Control method and system of optical storage direct flexible system

Abstract

The invention relates to the technical field of building electrics, and particularly provides a control method and system of an optical storage direct flexible system. The control method comprises the following steps: acquiring power load data of a target building within a preset time; acquiring illumination meteorological data in the preset time; obtaining a power load prediction curve according to the illumination meteorological data and the power load data; based on the power load prediction curve, constructing a control relationship between the direct current bus voltage at the grid-connected part of the optical storage direct-flexible system and the total grid-connected power; and based on the control relationship, performing power regulation according to the voltage change condition of the direct current bus. According to the invention, when the real-time power deviates from the predicted optimal load value, such as the inflection point, the optical storage direct-current flexible system can automatically deploy the power of the energy equipment connected to the direct-current bus. And system power response is completed according to the flexible capacity potential of the equipment, so that the system operation state tends to be economically optimal.

Description

Technical Field

[0001] The present invention belongs to the technical field of building electricity, and particularly relates to a control method and system for a photovoltaic-storage-direct-current-flexible system. Background Art

[0002] As a research hotspot in the field of new energy power generation in recent years, the "photovoltaic-storage-direct-current-flexible" system has been gradually widely applied to various buildings and power distribution scenarios integrating new energy technologies. The so-called "photovoltaic-storage-direct-current-flexible" refers to a direct-current power distribution system composed of photovoltaic power generation equipment, energy storage devices, direct-current loads, and alternating-current flexible loads. Such a system is usually connected to the traditional backbone power grid through a power conversion device, so that while having a strong self-organizing operation ability, it can also serve as a flexible load of the power grid to provide dynamic power regulation ability for the regional power grid. Under the background of high proportion penetration of clean energy, the photovoltaic-storage-direct-current-flexible system helps to improve the supply-demand structure of the traditional power system dominated by rigid loads and achieve higher operation flexibility and energy efficiency optimization.

[0003] Currently, to improve the comprehensive energy utilization efficiency, the photovoltaic-storage-direct-current-flexible system is increasingly combined with high-rise buildings, which helps to enhance the power coordination ability between the building complex and the regional power grid while meeting the internal supply-demand balance of the building. In such a fusion scenario, the building power distribution and utilization system is no longer a single energy consumption body, but is transformed into a flexible response energy hub through the photovoltaic-storage-direct-current-flexible architecture. This transformation not only involves the reconstruction of the microgrid topology structure and the optimization of the grid connection control strategy, but also brings higher requirements for the control accuracy, response speed, and regulation ability of the internal electrical equipment of the system.

[0004] However, the control means of the photovoltaic-storage-direct-current-flexible system in the prior art still have great limitations. Especially under working conditions such as system grid connection, grid disconnection, or load mutation, the traditional control strategy lacks a refined dynamic response mechanism, and often due to problems such as control hysteresis and rough regulation, it causes severe fluctuations in the bus voltage of the regional power grid, thus posing an impact on the stability of the power system. For example, in the existing system, there is a lack of a mechanism to dynamically adjust the grid connection voltage or the system power response according to the actual light, meteorological changes, and load change trends; the cooperation ability between the internal energy units of the system is weak, and it is difficult to achieve flexible regulation control based on prediction data. Therefore, how to improve the control flexibility and response accuracy of the photovoltaic-storage-direct-current-flexible system in the interaction process with the regional power system has become a key problem that urgently needs to be solved in the current technical field. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies in the prior art and provide a control method and system for a photovoltaic-storage-direct-current-flexible system that can make full use of the flexible capacity potential of each energy device and reduce the impact of the system on the power system.

[0006] To solve the above technical problems, the technical method adopted by the present invention is as follows: The present invention discloses a control method for a photovoltaic-storage-direct-current-flexible (PV-SD-F) system, which includes the following steps:

[0007] S1. Obtain the power load data of the target building within a preset time;

[0008] S2. Obtain the illumination meteorological data within the preset time, including air temperature, illumination intensity, historical energy storage capacity level, historical electricity price, and photovoltaic power generation fluctuation characteristics;

[0009] S3. According to the power load data and the illumination meteorological data, construct a short-term power load prediction curve and a photovoltaic power generation power prediction model;

[0010] S4. Based on the load prediction curve, construct a droop control relationship between the DC bus voltage and the total grid-connected power at the grid connection of the PV-SD-F system, including setting the inflection point voltage U0 and the droop coefficient L(x);

[0011] S5. Based on the droop control relationship, adjust the operation of the photovoltaic power generation system and the energy storage system in real time according to the change of the DC bus voltage.

[0012] Further, in step S3, the power load prediction curve is a short-term prediction curve constructed within a rolling time window, and the power load data includes the power load level PT within a unit time T and the load curve PnT within an nT historical time period.

[0013] Further, in step S4, constructing the droop control relationship between the DC bus voltage and the total grid-connected power at the grid connection of the PV-SD-F system includes:

[0014] V d = V0 + ΔV(t) = V0 + L(x)·(P0 - P r )

[0015] In the formula, P0 is the system grid-connected power prediction reference value based on the daily power load prediction curve, P r is the grid-connected power actually fed into the DC bus by the commercial power, V0 is the rated value of the DC bus voltage, and L(x) is the droop coefficient of the droop control curve; V d is the current DC bus voltage, and ΔV(t) is the difference between the real-time load and the predicted load during the operation at time t.

[0016] Further, construct a voltage-power droop control curve composed of a first droop control curve and a second droop control curve, where the first droop control curve is applicable to the voltage range (U0, U1] and the power range [P0, P1), and its slope is L1;

[0017] The second droop control curve is applicable to the voltage range [U2, U0) and the power range (P1, P2], and its slope is L2, and L1 > L2. By judging the range where the DC bus voltage Vd is located, the droop coefficient L(x) to be adopted is determined.

[0018] Among them, the inflection point voltage U0 is dynamically adjusted according to the following formula:

[0019] In the formula: U1 and U2 are respectively the upper limit value and the lower limit value allowed for the DC bus voltage, a is the adjustment coefficient, satisfying 0.85 ≤ a ≤ 0.95, U(t) and I(t) are respectively the DC bus voltage and current within the unit time T, PT is the building load prediction value within the unit time T, I0 is the rated current at the grid connection of the PV-FESS system. Based on the control curve and the dynamic U0, the grid-connected power and the bus voltage are accurately controlled.

[0020] Further, the inflection point power P1 is determined by the following formula: P1 = U0 × I0, where I0 is the system rated current at the grid connection;

[0021] Current bus voltage V d = V0 + ΔV(t) = V0 + L(x)·(P e0 - P r );

[0022] In the formula, V0 is the rated value of the bus voltage, P e0 is the grid-connected power reference value obtained based on the daily power load prediction, P r is the actual power fed into the bus by the utility grid, and L(x) is the selected droop coefficient.

[0023] Further, the value of the droop coefficient L(x) is as follows: In the formula, L1 and L2 are respectively the slopes corresponding to the voltage ranges (U0, U1] and [U2, U0), and L1 > L2.

[0024] Further, in the step S3, the photovoltaic power prediction model is: P = L × S × α × Q; in the formula, P is the total predicted photovoltaic power, L is the total solar irradiance, S is the sum of the areas of all photovoltaic modules on the outer wall of the building, α is the module conversion efficiency, and Q is the energy consumption conversion coefficient.

[0025] Further, the total solar irradiance L = k(x·T h + y·T1 + z·M + ΔL); in the formula, L is the total solar irradiance; ΔL is the change in the total solar irradiance; k is the daily solar radiation); T his the daily maximum temperature; Tl is the daily minimum temperature, M is the ultraviolet radiation intensity, and x, y, and z are the weather coefficients in the power prediction model.

[0026] The present invention also discloses a control system for a photovoltaic-storage-direct-current-soft (PV-SD-DC) system, comprising:

[0027] A data acquisition module, configured to acquire real-time operating parameters of a photovoltaic power generation unit, a energy storage unit, and a load unit, as well as power load data and light and meteorological data of a target building within a preset time;

[0028] A power load prediction module, configured to generate a power load prediction curve according to the load data and the light and meteorological data;

[0029] A control module, configured to execute the above-mentioned control method for the PV-SD-DC system based on the prediction curve;

[0030] An execution module, configured to drive a power regulation device to perform a power adjustment operation according to a control instruction of the control module;

[0031] A communication module, configured to perform data interaction with a microgrid monitoring system and each power conversion device;

[0032] An edge intelligence optimization module, configured to be deployed in a system area sub-node, acquire local load data, energy storage status, electricity price, and micro-meteorological data, perform machine learning model prediction through an edge computing unit, and generate local response control instructions to adjust the operating status of a load or an energy storage device in real time.

[0033] Further, the control module further comprises:

[0034] A judgment unit and an instruction generation unit, configured to judge whether a flexible control condition is satisfied currently and generate a flexible control instruction;

[0035] An overall coordination module, configured to iteratively adjust the on-grid capacity of each load device itself in real time according to the dynamic power regulation capabilities of load devices in the PV-SD-DC system;

[0036] The execution module includes an energy storage control unit and a photovoltaic regulation unit, which are respectively configured to control energy storage charging and discharging and photovoltaic output modes.

[0037] Advantageous effects:

[0038] Compared with the prior art, the present invention realizes the dynamic matching of the grid-connected voltage and the internal operating power of the photovoltaic-storage-direct-current flexible system by setting a dynamic inflection point voltage U0. The system obtains the power load data within a preset time and combines it with the illumination and meteorological data closely related to the DC bus voltage to realize the dynamic update of the voltage regulation strategy. Near the inflection point voltage, the system adjusts the power of each energy device connected to the bus in real time according to the current load trend and power deviation, effectively matching the power supply and demand, and ensuring the voltage stability and grid connection security of the system.

[0039] Secondly, the system innovatively introduces a droop control mechanism based on power deviation prediction. When there is a deviation between the real-time operating power and the predicted optimal load value (such as at the inflection point), the system can automatically coordinate the outputs of devices such as photovoltaic and energy storage, and complete the power response based on their respective flexible capacity potentials. Through this droop control relationship, not only the automatic and disturbance-free switching and coordinated operation of multi-source energy devices are realized, but also the real-time performance and stability of the system response are greatly improved.

[0040] Finally, by integrating power load prediction, meteorological data analysis and flexible control strategies, the photovoltaic-storage-direct-current flexible system can approach the economic optimal state of system operation while meeting the power supply demand and system stability according to the flexible capacity and regulation ability of different devices. This method avoids the energy efficiency loss caused by the rigid scheduling of devices in traditional control strategies, and shows significant cost control advantages and operation efficiency improvement in dynamic load scenarios, which is one of the key supporting technologies for the intelligent and efficient development of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic flow chart of the steps of the control method of the photovoltaic-storage-direct-current flexible system in Embodiment 1;

[0042] Figure 2 is a schematic flow chart of the steps of the control system of the photovoltaic-storage-direct-current flexible system in Embodiment 2;

[0043] Figure 3 is a schematic block diagram of the main structure of the control system of the photovoltaic-storage-direct-current flexible system in Embodiment 2. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0045] Embodiment 1

[0046] Referring to Figure 1 , the present invention provides a control method for a photovoltaic-storage-direct-current flexible system, including:

[0047] S1, obtaining the power load data of the target building within a preset time;

[0048] S2. Obtain the lighting meteorological data within a preset time;

[0049] S3. Obtain the power load prediction curve based on the lighting meteorological data and the power load data;

[0050] S4. Based on the power load prediction curve, construct the control relationship between the DC bus voltage and the total grid-connected power at the grid connection of the photovoltaic-storage-direct-current-soft (PV-ESS-DC-Soft) system;

[0051] S5. Adjust the power according to the change of the DC bus voltage based on the control relationship.

[0052] Steps S1 and S2 are both data acquisition processes and can be carried out simultaneously. In the method of the present invention, the serial numbers are for identification purposes and do not limit the sequence.

[0053] Preferably, in step S1, the target building is a PV-ESS-DC-Soft building. The power load data is past power load data, i.e., historical data.

[0054] The power load data covers data sources with various time spans. Specifically, these data can be from the power load records of the past year to reflect the load change trend of one year. Such data helps to analyze the annual load change law. Similarly, it can also be the actual data collected in the past several preset times, which can be set to several hours according to the need of data collection. This setting is applicable to real-time monitoring of the power load to quickly respond to load changes and ensure the stable operation of the power system.

[0055] The preset time can also be set to several days. This setting is applicable to predicting the power load in the future for a period of time and providing a reference for the power system planning and management.

[0056] The power load prediction curve provided by the present invention can be considered as a short-term power load prediction curve to facilitate the short-term scheduling and optimization of the power system. This short-term prediction curve has high accuracy and real-time performance, which helps to improve the operation efficiency and safety of the power system.

[0057] Preferably, for the past power load data, with T as the time unit, obtain the power load level P of the target building in the past unit time T through a clustering method T , and the power load curve P in the past nT time nT ; where the clustering method can be the Hierarchical Methods, Partitioning Methods, K-means clustering algorithm or the Model-Based Methods. Further preferably, the clustering method includes the K-means method.

[0058] Preferably, in step S2, obtain the historical power load level P T and the power load curve P nT of the target building, and record the temperature data Temp, light intensity data S P in the data during the historical period nT, as well as the photovoltaic power fluctuation characteristic f, historical load level L, historical energy storage capacity level C P in the target building, and historical electricity price F P .

[0059] Preferably, in step S3, obtaining the power load prediction curve based on the light meteorological data and the power load data includes:

[0060] Obtain the power load prediction curve based on the light meteorological data and the power load data, and use the preset feeder voltage stability threshold as the optimization benchmark to obtain the power load prediction curve.

[0061] The feeder voltage stability threshold is preset. For optimizing subsequent steps, optimization means starting with the threshold as the optimization starting point.

[0062] Through step S3 of the present invention, using the feeder voltage stability threshold as the optimization benchmark, obtain the power load prediction curve for the short term (such as the future unit time t). The purpose of using the feeder voltage stability threshold as the optimization benchmark is:

[0063] 1. Ensure that when the target building is connected to the target area power grid, it will not cause a large impact on the area power grid due to voltage fluctuations. This helps reduce the operation risk of the power system and improve the power grid stability;

[0064] 2. By optimizing the power load prediction curve, the operation strategy of the power system can be adjusted in real time to ensure that the voltage fluctuates within a reasonable range, avoid damage to power grid equipment caused by abnormal voltage, and contribute to the stability of the regional voltage.

[0065] 3. Through the accurate prediction and optimized adjustment of the power load, the power grid loss can be reduced and the overall operation efficiency of the power system can be improved.

[0066] Preferably, in step S4, based on the power load prediction curve, constructing the control relationship between the DC bus voltage and the total grid-connected power at the grid connection of the photovoltaic-storage-direct-soft system includes:

[0067] Based on the power load prediction curve, construct the total grid-connected gateway droop control curve.

[0068] In view of the characteristics of the power load forecasting curve, the present invention constructs a voltage-power droop control curve of the rectifier between the AC power grid in the target area and the DC power grid of the building with direct current power supply from photovoltaic and energy storage systems. The voltage-power droop control curve consists of two droop control curves, and different droop control curves are connected by inflection points. Furthermore, the control relationship between the DC bus voltage and the total grid-connected power at the grid connection point of the DC system of the photovoltaic and energy storage system with direct current power supply is determined according to the voltage-power droop control curve.

[0069] Specifically, the voltage-power droop control curve consists of two droop control curves, which are connected by an inflection point. The voltage and power values at the inflection point are key parameters, which determine the operating state of the rectifier under different load conditions.

[0070] The two droop control curves are the first droop control curve and the second droop control curve respectively. The slope of the first droop control curve is L1, and the corresponding DC bus voltage range of the photovoltaic and energy storage distribution system is (U0, U1], and the power range is [P0, P1). Let the droop control curve two be L2, and the corresponding DC bus voltage range is [U2, U0), and the power range is (P1, P2], where L1 > L2, U1 is the highest operating voltage allowed by the DC power grid, U2 is the lowest operating voltage allowed by the DC power grid, U0 is the inflection point voltage, P0 (0 is a number) is the power output by the rectifier when the operating voltage of the DC power grid is U2, P2 is the power output by the AC / DC converter when the operating voltage of the DC power grid is U1, P1 is the inflection point power, and the power transmitted by the rectifier to the DC power grid is recorded as the positive direction.

[0071] The dynamic adjustment of the inflection point voltage U0 and the inflection point power P1 in the present invention are important parameters:

[0072]

[0073] In the above formula, U1 and U2 are the upper limit and the lower limit of the voltage range of the direct current power supply distribution system, the coefficient a is the inflection point voltage adjustment coefficient, and the value range is 0.85 ≤ a ≤ 0.95. U(t) and I(t) respectively refer to the voltage and current at the grid connection point of the DC system of the photovoltaic and energy storage system with direct current power supply at time t, and the current I0 refers to the rated current at the grid connection point of the DC system of the photovoltaic and energy storage system with direct current power supply. The purpose of the above formula is to dynamically adjust the inflection point voltage based on the historical load level PT of the target building's past power load level within the unit time T.

[0074] The above formula has significant advantages compared with the traditional calculation method of the voltage-power droop control curve.

[0075] First, the above formula fully considers the historical load level of the PV-storage-DC-AC system in the target building during the calculation process. This is because the output level of PV power generation is affected by meteorological conditions and fluctuates greatly. During the historical unit time T, when the PV power generation system supplies power, it often deviates from the rated power supply. Specifically:

[0076] During the past unit time T, due to the instability of PV power generation level, there may be a difference between the power supply of the PV-storage-DC-AC system and the actual demand. This difference leads to fluctuations in the power load level.

[0077] During the past unit time T, the electric power at the grid connection point between the DC system of the PV-storage-DC-AC system and the AC grid in the target area will change due to the change of PV power generation level. This change directly affects the electric power input from the AC grid in the target area to the target building.

[0078] Due to historical load fluctuations, the supplementary input power level from the AC grid in the target area to the target building will also change accordingly. This change is dynamic and needs to be adjusted according to the actual situation.

[0079] The formula of the present invention realizes the dynamic adjustment of the inflection point voltage in the following way:

[0080] During the unit time T, the power load level PT is an important parameter in the inflection point voltage U0. Therefore, when PT changes, the inflection point voltage U0 will also be adjusted accordingly.

[0081] By dynamically adjusting the inflection point voltage, the voltage-power droop control curve can be adjusted more precisely. This adjustment method considers factors such as historical load fluctuations, changes in electric power at the grid connection point, and supplementary input power level, thereby improving the accuracy and adaptability of the voltage-power droop control curve.

[0082] In short, during the unit time T, the electric power input from the AC grid in the target area to the target building at the grid connection point between the DC system of the PV-storage-DC-AC system and the AC grid in the target area, and then dynamically adjust the inflection point voltage based on the supplementary input power level from the AC grid in the target area to the target building. The supplementary input power level will affect the power load level PT during the past unit time T. Since PT is a parameter in the inflection point voltage, it will affect the inflection point voltage U0. Therefore, based on the above formula, the way of dynamically adjusting the inflection point voltage can make the voltage-power droop control curve based on the inflection point voltage be adjusted more precisely. By dynamically adjusting the inflection point voltage, more precise voltage-power droop control is achieved, providing more efficient and reliable support for the energy management of the building.

[0083] At the same time, the calculation method of the inflection point power is as follows:

[0084] P1 = U0 × I0

[0085] By adding an inflection point to the control voltage-power droop control curve of the grid-connected transformer and setting the inflection point voltage as the DC limit voltage of the PV-storage DC-AC flexible system, when the power transmitted by the grid-connected transformer to the AC side approaches the rated power, the absolute value of the slope of the droop control curve is very high. When the operating voltage of the system reaches the inflection point, the adjustable power space of the grid-connected transformer is relatively low. At this time, the DC bus voltage will be much lower than the system-permitted DC limit voltage. At this time, even if there are fluctuations in the voltage of the DC conversion device of the PV modules in the PV-storage DC-AC flexible system, it is sufficient to cause the DC conversion device to change the operating maximum power tracking operation mode before the operating voltage of the DC grid reaches the DC limit voltage, preventing the system from having too high a voltage. At the same time, the adjustable power space of the grid-connected transformer is relatively low, enabling the PV modules to operate at a relatively high power level.

[0086] Therefore, through the coordinated control method of the present invention, if the grid-connected transformer fails, no additional power input is required, and the internal power self-sufficiency of the PV-storage DC-AC flexible system can be satisfied in a short time, achieving both the full utilization of PV resources and no overvoltage generation.

[0087] According to the voltage-power droop control curve, the control relationship between the DC bus voltage and the total grid-connected power at the grid connection of the DC system of the PV-storage DC-AC flexible system can be determined. By adjusting the output voltage of the rectifier, precise control of the DC bus voltage can be achieved. According to the voltage-power droop control curve, the output power of the rectifier is adjusted in real time to adapt to the load changes of the power grid.

[0088] By constructing the voltage-power droop control curve, it helps to achieve the stable operation of the power grid and the efficient utilization of energy.

[0089] By determining the control relationship between the DC bus voltage and power on the PV-storage DC-AC flexible system of the target building, power adjustment can be flexibly performed according to the change of the DC bus voltage.

[0090] Preferably, the constructed control relationship includes:

[0091] V d = V0 + ΔV(t) = V0 + L(x)·(P e0 -P r )

[0092]

[0093] In the formula, P e0is the benchmark value of the system grid-connected power prediction based on the daily power load prediction curve, Pr is the actual grid-connected power fed by the commercial power to the DC bus, V0 is the rated value of the DC bus voltage, and L(x) is the droop coefficient of the droop control curve; Vd is the current DC bus voltage, and ΔV(t) is the difference between the operating real-time load and the predicted load within time t;

[0094] In the formula, L1 and L2 are the slopes corresponding to the voltage intervals (U0, U1] and [U2, U0) respectively, and L1 > L2.

[0095] Preferably, the photovoltaic power prediction model is: P = L × S × α × Q; in the formula, P is the total predicted photovoltaic power, L is the total solar irradiance, S is the sum of the areas of all photovoltaic modules on the exterior wall of the building, α is the module conversion efficiency, and Q is the energy consumption conversion coefficient.

[0096] Integrate the photovoltaic crystalline silicon modules on the exterior wall of the building over time and sum them up to obtain the predicted photovoltaic power data in the short term in the future.

[0097] The following describes the specific acquisition methods of the parameters L and α in the power prediction model.

[0098] (1) In the formula, the acquisition method of L:

[0099] L = k(x·T h +y·T1+z·M+ΔL)

[0100] where, L——is the total solar irradiance (MJ / ㎡); ΔL is the change in the total solar irradiance; k is the daily solar radiation (MJ / ㎡ / day); T h is the daily maximum temperature (℃); T l is the daily minimum temperature (C), and M is the ultraviolet radiation intensity. x, y, and z are the weather coefficients in the power prediction model, and these weather coefficients can be preset according to empirical values or obtained by fitting historical data.

[0101] (2) In the formula, the acquisition method of α:

[0102] Extract the photovoltaic performance parameter data of the photovoltaic crystalline silicon modules on the exterior wall of the building according to the photovoltaic-storage-direct-flexible photovoltaic power generation modules (such as photovoltaic arrays), and calculate the energy conversion rate of the photovoltaic crystalline silicon modules on the exterior wall of the building according to the photovoltaic performance parameter data of the photovoltaic crystalline silicon modules on the exterior wall of the building:

[0103]

[0104] Among them, α is the power conversion efficiency of the photovoltaic module; Po is the rated output power of the photovoltaic module, which is obtained from the photovoltaic performance parameter data of the photovoltaic crystalline silicon module on the outer wall of the building; Pi is the sunlight intensity received by the photovoltaic module, which is obtained from the module information and solar irradiance. j(t) is the attenuation adjustment coefficient, which is the aging parameter curve determined in the aging experiment when the photovoltaic module leaves the factory.

[0105] Therefore, predict the power of the rooftop crystalline silicon module according to the solar radiation amount and the conversion rate of the crystalline silicon module on the outer wall of the building, and predict the total power generation P of the photovoltaic module according to the solar radiation amount and the conversion rate of the crystalline silicon module on the outer wall of the building.

[0106] Embodiment 2

[0107] This embodiment provides a control system for a photovoltaic-storage-direct-soft system, which is used to support the implementation of an integrated operation strategy of photovoltaic-storage-flexible load with multi-source coordination and multi-objective optimization.

[0108] The control system includes the following modules:

[0109] The data acquisition module is used to collect the real-time operation parameters of the photovoltaic power generation unit, energy storage unit and various load units in the system; in addition, it is also used to collect external environment parameters such as historical and current power load data, light intensity, and meteorological information of the target building within a preset time period.

[0110] The power load prediction module is used to establish a load-weather joint prediction model according to the collected power load data and light meteorological data, and generate a power load prediction curve for the future regulation period, providing prior data support for subsequent control optimization.

[0111] The control module is used to execute the multi-objective collaborative control strategy of the photovoltaic-storage-direct-soft system based on multi-source information such as the power load prediction curve and real-time operation status. Specifically, it includes judging whether the current system meets the flexible control conditions, and generating corresponding flexible control instructions when the conditions are met. This module also includes an overall coordination module, which is used to iteratively adjust the on-grid capacity of each load unit in real time based on the dynamic adjustment ability of the load equipment, realizing the coordinated scheduling and flexible response of system resources.

[0112] The execution module is used to receive the control instructions issued by the control module, and drive various power regulation devices, such as photovoltaic inverters, energy storage converters and load control devices, to complete operations such as adjusting the power output mode, controlling the charge and discharge of the energy storage, and setting the load power. This module further includes an energy storage control unit and a photovoltaic regulation unit inside, which respectively execute the orderly charge and discharge control of the energy storage system and the mode control of the photovoltaic output power.

[0113] A communication module, which is used to establish a two-way data interaction channel with the microgrid monitoring system and each power conversion device, so as to ensure the real-time nature of control instruction transmission and the integrity of operation data feedback.

[0114] An edge intelligent optimization module, which is used to be deployed on the edge nodes in the system area, to realize the acquisition of local load data, energy storage status, electricity price information and micro-meteorological data, and combine with the machine learning model in the embedded edge computing unit to predict the local load trend or fluctuation situation in a short time, and generate local response control instructions, so as to realize the fast and adaptive adjustment of local loads or energy storage devices.

[0115] Through the collaborative operation of the above modules, this control system realizes the integration of perception, prediction, decision-making and control of the photovoltaic-storage-flexible load system, and has the characteristics of strong real-time nature, flexible response, high control accuracy, etc., and is especially suitable for the dynamic load regulation scenarios of building-type or park-type photovoltaic-storage systems.

[0116] It should be noted that the control system of the photovoltaic-storage-direct-soft system in this embodiment can support the execution of each step of the control method in Embodiment 1, and each functional module can be correspondingly implemented according to the logical requirements of the control method, so as to realize the joint regulation of photovoltaic output, energy storage scheduling and flexible load operation status.

[0117] Further preferably, referring to Figure 3 , the control method of the photovoltaic-storage-direct-soft system further includes:

[0118] S6, according to the dynamic power regulation capacity on the load devices in the photovoltaic-storage-direct-soft system, iteratively adjust the preset on-grid capacity of each load device in real time.

[0119] In the current energy management field, the optimal control of the photovoltaic-storage-direct-soft system of the target building is particularly important. The present invention realizes the coordinated control of the bus voltage in the photovoltaic-storage-direct-soft system of the target building by evaluating the dynamic power regulation capacity on many load devices in the photovoltaic-storage-direct-soft system of the target building and iteratively optimizing the preset on-grid capacity regulation of each load device in real time. It realizes the continuous adjustment of the capacity regulation strategy of each load device, so that while meeting the actual needs, it can reduce energy consumption as much as possible.

[0120] In summary, the present invention proposes a control method and system for a photovoltaic-storage-direct-soft system based on a dynamic inflection point voltage regulation and droop control strategy. This method realizes the dynamic regulation and optimization of the system operation state by collecting the power load data within a preset time range and combining with the illumination meteorological information closely related to the DC bus voltage.

[0121] Specifically, first, by obtaining the real-time solar radiation amount and combining it with the conversion rate of the crystalline silicon components on the outer wall of the building, the power of the rooftop crystalline silicon components and the total power generation P of the photovoltaic components are respectively predicted, providing an initial reference for the load prediction and power scheduling of the system. At the same time, the present invention further introduces the corresponding relationship between the building load prediction data and the rated bus, and closely associates this information with the DC bus voltage as an important basis for subsequent system control.

[0122] In terms of obtaining the key prediction information of building energy consumption and the power supply side, the present invention includes but is not limited to the following: real-time monitoring and recording of the daily temperature changes to predict its actual impact on the building load; analyzing and predicting the power fluctuation of the photovoltaic power generation system to accurately grasp its output characteristics; collecting and analyzing historical load data to speculate on the future load change trend; real-time monitoring of the current capacity status of the energy storage device to provide a decision-making basis for subsequent power allocation and energy storage scheduling; and intelligently adjusting the operation strategy of the energy devices according to the time-of-use electricity price changes in the power market, thereby improving the economic efficiency of the system operation.

[0123] Then, in terms of the control strategy, the present invention proposes to set a dynamic inflection point voltage U0 in the droop control curve, and this voltage inflection point is used to reflect the mapping relationship between the grid-connected voltage regulation and the internal operating power of the system (especially the supply-demand balance within the photovoltaic-battery-direct-current-flexible system). When the system detects a deviation between the real-time power and the predicted optimal load value, that is, based on this dynamic inflection point U0, a response mechanism is started, and the droop control logic is used to automatically allocate the power output of each energy device connected to the DC bus. The system dynamically adjusts the power distribution of each device according to the flexible capacity potential of each device, realizing the rapid response and optimal coordination of the system power.

[0124] Through the above control method, the present invention not only realizes the refined allocation and high-efficiency operation within the photovoltaic-battery-direct-current-flexible system, but also effectively alleviates the possible impact on the regional power system during the grid connection process of the photovoltaic-battery-direct-current-flexible system, improves the rapid response ability of the system to load changes and the overall operation stability of the system, and significantly improves problems such as the lag of the control strategy and large bus voltage fluctuations existing in the prior art. At the same time, this solution provides a more efficient, flexible and economic control mechanism for building energy management, and has a wide application prospect.

[0125] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. It should be noted that although the steps are described in a specific order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of the present invention, it is not necessary for different steps to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these changes are all within the protection scope of the present invention. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A control method for a photovoltaic-storage-direct-current-soft system, characterized in that It includes the following steps: S1. Obtain the power load data of the target building within a preset time; S2. Obtain the illumination meteorological data within the preset time, including air temperature, illumination intensity, historical energy storage capacity level, historical electricity price, and photovoltaic power generation fluctuation characteristics; S3. Construct a short-term power load prediction curve and a photovoltaic power generation prediction model based on the power load data and the illumination meteorological data; S4. Based on the load prediction curve, construct a droop control relationship between the DC bus voltage and the total grid-connected power at the grid connection of the photovoltaic energy storage direct current flexible (PESDCF) system, including setting the inflection point voltage U0 and the droop coefficient L(x); S5. Based on the droop control relationship, adjust the operation of the photovoltaic power generation system and the energy storage system in real time according to the change of the DC bus voltage.

2. The control method of the optical storage direct current flexible system according to claim 1, wherein In the step S3, the power load prediction curve is a short-term prediction curve constructed within a rolling time window, and the power load data includes the power load level PT within a unit time T and the load curve PnT within an nT historical time period.

3. The control method of the optical storage direct-soft system according to claim 1, characterized in that In the step S4, constructing the droop control relationship between the DC bus voltage and the total grid-connected power at the grid connection of the PESDCF system includes: V d = V0 + ΔV(t) = V0 + L(x)·(P0 - P r ) Wherein, P0 is the system grid connection power prediction reference value based on the daily power load prediction curve, and P r is the grid connection power actually fed by the commercial power to the DC bus, V0 is the rated value of the DC bus voltage, and L(x) is the droop coefficient of the droop control curve; V d is the current DC bus voltage, and ΔV(t) is the difference between the operating real-time load and the predicted load within time t.

4. The control method of the optical storage direct current flexible system according to claim 3, characterized in that Construct a voltage-power droop control curve composed of a first droop control curve and a second droop control curve, where the first droop control curve is applicable to the voltage range (U0, U1] and the power range [P0, P1), and its slope is L1; The second droop control curve is applicable to the voltage range [U2, U0) and the power range (P1, P2], and its slope is L2, and L1 > L2. By judging the interval where the DC bus voltage Vd is located, determine the adopted droop coefficient L(x). Among them, the inflection point voltage U0 is dynamically adjusted according to the following formula: In the formula: U1 and U2 are respectively the upper limit value and the lower limit value allowed for the DC bus voltage, a is an adjustment coefficient, satisfying 0.85 ≤ a ≤ 0.95, U(t) and I(t) are respectively the DC bus voltage and current within a unit time T, PT is the predicted value of the building load within a unit time T, I0 is the rated current at the grid connection of the PESDCF system, and based on the control curve and the dynamic U0, precise control of the grid-connected power and the bus voltage is realized.

5. The control method of the optical storage direct current and flexible system according to claim 4, characterized in that The inflection point power P1 is determined by the following formula: P1 = U0 × I0, where I0 is the rated current of the system at the grid connection; Current bus voltage V d = V0 + ΔV(t) = V0 + L(x)·(P e0 - P r ); Where, V0 is the rated value of the bus voltage, and P e0 is the grid-connected power reference value obtained based on the daily power load forecast, and P r is the actual power fed into the bus by the mains, and L(x) is the selected droop coefficient.

6. The control method of the optical storage direct current flexible system according to claim 5, wherein The value of the sag coefficient L(x) is as follows: where L1 and L2 are the slopes corresponding to the voltage ranges (U0, U1] and [U2, U0), respectively, and L1 > L2.

7. The control method of the optical storage direct current flexible system according to claim 1, characterized in that In the step S3, the photovoltaic power generation prediction model is: P = L × S × α × Q; in the formula, P is the total predicted photovoltaic power generation, L is the total solar irradiance, S is the sum of the areas of all photovoltaic modules on the outer wall of the building, α is the module conversion efficiency, and Q is the energy consumption conversion coefficient.

8. The control method of the optical storage direct current flexible system according to claim 7, characterized in that The total solar irradiance L = k(x·T h + y·T1 + z·M + ΔL); where L is the total solar irradiance; ΔL is the change in the total solar irradiance; k is the daily solar radiation); T h is the daily maximum temperature; Tl is the daily minimum temperature, M is the ultraviolet radiation intensity, and x, y, and z are weather coefficients in the power prediction model.

9. A control system for a photovoltaic-storage-direct-current flexible system, characterized in that, It includes: A data acquisition module, used to collect the real-time operation parameters of the photovoltaic power generation unit, the energy storage unit, and the load unit, as well as the power load data and the illumination meteorological data of the target building within a preset time; A power load prediction module, used to generate a power load prediction curve according to the load data and the illumination meteorological data; A control module, used to execute the PESDCF system control method according to any one of claims 1 to 8 based on the prediction curve; An execution module, used to drive the power adjustment device to perform a power adjustment operation according to the control instruction of the control module. A communication module, which is used for data interaction with the microgrid monitoring system and each power conversion device; An edge intelligent optimization module, which is used to be deployed in the system area sub-nodes, collect local load data, energy storage status, electricity price and micro-meteorological data, execute machine learning model prediction through the edge computing unit, and generate local response control instructions to adjust the operation status of the load or energy storage device in real time.

10. The control system of the optical storage direct current flexible system according to claim 9, characterized in that, The control module further includes: A judgment unit and an instruction generation unit, which are used to judge whether the flexible control condition is satisfied currently and generate flexible control instructions; An overall coordination module, which is used to iteratively adjust the online capacity of each load device in real time according to the dynamic power regulation ability of the load devices in the optical storage direct current flexible (OSDCF) system; The execution module includes an energy storage control unit and a photovoltaic regulation unit, which are respectively used to control the energy storage charge and discharge and the photovoltaic output mode.

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