment, the load monitoring equipment refers to equipment used for measuring and monitoring load level and energy consumption of a power system, such as an electric energy meter and an EMS system.

[0080] In this embodiment, the collection data refers to data obtained by collecting operation data of the power generation equipment, the energy storage equipment, and the load monitoring equipment.

[0081] In this embodiment, the time sequence feature is the time sequence feature corresponding to the collected data.

[0082] In this embodiment, the initial data is data generated by the collected data according to the time sequence feature of the collected data.

[0083] In this embodiment, the load-energy-storage-power generation analysis data is data obtained by analyzing and processing the initial data by the load analysis module, and is about the correlation between the load, energy storage and power generation of the power system.

[0084] In this embodiment, the real-time power grid load data is real-time monitoring data of the power grid load.

[0085] In this embodiment, the historical load data is historical data of the power grid load stored in the database.

[0086] In this embodiment, the load change trend is the change mode and trend of the load in the future period, for example, the load peak-valley change trend in the future day.

[0087] In this embodiment, the load prediction curve is a curve obtained by the prediction analysis module for predicting the change of the load data in the future period.

[0088] In this embodiment, the preset control strategy is a strategy preset and used for controlling the energy storage device and the power generation device.

[0089] The implementation principle and beneficial effects of the embodiment are as follows: the data acquisition module can accurately acquire the collected data of the power generation device, the energy storage device and the load monitoring device, generate the initial data, improve the accuracy and comprehensiveness of data acquisition, then the load analysis module can process and analyze the power generation, energy storage and load data, at the same time, the prediction analysis module analyzes and processes the real-time power grid load data and historical load prediction, predicts the future change trend of the load, and generates the load prediction curve, realizes the advanced prediction of the load, and then the control module controls the energy storage and power generation devices, reduces the influence of load fluctuation on the stability of the power grid, and improves the control effect of the control system.

[0090] The photovoltaic energy storage coordinated control system provided by the embodiment of the application has the characteristics that the data acquisition module comprises:

[0091] The power generation data acquisition submodule is used for acquiring the power generation plan and the power generation data of the power generation device in real time.

[0092] The energy storage data acquisition submodule is used for acquiring the operation data of the energy storage device in real time and generating energy storage data.

[0093] The load data acquisition submodule is used for monitoring the load state of the power grid in real time based on the load monitoring device and generating load data.

[0094] The power generation data, the energy storage data and the load data constitute acquisition data.

[0095] In this embodiment, the power generation plan is a plan for arranging power generation resources of a power system.

[0096] In this embodiment, the power generation data are operation parameters in the process of power generation of a power station, for example, power generation capacity of the power station, power output of each power generation device and power generation efficiency.

[0097] In this embodiment, the energy storage data are parameter data in the process of operation of an energy storage device, including energy storage capacity, charging and discharging efficiency and energy storage technology type.

[0098] In this embodiment, the load state is a load level and energy consumption condition of the power system.

[0099] In this embodiment, the load data are data of real-time load of the power system obtained through the load monitoring device, including load capacity and load distribution.

[0100] The implementation principle and beneficial effects of this embodiment are as follows: through the power generation data acquisition submodule, the day-ahead designated power generation plan and the real-time generated day-after power generation plan can be obtained, so that the system can plan the power generation plans of each power station in future time periods, and the unified coordination efficiency of the system is improved; meanwhile, through the energy storage data acquisition submodule, the energy storage data of each energy storage device can be obtained in real time, and the acquisition data are generated in combination with the acquired load data, so as to provide accurate data support for subsequent regulation and control of the power generation device and the energy storage device.

[0101] The data acquisition module further includes:

[0102] The preprocessing submodule is used for preprocessing the power generation data, the energy storage data and the load data to obtain preprocessing data.

[0103] The time sequence feature extraction submodule is used for extracting time sequence features of the preprocessing data to generate time sequence data.

[0104] The initial data generation submodule is used for analyzing and processing the time sequence data to obtain initial data.

[0105] In this embodiment, preprocessing is a process of cleaning, converting and arranging original acquisition data before data analysis of the acquired data, which is used for improving data quality and laying a foundation for subsequent data analysis.

[0106] In this embodiment, the preprocessed data is data obtained after preprocessing the collected data;

[0107] In this embodiment, the time series data is data obtained after binding the time series features extracted from the preprocessed data and the corresponding collected data.

[0108] The implementation principle and beneficial effects of the present embodiment are as follows: the power generation data, energy storage data and load data collected are preprocessed by the preprocessing submodule, which can eliminate noise in the collected data and improve the data quality of the collected data, reduce the influence of data missing, data abnormality and noise interference on the data quality, and improve the accuracy and reliability of subsequent data analysis. At the same time, the time series data generated based on the extracted time series features can facilitate subsequent time positioning and rechecking of the data.

[0109] The photovoltaic energy storage coordinated control system provided by the embodiment of the present application is characterized in that the load analysis module comprises:

[0110] The capacity acquisition submodule is configured to analyze the initial data to obtain the power generation capacity and the energy storage capacity, and generate capacity data.

[0111] The comparison submodule is configured to obtain and compare the load data at the same time as the capacity data in the initial data, and establish a mapping relationship between the capacity data and the load data to obtain load-energy-generation analysis data.

[0112] In this embodiment, the power generation capacity is the power output capability of a power generation device or a power generation station, which is usually represented in the form of power and is one of the important indicators for evaluating the power supply capability of the power generation device or the power generation station.

[0113] In this embodiment, the energy storage capacity is the amount of energy that can be stored by an energy storage device, which is usually represented in units of energy, such as kilowatt-hours or megawatt-hours.

[0114] In this embodiment, the capacity data is data generated based on the power generation capacity and the energy storage capacity.

[0115] The implementation principle and beneficial effects of the present embodiment are as follows: the power generation capacity and the energy storage capacity in the initial data are analyzed and extracted by the capacity acquisition submodule to generate capacity data, and the load data at the same time is obtained to facilitate subsequent comparison and analysis. Then, the capacity data and the load data at the same time are compared and analyzed by the comparison submodule, and a mapping relationship is established to obtain load-energy-generation analysis data, which not only accurately reflects the data changes between the load, energy storage and power generation data at each time, but also provides data support for subsequent regulation and control of the energy storage device and the power generation device.

[0116] The photovoltaic energy storage coordination control system provided by the embodiment of the present application is characterized in that the prediction analysis module comprises:

[0117] The real-time data extraction submodule is configured to extract the load real-time data from the initial data;

[0118] The feature extraction submodule is configured to extract features of the load real-time data to obtain load feature information;

[0119] The historical data screening submodule is configured to screen load historical data with a matching degree greater than a first preset degree from the load historical database based on the load feature information;

[0120] The prediction analysis submodule is configured to analyze the load real-time data and the load historical data, judge the load change trend, and generate a load prediction curve.

[0121] In the embodiment, the load real-time data refers to real-time data of a load in a power system;

[0122] In the embodiment, the load feature information refers to feature information obtained by the feature extraction submodule from the load real-time data;

[0123] In the embodiment, the load historical database refers to a database storing historical load data in a power system, and is configured to match corresponding load historical data based on the input load feature information;

[0124] In the embodiment, the first preset degree refers to a threshold value for screening load historical data meeting preset conditions from the load historical database;

[0125] In the embodiment, the load historical data refers to historical data selected from the load historical database.

[0126] The implementation principle and beneficial effects of the embodiment are as follows: the real-time data extraction submodule extracts preprocessed load real-time data with high information accuracy from the initial data, the feature extraction submodule extracts features of the extracted load real-time data, and the historical data screening submodule screens highly matched load historical data from the load historical database based on the extracted load feature information, so that the prediction analysis submodule can compare and analyze the load real-time data and the load historical data, accurately predict the change trend of the load at a future time, and generate a load prediction curve, thereby improving the monitoring level of the load state.

[0127] The photovoltaic energy storage coordination control system provided by the embodiment of the present application is characterized in that the prediction analysis module further comprises:

[0128] a real-time numerical analysis submodule configured to analyze the real-time load data and corresponding sampling time, and to calculate a real-time numerical change rate of the load;

[0129] an incremental analysis submodule configured to calculate an increment of the load data between each adjacent sampling point in a preset time period, and to obtain an adjacent incremental change rate;

[0130] a time period division submodule configured to divide the preset time period into a plurality of sampling periods;

[0131] a period analysis submodule configured to analyze the real-time load data in each sampling period, and to obtain a period numerical change rate and a period incremental change rate;

[0132] a numerical feature extraction submodule configured to perform feature analysis on all real-time numerical change rates of the load in the preset time period and all period numerical change rates, and to obtain a load numerical feature;

[0133] an incremental feature extraction submodule configured to perform feature analysis on all adjacent incremental change rates in the preset time period and all period incremental change rates, and to obtain a load incremental feature;

[0134] a first historical data acquisition submodule configured to obtain a time feature based on a time length of the preset time period and data sampling time in the preset time period, and to select first historical data from a load historical database;

[0135] a second historical data acquisition submodule configured to select second historical data from the load historical database based on the load numerical feature and the load incremental feature;

[0136] a data processing submodule configured to process the real-time load data by using a preset function, and to obtain a real-time load curve;

[0137] a predicted value calculation submodule configured to obtain a first predicted value and a second predicted value based on the real-time load curve, and in combination with the first historical data and the second historical data;

[0138] an incremental prediction submodule configured to obtain a load predicted increment at a future corresponding time based on the first predicted value and the second predicted value;

[0139] a trend prediction submodule configured to predict a predicted trend of the load in combination with a first trend of the first historical data and a second trend of the second historical data;

[0140] a curve generation submodule configured to generate a load predicted curve based on a time sequence feature of the future time, and the corresponding load predicted increment and predicted trend.

[0141] In this embodiment, the sampling time is the sampling time corresponding to the real-time load data;

[0142] In this embodiment, the real-time value change rate of the load is a change rate of a real-time value of the load over time;

[0143] In this embodiment, the preset time period is a time interval set in advance;

[0144] In this embodiment, the sampling point is a time node corresponding to a sampling time;

[0145] In this embodiment, the adjacent incremental change rate is a change rate of a load data increment between adjacent sampling points;

[0146] In this embodiment, the sampling period is a plurality of time periods with the same time interval obtained by dividing the preset time period by a time period division module;

[0147] In this embodiment, the period value change rate is a change rate of a load value in each sampling period;

[0148] In this embodiment, the period incremental change rate is a change rate of a load increment in each sampling period;

[0149] In this embodiment, the load value feature is a numerical feature obtained by performing feature analysis on all real-time value change rates of the load in the preset time period and the period value change rates;

[0150] In this embodiment, the load increment feature is an incremental feature obtained by performing feature analysis on all adjacent incremental change rates and the period incremental change rates in the preset time period, and used for representing a change of the load increment;

[0151] In this embodiment, the time length is a time interval length of the preset time period;

[0152] In this embodiment, the data sampling time is a sampling time corresponding to the sampled data;

[0153] In this embodiment, the time feature is a feature about the time length of the preset time period and the sampling time; in general, the longer the sampling time length, the higher the accuracy and reliability of the data, and the sampling time can reflect the influence of factors such as peak and valley and season on the load data; the historical data with similar time and length can be selected as a reference in the load historical database according to the time feature;

[0154] In this embodiment, the first historical data is historical data selected from the load historical database according to the time feature;

[0155] In this embodiment, the second historical data is historical data selected from the load historical database according to the load value feature and the load increment feature;

[0156] In this embodiment, the preset function for analyzing and processing the load real-time data to obtain the corresponding numerical curve is a preset function;

[0157] In this embodiment, the first prediction value is a load prediction value obtained by predicting and analyzing the load real-time curve and the first historical data;

[0158] In this embodiment, the second prediction value is a load prediction value obtained by predicting the load real-time curve and the second historical data, corresponding to the first prediction value;

[0159] In this embodiment, the load prediction increment is an incremental prediction data of the load in the future period obtained by comprehensively combining the first prediction value and the second prediction value;

[0160] In this embodiment, the first trend is the trend data of the load in the first historical data;

[0161] In this embodiment, the second trend is the trend data of the load in the second historical data, corresponding to the first trend;

[0162] In this embodiment, the predicted trend is the trend prediction data of the load in the future period obtained by comprehensively analyzing the first trend and the second trend.

[0163] The implementation principle and beneficial effects of the embodiment are as follows: the load real-time data is analyzed and incrementally analyzed to obtain the numerical change rate and the incremental change rate of the load real-time data, and then the corresponding numerical features and incremental features are obtained through feature analysis, and similar second historical data is selected in the load historical database; at the same time, the first historical data is selected in the historical database according to the time characteristics of the load real-time data, and the trend of the load in the future moment and the numerical increment are predicted by combining the first historical data and the second historical data, the change law of the load is comprehensively analyzed from multiple angles, and the prediction accuracy of the load prediction trend and the load prediction curve is improved.

[0164] The photovoltaic energy storage coordination control system provided by the embodiment of the application has the characteristics that the control module comprises:

[0165] The first analysis submodule is configured to compare and analyze the load data and the power generation data at the same moment to obtain a first analysis result;

[0166] The strategy matching submodule is configured to adopt a first control strategy when the first analysis result meets a first preset condition;

[0167] The second analysis submodule is configured to compare and analyze the load data and the power generation data and the energy storage data to obtain a second analysis result when the first analysis result does not meet the first preset condition;

[0168] adopting a second regulation strategy when the second analysis result meets a second preset condition;

[0169] adopting a third regulation strategy when the second analysis result does not meet the second preset condition;

[0170] a high-low peak analysis submodule, configured to obtain a load peak period corresponding to load data and a generation peak period corresponding to generation data based on load-energy-generation analysis data, in combination with historical load data and obtained generation plans, and to obtain a correlation between energy storage data and the load peak period and the generation peak period;

[0171] a comprehensive regulation submodule, configured to generate a strategy adjustment instruction based on the correlation, to adjust the first regulation strategy, the second regulation strategy, and the third regulation strategy, and to generate a comprehensive regulation strategy;

[0172] an instruction generation submodule, configured to select corresponding regulation instructions in an instruction database based on the comprehensive regulation strategy;

[0173] an instruction verification submodule, configured to perform control verification on the regulation instructions, to store the regulation instructions that pass the verification in an instruction pool, to select, in the instruction database, replacement instructions corresponding to the regulation instructions that do not pass the verification and to perform verification on the replacement instructions, and to store the replacement instructions that pass the verification in the instruction pool;

[0174] an instruction sending submodule, configured to send the regulation instructions and the replacement instructions in the instruction pool to corresponding target devices and to perform regulation;

[0175] a regulation data acquisition submodule, configured to acquire process data in a regulation process and regulation data after the regulation ends in real time, to compare the regulation data with target regulation effects corresponding to the comprehensive regulation instructions, and to output and display a regulation effect analysis table.

[0176] In this embodiment, the first analysis result is a result obtained by comparing and analyzing load data and generation data at each same time point by the first analysis submodule;

[0177] In this embodiment, the first preset condition is a threshold condition for comparing with the first analysis result to determine a corresponding regulation strategy, and is pre-set, for example, a load amount and a load prediction increment are less than a generation capacity and a capacity increment corresponding to a generation plan at the same time, in which case a total output amount of generation equipment is greater than a total load amount in a power system, and an excess output amount flows to energy storage equipment for energy storage;

[0178] In this embodiment, the first regulation strategy is a regulation strategy adopted when the first analysis result meets the first preset condition;

[0179] In this embodiment, the second analysis result: when the first analysis result does not meet the first preset condition, the result obtained by comparing and analyzing the load data with the power generation data and the energy storage data;

[0180] In this embodiment, the second preset condition: the threshold condition used for comparing with the second analysis result to determine the corresponding regulation strategy, is pre-set, corresponding to the first preset condition, for example, the load amount and the load prediction increment are greater than the power generation capacity at the same time, but less than the sum of the power generation capacity and the energy storage capacity at the same time, under this condition, the power generation equipment and the energy storage equipment simultaneously act as power supply to deliver electric energy to the load in the power system;

[0181] In this embodiment, the second regulation strategy: the regulation strategy taken when the second analysis result meets the second preset condition;

[0182] In this embodiment, the third regulation strategy: the regulation strategy taken when the second analysis result does not meet the second preset condition, for example, when the total load is greater than the sum of the total power generation capacity and the total energy storage capacity, the strategy of coordinating the grid-connected operation of each off-grid power station;

[0183] In this embodiment, the load peak period: the peak period of the load curve, for example, the peak period of electricity use;

[0184] In this embodiment, the power generation peak period: the peak period of the power generation equipment power generation, for example, the power peak period of the daily power generation equipment;

[0185] In this embodiment, the correlation: the relationship between the energy storage data and the load peak period and the power generation peak period;

[0186] In this embodiment, the strategy adjustment instruction: the instruction used for adjusting the generated regulation strategy obtained by analyzing and processing the correlation according to the comprehensive regulation submodule;

[0187] In this embodiment, the comprehensive adjustment strategy: the regulation strategy obtained by adjusting the regulation strategy based on the strategy adjustment instruction;

[0188] In this embodiment, the regulation instruction: the coordination and control instruction obtained by screening in the instruction database based on the comprehensive adjustment strategy;

[0189] In this embodiment, the control verification: a verification technology used for verifying and ensuring the integrity and correctness of the regulation instruction in the transmission process, to ensure the correct transmission and execution of the regulation instruction;

[0190] In this embodiment, the instruction pool: a data pool used for temporarily storing the regulation instruction and the alternative instruction;

[0191] In this embodiment, the replacement instruction is: a replacement instruction corresponding to the unverified regulation instruction selected in the instruction database;

[0192] In this embodiment, the process data is: data collected by the power generation equipment, energy storage equipment and load monitoring equipment during the regulation process;

[0193] In this embodiment, the regulation data is: the collected data of each device after the regulation ends;

[0194] In this embodiment, the target regulation effect is: an ideal regulation effect corresponding to the comprehensive regulation instruction, which is used for comparison and analysis with the actual regulation data to determine the actual achievement effect of the regulation;

[0195] In this embodiment, the regulation effect analysis table is: an analysis table for representing the regulation effect of the regulation instruction.

[0196] The implementation principle and beneficial effects of the embodiment are as follows: the first analysis submodule and the second analysis submodule are used to compare and analyze the load data, power generation data and energy storage data at the same time, and the corresponding regulation strategy is obtained based on the obtained analysis result, which improves the regulation capability of the system. At the same time, the high-low peak analysis submodule is used to analyze the correlation between the energy storage data and the load peak period and the power generation peak period, and the regulation strategy is adjusted, so that the regulation effect of the regulation strategy on the power system can be improved. The process data and the regulation data during the regulation process are obtained, the regulation effect is analyzed and determined, and the regulation strategy and the regulation instruction can be continuously optimized in the future.

[0197] The photovoltaic energy storage coordinated control system provided in the embodiment of the application has the characteristics that the instruction generation submodule comprises:

[0198] The power generation data group generation unit is used to analyze the load-energy storage-power generation analysis data, obtain the operation data of each distributed photovoltaic power generation station, and construct the station power generation data group A=(Xi, i=1, 2, …, n), wherein Xi represents the data of the i th power generation station.

[0199] The standard data acquisition unit is used to acquire the standard operation data group B corresponding to the station power generation data group based on the preset power generation plan.

[0200] The state determination unit is used to perform state analysis on the load-energy storage-power generation analysis data, and obtain an operation state change table about the real-time changes of the load state, the energy storage state and the power generation state.

[0201] A deviation calculation unit is configured to calculate, based on the power station power generation data set and the standard operation data set and in combination with the operation state change table, frequency deviation and power deviation on both sides of a corresponding grid point switch of each power station by using a preset data processing model, and generate a deviation data set C=(Δfi, Δpi);

[0202] A grid-connected and off-grid data statistical unit is configured to compare and analyze the deviation data set with preset grid-connected and off-grid conditions, and obtain a grid-connected and off-grid data set D=(D1, D2);

[0203]

[0204]

[0205]

[0206] wherein Xij represents a parameter value of the jth parameter of the ith power station; Bij represents a standard data value corresponding to the jth parameter of the ith power station in the standard operation data set; γi represents a preset threshold value of the ith power station under the preset grid-connected and off-grid condition; δi represents a preset threshold value of the ith power station under the off-grid threshold condition; Δfi represents the frequency deviation of the ith power station; fi1 represents the steady-state frequency of the ith power station; fi2 represents the average peak value frequency of the ith power station; αi represents the frequency adjustment coefficient of the ith power station; Δpi represents the power deviation of the ith power station; pi1 represents the steady-state power of the ith power station; pi2 represents the average peak value power of the ith power station; βi represents the power adjustment coefficient of the ith power station; μ ij represents the parameter weight of the jth parameter of the ith power station; D1 represents the grid-connected power station data set satisfying ; and D2 represents the off-grid power station data set satisfying .

[0207] A grid-connected and off-grid control unit is configured to obtain control port information of the grid-connected power stations and the off-grid power stations based on the grid-connected and off-grid data set and a preset tie-line power, and filter grid-connected control instructions corresponding to the grid-connected power stations and off-grid control instructions corresponding to the off-grid power stations in the instruction database respectively.

[0208] An instruction adjustment unit is configured to adjust the grid-connected control instructions and the off-grid control instructions based on a comprehensive control strategy, and generate grid-connected and off-grid control instructions.

[0209] In this embodiment, the power station power generation data set is a data set composed of operation data of each photovoltaic power station;

[0210] In this embodiment, the preset power generation plan is a day-ahead power generation plan of each power station, which is preset.

[0211] In this embodiment, the standard operation data set is a data set composed of ideal power generation operation data corresponding to the preset power generation plan;

[0212] In this embodiment, the operation state change table is a state change table generated by the state determination unit and used to represent the load state, the energy storage state, and the power generation state;

[0213] In this embodiment, the preset data processing model is a model used to calculate the frequency deviation and the power deviation on both sides of the corresponding grid point switch of each power station, and is preset;

[0214] In this embodiment, the deviation data set is a data set composed of the frequency deviation and the power deviation on both sides of the corresponding grid point switch of each power station;

[0215] In this embodiment, the preset grid-connected and off-grid condition is a threshold condition used to determine whether each power station meets the grid-connected and off-grid operation, and is preset;

[0216] In this embodiment, the grid-connected and off-grid data set is a data set composed of the grid-connected data set and the off-grid data set obtained by comparative analysis;

[0217] In this embodiment, the preset tie-line power is the power transmitted by the tie-line in the power system to realize power interconnection and energy exchange, and is preset;

[0218] In this embodiment, the control port information is the port information of each station used to receive control instructions;

[0219] In this embodiment, the grid-connected control instruction is a control instruction for connecting the power station to the power network;

[0220] In this embodiment, the off-grid control instruction is a control instruction for disconnecting the power station from the power network to realize independent operation of the power station;

[0221] In this embodiment, the grid-connected and off-grid regulation instruction is a regulation instruction generated by adjusting the grid-connected control instruction and the off-grid control instruction by the instruction adjustment unit.

[0222] Principles and beneficial effects of the embodiment: the application realizes real-time monitoring on the operation data of each distributed photovoltaic power generation station through the power generation data generation unit, and obtains the standard operation data corresponding to the power generation plan, and then determines the load state, the energy storage state and the power generation state in the power network through the state determination unit, accurately obtains the operation state of each station, at the same time, the on-grid and off-grid data of each station are statistically analyzed through the deviation calculation unit and the on-grid and off-grid data statistical unit, which provides accurate data support for the subsequent on-grid and off-grid state switching of each station, and the on-grid and off-grid control instructions of the station are accurately adjusted combined with the comprehensive control strategy, reduces the fluctuation of the system in the process of on-grid and off-grid, and improves the system stability of the power system in the on-grid and off-grid operation.

[0223] Finally, it should be pointed out that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A photovoltaic energy storage coordinated control system, characterized in that, The method comprises the following steps: A data acquisition module is configured to acquire real-time data of power generation equipment, energy storage equipment and load monitoring equipment, and generate initial data according to the time sequence characteristics of data acquisition; A load analysis module is configured to process and analyze the initial data, and generate load-energy storage-power generation analysis data; A prediction analysis module is configured to analyze real-time grid load data and historical load data in the initial data, predict load variation trend and generate a load prediction curve; A regulation module is configured to regulate the energy storage equipment and the power generation equipment based on the load-energy storage-power generation analysis data and the load prediction curve, and in combination with a preset regulation strategy; The load analysis module comprises: A capacity acquisition sub-module is configured to analyze the initial data to obtain power generation capacity and energy storage capacity, and generate capacity data; A comparison sub-module is configured to acquire load data at the same time as the capacity data in the initial data and perform comparative analysis, and establish a mapping relationship between the capacity data and the load data to obtain load-energy storage-power generation analysis data; The prediction analysis module comprises: A real-time data extraction sub-module is configured to extract load real-time data in the initial data; A feature extraction sub-module is configured to extract load feature information from the load real-time data; A historical data screening sub-module is configured to screen load historical data with a matching degree greater than a first preset degree from a load historical database based on the load feature information; A prediction analysis sub-module is configured to analyze the load real-time data and the load historical data, judge load variation trend and generate a load prediction curve; The regulation module comprises: A first analysis sub-module is configured to compare and analyze load data and power generation data at the same time to obtain a first analysis result; A strategy matching sub-module is configured to adopt a first regulation strategy when the first analysis result meets a first preset condition; A second analysis sub-module is configured to compare and analyze load data, power generation data and energy storage data when the first analysis result does not meet the first preset condition to obtain a second analysis result; A third regulation strategy is adopted when the second analysis result does not meet a second preset condition; A high-low peak analysis sub-module is configured to acquire load peak time periods corresponding to load data and power generation peak time periods corresponding to power generation based on the load-energy storage-power generation analysis data, in combination with the historical load data and the acquired power generation plan, and acquire the association relationship between energy storage data and the load peak time periods and the power generation peak time periods; A comprehensive regulation sub-module is configured to generate strategy adjustment instructions based on the association relationship, adjust the first regulation strategy, the second regulation strategy and the third regulation strategy, and generate a comprehensive regulation strategy; An instruction generation sub-module is configured to select corresponding regulation instructions from an instruction database based on the comprehensive regulation strategy. ​ The instruction verification submodule is configured to perform control verification on the control instructions, store the control instructions passing the verification in an instruction pool, select substitute instructions corresponding to the control instructions failing the verification from the instruction database, perform verification on the substitute instructions, and store the substitute instructions passing the verification in the instruction pool; The instruction sending submodule is configured to send the control instructions and the substitute instructions in the instruction pool to corresponding target devices and perform control. The control data acquisition submodule is configured to acquire process data in a control process and control data after the control ends, compare the control data with target control effects corresponding to the comprehensive control instructions, obtain a control effect analysis table, and output and display the control effect analysis table.

2. The photovoltaic energy storage coordinated control system according to claim 1, wherein, The data acquisition module comprises: The power generation data acquisition submodule is configured to acquire power generation data of a power generation plan and power generation equipment in real time. The energy storage data acquisition submodule is configured to acquire operation data of energy storage equipment in real time and generate energy storage data. The load data acquisition submodule is configured to monitor a load state of a power grid in real time based on a load monitoring device and generate load data. The power generation data, the energy storage data, and the load data constitute acquisition data.

3. The photovoltaic energy storage coordinated control system according to claim 2, wherein, The data acquisition module further comprises: The preprocessing submodule is configured to preprocess the power generation data, the energy storage data, and the load data to obtain preprocessed data. The time sequence feature extraction submodule is configured to extract time sequence features from the preprocessed data to generate time sequence data. The initial data generation submodule is configured to analyze and process the time sequence data to obtain initial data.

4. The photovoltaic energy storage coordinated control system of claim 1, wherein, The prediction analysis module further comprises: The real-time numerical value analysis submodule is configured to analyze the load real-time data and corresponding sampling time points to calculate a load real-time numerical value change rate. The incremental analysis submodule is configured to calculate an increment of load data between each adjacent sampling point in a preset time period and obtain an adjacent incremental change rate. The time period division submodule is configured to divide the preset time period to obtain a plurality of sampling periods. The period analysis submodule is configured to analyze load real-time data in each sampling period to obtain a period numerical value change rate and a period incremental change rate. The numerical value feature extraction submodule is configured to analyze features of all load real-time numerical value change rates and period numerical value change rates in the preset time period to obtain load numerical value features. The incremental feature extraction submodule is configured to analyze features of all adjacent incremental change rates and period incremental change rates in the preset time period to obtain load incremental features. The first historical data acquisition submodule is configured to obtain a time feature based on a time length of the preset time period and data sampling time points in the preset time period, and select first historical data from a load historical database. The second historical data acquisition submodule is configured to select second historical data from the load historical database based on the load numerical value features and the load incremental features. The data processing submodule is configured to process the load real-time data by using a preset function to obtain a load real-time curve. The prediction value calculation submodule is configured to obtain a first prediction value and a second prediction value based on the real-time load curve and in combination with the first historical data and the second historical data; The incremental prediction submodule is configured to obtain a load prediction increment at a future time based on the first prediction value and the second prediction value; The trend prediction submodule is configured to obtain a predicted trend of the load by combining a first trend of the first historical data and a second trend of the second historical data; The curve generation submodule is configured to generate a load prediction curve based on the time sequence characteristics at the future time, the corresponding load prediction increment and the predicted trend.

5. The photovoltaic energy storage coordinated control system of claim 1, wherein, The instruction generation submodule comprises: The power generation data set generation unit is configured to analyze the load- energy storage- power generation analysis data to obtain operation data of each distributed photovoltaic power station, and to construct a power station power generation data set A=(Xi, i=1, 2, …, n), wherein Xi represents the data of the i th power station; The standard data acquisition unit is configured to acquire a standard operation data set B corresponding to the power station power generation data set based on a preset power generation plan; The state determination unit is configured to analyze the load- energy storage- power generation analysis data to obtain an operation state change table about real-time changes in load state, energy storage state and power generation state; The bias calculation unit is configured to calculate the frequency bias and the power bias on both sides of the point switch of each power station based on the power station power generation data set and the standard operation data set, and in combination with the operation state change table, by using a preset data processing model, to generate a bias data set C=(Δfi, Δpi); The on- and off-grid data statistical unit is configured to compare and analyze the bias data set with a preset on- and off-grid condition to obtain an on- and off-grid data set D=(D1, D2); Wherein, Xij represents the parameter value of the j th parameter under the i th station; Bij represents the standard data value corresponding to the j th parameter under the i th station in the standard operation data set; γi represents a preset threshold value of the i th station under the preset on- and off-grid condition; δi represents a preset threshold value of the i th station under the off-grid threshold condition; Δfi represents the frequency bias of the i th station; fi1 represents the steady-state frequency of the i th station; fi2 represents the average peak value frequency of the i th station; αi represents the frequency adjustment coefficient of the i th station; Δpi represents the power bias of the i th station; pi1 represents the steady-state power of the i th station; pi2 represents the average peak value power of the i th station; βi represents the power adjustment coefficient of the i-th station; μ ij βi represents the power adjustment coefficient of the i-th station; μ ij βi represents the power adjustment coefficient of the i-th station; μ ij βi represents the power adjustment coefficient of the i-th station; μ ij D1 denotes a grid-connected power plant data packet satisfying D2 denotes an off-grid power plant data packet satisfying ​ The on- and off-grid control unit is configured to obtain the control port information of the on-grid station and the off-grid station based on the on- and off-grid data set and the preset tie line power, and to filter the on-grid control instruction corresponding to the on-grid station and the off-grid control instruction corresponding to the off-grid station in the instruction database respectively; The instruction adjustment unit is configured to adjust the on-grid control instruction and the off-grid control instruction based on the comprehensive regulation strategy to generate on- and off-grid regulation instructions.

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