Intelligent scheduling control method, system and equipment of optical storage hybrid inverter and medium

Abstract

The invention provides an intelligent scheduling control method, system and device of an optical storage hybrid inverter, and a medium, and belongs to the technical field of new energy power systems.The method comprises the steps that a scheduling controller collects the output power of a photovoltaic battery, the electric quantity state of an energy storage battery, the power grid electricity price and the load power; generating a photovoltaic power prediction value and a load prediction value by using the prediction model; solving an optimal power distribution scheme by taking economy optimization as a target to obtain photovoltaic side target power, energy storage side target power and power grid interaction target power; the duty ratio of the photovoltaic side bidirectional DC / DC converter is adjusted according to the photovoltaic side target power to achieve maximum power point tracking of the photovoltaic cell, the duty ratio of the energy storage side bidirectional DC / DC converter is adjusted according to the energy storage side target power to achieve charging and discharging power control of the energy storage cell, and the output current of the grid-connected inverter is adjusted according to the power grid interaction target power. According to the invention, efficient and intelligent control of the optical storage hybrid inverter can be realized.

Description

Technical Field

[0001] The present application belongs to the technical field of new energy power systems, and specifically relates to an intelligent dispatching control method, system, equipment and medium for a photovoltaic and energy storage hybrid inverter. Background Art

[0002] With the development of new energy, photovoltaic power generation is increasingly widely used. However, photovoltaic power generation is greatly affected by natural conditions and has obvious intermittent and volatile characteristics, which means that when photovoltaic power generation is connected to the power grid, it will impact the stable operation of the power grid, such as voltage fluctuations, frequency offsets, etc. To solve these problems, a photovoltaic storage hybrid system combining energy storage system with photovoltaic power generation system has emerged.

[0003] As the core equipment connecting photovoltaic cells, energy storage batteries and power grids, the scheduling and control method of the photovoltaic storage hybrid inverter plays a decisive role in the performance of the entire photovoltaic storage hybrid system. There are many problems with the existing scheduling and control methods of photovoltaic storage hybrid inverters. First, some control methods are only based on simple rules for scheduling, such as fixed charging and discharging time settings or energy storage control only according to the size of photovoltaic power. This method cannot adapt to complex and changeable actual working conditions, such as differences in meteorological conditions in different regions, dynamic changes in loads, and real-time fluctuations in grid electricity prices, making it difficult to give full play to the advantages of photovoltaic storage hybrid systems. Secondly, although some control methods take actual working conditions into consideration, they are seriously insufficient in comprehensively coordinating the relationship between photovoltaics, energy storage and power grids. For example, when photovoltaic power is in excess, energy storage charging is not reasonably and safely performed, resulting in serious light abandonment. When the load is at peak and photovoltaic power is insufficient, it is not possible to optimally choose whether to discharge from energy storage or purchase electricity from the grid, resulting in energy waste and increased system operating costs. Thirdly, when dealing with sudden failures and abnormal situations, the response speed is slow. For example, when the power grid fails or the energy storage battery is overcharged or over-discharged, it is impossible to take effective protection measures and adjustment strategies in time, making it difficult to ensure the stable operation of the system, which may cause equipment damage or even power outages, poor user experience and even economic losses. Summary of the invention

[0004] In a first aspect, an embodiment of the present application provides an intelligent dispatching control method for a photovoltaic-storage hybrid inverter, wherein the photovoltaic-storage hybrid inverter includes a photovoltaic-side DC / DC converter, a storage-side bidirectional DC / DC converter, a grid-connected inverter, and a dispatching controller; The method comprises the following steps: S1. The dispatch controller collects the output power of photovoltaic cells, the power status of energy storage batteries, the power price of the power grid and the load power, and uses the prediction model to generate photovoltaic power prediction values ​​and load prediction values; S2. Based on the photovoltaic power prediction value and the load prediction value, the optimal power allocation scheme is solved with the goal of economic optimization, and the photovoltaic side target power, the energy storage side target power and the grid interaction target power are obtained; S3. According to the target power of the photovoltaic side, the duty cycle of the bidirectional DC / DC converter on the photovoltaic side is adjusted to realize the maximum power point tracking of the photovoltaic cell. According to the target power of the energy storage side, the duty cycle of the bidirectional DC / DC converter on the energy storage side is adjusted to realize the charging and discharging power control of the energy storage battery. According to the target power of the grid interaction, the output current of the grid-connected inverter is adjusted.

[0005] Furthermore, the specific steps of step S1 are as follows: S11. Collect the output power and meteorological data of photovoltaic cells, the power status, full power and temperature of energy storage batteries, the voltage, frequency and electricity price of the power grid, and the power consumption of loads; S12. Filtering and normalizing the collected data; S13. Based on the historical photovoltaic cell output power and meteorological data, the photovoltaic power within a set time period in the future is predicted using the LSTM neural network as the photovoltaic power prediction value; S14. Based on the historical load power consumption, the ARIMA model is used to predict the load demand power in the future set time period as the load prediction value.

[0006] Furthermore, the specific steps of step S2 are as follows: S21. Construct the objective function with the minimum grid power purchase cost, battery charge and discharge protection, grid fluctuation suppression and dynamic electricity price response optimization as the target sub-items, and set the power balance constraint conditions, energy storage battery constraint conditions and grid interaction constraint conditions; S22. Adjust the weight coefficient of each target sub-item in the objective function according to real-time demand, use the model predictive control algorithm to solve the objective function, initialize the target period and time window, and solve the optimal target power allocation sequence of the target period according to the time window.

[0007] Furthermore, in step S21, the objective function is constructed as follows:

[0008] in, is the grid electricity price, Purchase power for the grid, is the maximum power state of the energy storage battery, Real-time power status of energy storage battery; , represents the maximum electricity price, Indicates the minimum electricity price, Indicates the charging or discharging power of the energy storage battery. Represents the energy storage battery power penalty coefficient, represents the penalty coefficient of power grid fluctuation, Indicates the dynamic electricity price response fluctuation coefficient; The constraints are as follows: Power balance constraints:

[0009] in, is the output power of the photovoltaic cell, The charging and discharging power of the energy storage battery, Purchase power for the grid, The power used by the load; Energy storage battery constraints:

[0010]

[0011] in, It is the real-time power status of the energy storage battery. is the lowest state of charge of the energy storage battery. is the maximum state of charge of the energy storage battery, To charge or discharge the energy storage battery, The maximum charging or discharging power of the energy storage battery; Grid interaction constraints:

[0012] in, Purchase power for the grid, The maximum power that can be purchased by the power grid.

[0013] Furthermore, the specific steps of step S22 are as follows: S221. Compare the real-time power status of the energy storage battery and the maximum power state of the energy storage battery , and in Less than , but when the difference between the two is less than the set threshold, the energy storage battery power penalty coefficient is increased in a preset manner ; S222. When the frequency of the power grid at adjacent moments is greater than the set threshold, the power grid power fluctuation penalty coefficient is increased in a preset manner ; S223. Initialize the target time period T as the prediction time domain and the time window N as the control time domain; S224. Obtain the photovoltaic power forecast value and load forecast value within the target period T, use the numerical optimization algorithm to solve the objective function within the time window [t, t+N], and obtain the optimal target power allocation sequence ; Among them, t is the time in the target period T, k is the time in the time window, represents the target power of the photovoltaic side at time k, represents the target power of the energy storage side at time k, represents the grid interaction target power at time k; S225. Verify whether the optimal target power allocation sequence as a solution satisfies the power balance constraint, the energy storage battery constraint, and the grid interaction constraint; If yes, go to step S226; If not, proceed to step S227; S226. Verify whether the following conditions are met: C grid ( k )≥C high And P bat ( k )>0 or C grid ( k )≤C low And P bat ( k )<0 Among them, C high represents the upper threshold of the power grid electricity price, C low Indicates the lower limit threshold of the power grid electricity price; If yes, force setting P bat ( k )=0, solve again and return to step S224; If not, proceed to step S3; S227. Correct the objective function using the penalty function method and solve it again, and return to step S224.

[0014] Furthermore, in step S224, the objective function is solved by the numerical optimization algorithm as follows: The numerical optimization algorithm is a particle swarm optimization algorithm; Initialize as the target power allocation sequence combination The position of the particle group; Calculate the objective function value of each particle and update the individual optimal solution and the global optimal solution; Update the particle position according to the following speed formula until the objective function value converges;

[0015] in, Indicates the update speed, represents the inertia weight, c 1 represents the individual learning factor, c 2 represents the group learning factor, r 1 、r2 Represents a random number between 0 and 1, p best represents the optimal historical position of the individual particle, g best represents the global optimal position of the group; The specific steps of correcting the objective function by the penalty function method in step S227 are as follows: Calculate the constraint violation g corresponding to the i-th constraint condition i (x); The correction function is constructed as follows: Corrected objective function = original objective function +

[0016] in, is the penalty factor for the i-th constraint.

[0017] Furthermore, the specific steps of step S3 are as follows: S31. Calculate the current photovoltaic output power P pv (k) and photovoltaic target power Deviation ΔP pv (k) Use the perturbation observation method or conductance increment method to adjust the duty cycle D of the bidirectional DC / DC converter on the photovoltaic side. pv (k) makes ΔP pv (k) is less than a set threshold; S32. Determine the target power of the energy storage side size; like >0, it is judged as charging state; Calculate the charging current ,in is the voltage of the energy storage battery; Adjusting the duty cycle of the bidirectional DC / DC converter on the energy storage side through the PWM signal , so that the actual charging current track ; like <0, determine the discharge state: Calculate the discharge current ,in is the voltage of the energy storage battery; By adjusting the duty cycle of the bidirectional DC / DC converter on the energy storage side Realize constant current or constant power discharge; S33. Determine the target power of grid interaction size; like >0, it is determined to be purchasing electricity from the grid; Calculate the grid current amplitude , controlling the input current amplitude from the grid to the grid-connected inverter according to the grid-connected current amplitude, and controlling the phase to be synchronized with the grid voltage; like <0, it is determined to sell electricity to the grid; Calculate the inverter output current amplitude , the output current amplitude of the grid-connected inverter to the grid is controlled according to the output current amplitude of the inverter, and the phase is controlled to be opposite to the grid voltage.

[0018] In a second aspect, the embodiment of the present application also provides an intelligent dispatching control system for a photovoltaic-storage hybrid inverter, wherein the photovoltaic-storage hybrid inverter includes a photovoltaic-side DC / DC converter, a storage-side bidirectional DC / DC converter, and a grid-connected inverter; The system comprises: The data collection and demand forecasting module is used to collect the output power of photovoltaic cells, the power status of energy storage batteries, the power price of the power grid and the load power, and use the forecasting model to generate the photovoltaic power forecast value and the load forecast value; The target solving module is used to solve the optimal power allocation scheme based on the photovoltaic power prediction value and the load prediction value, with the goal of optimal economic efficiency, and obtain the photovoltaic side target power, the energy storage side target power and the grid interaction target power; The power adjustment module is used to adjust the duty cycle of the bidirectional DC / DC converter on the photovoltaic side according to the target power on the photovoltaic side to achieve maximum power point tracking of the photovoltaic cell, adjust the duty cycle of the bidirectional DC / DC converter on the energy storage side according to the target power on the energy storage side to achieve charging and discharging power control of the energy storage battery, and adjust the output current of the grid-connected inverter according to the grid interaction target power.

[0019] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the intelligent scheduling control method for the photovoltaic-storage hybrid inverter as described in the first aspect are implemented.

[0020] In a fourth aspect, an embodiment of the present application further provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the intelligent scheduling control method for the photovoltaic-storage hybrid inverter as described in the first aspect are implemented.

[0021] It can be seen from the above technical solutions that this application has the following advantages: The intelligent dispatching control method, system, device and medium of the photovoltaic-storage hybrid inverter provided in this application realizes the optimal power distribution between photovoltaic, energy storage and power grid through intelligent dispatching control, improves energy utilization efficiency, reduces energy waste, effectively utilizes photovoltaic power generation, and reduces dependence on traditional power grids; with the goal of optimal economic efficiency, it takes into account multiple factors such as the power purchase cost of the power grid, the protection of energy storage battery charging and discharging, the suppression of power grid fluctuations, and the optimization of dynamic electricity price response, and can reasonably arrange the charging and discharging of energy storage batteries in different electricity price periods, reducing the operating cost of the system. It can monitor parameters such as grid voltage and frequency, and adjust power distribution according to grid fluctuations, effectively suppress grid fluctuations, and enhance the adaptability of photovoltaic-storage hybrid inverters in complex power grid environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the description will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 It is a flow chart of the intelligent dispatching control method of the photovoltaic-storage hybrid inverter of the present invention.

[0024] Figure 2 Schematic diagram of the intelligent dispatching control system of the photovoltaic-storage hybrid inverter of the present invention. DETAILED DESCRIPTION

[0025] In the specific steps of the intelligent dispatching control method of the photovoltaic storage hybrid inverter, which will be described in detail below, various embodiments of the present disclosure will be described more comprehensively. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0026] For example, with the development of new energy, the scope of use of photovoltaic power generation is also expanding. However, photovoltaic power generation is highly dependent on natural conditions and exhibits significant intermittent and volatile characteristics. This makes it difficult for photovoltaic power generation to be integrated into the power grid, which brings many challenges to the stable operation of the power grid, such as voltage fluctuations and frequency deviations. In order to effectively solve the above problems, the photovoltaic storage hybrid system came into being, which combines the energy storage system with the photovoltaic power generation system.

[0027] The photovoltaic storage hybrid inverter is a key device that connects photovoltaic cells, energy storage batteries and power grids. Its dispatching and control method has a vital impact on the performance of the entire photovoltaic storage hybrid system. However, the current dispatching and control methods of photovoltaic storage hybrid inverters have many defects. On the one hand, some control methods only perform dispatching operations based on relatively simple rules, such as pre-setting fixed charging and discharging time, or simply implementing energy storage control based on the size of photovoltaic power. This dispatching method is powerless in the face of complex and changeable actual working conditions. For example, it is difficult to adapt to the differences in meteorological conditions in different regions, the dynamic changes in loads, and the real-time fluctuations in grid electricity prices, and it is impossible to fully tap the advantages of the photovoltaic storage hybrid system. On the other hand, although some control methods take actual working conditions into consideration, there are obvious shortcomings in the comprehensive coordination of the relationship between photovoltaics, energy storage and the grid. For example, when photovoltaic power is in excess, energy storage charging cannot be carried out reasonably and safely, resulting in a more serious phenomenon of abandonment of light; when the load is at a peak and the photovoltaic power is insufficient, it is impossible to optimally choose whether to discharge from energy storage or purchase electricity from the grid, resulting in energy waste and an increase in system operating costs. In addition, its response speed is relatively slow when dealing with sudden failures and abnormal situations. For example, when a power grid failure occurs, or the energy storage battery has problems such as overcharging or over-discharging, it is impossible to take effective protection measures and adjustment strategies in time, making it difficult to ensure the stable operation of the system, which may cause equipment damage or even power outages, affecting not only the user experience but also causing economic losses.

[0028] In view of the above problems, this embodiment provides an intelligent dispatching control method for a photovoltaic hybrid inverter. By integrating the photovoltaic side DC / DC converter, the energy storage side bidirectional DC / DC converter, the grid-connected inverter and the dispatching controller, the hardware coordinated control of the photovoltaic and energy storage hybrid system is realized. The bidirectional DC / DC converter is used to improve the charging and discharging efficiency and reduce energy loss. The duty cycle adjustment is used to achieve rapid power tracking and adapt to dynamic load and electricity price changes.

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 The figure is a flow chart of an intelligent dispatching control method of a photovoltaic-storage hybrid inverter in a specific embodiment, wherein the photovoltaic-storage hybrid inverter includes a photovoltaic-side DC / DC converter, a storage-side bidirectional DC / DC converter, a grid-connected inverter, and a dispatching controller; The method comprises the following steps: S1. The dispatch controller collects the output power of photovoltaic cells, the power status of energy storage batteries, the power price of the power grid and the load power, and uses the prediction model to generate photovoltaic power prediction values ​​and load prediction values; It should be noted that by collecting the output power of photovoltaic cells, the power status of energy storage batteries, the power price of the power grid and the load power, data support is provided for the subsequent power allocation solution. At the same time, the use of the prediction model to generate photovoltaic power prediction values ​​and load prediction values ​​can predict the future energy supply and demand in advance, providing a basis for optimizing the power allocation plan. S2. Based on the photovoltaic power prediction value and the load prediction value, the optimal power allocation scheme is solved with the goal of economic optimization, and the photovoltaic side target power, the energy storage side target power and the grid interaction target power are obtained; It should be noted that, with the goal of economic optimization, the target power of the photovoltaic side, the target power of the energy storage side, and the target power of grid interaction are solved, and the optimal scheduling of the photovoltaic and energy storage hybrid inverters under different operating conditions is achieved, which reduces the operating cost and improves the energy utilization efficiency. S3. According to the target power of the photovoltaic side, the duty cycle of the bidirectional DC / DC converter on the photovoltaic side is adjusted to realize the maximum power point tracking of the photovoltaic cell, according to the target power of the energy storage side, the duty cycle of the bidirectional DC / DC converter on the energy storage side is adjusted to realize the charge and discharge power control of the energy storage battery, and according to the target power of the grid interaction, the output current of the grid-connected inverter is adjusted; It should be noted that according to the target power obtained by the solution, the corresponding parameters of the bidirectional DC / DC converter on the photovoltaic side, the bidirectional DC / DC converter on the energy storage side and the grid-connected inverter are adjusted respectively, so as to realize the maximum power point tracking of the photovoltaic cell, the charge and discharge power control of the energy storage battery and the output current regulation of the grid-connected inverter, ensure the implementation of the power distribution plan, improve the control accuracy and operation efficiency, and enable the photovoltaic and storage hybrid inverter to operate according to the optimal plan.

[0031] This embodiment can achieve efficient and intelligent control of the photovoltaic and energy storage hybrid inverter through data collection and prediction, power allocation solution solution and intelligent scheduling control process of power regulation.

[0032] Further, as a refinement and expansion of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process in this embodiment, another intelligent scheduling control method of a photovoltaic storage hybrid inverter is provided, wherein the photovoltaic storage hybrid inverter includes a photovoltaic side DC / DC converter, a storage side bidirectional DC / DC converter, a grid-connected inverter and a scheduling controller; The method comprises the following steps: S1. The dispatch controller collects the output power of the photovoltaic cell, the power state of the energy storage battery, the power price of the grid and the load power, and uses the prediction model to generate the photovoltaic power prediction value and the load prediction value; the specific steps of step S1 are as follows: S11. Collect the output power and meteorological data of photovoltaic cells, the power status, full power and temperature of energy storage batteries, the voltage, frequency and electricity price of the power grid, and the power consumption of loads; Exemplarily, the meteorological data includes air temperature and light intensity; S12. Filtering and normalizing the collected data; Specifically, the Kalman filter method or the sliding average method is used to eliminate noise for filtering and normalize the data to the range of [0,1]; S13. Based on the historical photovoltaic cell output power and meteorological data, the photovoltaic power within a set time period in the future is predicted using the LSTM neural network as the photovoltaic power prediction value; Specifically, the photovoltaic power prediction value is solved as follows:

[0033] in, G ( k ) is the predicted value of light intensity, T ( k ) is the predicted temperature value; S14. Based on the historical load power consumption, the ARIMA model is used to predict the load demand power in the future set time period as the load prediction value; Specifically, the load prediction value is solved as follows:

[0034] It should be noted that the accuracy of the data is improved by collecting meteorological data, the full power and temperature of the energy storage battery, and filtering and normalizing the collected data; the photovoltaic power and load demand power are predicted through the LSTM neural network and ARIMA model, which improves the prediction accuracy, provides a more accurate basis for solving the power allocation plan, and enhances the intelligence level of the system; S2. Based on the photovoltaic power prediction value and the load prediction value, the optimal power allocation scheme is solved with the goal of economic optimization to obtain the photovoltaic side target power, the energy storage side target power and the grid interaction target power; the specific steps of step S2 are as follows: S21. Construct an objective function with the minimum grid power purchase cost, battery charge and discharge protection, grid fluctuation suppression and dynamic electricity price response optimization as the target sub-items, and set power balance constraints, energy storage battery constraints and grid interaction constraints; in step S21, the objective function is constructed as follows:

[0035] in, is the grid electricity price, Purchase power for the grid, is the maximum power state of the energy storage battery, Real-time power status of energy storage battery; , represents the maximum electricity price, Indicates the minimum electricity price, Indicates the charging or discharging power of the energy storage battery. Represents the energy storage battery power penalty coefficient, represents the penalty coefficient of power grid fluctuation, Indicates the dynamic electricity price response fluctuation coefficient; Specifically, It can prevent the energy storage battery from overcharging / over-discharging. Can smooth grid-connected power; through Encourage increasing energy storage charging and discharging power during periods of high electricity price differences; The larger the price difference is, the more it encourages energy storage charging and discharging, which is converted into a minimization target through the negative sign; The constraints are as follows: Power balance constraints:

[0036] in, is the output power of the photovoltaic cell, The charging and discharging power of the energy storage battery, Purchase power for the grid, The power used by the load; Energy storage battery constraints:

[0037]

[0038] in, It is the real-time power status of the energy storage battery. is the lowest state of charge of the energy storage battery. is the maximum state of charge of the energy storage battery, To charge or discharge the energy storage battery, The maximum charging or discharging power of the energy storage battery; Grid interaction constraints:

[0039] in, Purchase power for the grid, The maximum power that can be purchased by the power grid; S22. According to the real-time demand, the weight coefficient of each target sub-item in the target function is adjusted, the target function is solved by the model predictive control algorithm, the target period and time window are initialized, and the optimal target power allocation sequence of the target period is solved according to the time window rolling; the specific steps of step S22 are as follows: S221. Compare the real-time power status of the energy storage battery and the maximum power state of the energy storage battery , and in Less than , but when the difference between the two is less than the set threshold, the energy storage battery power penalty coefficient is increased in a preset manner ; S222. When the frequency of the power grid at adjacent moments is greater than the set threshold, the power grid power fluctuation penalty coefficient is increased in a preset manner ; S223. Initialize the target time period T as the prediction time domain and the time window N as the control time domain; For example, T =24 hours, N =4 hours; S224. Obtain the photovoltaic power forecast value and load forecast value within the target period T, use the numerical optimization algorithm to solve the objective function within the time window [t, t+N], and obtain the optimal target power allocation sequence ; Among them, t is the time in the target period T, k is the time in the time window, represents the target power of the photovoltaic side at time k, represents the target power of the energy storage side at time k, represents the grid interaction target power at time k; The objective function is solved by numerical optimization algorithm as follows: The numerical optimization algorithm is a particle swarm optimization algorithm; Initialize as the target power allocation sequence combination The position of the particle group; Calculate the objective function value of each particle and update the individual optimal solution and the global optimal solution; Update the particle position according to the following speed formula until the objective function value converges;

[0040] in, Indicates the update speed, represents the inertia weight, c 1 represents the individual learning factor, c 2 represents the group learning factor, r 1 、r 2Represents a random number between 0 and 1, p best represents the optimal historical position of the individual particle, g best represents the global optimal position of the group; Specifically, the numerical optimization algorithm selected the particle swarm optimization algorithm and gave a speed formula for particle position update, which provided algorithm support for solving the optimal target power allocation sequence and improved the efficiency and accuracy of the solution. It should be noted that the numerical optimization algorithm can use the quadratic programming QP algorithm to solve the optimal target power sequence in addition to the particle swarm optimization algorithm; S225. Verify whether the optimal target power allocation sequence as a solution satisfies the power balance constraint, the energy storage battery constraint, and the grid interaction constraint; If yes, go to step S226; If not, proceed to step S227; S226. Verify whether the following conditions are met: C grid ( k )≥C high And P bat ( k )>0 or C grid ( k )≤C low And P bat ( k )<0 Among them, C high represents the upper threshold of the power grid electricity price, C low Indicates the lower limit threshold of the power grid electricity price; If yes, force setting P bat ( k )=0, solve again and return to step S224; If not, proceed to step S3; S227. Correct the objective function by penalty function method and solve it again, and return to step S224; The specific steps of correcting the objective function by penalty function method are as follows: Calculate the constraint violation g corresponding to the i-th constraint condition i (x); The correction function is constructed as follows: Corrected objective function = original objective function +

[0041] in, is the penalty factor of the i-th constraint; For example, if the solution violates the SOC constraint (such as SOC ( t +1)> SOCmax ), then the objective function is adjusted by the penalty function method: Corrected objective function = original objective function +

[0042] Specifically, when the optimal target power allocation sequence does not meet the constraint conditions, the constraint violation amount is calculated and a correction function is constructed, and the objective function is corrected and then solved again, thus ensuring the feasibility and accuracy of the solution result. It should be noted that multiple target sub-items based on minimizing the cost of power purchase from the power grid, battery charge and discharge protection, power grid fluctuation suppression, and dynamic electricity price response optimization are taken into consideration in the objective function, making the objective function comprehensive and reasonable; by setting power balance constraints, energy storage battery constraints, and power grid interaction constraints, the feasibility and safety of the power allocation scheme are ensured, and the problems of overcharging and over-discharging of energy storage batteries, power grid fluctuations, and power purchases at peak electricity prices and power sales at low electricity prices are avoided, thereby extending the service life of energy storage batteries, maintaining grid stability, and dynamically responding to electricity prices; S3. According to the photovoltaic side target power, the duty cycle of the bidirectional DC / DC converter on the photovoltaic side is adjusted to realize the maximum power point tracking of the photovoltaic cell, and according to the energy storage side target power, the duty cycle of the bidirectional DC / DC converter on the energy storage side is adjusted to realize the charge and discharge power control of the energy storage battery, and the output current of the grid-connected inverter is adjusted according to the grid interaction target power; the specific steps of step S3 are as follows: S31. Calculate the current photovoltaic output power P pv (k) and photovoltaic target power Deviation ΔP pv (k) Use the perturbation observation method or conductance increment method to adjust the duty cycle D of the bidirectional DC / DC converter on the photovoltaic side. pv (k) makes ΔP pv (k) is less than a set threshold; Specifically, when using the perturbation and observation method, the duty cycle adjustment step is Adaptive adjustment based on changes in illumination:

[0043] Among them, k is the dynamic coefficient; S32. Determine the target power of the energy storage side size; like >0, it is judged to be in charging state; Calculate the charging current ,in is the voltage of the energy storage battery; Adjusting the duty cycle of the bidirectional DC / DC converter on the energy storage side through PWM signal , so that the actual charging current track ; like <0, determine the discharge state: Calculate the discharge current ,in is the voltage of the energy storage battery; By adjusting the duty cycle of the bidirectional DC / DC converter on the energy storage side Realize constant current or constant power discharge; S33. Determine the target power of grid interaction size; like >0, it is determined to be purchasing electricity from the grid; Calculate the grid current amplitude , controlling the input current amplitude from the grid to the grid-connected inverter according to the grid-connected current amplitude, and controlling the phase to be synchronized with the grid voltage; like <0, it is determined to sell electricity to the grid; Calculate the inverter output current amplitude , controlling the output current amplitude of the grid-connected inverter to the grid according to the output current amplitude of the inverter, and controlling the phase to be opposite to the grid voltage; Specifically, the duty cycle of the bidirectional DC / DC converter on the photovoltaic side is adjusted by adopting the perturbation observation method or the conductance increment method, the charging or discharging state is judged and controlled accordingly according to the size of the target power on the energy storage side, and the purchase of electricity from the grid or the sale of electricity to the grid is judged according to the size of the grid interaction target power and the output current of the grid-connected inverter is controlled, so that the power regulation process is accurate and reliable.

[0044] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0045] like Figure 2 As shown, the following is an embodiment of the intelligent scheduling and control system of the photovoltaic and storage hybrid inverter provided by the embodiment of the present disclosure. The system and the intelligent scheduling and control method of the photovoltaic and storage hybrid inverter of the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the intelligent scheduling and control system of the photovoltaic and storage hybrid inverter, please refer to the embodiment of the intelligent scheduling and control method of the photovoltaic and storage hybrid inverter mentioned above.

[0046] The photovoltaic-storage hybrid inverter includes a photovoltaic-side DC / DC converter, an energy storage-side bidirectional DC / DC converter and a grid-connected inverter; The system comprises: The data collection and demand forecasting module is used to collect the output power of photovoltaic cells, the power status of energy storage batteries, the power price of the power grid and the load power, and use the forecasting model to generate the photovoltaic power forecast value and the load forecast value; The target solving module is used to solve the optimal power allocation scheme based on the photovoltaic power prediction value and the load prediction value, with the goal of optimal economic efficiency, and obtain the photovoltaic side target power, the energy storage side target power and the grid interaction target power; The power adjustment module is used to adjust the duty cycle of the bidirectional DC / DC converter on the photovoltaic side according to the target power on the photovoltaic side to achieve maximum power point tracking of the photovoltaic cell, adjust the duty cycle of the bidirectional DC / DC converter on the energy storage side according to the target power on the energy storage side to achieve charging and discharging power control of the energy storage battery, and adjust the output current of the grid-connected inverter according to the grid interaction target power.

[0047] This embodiment realizes intelligent scheduling and control of the photovoltaic-storage hybrid inverter through the interactive collaboration of the data collection and demand prediction module, the target solution module and the power adjustment module.

[0048] The intelligent dispatching control method of the photovoltaic storage hybrid inverter provided in the embodiment of the present application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiment of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange different components. In the embodiment of the present invention, the electronic device includes but is not limited to a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0049] The electronic device may include a processor, an external memory interface, an internal memory, a universal serial bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, buttons, a camera, a display, and a SIM card interface, etc.

[0050] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0051] The processor may include one or more processing units, for example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0052] The processor can be the nerve center and command center of the electronic device. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0053] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. The memory may store instructions or data that the processor has just used or is cyclically used. If the processor needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor, and thus improves system efficiency.

[0054] The above-mentioned electronic device realizes the dispatching controller of the intelligent dispatching control method of the photovoltaic-storage hybrid inverter of the present application, which collects the output power of the photovoltaic cell, the power state of the energy storage battery, the grid electricity price and the load power, and uses the prediction model to generate the photovoltaic power prediction value and the load prediction value; based on the photovoltaic power prediction value and the load prediction value, the optimal power allocation scheme is solved with the economic optimization as the goal, and the photovoltaic side target power, the energy storage side target power and the grid interaction target power are obtained; according to the photovoltaic side target power, the duty cycle of the photovoltaic side bidirectional DC / DC converter is adjusted to realize the maximum power point tracking of the photovoltaic cell, according to the energy storage side target power, the duty cycle of the energy storage side bidirectional DC / DC converter is adjusted to realize the charging and discharging power control of the energy storage battery, and according to the grid interaction target power, the output current of the grid-connected inverter is adjusted. The technical solution achieves the intelligent dispatching control process through data collection and prediction, power allocation scheme solution and power adjustment, which can achieve the beneficial effect of efficient and intelligent control of the photovoltaic-storage hybrid inverter.

[0055] The storage medium provided in the present application stores a program product that can implement an intelligent scheduling control method for a photovoltaic-storage hybrid inverter.

[0056] The intelligent dispatching and control method of the photovoltaic-storage hybrid inverter includes: the dispatching controller collects the output power of the photovoltaic cell, the power state of the energy storage battery, the grid electricity price and the load power, and uses the prediction model to generate the photovoltaic power prediction value and the load prediction value; based on the photovoltaic power prediction value and the load prediction value, the optimal power allocation scheme is solved with the economic optimization as the goal, and the photovoltaic side target power, the energy storage side target power and the grid interaction target power are obtained; according to the photovoltaic side target power, the duty cycle of the photovoltaic side bidirectional DC / DC converter is adjusted to realize the maximum power point tracking of the photovoltaic cell, according to the energy storage side target power, the duty cycle of the energy storage side bidirectional DC / DC converter is adjusted to realize the charging and discharging power control of the energy storage battery, and the output current of the grid-connected inverter is adjusted according to the grid interaction target power.

[0057] In some possible embodiments, the intelligent scheduling control method of the photovoltaic-storage hybrid inverter disclosed in the present invention can be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above "Exemplary Method" section of this specification according to various exemplary embodiments of the present disclosure.

[0058] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0059] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent dispatching control method for a photovoltaic-storage hybrid inverter, characterized in that: The photovoltaic-storage hybrid inverter includes a photovoltaic-side DC / DC converter, an energy storage-side bidirectional DC / DC converter, a grid-connected inverter, and a dispatch controller; The method comprises the following steps: S1. The dispatch controller collects the output power of photovoltaic cells, the power status of energy storage batteries, the power price of the power grid and the load power, and uses the prediction model to generate photovoltaic power prediction values ​​and load prediction values; S2. Based on the photovoltaic power prediction value and the load prediction value, the optimal power allocation scheme is solved with the goal of economic optimization, and the photovoltaic side target power, the energy storage side target power and the grid interaction target power are obtained; S3. According to the target power of the photovoltaic side, the duty cycle of the bidirectional DC / DC converter on the photovoltaic side is adjusted to realize the maximum power point tracking of the photovoltaic cell. According to the target power of the energy storage side, the duty cycle of the bidirectional DC / DC converter on the energy storage side is adjusted to realize the charging and discharging power control of the energy storage battery. According to the target power of the grid interaction, the output current of the grid-connected inverter is adjusted.

2. The intelligent dispatching control method of the photovoltaic hybrid inverter according to claim 1 is characterized in that: The specific steps of step S1 are as follows: S11. Collect the output power and meteorological data of photovoltaic cells, the power status, full power and temperature of energy storage batteries, the voltage, frequency and electricity price of the power grid, and the power consumption of loads; S12. Filtering and normalizing the collected data; S13. Based on the historical photovoltaic cell output power and meteorological data, the photovoltaic power within a set time period in the future is predicted using the LSTM neural network as the photovoltaic power prediction value; S14. Based on the historical load power consumption, the ARIMA model is used to predict the load demand power in the future set time period as the load prediction value.

3. The intelligent dispatching control method of the photovoltaic hybrid inverter according to claim 2 is characterized in that: The specific steps of step S2 are as follows: S21. Construct the objective function with the minimum grid power purchase cost, battery charge and discharge protection, grid fluctuation suppression and dynamic electricity price response optimization as the target sub-items, and set the power balance constraint conditions, energy storage battery constraint conditions and grid interaction constraint conditions; S22. Adjust the weight coefficient of each target sub-item in the objective function according to real-time demand, use the model predictive control algorithm to solve the objective function, initialize the target period and time window, and solve the optimal target power allocation sequence of the target period according to the time window.

4. The intelligent dispatching control method of the photovoltaic-storage hybrid inverter according to claim 3 is characterized in that: In step S21, the objective function is constructed as follows: in, is the grid electricity price, Purchase power for the grid, is the maximum power state of the energy storage battery, Real-time power status of the energy storage battery; , represents the maximum electricity price, Indicates the minimum electricity price, Indicates the charging or discharging power of the energy storage battery. Represents the energy storage battery power penalty coefficient, represents the penalty coefficient of power grid fluctuation, Indicates the dynamic electricity price response fluctuation coefficient; The constraints are as follows: Power balance constraints: in, is the output power of the photovoltaic cell, The charging and discharging power of the energy storage battery, Purchase power for the grid, The power used by the load; Energy storage battery constraints: in, It is the real-time power status of the energy storage battery. is the lowest state of charge of the energy storage battery. is the maximum state of charge of the energy storage battery, To charge or discharge the energy storage battery, The maximum charging or discharging power of the energy storage battery; Grid interaction constraints: in, Purchase power for the grid, The maximum power that can be purchased by the power grid.

5. The intelligent dispatching control method of the photovoltaic-storage hybrid inverter according to claim 3 is characterized in that: The specific steps of step S22 are as follows: S221. Compare the real-time power status of the energy storage battery and the maximum power state of the energy storage battery , and in Less than , but when the difference between the two is less than the set threshold, the energy storage battery power penalty coefficient is increased in a preset manner ; S222. When the frequency of the power grid at adjacent moments is greater than the set threshold, the power grid power fluctuation penalty coefficient is increased in a preset manner ; S223. Initialize the target time period T as the prediction time domain and the time window N as the control time domain; S224. Obtain the photovoltaic power forecast value and load forecast value within the target period T, use the numerical optimization algorithm to solve the objective function within the time window [t, t+N], and obtain the optimal target power allocation sequence ; Among them, t is the time in the target period T, k is the time in the time window, represents the target power of the photovoltaic side at time k, represents the target power of the energy storage side at time k, represents the grid interaction target power at time k; S225. Verify whether the optimal target power allocation sequence as a solution satisfies the power balance constraint, the energy storage battery constraint, and the grid interaction constraint; If yes, go to step S226; If not, proceed to step S227; S226. Verify whether the following conditions are met: C grid ( k )≥C high And P bat ( k )>0 or C grid ( k )≤C low And P bat ( k )<0 Among them, C high represents the upper threshold of the power grid electricity price, C low Indicates the lower limit threshold of the power grid electricity price; If yes, force setting P bat ( k )=0, solve again and return to step S224; If not, proceed to step S3; S227. Correct the objective function using the penalty function method and solve it again, and return to step S224.

6. The intelligent dispatching control method of the photovoltaic-storage hybrid inverter according to claim 5 is characterized in that: In step S224, the objective function is solved by the numerical optimization algorithm as follows: The numerical optimization algorithm is a particle swarm optimization algorithm; Initialize as the target power allocation sequence combination The position of the particle group; Calculate the objective function value of each particle and update the individual optimal solution and the global optimal solution; Update the particle position according to the following speed formula until the objective function value converges; in, Indicates the update speed, represents the inertia weight, c1 represents the individual learning factor, c2 represents the group learning factor, r1 and r2 represent random numbers between 0 and 1, and p best represents the optimal historical position of the individual particle, g best represents the global optimal position of the group; The specific steps of correcting the objective function by the penalty function method in step S227 are as follows: Calculate the constraint violation g corresponding to the i-th constraint condition i (x); The correction function is constructed as follows: Corrected objective function = original objective function + in, is the penalty factor for the i-th constraint.

7. The intelligent dispatching control method of the photovoltaic-storage hybrid inverter according to claim 1 is characterized in that: The specific steps of step S3 are as follows: S31. Calculate the current photovoltaic output power P pv (k) and photovoltaic target power Deviation ΔP pv (k) Use the perturbation observation method or conductance increment method to adjust the duty cycle D of the bidirectional DC / DC converter on the photovoltaic side. pv (k) makes ΔP pv (k) is less than a set threshold; S32. Determine the target power of the energy storage side size; like >0, it is judged to be in charging state; Calculate the charging current ,in is the voltage of the energy storage battery; Adjusting the duty cycle of the bidirectional DC / DC converter on the energy storage side through PWM signal , so that the actual charging current track ; like <0, determine the discharge state: Calculate the discharge current ,in is the voltage of the energy storage battery; By adjusting the duty cycle of the bidirectional DC / DC converter on the energy storage side Realize constant current or constant power discharge; S33. Determine the target power of grid interaction size; like >0, it is determined to be purchasing electricity from the grid; Calculate the grid current amplitude , controlling the input current amplitude from the grid to the grid-connected inverter according to the grid-connected current amplitude, and controlling the phase to be synchronized with the grid voltage; like <0, it is determined to sell electricity to the grid; Calculate the inverter output current amplitude , the output current amplitude of the grid-connected inverter to the grid is controlled according to the output current amplitude of the inverter, and the phase is controlled to be opposite to the grid voltage.

8. An intelligent dispatching and control system for a photovoltaic and energy storage hybrid inverter, characterized in that: The photovoltaic-storage hybrid inverter includes a photovoltaic-side DC / DC converter, an energy storage-side bidirectional DC / DC converter and a grid-connected inverter; The system comprises: The data collection and demand forecasting module collects the output power of photovoltaic cells, the power status of energy storage batteries, the power price of the power grid and the load power, and uses the forecasting model to generate the photovoltaic power forecast value and the load forecast value; The target solution module solves the optimal power allocation scheme based on the photovoltaic power prediction value and the load prediction value, with the goal of economic optimization, and obtains the photovoltaic side target power, the energy storage side target power and the grid interaction target power; The power adjustment module is used to adjust the duty cycle of the bidirectional DC / DC converter on the photovoltaic side according to the target power on the photovoltaic side to achieve maximum power point tracking of the photovoltaic cell, adjust the duty cycle of the bidirectional DC / DC converter on the energy storage side according to the target power on the energy storage side to achieve charging and discharging power control of the energy storage battery, and adjust the output current of the grid-connected inverter according to the grid interaction target power.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the intelligent dispatching control method for the photovoltaic hybrid inverter as described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent scheduling control method for the photovoltaic-storage hybrid inverter as described in any one of claims 1 to 7 are implemented.

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