New energy power station multi-type energy storage and photovoltaic primary side cooperative grid-connected control method

Abstract

The invention relates to a multi-type energy storage and photovoltaic primary side cooperative grid-connected control method for a new energy power station. The method comprises the following steps: collecting operation data of a photovoltaic array, a multi-type energy storage system and a power grid of the new energy power station; constructing a prediction model, wherein the prediction model predicts photovoltaic output according to the photovoltaic array operation data; the prediction model predicts a power grid load demand according to the power grid operation data; the method is based on prediction of photovoltaic output, prediction of power grid load demands and operation data of multiple types of energy storage systems. Optimizing the charging and discharging strategies of the various energy storage systems and the power distribution proportion of the various energy storage systems through a multi-target optimization algorithm with the minimum grid-connected power fluctuation, the longest service life of the energy storage systems and the lowest operation cost as targets; and controlling the photovoltaic array and each type of energy storage system based on the optimized charging and discharging strategy of each type of energy storage system and the power distribution proportion of each type of energy storage system.

Description

Technical Field

[0001] This invention relates to a method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants, belonging to the field of new energy engineering technology. Background Technology

[0002] As the global energy structure shifts towards cleaner and lower-carbon energy, photovoltaic (PV) power generation, as an important component of new energy sources, has seen its installed capacity grow rapidly. However, PV power generation is significantly affected by natural factors such as sunlight and temperature, resulting in strong intermittency, fluctuations, and randomness in output. Large-scale grid-connected PV systems can impact the frequency stability, voltage quality, and power flow distribution of the power grid, potentially leading to safety incidents such as grid disconnection in severe cases.

[0003] To mitigate fluctuations in photovoltaic (PV) output and improve grid connection stability, energy storage systems are widely used in new energy power plants. Commonly used energy storage types include lithium-ion battery storage (high energy density), lead-acid battery storage (low cost), and flywheel storage (high power density, fast response). Combining multiple types of energy storage can achieve complementary advantages. However, in existing technologies, energy storage systems and PV systems are mostly controlled independently, lacking an effective collaborative scheduling mechanism. On the one hand, energy storage charging and discharging strategies do not fully consider the real-time output characteristics of the PV primary side, leading to wasted energy storage capacity or delayed charging and discharging responses. On the other hand, power allocation among different types of energy storage is unreasonable, failing to fully leverage the technological advantages of different energy storage types, and lacking a comprehensive protection mechanism for PV-energy storage collaborative operation, resulting in inadequate system operational safety. Furthermore, traditional PV output and load forecasting methods have low accuracy, making it difficult to support efficient collaborative control decisions, thus hindering the improvement of new energy absorption rates and system operational economics. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants.

[0005] The technical solution of the present invention is as follows: On the one hand, this invention provides a method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants, including the following steps: Collect operational data from photovoltaic arrays in new energy power plants, various types of energy storage systems, and the power grid; A prediction model is constructed, which predicts photovoltaic output based on photovoltaic array operation data; the prediction model also predicts grid load demand based on grid operation data. Based on predicted photovoltaic output, predicted grid load demand, and operational data of various types of energy storage systems, a multi-objective optimization algorithm is used to optimize the charging and discharging strategies of various types of energy storage systems and the power allocation ratio of various types of energy storage systems, with the objectives of minimizing grid-connected power fluctuations, maximizing the lifespan of energy storage systems, and minimizing operating costs. The photovoltaic array and various types of energy storage systems are controlled based on the optimized charging and discharging strategies of each type of energy storage system and the power allocation ratio of each type of energy storage system.

[0006] Preferably, the method further includes real-time monitoring of the operating parameters of the photovoltaic array, multiple types of energy storage systems, and grid connection points, and setting thresholds for each. When the operating parameters exceed the preset thresholds, overload protection, overvoltage protection, or overload protection mechanisms are activated.

[0007] Preferably, the multi-type energy storage system includes a lithium battery energy storage system, a lead-acid battery energy storage system, and a flywheel energy storage system.

[0008] Preferably, the photovoltaic array operation data and the power grid operation data are preprocessed, specifically as follows: The Kalman filter algorithm is used to filter the photovoltaic array operation data and the power grid operation data, and then the filtered data is normalized by the max-min normalization method.

[0009] Preferably, the photovoltaic array operating data includes output voltage, output current, and output power; The operating data of the various types of energy storage systems include the battery state of charge, battery charging and discharging power, and battery temperature of each type of energy storage system. The power grid operation data includes power grid frequency, power grid voltage, and power grid load.

[0010] Preferably, the prediction model is constructed based on a hybrid model of long short-term memory network and attention mechanism.

[0011] Preferably, the multi-objective optimization algorithm is the NSGA-III algorithm.

[0012] On the other hand, the present invention also provides a multi-type energy storage and photovoltaic primary side coordinated grid connection control system for new energy power plants, including a data acquisition module, a prediction module, a control strategy optimization module and a control module; The data acquisition module is used to collect operational data from photovoltaic arrays in new energy power plants, various types of energy storage systems, and the power grid. The prediction module is used to construct a prediction model, which predicts photovoltaic output based on photovoltaic array operation data and predicts grid load demand based on grid operation data. The control strategy optimization module is used to optimize the charging and discharging strategies of various types of energy storage systems and the power allocation ratio of various types of energy storage systems based on the predicted photovoltaic output, predicted grid load demand, and operation data of various types of energy storage systems. The optimization algorithm aims to minimize grid-connected power fluctuations, maximize the lifespan of energy storage systems, and minimize operating costs. The control module is used to control the photovoltaic array and various types of energy storage systems based on the optimized charging and discharging strategies of each type of energy storage system and the power allocation ratio of each type of energy storage system.

[0013] In another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the present invention.

[0014] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the present invention.

[0015] The present invention has the following beneficial effects: 1. This invention uses high-precision photovoltaic power output and load prediction to formulate dynamic charging and discharging strategies and adjust them in real time, effectively smoothing out photovoltaic power output fluctuations, stabilizing grid-connected power fluctuations, significantly improving grid frequency and voltage stability, and reducing the impact of photovoltaic grid connection on the grid.

[0016] 2. This invention uses a multi-objective optimization algorithm to allocate power to multiple types of energy storage, giving full play to the advantages of high energy density of lithium battery energy storage and fast response of flywheel energy storage, avoiding overuse of a single type of energy storage, extending the overall life of the energy storage system, and reducing the operation and maintenance costs of the energy storage system.

[0017] 3. This invention monitors key operating parameters in real time, and multiple protection mechanisms are activated in tandem to quickly respond to abnormal situations such as overload, overvoltage, and islanding, ensuring the safety of the power plant and the power grid. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0021] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0023] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0024] See Figure 1 In some embodiments, a method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants is proposed, including the following steps: Collect operational data from photovoltaic arrays in new energy power plants, various types of energy storage systems, and the power grid; A prediction model is constructed, which predicts photovoltaic output based on photovoltaic array operation data; the prediction model also predicts grid load demand based on grid operation data. Based on predicted photovoltaic output, predicted grid load demand, and operational data of various types of energy storage systems, a multi-objective optimization algorithm is used to optimize the charging and discharging strategies of various types of energy storage systems and the power allocation ratio of various types of energy storage systems, with the objectives of minimizing grid-connected power fluctuations, maximizing the lifespan of energy storage systems, and minimizing operating costs. The photovoltaic array and various types of energy storage systems are controlled based on the optimized charging and discharging strategies of each type of energy storage system and the power allocation ratio of each type of energy storage system.

[0025] In some embodiments, the method further includes real-time monitoring of the operating parameters of the photovoltaic array, multiple types of energy storage systems, and grid connection points, and setting thresholds for each. When the operating parameters exceed the preset thresholds, overload protection, overvoltage protection, or an overload protection mechanism is activated.

[0026] In one specific embodiment, the preset thresholds include a photovoltaic array output voltage threshold of 280-320V, an energy storage system SOC threshold of 20%-90%, and a grid frequency threshold of 49.5-50.5Hz.

[0027] In one specific embodiment, the battery capacity, internal resistance, and charge / discharge efficiency of various types of energy storage systems are periodically tested, and the energy storage system status parameters are updated based on the test results.

[0028] In some embodiments, the multi-type energy storage system includes a lithium battery energy storage system, a lead-acid battery energy storage system, and a flywheel energy storage system.

[0029] In some embodiments, the photovoltaic array operation data and the power grid operation data are preprocessed, specifically as follows: The Kalman filter algorithm is used to filter the photovoltaic array operation data and the power grid operation data, and then the filtered data is normalized by the max-min normalization method.

[0030] In some embodiments, the photovoltaic array operating data includes output voltage, output current, and output power; The operating data of the various types of energy storage systems include the battery state of charge, battery charging and discharging power, and battery temperature of each type of energy storage system. The power grid operation data includes power grid frequency, power grid voltage, and power grid load.

[0031] In some embodiments, the prediction model is constructed based on a hybrid model of long short-term memory network and attention mechanism.

[0032] In some embodiments, the multi-objective optimization algorithm is the NSGA-III algorithm.

[0033] In a specific embodiment, taking a 100MW centralized photovoltaic power station as an application scenario, the power station is equipped with multiple types of energy storage systems, including 15MW / 60MWh lithium battery energy storage (for long-term energy regulation), 10MW / 10MWh lead-acid battery energy storage (for medium-term power compensation) and 5MW / 0.5MWh flywheel energy storage (for short-term rapid response), aiming to achieve coordinated grid-connected control of energy storage and photovoltaic primary side through the method of the present invention; Hall voltage sensors (measurement range 0-500V) and current sensors (measurement range 0-2000A) are deployed at the output terminals of the 30 combiner boxes of the photovoltaic array; SOC sensors, temperature sensors (measurement range -20℃-85℃), and power sensors (measurement range -10MW-10MW) are deployed on the DC side of the lithium battery, lead-acid battery, and flywheel energy storage system, respectively; and frequency sensors (measurement range 45-55Hz), voltage sensors (measurement range 0-120kV), and load power sensors (measurement range 0-120MW) are deployed at the 110kV grid connection node. Based on the aforementioned sensors, operational data from the power plant's photovoltaic array, various energy storage systems, and the power grid are collected. Sampling intervals of 1 second are used to collect real-time data on the photovoltaic array's output voltage (average approximately 300V), current (average approximately 1800A), and power (real-time fluctuation range 20-95MW); the SOC value of various energy storage systems (lithium battery 60%-85%, lead-acid battery 50%-75%, flywheel energy storage 80%-90%), charge / discharge power (lithium battery - 15MW-15MW, lead-acid battery - 10MW-10MW, flywheel energy storage - 5MW-5MW), and temperature (average 25℃); and the power grid's frequency (average 50Hz), voltage (average 110kV), and load power (real-time range 30-110MW). The Kalman filter algorithm is used to filter the collected voltage, current and other noisy data to remove errors caused by environmental electromagnetic interference. The maximum-minimum normalization method is used to map the filtered data to the [0,1] interval. The processed photovoltaic power data is 0.21-0.99 and the grid load data is 0.27-1.00, which is convenient for subsequent prediction model input.

[0034] In one specific embodiment, the hybrid model of long short-term memory network and attention mechanism includes three hidden layers (each with 128, 64, and 32 neurons respectively), and the attention layer adopts an additive attention mechanism. The training dataset consists of photovoltaic power output data (sampling interval of 15 minutes), grid load data, and environmental data (light intensity and temperature) of the power station over the past year, which are divided into a training set (8840 data points) and a test set (3780 data points) in a 7:3 ratio. After training, the mean absolute percentage error (MAPE) of the model is 4.2%.

[0035] Input the processed real-time parameter data (series data of the past 1 hour), and the model outputs the photovoltaic power output curve (predicted peak of 92MW and valley of 25MW) and the grid load demand curve (predicted peak of 105MW and valley of 32MW) for the next 30 minutes.

[0036] In one specific embodiment, the objectives of the multi-objective optimization algorithm are: to minimize grid-connected power fluctuation (objective weight 0.4), maximize the lifespan of the energy storage system (objective weight 0.3), and minimize operating costs (objective weight 0.3). The constraints are: lithium battery SOC ≥ 20% and ≤ 90%, lead-acid battery SOC ≥ 20% and ≤ 80%, flywheel energy storage SOC ≥ 10% and ≤ 95%; the charging and discharging power of each energy storage system shall not exceed the rated power.

[0037] After 100 iterations, the NSGA-Ⅲ algorithm outputs the optimal charging and discharging strategy: when the photovoltaic output is higher than the load demand (e.g., photovoltaic output 92MW, load 80MW), the lithium battery is charged at 5MW power, the lead-acid battery is charged at 3MW power, and the flywheel energy storage is charged at 2MW power; when the photovoltaic output is lower than the load demand (e.g., photovoltaic output 25MW, load 40MW), the lithium battery is discharged at 10MW power, the lead-acid battery is discharged at 4MW power, and the flywheel energy storage is discharged at 1MW power, with a power allocation ratio of lithium battery: lead-acid battery: flywheel energy storage = 5:2:1.

[0038] Based on the established charging and discharging strategy and real-time parameter deviations (such as the actual photovoltaic output being 3MW higher than the predicted value), dynamic control commands are generated: the active power output of the photovoltaic inverter is adjusted (reduced by 3MW), while the lithium battery charging power is increased from 5MW to 6MW.

[0039] Control commands are sent via 5G communication to 20 photovoltaic inverters (model SG1250HX, rated power 5MW) and 3 sets of energy storage converters (PCS, lithium battery PCS rated power 15MW, lead-acid battery PCS rated power 10MW, flywheel energy storage PCS rated power 5MW). The response time of the inverters and PCS is ≤50ms, realizing the coordinated regulation of photovoltaic output and energy storage charging and discharging.

[0040] In one specific embodiment, key parameters are monitored at 0.1-second intervals. When encountering short-term cloud cover, the photovoltaic output drops sharply from 80MW to 50MW, causing a 37.5% fluctuation in grid-connected power. Simultaneously, the grid frequency drops to 49.4Hz (below the preset threshold of 49.5Hz). The frequency protection mechanism is activated, and a command is sent to the energy storage converter. The flywheel energy storage discharges rapidly at 5MW rated power (response time 0.2 seconds), and the lead-acid battery discharges at 10MW power. Within 3 seconds, the grid-connected power is stabilized at 75MW, and the grid frequency recovers to 49.6Hz. When the temperature of a certain group of lithium batteries is detected to rise to 86℃ (above the preset threshold of 85℃), over-temperature protection is activated, cutting off the charging and discharging circuit of that group of lithium batteries. At the same time, the power allocation of other energy storage systems is adjusted to ensure continuous system operation.

[0041] In some embodiments, a multi-type energy storage and photovoltaic primary side coordinated grid connection control system for new energy power plants is also proposed, including a data acquisition module, a prediction module, a control strategy optimization module, and a control module; The data acquisition module is used to collect operational data from photovoltaic arrays in new energy power plants, various types of energy storage systems, and the power grid. The prediction module is used to construct a prediction model, which predicts photovoltaic output based on photovoltaic array operation data and predicts grid load demand based on grid operation data. The control strategy optimization module is used to optimize the charging and discharging strategies of various types of energy storage systems and the power allocation ratio of various types of energy storage systems based on the predicted photovoltaic output, predicted grid load demand, and operation data of various types of energy storage systems. The optimization algorithm aims to minimize grid-connected power fluctuations, maximize the lifespan of energy storage systems, and minimize operating costs. The control module is used to control the photovoltaic array and various types of energy storage systems based on the optimized charging and discharging strategies of each type of energy storage system and the power allocation ratio of each type of energy storage system.

[0042] In some embodiments, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.

[0043] In some embodiments, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the method as described in any embodiment of the present invention.

[0044] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0045] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0046] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0048] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants, characterized in that, Includes the following steps: Collect operational data from photovoltaic arrays in new energy power plants, various types of energy storage systems, and the power grid; A prediction model is constructed, which predicts photovoltaic output based on photovoltaic array operation data; the prediction model also predicts grid load demand based on grid operation data. Based on predicted photovoltaic output, predicted grid load demand, and operational data of various types of energy storage systems, a multi-objective optimization algorithm is used to optimize the charging and discharging strategies of various types of energy storage systems and the power allocation ratio of various types of energy storage systems, with the objectives of minimizing grid-connected power fluctuations, maximizing the lifespan of energy storage systems, and minimizing operating costs. The photovoltaic array and various types of energy storage systems are controlled based on the optimized charging and discharging strategies of each type of energy storage system and the power allocation ratio of each type of energy storage system.

2. The method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in a new energy power plant according to claim 1, characterized in that, The method also includes real-time monitoring of the operating parameters of the photovoltaic array, various types of energy storage systems, and grid connection points, and setting thresholds for each. When the operating parameters exceed the preset thresholds, overload protection, overvoltage protection, or overload protection mechanisms are activated.

3. The method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants according to claim 1, characterized in that, The various types of energy storage systems include lithium battery energy storage systems, lead-acid battery energy storage systems, and flywheel energy storage systems.

4. The method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants according to claim 1, characterized in that, Preprocessing of photovoltaic array operation data and grid operation data is performed as follows: The Kalman filter algorithm is used to filter the photovoltaic array operation data and the power grid operation data, and then the filtered data is normalized by the max-min normalization method.

5. The method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants according to claim 1, characterized in that, The photovoltaic array's operating data includes output voltage, output current, and output power; The operating data of the various types of energy storage systems include the battery state of charge, battery charging and discharging power, and battery temperature of each type of energy storage system. The power grid operation data includes power grid frequency, power grid voltage, and power grid load.

6. The method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in a new energy power plant according to claim 1, characterized in that, The prediction model is constructed based on a hybrid model of long short-term memory network and attention mechanism.

7. The method for coordinated grid connection control of multiple types of energy storage and photovoltaic primary side in new energy power plants according to claim 1, characterized in that, The multi-objective optimization algorithm is the NSGA-Ⅲ algorithm.

8. A multi-type energy storage and photovoltaic primary-side coordinated grid-connected control system for new energy power plants, characterized in that, It includes a data acquisition module, a prediction module, a control strategy optimization module, and a control module; The data acquisition module is used to collect operational data from photovoltaic arrays in new energy power plants, various types of energy storage systems, and the power grid. The prediction module is used to construct a prediction model, which predicts photovoltaic output based on photovoltaic array operation data and predicts grid load demand based on grid operation data. The control strategy optimization module is used to optimize the charging and discharging strategies of various types of energy storage systems and the power allocation ratio of various types of energy storage systems based on the predicted photovoltaic output, predicted grid load demand, and operation data of various types of energy storage systems. The optimization algorithm aims to minimize grid-connected power fluctuations, maximize the lifespan of energy storage systems, and minimize operating costs. The control module is used to control the photovoltaic array and various types of energy storage systems based on the optimized charging and discharging strategies of each type of energy storage system and the power allocation ratio of each type of energy storage system.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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