Cooperative control method, system, device and medium based on optical storage fusion system

Abstract

The embodiment of the application discloses a kind of based on the collaborative control method, system, equipment and medium of light storage fusion system;The method comprises: obtaining various data and sending to cloud platform by central controller;Various data are cleaned by cloud platform, and photovoltaic power and load power are initially predicted;Set the benefit formula of light storage fusion system and the calculation method related to its influence factor on cloud platform;Get strategy algorithm under different working conditions;According to strategy algorithm, charge-discharge control instruction and energy storage system control instruction are issued to central controller;The charge-discharge control method of different working conditions and different characteristic attributes of user, subsequent cloud platform can be adjusted according to different needs Intelligent collaborative method, remove the coupling between local algorithm, intelligently adjust to make light storage fusion system operating condition and energy consumption optimal, fully utilize the charge-discharge capacity of energy storage system, improve comprehensive income, reduce energy consumption.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of light storage control, in particular to a collaborative control method, system, device and medium based on a light storage fusion system. BACKGROUND

[0002] At present, the user-side photovoltaic system has the phenomenon of light abandonment due to the large electricity planning in the early stage or the subsequent user peak-shaving electricity and the like, and the user can obtain better benefits by installing an energy storage system. An excellent light storage charging and discharging collaborative control method helps to maximize the profit.

[0003] One of the profit modes of energy storage is peak-valley arbitrage, that is, charging at a low price during the electricity valley and discharging to supply the user during the electricity peak, so that the user can save the electricity cost, the photovoltaic system can be combined to form a light storage fusion system to avoid the light abandonment phenomenon, and self-generation and self-use can be realized in some special scenarios. How to realize safe and stable operation and benefit optimization through the collaborative control of the light storage system becomes the focus of customers.

[0004] The fundamental purpose of the user-side energy storage user is to realize rapid return on investment and have the best benefits. In the daily operation process, the regulation and control based on the real-time strategy help to maximize the profit and help the user to realize the fine control of the energy storage system. How to comprehensively utilize the energy storage system and the photovoltaic system for fine control to maximize the benefits will be the core competitiveness of the energy storage integrator.

[0005] The existing technical solutions are more independent of the photovoltaic and energy storage systems, or the energy storage system only charges when the photovoltaic system generates power, and controls the photovoltaic system when the photovoltaic system flows back. However, in the case of a single system, the energy storage system and the photovoltaic system are not independent of each other and cannot know the operation state of the photovoltaic system; in the case of simple coupling, the two systems are not deeply collaborative and the benefits cannot be maximized; according to the preset strategy logic, adaptive strategy adjustment cannot be performed; the energy storage end energy consumption cannot be reduced and the photovoltaic end light abandonment phenomenon exists. SUMMARY

[0006] The embodiment of the present application provides a collaborative control method, system, device and medium based on a light storage fusion system to solve the problems in the prior art that the energy storage system and the photovoltaic system are not deeply collaborative, the benefits cannot be maximized, adaptive strategy adjustment cannot be performed, and the energy storage end energy consumption cannot be reduced.

[0007] In a first aspect, the embodiment of the present application provides a collaborative control method based on a light storage fusion system, which comprises the following steps:

[0008] Various data are acquired and sent to a cloud platform through a central controller, wherein the data includes energy storage data, local photovoltaic data and external environment data;

[0009] Clean various types of data through the cloud platform, and preliminarily predict photovoltaic power and load power;

[0010] Set the benefit formula of the light-storage-fusion system and the calculation method of the related influence factors on the cloud platform;

[0011] Obtain the strategy algorithm under different working conditions, wherein the strategy algorithm includes the SOC range, power range, energy storage temperature control logic and photovoltaic control algorithm under each period;

[0012] According to the strategy algorithm, issue the charge-discharge control instruction and the energy storage system control instruction to the central controller.

[0013] Further, the various types of data are obtained and sent to the cloud platform through the central controller, wherein the data includes: energy storage data, local photovoltaic data and external environment data, including:

[0014] Collect and correct the energy storage data, wherein the energy storage data includes: the opening / closing time of each charge-discharge cycle, battery charge-discharge power, battery temperature change, SOC value, chargeable power, AC charge-discharge power, energy storage system auxiliary component power loss and thermal management system energy consumption;

[0015] Collect and dynamically adjust the local photovoltaic data, wherein the local photovoltaic data includes: string capacity, PV power generation, inverter power generation, daily peak AC / DC power and daily grid-connected duration;

[0016] Collect external environment data through the cloud platform or a collector, and adjust the parameters of the external environment data, wherein the external environment data includes: solar radiation, weather conditions, ambient temperature, historical data and real-time data of photovoltaic installation angle and orientation, and predicted data;

[0017] Send various types of data to the cloud platform through the central controller.

[0018] Further, the various types of data are obtained and sent to the cloud platform through the central controller, wherein the data includes: energy storage data, local photovoltaic data and external environment data, including:

[0019] Statistically clean the data on the cloud platform, record the data cleaning ratio value, and process the confidence of data with different cleaning ratio values, thereby reducing the weight of low-confidence data in the related formula;

[0020] Preliminarily predict the photovoltaic power and load power.

[0021] Further, the benefit formula of the light-storage-fusion system and the calculation method of the related influence factors are set on the cloud platform, including:

[0022] Customer daily income = energy storage income + photovoltaic income;

[0023] The energy storage benefit = (actual discharge amount at the sharp grid end - charging amount) * sharp price + (actual discharge amount at the peak grid end - charging amount) * peak price + (actual discharge amount at the flat grid end - charging amount) * flat price + (actual discharge amount at the valley grid end - charging amount) * valley price;

[0024] The actual discharge amount at the grid end = battery end consumption amount of the energy storage system * discharge efficiency of the energy storage system - power consumption amount of the energy storage system;

[0025] The actual charging amount at the grid end = battery end charging amount of the energy storage system / charging efficiency of the energy storage system + power consumption amount of the energy storage system;

[0026] The charging and discharging efficiency of the energy storage system is obtained by statistical analysis of historical charging and discharging data;

[0027] The photovoltaic benefit = actual discharge amount of photovoltaic discharge to the sharp grid end * sharp price + actual discharge amount of photovoltaic discharge to the peak grid end * peak price + actual discharge amount of photovoltaic discharge to the flat grid end * flat price + actual discharge amount of photovoltaic discharge to the valley grid end * valley price;

[0028] In the case of constant energy storage and photovoltaic capacity, high-power discharge is performed using the sharp peak end energy storage; and in the valley flat period, photovoltaic charging is performed.

[0029] Further, the division condition acquisition strategy algorithm, wherein the strategy algorithm includes an SOC range, a power range, an energy storage temperature control logic and a photovoltaic control algorithm under each period, and includes:

[0030] According to historical experimental data or actual working condition data, the maximum efficiency of the energy storage under each local operating environment is obtained;

[0031] The charging and discharging efficiency of the energy storage system under each charging and discharging rate and each temperature is obtained through historical charging and discharging data analysis;

[0032] The theoretical available power of photovoltaic under each period is obtained by acquiring photovoltaic related information, and the residual power value for energy storage charging under each period is obtained in combination with the power prediction of the load;

[0033] The size of the theoretical energy required to be provided by the energy storage is calculated as:

[0034] ;

[0035] The corresponding maximum energy storage benefit is * the electricity price;

[0036] The theoretical maximum energy storage benefit under each period is obtained by sliding, and the benefit is positive and negative;

[0037] Then, determine the percentage of energy that needs to be stored during the period of maximum benefit, obtain the corresponding change in SOC value, and deduce the SOC value that energy storage needs to achieve in each period to meet the maximum benefit. This value is the theoretical optimal SOC value in that period.

[0038] The optimal charging and discharging strategy includes charging and discharging power and temperature control strategy. By obtaining the change of SOC value during this period and combining the energy consumption value under the comprehensive model of cloud platform storage, the theoretical charging and discharging power and temperature control strategy for this period can be obtained.

[0039] Among them, both photovoltaic power generation prediction and load power prediction are updated in real time, with a minimum granularity of ≤15min;

[0040] When issuing the target strategy for the next day, the target strategy for the photovoltaic and energy storage system includes the time period, charging and discharging power, SOC, and temperature control strategy, and reserves the set margin to ensure the system's revenue.

[0041] During the initial operation phase, the margin value is set relatively large. The original local SOC setting of the strategy is set to SOC1. When the cloud platform calculates that the optimal SOC value for this period is SOC2, the final strategy execution is as follows:

[0042] SOC3 = a * SOC1+b *SOC2, a+b =1;

[0043] Where a is the local policy weight and b is the cloud platform algorithm weight;

[0044] The algorithm weights of cloud platforms need to be dynamically adjusted based on historical data confidence and data accumulation.

[0045] Theoretically optimal local charge and discharge control strategy is obtained by sampling the same method.

[0046] Furthermore, the step of issuing charging / discharging control commands and energy storage system control commands to the central controller according to the strategy algorithm includes:

[0047] Based on the fitted charging and discharging time period, SOC threshold range, power range and temperature control strategy obtained from the cloud platform, charging and discharging control commands and energy storage system control commands are sent to the central controller.

[0048] The energy storage system uses the optimal intelligent cooperative control method obtained from the target optimization model to control the energy storage controller's charge / discharge cutoff period, SOC, charge / discharge power, and thermal management control.

[0049] The central controller dispatches the subordinate actuator units to complete the task based on the control commands issued by the cloud server.

[0050] In a second aspect, the embodiments of the present application also provide a coordinated control system based on a light-storage hybrid system, comprising: a cloud platform, a central controller, a storage system and a photovoltaic system; the central controller is connected to the cloud platform, the storage system and the photovoltaic system;

[0051] Various types of data are acquired and sent to the cloud platform through the central controller, wherein the data includes: storage data, local photovoltaic data and external environment data;

[0052] The cloud platform cleans the various types of data and preliminarily predicts photovoltaic power and load power;

[0053] The cloud platform sets a light-storage hybrid system benefit formula and a calculation method related to its influence factors;

[0054] A strategy algorithm is obtained under different working conditions, wherein the strategy algorithm includes an SOC range, a power range, a storage temperature control logic and a photovoltaic control algorithm under each time period;

[0055] The cloud platform issues a charge-discharge control instruction and a storage system control instruction to the central controller according to the strategy algorithm;

[0056] The central controller controls the photovoltaic system according to the charge-discharge control instruction and controls the storage system according to the storage system control instruction.

[0057] Further, a converter and a thermal management system are further included, the converter is used to collect charge-discharge data and perform charge-discharge power, and the thermal management system is used to control the temperature of the storage system.

[0058] In a third aspect, the embodiments of the present application also provide a computer device, comprising: a memory and one or more processors;

[0059] The memory is used to store one or more programs;

[0060] When the one or more programs are executed by the one or more processors, the one or more processors implement the coordinated control method based on the light-storage hybrid system as described above.

[0061] In a fourth aspect, the embodiments of the present application also provide a storage medium containing computer executable instructions, which are used to execute the coordinated control method based on the light-storage hybrid system as described above when executed by a computer processor.

[0062] The embodiments of the present application obtain various data and send the data to a cloud platform through a central controller, wherein the data includes energy storage data, local photovoltaic data and external environment data; the cloud platform cleans the various data and preliminarily predicts photovoltaic power and load power; the cloud platform sets an energy storage and photovoltaic fusion system benefit formula and a calculation method related to influence factors; obtains a strategy algorithm under different conditions, wherein the strategy algorithm includes an SOC range, a power range, energy storage temperature control logic and a photovoltaic control algorithm under each period; according to the strategy algorithm, issues a charge and discharge control instruction and an energy storage system control instruction to the central controller; a charge and discharge control method for different conditions and different characteristic attributes of users, the cloud platform can adjust an intelligent collaborative method according to different needs, uncouples between local algorithms, intelligently adjusts to optimize operation conditions and energy consumption of the energy storage and photovoltaic fusion system, fully utilizes charge and discharge capacity of the energy storage system, improves comprehensive benefits and reduces energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 is a flowchart of a collaborative control method based on an energy storage and photovoltaic fusion system provided by the embodiments of the present application;

[0064] Figure 2 is a graph of energy consumption values under various comprehensive models of a collaborative control method based on an energy storage and photovoltaic fusion system provided by the embodiments of the present application;

[0065] Figure 3 is a graph of residual power values that can be used for energy storage charging in theory under each period of a collaborative control method based on an energy storage and photovoltaic fusion system provided by the embodiments of the present application;

[0066] Figure 4 is a structural schematic diagram of a collaborative control system based on an energy storage and photovoltaic fusion system provided by the embodiments of the present application;

[0067] Figure 5 is a structural schematic diagram of a computer device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0068] In order to make the purposes, technical solutions and advantages of the present application clearer, the following further describes the specific embodiments of the present application with reference to the drawings. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the convenience of description, only parts related to the present application are shown in the drawings, but not all. Before discussing the example embodiments in more detail, it should be mentioned that some example embodiments are described as processes or methods depicted as flowcharts. Although the flowchart describes each operation (or step) as a sequential process, many of the operations can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, etc.

[0069] The embodiment of the present application establishes a set of collaborative control methods based on a light storage fusion system, solves the problems that the existing technology does not deeply cooperate with the energy storage system and photovoltaic, the benefits cannot be maximized, and the self-use strategy cannot be adjusted, and the energy consumption of the energy storage end cannot be reduced.

[0070] SOC (State of Charge, state of charge) is an important parameter in the battery management system, which is used to represent the current charging level of the battery, that is, the proportion of the stored electric energy in the battery to its total capacity.

[0071] The PV power generation capacity refers to the total amount of electric energy generated by the photovoltaic power generation system.

[0072] The collaborative control method based on the light storage fusion system provided in the embodiment can be executed by a collaborative control system based on the light storage fusion system. The collaborative control system based on the light storage fusion system can be realized by software and / or hardware, and integrated in a collaborative control device based on the light storage fusion system. The collaborative control device based on the light storage fusion system can be a computer or other device.

[0073] Figure 1 A flowchart of a collaborative control method based on a light storage fusion system provided by the embodiment of the present application is shown in FIG. 1. Referring to FIG. 1, the method includes the following steps: Figure 1

[0074] Step 100, acquiring various data and sending to the cloud platform through the central controller, wherein the data includes: energy storage data, local photovoltaic data and external environment data.

[0075] ​Specifically, the energy storage data is collected and corrected, wherein the energy storage data includes: the opening / closing time of each charge / discharge cycle, the battery charge / discharge power, the temperature change of the battery cell, the SOC value, the chargeable / dischargeable power, the AC measured charge / discharge power, the energy loss of the energy storage system auxiliary component, and the energy consumption of the thermal management system.

[0076] Specifically, the local photovoltaic data is collected and dynamically adjusted, wherein the local photovoltaic data includes: the string capacity, the PV power generation, the inverter power generation, the daily peak AC / DC power, and the daily grid-connected duration.

[0077] Specifically, the external environment data is collected by crawling or collecting through the cloud platform, and the external environment data is parameter-adjusted, wherein the external environment data includes: local environment / temperature / load related information, such as solar radiation, weather conditions (overcast, foggy, etc.), environmental temperature, photovoltaic installation angle and orientation, historical data, real-time data, and prediction data.

[0078] The battery efficiency of the energy storage, the PCS efficiency, the energy loss of the energy storage system auxiliary component, and the energy consumption of the thermal management system are initially laboratory data, and are corrected according to the actual situation as the actual operation. The string capacity of the photovoltaic is dynamically adjusted according to the actual situation, and the initial value is entered. The local environmental temperature, such as solar radiation intensity and weather conditions, is obtained by crawling information through the cloud platform. Some actual data, such as environmental temperature, temperature and humidity, and actual solar radiation, are collected by the collector, and the prediction of the available power of the photovoltaic is obtained by parameter correction. After collecting and processing various data, the central controller sends the data to the cloud platform.

[0079] Step 200: cleaning various data through the cloud platform, and preliminarily predicting photovoltaic power and load power.

[0080] Optionally, the central controller can be real-time transmission or transmission after a specific trigger condition is reached, and the cloud platform has a data cleaning function.

[0081] Specifically, the cloud platform calculates the data cleaning ratio, records the data cleaning ratio value, processes the confidence of data with different cleaning ratio values, and reduces the weight of low-confidence data in the related formula; and preliminarily predicts the photovoltaic power and the load power.

[0082] For example, the cloud platform calculates the data cleaning ratio, records the data cleaning ratio value, and when the data cleaning ratio value < the first data cleaning ratio value, such as 2%, it is assumed that the data in this period is valid, and the confidence of the cloud platform in this data is 100%; when the first data cleaning ratio value < the data cleaning ratio value < the second data cleaning ratio value, such as 5%, the confidence of the cloud platform in this data is 50%; and when the data cleaning ratio value > the third data cleaning ratio value, such as 10%, the confidence of the cloud platform in this data is 20%. It should be noted that three intervals are used in this embodiment, and as an option, the intervals can be adjusted according to actual implementation.

[0083] For example, the photovoltaic power and the load power are preliminarily predicted, the photovoltaic power is mainly predicted for data in the next 15 minutes to the next day, and the load prediction includes the maximum load power and the prediction for a short period of minutes to the next day. In this relationship application, the data with low confidence is processed with a weight. There are various methods for predicting photovoltaic and load power, and the embodiments of the present application are not limited thereto.

[0084] As an embodiment, the average charging and discharging electric energy of the historical value extraction is W1, the average charging and discharging electric energy in the past 5 minutes is W2, the average charging and discharging electric energy in the past 30 minutes is W3, and the average electric energy in the next 5 minutes is predicted to be (a*W1+b*W2+c*W3), where a+b+c=1. In the case of low initial data accumulation, a is small, such as 0.1, b is large, such as 0.7, and c is intermediate, such as 0.2. With the change of the data volume, the weights are adaptively adjusted. Similarly, the predicted data of photovoltaic and load in a period of time can be obtained.

[0085] Step 300, setting the benefit formula of the light-storage-fusion system and the calculation method of its influence factors on the cloud platform.

[0086] Among them, the daily income of the customer = the energy storage income + the photovoltaic income;

[0087] The energy storage income = (the actual discharging amount of the grid end - the charging amount) * the peak price + (the actual discharging amount of the grid end - the charging amount) * the peak price + (the actual discharging amount of the grid end - the charging amount) * the flat price + (the actual discharging amount of the grid end - the charging amount) * the valley price;

[0088] The actual discharging amount of the grid end = the battery end consumption electric quantity of the energy storage system * the discharging efficiency of the energy storage system - the power consumption of the energy storage system;

[0089] Actual charging amount at grid side = battery charging amount at battery side of energy storage system / charging efficiency of energy storage system + energy consumption of energy storage system;

[0090] Charging and discharging efficiency of energy storage system is obtained by statistical analysis of historical charging and discharging data;

[0091] Photovoltaic income = actual discharging amount of photovoltaic to grid at peak * peak price + actual discharging amount of photovoltaic to grid at peak * peak price + actual discharging amount of photovoltaic to grid at flat * flat price + actual discharging amount of photovoltaic to grid at valley * valley price;

[0092] In the case of constant energy storage and photovoltaic capacity, high-power discharging is performed by using energy storage at peak side; in valley and flat period, photovoltaic charging is performed, and if photovoltaic charging does not meet the full charging condition of energy storage, a small amount of power supply can be used for supplement to achieve optimal income.

[0093] Step 400, obtaining strategy algorithm under different working conditions, wherein the strategy algorithm includes SOC range, power range, energy storage temperature control logic and photovoltaic control algorithm under each period.

[0094] Embodiments of the present application mainly aim at self-generation, self-use and grid-connected system, and therefore photovoltaic power generation needs to be maximized. Working conditions are distinguished mainly by charging and discharging control of energy storage system, and at the same time, in order to meet engineering application, there is a preset logic framework locally, and the cloud platform can quickly realize by issuing set period, SOC, power and temperature control logic.

[0095] For example, working condition 1 prioritizes charging: energy storage allows charging P1 kW to SOC1 (allowing power supply by using city power, if photovoltaic power is greater than the set value, more charging is performed, and when photovoltaic power is large, SOC1 range can be exceeded, but not exceeding the safe threshold SOC2 of the system), and no discharging is performed; working condition 2 photovoltaic consumption: allowing photovoltaic charging and not allowing energy storage discharging; working condition 3 photovoltaic and energy storage charging and discharging: allowing photovoltaic charging and allowing maximum discharging power P2 kW, and discharging to SOC3; working condition 4 photovoltaic restriction: energy storage does not charge or discharge, and photovoltaic is controlled; in the above working conditions 1-4, period, SOC value and P value can be manually set or set by cloud platform.

[0096] As an embodiment: for a certain region, first select months: January, February, June, July, August and December; execute working condition 1 in valley period 0-7 (settable) to charge P1 kW (such as 35 kW, settable) to SOC1 (such as 50%, settable) without allowing discharging; execute working condition 3 in flat period 7-16 (settable) to allow photovoltaic charging and allow discharging to SOC2 (such as 70%, settable); execute working condition 3 in peak period 16-24 (settable) to allow photovoltaic charging and allow discharging to SOC3 (such as 5%, settable)

[0097] Month: 3-5, 9-11; valley period 0-6 (can be set) to perform 1, charging -35kW (can be set), charged to SOC1 (95%, can be set), not allowed to discharge; flat period 6-11 (can be set) to perform 3, allow photovoltaic charging, and allow discharge to SOC2 (5%, can be set); valley period 11-14 (can be set) to perform 1, charging -60kW, charged to SOC1 (70%, can be set), not allowed to discharge; photovoltaic large time can exceed the set SOC1 range, the maximum is set upper limit SOC (95%); flat period 14-16 (can be set) to perform 2, allow charging, not allowed to discharge; peak period 16-24 (can be set) to perform 3, allow photovoltaic charging, and allow discharge to SOC3 (5%, can be set)

[0098] Among them, in order to make the income in each working condition optimal, it is necessary to comprehensively evaluate the historical charging and discharging data, photovoltaic power generation situation, and local operating environment.

[0099] First, the maximum efficiency of the energy storage under each local operating environment is obtained. According to historical experimental data or actual working condition data, a table similar to Figure 2 can be obtained, and the energy consumption value under each comprehensive model is obtained: for example, the thermal management power of different initial temperatures and different SOCs at P1 rate is summarized in the following table, for example, Figure 2 , the average power of thermal management is 3kW (the following values are examples) when the charging initial temperature is -15℃ and the SOC is 10%. In actual use, the system power consumption at each power, temperature, and SOC needs to be obtained comprehensively in the database on the cloud server.

[0100] Among them, through historical charging and discharging data analysis, the charging and discharging efficiency of the energy storage system at each charging and discharging rate and each temperature can be obtained. Figure 2 The values in the table ofwill be updated according to the confidence of subsequent data to improve robustness, and relevant nonlinear models will be gradually established. In actual use, multivariate nonlinear relationship equations or Kalman filtering can be applied, which will not be described here.

[0101] In the above embodiment, the optimal charging and discharging power and temperature control strategy in each period can be obtained.

[0102] Secondly, the theoretical available power of photovoltaic in each period can be obtained by obtaining the photovoltaic related information, and the theoretical residual power value available for energy storage charging in each period can be obtained by combining the power prediction of the load, as shown in the table in Figure 3

[0103] Therefore, the theoretical size of the electrical energy that needs to be provided by the energy storage from 7 to 8 is calculated as: The corresponding maximum energy storage benefit is The electricity price; the sliding difference can obtain the maximum energy storage benefit in each period, the benefit is positive and negative; then judge the corresponding energy storage percentage in the maximum benefit period, obtain the corresponding SOC value change, finally it is known that the energy storage needs to reach the SOC value in each period to meet the maximum benefit, which is the theoretical optimal SOC value in the period.

[0104] The optimal charging and discharging strategy includes charging and discharging power and temperature control strategy. The SOC value change in the period obtained by the above method, combined with the energy consumption value of the comprehensive model stored in the cloud platform, can obtain the charging and discharging power and temperature control strategy in the period, and reduce the power loss.

[0105] In actual use, photovoltaic power prediction and load power prediction are updated in real time, and the minimum granularity is ≤15 min. The light storage system reserves a certain margin to ensure system benefit when issuing the next day target strategy (period, charging and discharging power, SOC, temperature control strategy). In the initial operation stage, the margin value should be set larger, such as the original strategy 0-7 point local SOC set as SOC1, the cloud platform calculates the SOC value of the period as SOC2 optimal, and the final strategy execution is SOC3 = a * SOC1+b *SOC2; a+b =1, wherein a is the local strategy weight, and b is the cloud platform algorithm weight. b will be dynamically adjusted combined with historical data confidence and data accumulation to reduce the algorithm deviation caused by data uncertainty. Similarly, the theoretical optimal local charging and discharging control strategy can be obtained.

[0106] Step 500, according to the strategy algorithm, the charging and discharging control instruction and the energy storage system control instruction are issued to the central controller.

[0107] Specifically, according to the obtained charging and discharging period, the SOC threshold range, the power range and the temperature control strategy fitted in the cloud platform, the charging and discharging control instruction and the energy storage system control instruction are issued to the central controller.

[0108] The energy storage system obtains the optimal intelligent collaborative control method according to the target optimization model to control the energy storage controller charging and discharging period, SOC, charging and discharging power and thermal management control.

[0109] The central controller controls the execution of the subordinate control actuator unit according to the control instruction issued by the cloud server. For example, the central controller controls the charging and discharging period, and issues the charging and discharging power to the converter to control the charging and discharging; the central controller issues the start / close thermal management instruction to the thermal management system.

[0110] The above, the embodiment of the application acquires various data and sends to the cloud platform through the central controller, wherein the data includes energy storage data, local photovoltaic data and external environment data; the cloud platform is used for cleaning various data and preliminarily predicting photovoltaic power and load power; the cloud platform is used for setting the benefit formula of the light-storage-fusion system and the calculation method related to the influence factor; the strategy algorithm is acquired under different working conditions, wherein the strategy algorithm includes the SOC range, the power range, the energy storage temperature control logic and the photovoltaic control algorithm under each period; the charge-discharge control instruction and the energy storage system control instruction are issued to the central controller according to the strategy algorithm; the charge-discharge control method of different working conditions and different characteristic properties of the user, the subsequent cloud platform can adjust the intelligent collaborative method according to different needs, uncouple the local algorithm, intelligently adjust the operation condition and energy consumption of the light-storage-fusion system, fully utilize the charge-discharge capacity of the energy storage system, improve the comprehensive income and reduce the energy consumption.

[0111] The embodiment of the application analyzes the current collected energy storage system data, photovoltaic data, power grid load conditions and climate data obtained by the cloud platform; the working conditions are identified for engineering application, and the charge-discharge power and available SOC interval of the energy storage equipment under each working condition in each period are obtained, so as to reduce energy consumption and improve income.

[0112] The embodiment of the application determines the charge-discharge control method of different working conditions and different characteristic properties of the user; the subsequent big data platform can adjust the intelligent collaborative method according to different needs, uncouple the local algorithm. Intelligent adjustment makes the operation condition and energy consumption of the light-storage-fusion system optimal, fully utilizes the charge-discharge capacity of the energy storage system, improves the comprehensive income, and the cloud platform is self-adaptive. With the increase of data volume, the algorithm robustness is stronger. The data of the cloud platform can be stored in distributed modules according to different customer working conditions, and different charge-discharge control modes are intelligently called according to the identification of working conditions.

[0113] Therefore, the embodiment of the application realizes the maximum income under the condition of minimum abandoned light quantity, peak clipping, demand, and anti-flow value, improves the user side income; intelligent adjustment makes the operation condition of the energy storage & photovoltaic optimal, fully utilizes the charge-discharge capacity of the light-storage system; the cloud platform is self-adaptive, and with the increase of data volume, the algorithm can be corrected in the cloud, and then real-time correction is performed according to the customer working condition. The cloud platform can reduce the early calibration and debugging work, and in the later use process, the adaptive control strategy is customized through different use conditions of the customer in real time.

[0114] On the basis of the above embodiment, please refer to Figure 4 The embodiment of the application provides a collaborative control system based on a light-storage-fusion system, which specifically comprises: a cloud platform, a central controller, an energy storage system and a photovoltaic system; the central controller is connected with the cloud platform, the energy storage system and the photovoltaic system.

[0115] Specifically, various types of data are acquired and sent to the cloud platform through the central controller, wherein the data includes energy storage data, local photovoltaic data, and external environment data; the cloud platform cleans the various types of data and preliminarily predicts photovoltaic power and load power; the cloud platform sets a light-storage-fusion system benefit formula and a calculation method related to its influence factors; a strategy algorithm is obtained under different working conditions, wherein the strategy algorithm includes an SOC range, a power range, an energy storage temperature control logic, and a photovoltaic control algorithm under each time period; the cloud platform issues a charge-discharge control instruction and an energy storage system control instruction to the central controller according to the strategy algorithm; and the central controller controls the photovoltaic system according to the charge-discharge control instruction and controls the energy storage system according to the energy storage system control instruction.

[0116] In some embodiments, a current transformer and a thermal management system are further included, the current transformer is used to collect charge-discharge data and perform charge-discharge power, and the thermal management system is used to control the temperature of the energy storage system.

[0117] The cloud server includes a data processing unit that analyzes and cleans data according to the energy storage information; and a logical judgment unit that obtains optimal energy storage charge-discharge control and energy storage system operating conditions by comprehensively considering the energy storage system and input data.

[0118] Meanwhile, the cloud platform can optimize and upgrade the charge-discharge control algorithm in the future; the central control unit includes a data uploading function and a scheduling module that schedules subordinate actuators according to the cloud server control method; the working condition acquisition module is used to acquire external operating environment information; and the photovoltaic system is used to acquire photovoltaic related information and control photovoltaic power generation.

[0119] The above embodiment of the application acquires various types of data and sends them to the cloud platform through the central controller, wherein the data includes energy storage data, local photovoltaic data, and external environment data; the cloud platform cleans the various types of data and preliminarily predicts photovoltaic power and load power; the cloud platform sets a light-storage-fusion system benefit formula and a calculation method related to its influence factors; a strategy algorithm is obtained under different working conditions, wherein the strategy algorithm includes an SOC range, a power range, an energy storage temperature control logic, and a photovoltaic control algorithm under each time period; the cloud platform issues a charge-discharge control instruction and an energy storage system control instruction to the central controller according to the strategy algorithm; and the central controller controls the photovoltaic system according to the charge-discharge control instruction and controls the energy storage system according to the energy storage system control instruction.

[0120] The cooperative control system based on the light storage fusion system provided by the embodiments of the present application can be used to execute the cooperative control method based on the light storage fusion system provided by the embodiments of the present application, and has corresponding functions and beneficial effects.

[0121] The embodiments of the present application further provide a computer device which can integrate the cooperative control system based on the light storage fusion system provided by the embodiments of the present application. Figure 5 FIG. 1 is a structural schematic diagram of a computer device provided by an embodiment of the present application. Referring to FIG. 1, Figure 5 The computer device includes an input device 33, an output device 34, a memory 32, and one or more processors 31; the memory 32 is used to store one or more programs; when the one or more programs are executed by the one or more processors 31, the one or more processors 31 implement the cooperative control method based on the light storage fusion system provided by the embodiments described above. The input device 33, the output device 34, the memory 32, and the processor 31 can be connected by a bus or other means, Figure 5 For example, the connection by the bus is taken as an example in the description.

[0122] The processor 31 executes various function applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 32, that is, implements the cooperative control method based on the light storage fusion system described above.

[0123] The computer device provided above can be used to execute the cooperative control method based on the light storage fusion system provided by the embodiments described above, and has corresponding functions and beneficial effects.

[0124] The embodiments of the present application further provide a storage medium containing computer executable instructions, which are used to execute a cooperative control method based on a light storage fusion system when executed by a computer processor. The cooperative control method based on the light storage fusion system includes: receiving an authentication request sent by a user through a secure communication protocol; obtaining identity information and system information of the user according to the authentication request of the user; wherein the identity information includes but is not limited to a username, a password, and biological feature information, and the system information includes hardware information and software configuration information of a user device or a system; encrypting the identity information and the system information of the user by using a symmetric encryption algorithm or an asymmetric encryption algorithm; establishing a secure connection with an authentication server, sending the encrypted identity information and system information to the authentication server through the connection, and instructing the authentication server to perform decryption and verification operations; receiving a verification result returned by the authentication server, if the verification result returned by the authentication server indicates that the identity information and the system information of the user are both verified, judging that the authentication of the user is successful, and granting the user corresponding access permissions, and if the verification result is not passed, judging that the authentication of the user fails, and rejecting the access request of the user.

[0125] Storage medium - any type of memory device or storage device. The term "storage medium" is intended to include an installation medium, e.g., a CD-ROM, floppy disks, or tape apparatus; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; or a nonvolatile memory such as a flash memory, a magnetic media (e.g., a hard disk or optical storage); registers or other similar types of memory elements, etc. The memory medium can also include other types of storage medium and combinations thereof. Moreover, the memory medium can be located in a first computer in which the programs are executed, or be located in a second different computer which connects to the first computer over a network, such as the Internet. The second computer can provide program instructions to the first computer for execution. The term "memory medium" can include two or more memory mediums which can reside in different locations, e.g., in different computers that are connected over a network. The memory medium can store the program instructions which can be executed by one or more processors (e.g., specifically implemented as a computer program).

[0126] Of course, the storage medium provided by the embodiments of the present application includes computer executable instructions, and the computer executable instructions are not limited to the cooperative control method of the optical storage hybrid system as described above, but can also perform the related operations in the cooperative control method of the optical storage hybrid system provided by any of the embodiments of the present application.

[0127] The cooperative control system of the optical storage hybrid system, the storage medium and the computer device provided in the above embodiments can execute the cooperative control method of the optical storage hybrid system provided by any of the embodiments of the present application, and the technical details not described in the above embodiments can be referred to the cooperative control method of the optical storage hybrid system provided by any of the embodiments of the present application.

[0128] The above is only the preferred embodiments of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments and replacements made by those skilled in the art will not deviate from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without deviating from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A collaborative control method based on a photovoltaic-storage fusion system, characterized in that, The method includes the following steps: Various types of data are acquired and sent to the cloud platform through the central controller. This data includes: energy storage data, local photovoltaic data, and external environmental data. Various types of data are cleaned through a cloud platform, and preliminary predictions of photovoltaic power and load power are made. Set up the calculation method for the benefit formula and related influencing factors of the photovoltaic-storage integration system on the cloud platform; The strategy algorithm is obtained under different operating conditions. The strategy algorithm includes the SOC range, power range, energy storage temperature control logic and photovoltaic control algorithm under each time period. Based on the strategy algorithm, charging and discharging control commands and energy storage system control commands are sent to the central controller. The calculation method for the benefit formula and its influencing factors of the photovoltaic-storage fusion system set on the cloud platform includes: Customer daily revenue = energy storage revenue + photovoltaic revenue; Energy storage revenue = (Actual discharge amount at peak grid end - Charging amount) * Peak price + (Actual discharge amount at peak grid end - Charging amount) * Peak price + (Actual discharge amount at flat grid end - Charging amount) * Flat price + (Actual discharge amount at valley grid end - Charging amount) * Valley price; Actual discharge at the grid end = Energy consumption at the battery end of the energy storage system * Discharge efficiency of the energy storage system – Energy consumption of the energy storage system; Actual charging amount at the grid end = Charging amount at the battery end of the energy storage system / Charging efficiency of the energy storage system + Power consumption of the energy storage system; The charging and discharging efficiency of the energy storage system is obtained from historical charging and discharging data. Photovoltaic revenue = Peak actual discharge amount from photovoltaic power to the grid * Peak price + Peak actual discharge amount from photovoltaic power to the grid * Peak price + Flat actual discharge amount from photovoltaic power to the grid * Grid price + Valley actual discharge amount from photovoltaic power to the grid * Valley price; Under constant energy storage and photovoltaic capacity, high-power discharge is achieved using peak-end energy storage; during off-peak hours, photovoltaic charging is used.

2. The collaborative control method based on a photovoltaic-storage fusion system according to claim 1, characterized in that, The acquisition of various types of data and their transmission to the cloud platform via the central controller includes: energy storage data, local photovoltaic data, and external environmental data, including: Collect and correct energy storage data. The energy storage data includes: the on / off time of each charge / discharge cycle, battery charge / discharge power, cell temperature change, SOC value, chargeable / dischargeable capacity, AC charge / discharge power, energy loss of auxiliary components of the energy storage system, and energy consumption of the thermal management system. Collect local photovoltaic data and dynamically adjust the local photovoltaic data, which includes: string capacity, PV power generation, inverter power generation, daily peak AC / DC power and daily grid connection duration; External environmental data is collected through cloud platform crawling or data collectors, and parameters of the external environmental data are adjusted. The external environmental data includes historical data, real-time data, and forecast data of solar radiation, weather conditions, ambient temperature, photovoltaic installation angle and orientation. Various types of data are sent to the cloud platform through the central controller.

3. The collaborative control method based on a photovoltaic-storage fusion system according to claim 1, characterized in that, The process of cleaning various types of data through a cloud platform and making preliminary predictions of photovoltaic power and load power includes: The proportion of data cleaning is statistically analyzed on the cloud platform, and the data cleaning proportion value is recorded. By processing the confidence of data with different cleaning proportion values, the weight of low-confidence data in the relevant formulas is reduced accordingly. A preliminary forecast of photovoltaic power and load power is made.

4. The collaborative control method based on a photovoltaic-storage fusion system according to claim 1, characterized in that, The operating condition acquisition strategy algorithm includes the SOC range, power range, energy storage temperature control logic, and photovoltaic control algorithm for each time period, including: Based on historical experimental data or actual operating data, the maximum energy storage efficiency under each local operating environment is obtained. Among them, by analyzing historical charge and discharge data, the charge and discharge efficiency of the energy storage system at each charge and discharge rate and at each temperature is obtained; By acquiring photovoltaic-related information, the theoretical power generation of photovoltaics in each time period is obtained. Combined with the power prediction of the load, the theoretical remaining power value for energy storage charging in each time period is obtained. The theoretical amount of electrical energy that needs to be provided by energy storage is calculated as follows: ； The corresponding maximum benefit of energy storage is Electricity price; The slippage yields the theoretical maximum energy storage return for each time period, with returns being either positive or negative. Then, determine the percentage of energy that needs to be stored during the period of maximum benefit, obtain the corresponding change in SOC value, and deduce the SOC value that energy storage needs to achieve in each period to meet the maximum benefit. This value is the theoretical optimal SOC value in that period. The optimal charging and discharging strategy includes charging and discharging power and temperature control strategy. By obtaining the change of SOC value during this period and combining the energy consumption value under the comprehensive model of cloud platform storage, the theoretical charging and discharging power and temperature control strategy for this period can be obtained. Among them, both photovoltaic power generation prediction and load power prediction are updated in real time, with a minimum granularity of ≤15min; When issuing the target strategy for the next day, the target strategy for the photovoltaic and energy storage system includes the time period, charging and discharging power, SOC, and temperature control strategy, and reserves the set margin to ensure the system's revenue. During the initial operation phase, the SOC of the original policy is set to SOC1 locally. When the cloud platform calculates that the optimal SOC value for this period is SOC2, the final policy execution is as follows: SOC3 = a * SOC1+b *SOC2, a+b =1; Where a is the local policy weight and b is the cloud platform algorithm weight; The algorithm weights of cloud platforms need to be dynamically adjusted based on historical data confidence and data accumulation. Theoretically optimal local charge and discharge control strategy is obtained by sampling the same method.

5. The collaborative control method based on a photovoltaic-storage fusion system according to claim 4, characterized in that, The step of issuing charging / discharging control commands and energy storage system control commands to the central controller according to the strategy algorithm includes: Based on the fitted charging and discharging time period, SOC threshold range, power range and temperature control strategy obtained from the cloud platform, charging and discharging control commands and energy storage system control commands are sent to the central controller. The energy storage system uses the optimal intelligent cooperative control method obtained from the target optimization model to control the energy storage controller's charge / discharge cutoff period, SOC, charge / discharge power, and thermal management control. The central controller dispatches the subordinate actuator units to complete the task based on the control commands issued by the cloud server.

6. A collaborative control system based on a photovoltaic-storage fusion system, implemented based on the method of claim 1, characterized in that, include: The system comprises a cloud platform, a central controller, an energy storage system, and a photovoltaic system; the central controller connects the cloud platform, the energy storage system, and the photovoltaic system. Various types of data are acquired and sent to the cloud platform through the central controller, including: energy storage data, local photovoltaic data, and external environmental data; The cloud platform is used to clean various types of data and to make preliminary predictions on photovoltaic power and load power. A calculation method for the benefit formula and influencing factors of the photovoltaic-storage fusion system is set on the cloud platform. The strategy algorithm is obtained under different operating conditions. The strategy algorithm includes the SOC range, power range, energy storage temperature control logic and photovoltaic control algorithm under each time period. The cloud platform sends charging and discharging control commands and energy storage system control commands to the central controller according to the strategy algorithm; The central controller controls the photovoltaic system according to the charge and discharge control commands, and controls the energy storage system according to the energy storage system control commands.

7. The collaborative control system based on the photovoltaic-storage fusion system according to claim 6, characterized in that, It also includes a converter and a thermal management system. The converter is used to collect charge and discharge data and execute charge and discharge power, and the thermal management system is used to control the temperature of the energy storage system.

8. A computer device, characterized in that, include: Memory and one or more processors; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a collaborative control method based on a photovoltaic-storage fusion system as described in any one of claims 1-5.

9. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform a collaborative control method based on a photovoltaic-storage fusion system as described in any one of claims 1-5.

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