Park energy regulation and control method based on source network load storage collaborative optimization

Abstract

The invention discloses a park energy regulation and control method based on source-grid-load-storage collaborative optimization, and the method comprises the steps: monitoring the power generation conditions of all power generation equipment related to a park in real time, and obtaining power generation prediction data through a power generation prediction algorithm model in combination with weather data and historical power generation data; monitoring the power consumption condition of each piece of load equipment in the park in real time, and obtaining power consumption prediction data through a power consumption prediction algorithm model in combination with a production scheduling plan and historical power consumption data; formulating a collaborative optimization control strategy including an energy storage charging strategy, an energy storage discharging strategy, a power generation equipment power generation adjustment strategy and / or a load equipment power consumption adjustment strategy based on the power generation prediction data and the power consumption prediction data; and executing the collaborative optimization control strategy to reduce the absolute value of the difference value between the power generation prediction data and the power consumption prediction data. Through accurate prediction and intelligent regulation and control of the power generation data and the power consumption data, the power generation capacity and the load demand of the park are effectively balanced, and energy waste is reduced.

Description

Technical Field

[0001] The present application relates to the field of smart grid technology, and more specifically, to a park energy control method based on coordinated optimization of source, grid, load and storage. Background Art

[0002] With the large-scale grid connection of new energy, park energy management faces challenges such as unstable new energy power generation and variable load demand. However, traditional energy regulation and management methods are difficult to effectively cope with these changes, resulting in energy waste and reduced grid stability. Therefore, it is urgent to develop an energy regulation technology that can monitor, predict and regulate in real time. Summary of the invention

[0003] In response to at least one defect or improvement need in the prior art, the present application provides a park energy control method and system based on coordinated optimization of source, grid, load and storage, which is used to at least reduce energy waste.

[0004] To achieve the above objectives, in a first aspect, the present application provides a park energy control method based on source-grid-load-storage collaborative optimization, including:

[0005] Real-time monitoring of the power generation of various power generation equipment related to the park, combined with weather data and historical power generation data, to obtain power generation forecast data through the power generation forecast algorithm model;

[0006] Real-time monitoring of the power consumption of each load device in the park, combined with production scheduling and historical power consumption data, to obtain power consumption forecast data through the power consumption forecast algorithm model;

[0007] Based on the power generation forecast data and the power consumption forecast data, formulate a collaborative optimization control strategy including an energy storage charging strategy, an energy storage discharging strategy, a power generation adjustment strategy for power generation equipment, and / or a power consumption adjustment strategy for load equipment;

[0008] The collaborative optimization control strategy is executed to reduce the absolute value of the difference between the power generation forecast data and the power consumption forecast data.

[0009] Further, the executing the collaborative optimization control strategy includes:

[0010] The cloud control platform sends the collaborative optimization control strategy to the power generation equipment and the energy storage system;

[0011] If the power generation forecast data is greater than the power consumption forecast data, the power generation equipment adjusts the output according to the power generation adjustment strategy of the power generation equipment, and the energy storage system is charged according to the energy storage charging strategy;

[0012] If the energy storage system still cannot completely absorb the surplus electricity, the cloud control platform will adjust the output of the power generation equipment through system instructions to reduce the amount of electricity connected to the grid to avoid impact on the power grid.

[0013] Further, the executing the collaborative optimization control strategy includes:

[0014] The cloud control platform sends the collaborative optimization control strategy to the power generation equipment and the energy storage system; the power generation equipment is a new energy power generation equipment;

[0015] If the power generation forecast data is less than the power consumption forecast data, the power generation equipment adjusts the output to the maximum according to the power generation adjustment strategy of the power generation equipment, and the energy storage system discharges according to the energy storage discharge strategy;

[0016] If the electricity demand still cannot be fully met, the cloud control platform executes the load equipment power consumption adjustment strategy to shut down some interruptible loads and adjust the power of the flexible and adjustable loads to meet the remaining electricity demand.

[0017] Furthermore, it also includes:

[0018] The edge control system collects actual data on power generation and power consumption, and obtains power generation forecast data and power consumption forecast data through the two forecasting algorithm models mentioned above;

[0019] The edge control system formulates the target value for regulating the charging and discharging power of the energy storage according to the actual data of power generation and power consumption in a first time period before a time point, and the predicted data of power generation and power consumption in a second time period thereafter;

[0020] The edge control system sends the energy storage charging and discharging power adjustment target value to the energy storage system;

[0021] The energy storage system performs charging and discharging operations according to the energy storage charging and discharging power adjustment target value to smooth power fluctuations.

[0022] Furthermore, the cloud control platform dynamically adjusts and optimizes the collaborative optimization control strategy based on real-time data feedback.

[0023] Furthermore, the edge control system dynamically adjusts and optimizes the energy storage charging and discharging power regulation target value according to real-time data feedback.

[0024] Furthermore, the method for ensuring the stability of the power grid frequency also includes:

[0025] Setting f Lmax <f<f Hmin It is the safe operating range of the power grid frequency;

[0026] Setting f LM <f≤f Lmax &f Hmin ≤f <f HM It is the power grid frequency early warning operation range;

[0027] Setting f Lmin ≤f≤f LM &f HM ≤f≤f H max It is the grid frequency alarm operation range;

[0028] Setting f <f Lmin &f>f H max It is the grid frequency collapse interval;

[0029] Where, f represents the real-time grid frequency; f Lmax It represents the first frequency below the rated grid frequency, which is a frequency less than the rated grid frequency; f Hmin It represents the first frequency above the rated grid frequency, which is a frequency greater than the rated grid frequency; f LM Indicates the second frequency below the rated grid frequency, f HM Indicates the second frequency above the rated grid frequency; f Lmin Indicates the third frequency below the rated grid frequency, f H max Indicates the third frequency above the rated grid frequency;

[0030] If the real-time grid frequency is within the grid frequency safe operation range, one or more of smooth output control, economic optimization control and operation mode optimization control of the power generation equipment are performed;

[0031] If the real-time grid frequency is within the grid frequency warning operation range, one or more of energy storage system output adjustment, power generation equipment output adjustment and flexible adjustable load adjustment are performed to return the grid frequency to the safe operation range;

[0032] If the real-time grid frequency is within the grid frequency alarm operation range, the generator is cut off to reduce load;

[0033] If the real-time grid frequency is in the grid frequency collapse interval, a power outage is performed for maintenance and a black start control is performed.

[0034] Furthermore, the method for ensuring voltage stability also includes:

[0035] Setting U Lmax <U<U Hmin It is the voltage safe operating range;

[0036] Setting U LM<U≤U Lmax &U Hmin ≤U HM It is the voltage warning operation range;

[0037] Setting U Lmin ≤U≤U LM &U HM ≤U≤U H max It is the voltage alarm operation range;

[0038] Setting U Lmin &U> H max is the voltage collapse interval;

[0039] Among them, U represents the real-time voltage; U Lmax Indicates the first voltage below the rated voltage, which is a voltage less than the rated voltage; U Hmin Indicates the first voltage above the rated voltage, which is a voltage greater than the rated voltage; U LM Indicates the second voltage below the rated voltage, U HM Indicates the second voltage above the rated voltage; U Lmin Indicates the third voltage below the rated voltage, U H max Indicates the third voltage above the rated voltage;

[0040] If the real-time voltage is within the voltage safety operation range, one or more of smooth output control, economic optimization control and operation mode optimization control of the power generation equipment are performed;

[0041] If the real-time voltage is within the voltage warning operation range, one or more of adjusting the reactive output of the energy storage system, the SVG compensation capacity, and the reactive output of the power generation equipment is adjusted to return the voltage to the safe operation range;

[0042] If the real-time voltage is within the voltage alarm operation range, the generator is cut off to reduce load and / or a high-power reactive compensation device is switched on;

[0043] If the real-time voltage is in the voltage collapse interval, a power outage is performed for maintenance and a black start control is performed.

[0044] In the second aspect, the present application provides a park energy control system based on source-grid-load-storage collaborative optimization, including:

[0045] The power generation side monitoring module is used to monitor the power generation status of various power generation equipment related to the park in real time, and obtain power generation forecast data through the power generation forecast algorithm model in combination with weather data and historical power generation data;

[0046] ​​The load-side monitoring module is used to monitor the power consumption of each load device in the park in real time, and obtain power consumption forecast data through the power consumption forecast algorithm model in combination with the production scheduling plan and historical power consumption data;

[0047] The cloud control platform is used to formulate a collaborative optimization control strategy including an energy storage charging strategy, an energy storage discharging strategy, a power generation equipment power generation adjustment strategy and / or a load equipment power consumption adjustment strategy based on the power generation forecast data and the power consumption forecast data; and is also used to send the collaborative optimization control strategy to the capacity energy storage system for execution, so as to reduce the absolute value of the difference between the power generation forecast data and the power consumption forecast data.

[0048] Furthermore, the energy storage system comprises:

[0049] An energy storage system, configured to charge according to the energy storage charging strategy obtained from the cloud control platform;

[0050] The power generation equipment is used to adjust the output according to the power generation adjustment strategy of the power generation equipment obtained from the cloud control platform if the power generation forecast data is greater than the power consumption forecast data; if the energy storage system still cannot completely absorb the surplus power, it is used to adjust its own output according to the system instructions obtained from the cloud control platform to reduce the amount of power connected to the grid to avoid impact on the power grid.

[0051] Furthermore, the energy storage system comprises an energy storage system and power generation equipment;

[0052] An energy storage system, configured to discharge energy according to the energy storage discharge strategy obtained from the cloud control platform;

[0053] A power generation device, if the power generation forecast data is less than the power consumption forecast data, is used to adjust the output to the maximum according to the power generation adjustment strategy of the power generation device obtained from the cloud control platform;

[0054] When the electricity demand still cannot be fully met, the cloud control platform is also used to execute the load equipment power consumption adjustment strategy to shut down some interruptible loads and adjust the power of flexible and adjustable loads to meet the remaining electricity demand.

[0055] Furthermore, it also includes:

[0056] The edge control system is used to collect actual power generation data and actual power consumption data, and respectively obtain power generation forecast data and power consumption forecast data through the two prediction algorithm models mentioned above; it is used to formulate the energy storage charging and discharging power adjustment target value based on the actual power generation data and the actual power consumption data in a first time period before a time point, and the power generation forecast data and the power consumption forecast data in a second time period thereafter; it is used to send the energy storage charging and discharging power adjustment target value to the energy storage system so that it can perform charging and discharging operations to smooth power fluctuations.

[0057] In general, the above technical solutions conceived by the present application can achieve the following beneficial effects compared with the prior art:

[0058] (1) This application effectively balances the power generation capacity and load demand of the park and reduces energy waste through accurate prediction and intelligent regulation of power generation data and power consumption data.

[0059] (2) Based on real-time and predicted power generation data and power consumption data, this application formulates a target value for energy storage charging and discharging power regulation, and performs charging and discharging operations on the energy storage system based on this value, thereby smoothing power fluctuations in real time, reducing the impact on the power grid, and improving the safety and reliability of power grid operation.

[0060] (3) This application optimizes the coordinated work between new energy power generation equipment and energy storage systems, improves the utilization rate and grid connection capacity of new energy, and saves energy resources.

[0061] (4) This application divides the fluctuation amplitudes of grid frequency and voltage into different zones, and implements corresponding control measures for different fluctuation amplitudes. Through the cooperation of active power control devices and source-grid-load-storage coordinated control systems, stable control of grid frequency and voltage in the entire area is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments 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 creative work.

[0063] Figure 1 A core flow chart of a park energy control method based on source-grid-load-storage collaborative optimization provided in an embodiment of the present application;

[0064] Figure 2 A rendering of the real-time coordinated control of source, grid, load and storage provided in the embodiment of the present application;

[0065] Figure 3A schematic diagram of power grid frequency fluctuation zoning provided in an embodiment of the present application;

[0066] Figure 4 A schematic diagram of grid frequency fluctuation zoning control provided in an embodiment of the present application;

[0067] Figure 5 A schematic diagram of voltage fluctuation zones provided in an embodiment of the present application;

[0068] Figure 6 A schematic diagram of voltage fluctuation zoning control provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0070] The terms "first", "second" or "nth" in the specification, claims or drawings of the present application are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0071] As described in the background technology section of the specification, traditional energy regulation and management methods are difficult to effectively cope with challenges such as unstable renewable energy generation and variable load demand. In view of this, this application proposes a park energy regulation method and system based on source-grid-load-storage collaborative optimization to at least reduce energy waste.

[0072] refer to Figure 1 An embodiment of the present application provides a park energy control method based on source-grid-load-storage collaborative optimization. The park energy control method can be described from the following two application scenarios.

[0073] Application scenario 1: The application scenario where the power generation of new energy is greater than (especially much greater than) the load power consumption.

[0074] The cloud system recommends and executes the maximum output control strategy of new energy based on the predicted data, and calculates the energy storage charging time period and power. If the energy storage system cannot fully absorb the surplus electricity, the output of photovoltaic and other new energy power generation equipment is adjusted through system instructions to reduce the amount of electricity connected to the grid, and reduce or even avoid the impact on the power grid. Based on real-time data feedback, the cloud control platform dynamically adjusts and optimizes the control strategy. In some embodiments, more specifically, the following sub-steps may also be included.

[0075] Step 11: Collection and prediction of power generation data.

[0076] The power generation side monitoring module collects the power generation data of wind power, photovoltaic and other new energy power generation equipment related to the park in real time, as well as related weather data (such as wind speed, light intensity, etc.), and uses historical power generation data and weather data to predict the new energy power generation in the future (such as the next 24 hours) through the power generation prediction algorithm model.

[0077] Step 12: Collection and prediction of load data.

[0078] The load-side monitoring module collects the power consumption data of each load point (load equipment) in the park, including historical power consumption data, production scheduling, etc. Based on the historical power consumption data and the current production scheduling, the power demand in the future is predicted through the power consumption prediction algorithm model. Both the power generation prediction algorithm model and the power consumption prediction algorithm model are adaptively developed based on the existing algorithm model and are not specifically limited here.

[0079] Step 13: Formulate control strategy.

[0080] The cloud control platform formulates the maximum output control strategy of new energy according to the new energy power generation forecast data and load forecast data, and calculates the energy storage charging time period and charging power to ensure that the energy storage system can fully absorb the surplus electricity and reduce the amount of electricity connected to the grid.

[0081] Step 14: Execution and adjustment of control strategies.

[0082] The cloud control platform sends control strategies to new energy power generation equipment and energy storage systems.

[0083] New energy power generation equipment adjusts its output according to the control strategy, and the energy storage system charges according to the charging power and time period.

[0084] If the energy storage system cannot fully absorb the surplus electricity, the cloud control platform will adjust the output of photovoltaic and other new energy power generation equipment through system instructions to reduce the amount of electricity connected to the grid and avoid impact on the power grid.

[0085] Preferably, based on real-time data feedback, the cloud control platform can dynamically adjust and optimize the corresponding control strategy.

[0086] Application scenario 2: The application scenario where the load power consumption is greater than (especially much greater than) the new energy power generation.

[0087] The cloud system implements the maximum output control of new energy based on the forecast data, and calculates the energy storage discharge time period and power. The output of new energy equipment is adjusted through system instructions, and it is recommended and possible to shut down some interruptible loads and adjust the power of flexible adjustable loads. The management personnel decide whether to adjust the power load. In some embodiments, more specifically, the following sub-steps may also be included.

[0088] Step 21: Collection and prediction of power generation data.

[0089] Step 22: Collection and prediction of load data. Step 21 and step 22 are similar to step 11 and step 12 of application scenario 1, respectively, and will not be repeated here.

[0090] Step 23: Formulate control strategy.

[0091] The cloud control platform formulates the maximum output control strategy and energy storage discharge plan based on the new energy power generation forecast data and load forecast data, and calculates the energy storage discharge time period and discharge power to meet the electricity demand.

[0092] Step 24: Execution of control strategy and load management.

[0093] The cloud control platform sends control strategies to new energy power generation equipment and energy storage systems.

[0094] New energy power generation equipment adjusts its output to maximum according to the control strategy.

[0095] The energy storage system discharges according to the discharge power and time period.

[0096] If the electricity demand still cannot be fully met, the cloud control platform will recommend and possibly execute a strategy to shut down some interruptible loads and adjust the power of the flexible and adjustable loads to meet the remaining electricity demand.

[0097] Preferably, based on real-time data feedback, the cloud control platform can dynamically adjust and optimize the corresponding control strategy.

[0098] This application effectively balances the power generation capacity and load demand of the park through accurate prediction and intelligent regulation of power generation data and power consumption data, reducing energy waste. This application optimizes the collaborative work of new energy power generation equipment and energy storage systems, improves the utilization rate and grid connection capacity of new energy, and saves energy resources.

[0099] Further preferably, the park energy regulation method may also include a power fluctuation smoothing mechanism. The edge control system (i.e., the local subsystem, there can be multiple, each responsible for a part, characterized by strong real-time performance and fast response speed in the local area network. The power fluctuation balance has high real-time requirements for collecting data and issuing control instructions, so edge computing is required) uses edge computing technology to formulate energy storage charging and discharging power adjustment target values ​​based on, for example, new energy power generation and load forecast data within the next 5 minutes, combined with actual power data in the previous 5 minutes, to smooth power fluctuations in real time and ensure the stability of grid frequency and voltage. In some embodiments, more specifically, the following sub-steps may be included.

[0100] Step 31: Collection and prediction of power generation and consumption data.

[0101] The edge control system collects new energy power generation data and load power consumption data in real time.

[0102] The corresponding algorithm model mentioned above is used to predict the renewable energy power generation and load demand in the next 5 minutes.

[0103] Step 32: Calculation of power fluctuation smoothing.

[0104] The edge control system calculates the power fluctuation based on the actual power data of the previous 5 minutes and the predicted power data of the next 5 minutes.

[0105] According to the power fluctuation situation, the target value for energy storage charging and discharging power adjustment is formulated to smooth out the power fluctuation.

[0106] Step 33: Execution and real-time adjustment of the power fluctuation smoothing strategy.

[0107] The edge control system sends the energy storage charging and discharging power adjustment target value to the energy storage system.

[0108] The energy storage system performs charging and discharging operations according to the adjustment target value to smooth out power fluctuations.

[0109] Preferably, the edge control system dynamically adjusts and optimizes the energy storage charging and discharging power based on real-time data feedback to ensure the stability of the grid frequency and voltage.

[0110] The effect diagram of real-time coordinated control of source, grid, load and storage can be referenced Figure 2 . Based on real-time and predicted power generation data and power consumption data, this application formulates the target value for energy storage charging and discharging power regulation, and performs charging and discharging operations on the energy storage system based on this value, thereby smoothing power fluctuations in real time, reducing the impact on the power grid, and improving the safety and reliability of power grid operation.

[0111] This application realizes accurate prediction of power generation and power consumption in the future by real-time monitoring and intelligent control of the power generation and load sides in the park, combined with weather data, historical power generation and power consumption data. On this basis, in view of the mismatch between new energy power generation and load power consumption, through the collaborative work of cloud and edge systems, a comprehensive optimization control strategy of source, network, load and storage is implemented to effectively balance energy supply and demand (to minimize the absolute value of the difference between power generation data and power consumption data, and realize the "peak shaving and valley filling" of power resources), reduce the impact on the power grid, and improve energy utilization efficiency.

[0112] Further preferably, the method for ensuring the stability of the power grid frequency may also include the following operations. When the power grid in the region is in a weakly connected state or off-grid operation state, if a large load or power supply disturbance occurs, it will cause a serious deviation of the power grid frequency. At this time, the emergency frequency adjustment strategy can be used to rebalance the power and restore the frequency to the allowable range.

[0113] First, the control levels are divided into the following categories.

[0114] (1) Local control layer

[0115] It consists of subsystems such as the photovoltaic power station monitoring system, wind farm monitoring system, energy storage monitoring system, energy router control system, substation monitoring system and load control terminal of the edge control system. The operation and maintenance personnel can achieve on-site management and control of their control objects through the local control layer, and provide a communication interface to exchange information with photovoltaic, wind power, energy storage, central air conditioning, machine tools and other equipment in the local area network through optical fiber or WiFi, and receive control instructions from the collaborative control layer through 5G signals.

[0116] (2) Active defense layer (active defense control layer)

[0117] The active defense layer adds an automatic control strategy for edge computing on the basis of the local control layer. The subsystems of each edge control system mainly complete the rapid and stable control and operation mode control of their respective modules. The equipment and the local control layer subsystem transmit data through the interface, obtain local sampling values, and quickly analyze the operating status of the equipment. By adjusting the energy storage output, cutting off the interruptible load, adjusting the adjustable load, and operating the fast switch, the frequency and voltage of the park are stabilized, playing the role of active defense and rapid adjustment.

[0118] (3) Collaborative control layer

[0119] The cloud application realizes the monitoring and control of the entire park in a steady state. Based on the data provided by related applications such as data acquisition and monitoring control, scheduling planning and load forecasting, the cloud system realizes functions such as energy forecasting, load management, optimized operation and economic dispatch, and maximizes the comprehensive utilization efficiency of energy in the network. The main functions include panoramic monitoring of sources, grids, loads and storage, forecasting of wind power and photovoltaic power generation, load forecasting, planning of sources, loads and storage, coordinated control of sources, grids, loads and storage, and distributed resource cluster control.

[0120] Then, according to the dynamic characteristics of the power grid frequency, the power grid frequency stability level is divided into four areas, and the frequency stability is controlled according to the area. The thresholds of the four areas ABCD can be set according to the actual situation. Figure 3 shown.

[0121] Safe operation zone A: f Lmax <f<f Hmin In this area, the frequency fluctuation is very small, generally near the rated frequency value, with very small deviation. In this range, the entire system can perform optimization control such as distributed power smoothing output control, economic optimization control, and operation mode optimization control. The active defense layer does not require any additional control.

[0122] Warning operation area B: f LM <f≤f Lmax &f Hmin ≤f <f HM When the frequency is in this area, there is a certain deviation between the frequency and the rated frequency value. The system can be returned to the safe operation area A by adjusting the energy storage output, adjusting the photovoltaic and wind turbine output, and adjusting the adjustable load.

[0123] Alarm operation C area: f Lmin ≤f≤f LM &f HM ≤f≤f H max The frequency fluctuation in this area is large. If fast and high-power matching control is not adopted, the system will collapse. The measures taken in this area are to cut off the machine and reduce the load.

[0124] Disintegrate D area: f <f Lmin &f>f H max When the frequency is in this area, the system is facing collapse and the load loses power. At this time, black start control is performed after power outage and maintenance.

[0125] According to the zoning of grid frequency fluctuations, the active power control device cooperates with the source-grid-load-storage coordinated control system to achieve stable control of the grid frequency in the entire area, such as Figure 4 shown.

[0126] Further preferably, the method for ensuring voltage stability may also include the following operations.

[0127] Voltage stability control is similar to grid frequency stability control, and also uses fluctuation zone control. Based on the voltage dynamic characteristics, the voltage stability level is divided into four areas. The thresholds of the four areas ABCD can be set according to actual conditions, such as Figure 5 shown.

[0128] Safe operation zone A: U Lmax <U<U Hmin In this area, the voltage fluctuation is very small, generally near the rated voltage, with very small deviation, and the entire system can perform optimization control such as distributed power smoothing output control, economic optimization control, and operation mode optimization control. The active defense layer does not require any additional control.

[0129] Warning operation area B: U LM <U≤U Lmax &U Hmin ≤U HM When the voltage is in this area, there is a certain deviation between the voltage and the rated voltage. The system can be returned to the safe operating area by adjusting the reactive output of energy storage, SVG compensation capacity, and reactive output of wind and solar power stations.

[0130] Alarm operation area C: U Lmin ≤U≤U LM &U HM ≤U≤U H max The voltage in this area fluctuates greatly. If fast and high-power matching control is not adopted, the system will collapse. The measures taken in this area include switching on and off high-power reactive compensation equipment and / or cutting off the machine to reduce the load.

[0131] Disintegrate D area: U Lmin &U> H max When the voltage is in this area, the system is facing collapse and the load loses power. At this time, after the power outage for maintenance, black start control is performed.

[0132] According to the zoning of voltage fluctuations, the active power control device cooperates with the source-grid-load-storage coordinated control system to achieve stable control of the grid voltage in the entire area, such as Figure 6 shown.

[0133] This application divides the fluctuation amplitudes of grid frequency and voltage into different zones, implements corresponding control measures for different fluctuation amplitudes, and realizes stable control of grid frequency and voltage in the entire area through the cooperation of active power control device and source-grid-load-storage collaborative control system.

[0134] ​​The aforementioned local control, active defense control, and collaborative control are some of the control methods. The manual control of the edge (edge ​​control system) subsystem, the automatic control of the edge subsystem, and the control of the cloud system (cloud control platform) comprehensively consider the source, grid, load, and storage conditions, and conduct comprehensive control in many aspects. Multiple control instructions can be packaged and issued with one click according to different partitions of grid frequency and voltage fluctuations, realizing cloud-edge coordinated integrated control of source, grid, load, and storage.

[0135] Another embodiment of the present application provides a park energy control system based on source-grid-load-storage collaborative optimization, and the park energy control system mainly includes the following parts.

[0136] Power generation side monitoring module: real-time monitoring of the power generation of wind power, photovoltaic and other new energy power generation equipment related to the park, combined with weather data and historical power generation data, and predicting the power generation in the future through the corresponding algorithm model.

[0137] Load-side monitoring module: monitors the power consumption of each load point in the park, including production scheduling, historical power consumption data, etc., and predicts the power demand in the future through the corresponding algorithm model.

[0138] Cloud control platform: Based on the forecast data of the power generation side and the load side, it conducts coordinated optimization control of the source, grid, load and storage, formulates and implements control strategies, including energy storage charging and discharging plans, new energy output adjustment and interruptible load management.

[0139] Preferably, the park energy control system may also include:

[0140] Edge control system: Deployed in the local area network, it is responsible for edge computing and rapid response of real-time data, smoothing power fluctuations of renewable energy power generation and loads, and ensuring grid stability.

[0141] The specific technical details and technical effects of the park energy control system can be referred to the aforementioned implementation examples of the park energy control method, which will not be repeated here.

[0142] It should be noted that the functional modules in the various embodiments of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product.

[0143] The flowchart and / or block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flowchart and / or block diagram can represent a part of a module, program segment or code, and a part of the above-mentioned module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. It should also be noted that each box in the block diagram or flowchart, and the combination of the boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0144] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the technical features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, and all of these combinations and / or combinations fall within the scope of the present application.

[0145] Although the present application has been shown and described with reference to specific exemplary embodiments of the present application, it should be understood by those skilled in the art that various changes in form and details may be made to the present application without departing from the spirit and scope of the present application as defined by the appended claims and their equivalents. Therefore, the scope of the present application should not be limited to the above-mentioned embodiments, but should be determined not only by the appended claims, but also by the equivalents of the appended claims.

Claims

1. A park energy control method based on source-grid-load-storage collaborative optimization, characterized in that: include: Real-time monitoring of the power generation of various power generation equipment related to the park, combined with weather data and historical power generation data, to obtain power generation forecast data through the power generation forecast algorithm model; Real-time monitoring of the power consumption of each load device in the park, combined with production scheduling and historical power consumption data, to obtain power consumption forecast data through the power consumption forecast algorithm model; Based on the power generation forecast data and the power consumption forecast data, formulate a collaborative optimization control strategy including an energy storage charging strategy, an energy storage discharging strategy, a power generation adjustment strategy for power generation equipment, and / or a power consumption adjustment strategy for load equipment; The collaborative optimization control strategy is executed to reduce the absolute value of the difference between the power generation forecast data and the power consumption forecast data.

2. The park energy control method according to claim 1, characterized in that: The executing of the collaborative optimization control strategy includes: The cloud control platform sends the collaborative optimization control strategy to the power generation equipment and the energy storage system; If the power generation forecast data is greater than the power consumption forecast data, the power generation equipment adjusts the output according to the power generation adjustment strategy of the power generation equipment, and the energy storage system is charged according to the energy storage charging strategy; If the energy storage system still cannot completely absorb the surplus electricity, the cloud control platform will adjust the output of the power generation equipment through system instructions to reduce the amount of electricity connected to the grid to avoid impact on the power grid.

3. The park energy control method according to claim 1 or 2, characterized in that: The executing of the collaborative optimization control strategy includes: The cloud control platform sends the collaborative optimization control strategy to the power generation equipment and the energy storage system; the power generation equipment is a new energy power generation equipment; If the power generation forecast data is less than the power consumption forecast data, the power generation equipment adjusts the output to the maximum according to the power generation adjustment strategy of the power generation equipment, and the energy storage system discharges according to the energy storage discharge strategy; If the electricity demand still cannot be fully met, the cloud control platform executes the load equipment power consumption adjustment strategy to shut down some interruptible loads and adjust the power of the flexible and adjustable loads to meet the remaining electricity demand.

4. The park energy control method according to claim 1, characterized in that: Also includes: The edge control system collects actual data on power generation and power consumption, and obtains power generation forecast data and power consumption forecast data through the two forecasting algorithm models mentioned above; The edge control system formulates the target value for regulating the charging and discharging power of the energy storage according to the actual data of power generation and power consumption in a first time period before a time point, and the predicted data of power generation and power consumption in a second time period thereafter; The edge control system sends the energy storage charging and discharging power adjustment target value to the energy storage system; The energy storage system performs charging and discharging operations according to the energy storage charging and discharging power adjustment target value to smooth power fluctuations and ensure the stability of grid frequency and voltage.

5. The park energy control method according to claim 4, characterized in that: Other methods to ensure grid frequency stability include: Setting f Lmax <f<f Hmin It is the safe operating range of the power grid frequency; Setting f LM <f≤f Lmax &f Hmin ≤f <f HM It is the power grid frequency early warning operation range; Setting f Lmin ≤f≤f LM &f HM ≤f≤f Hmax It is the grid frequency alarm operation range; Setting f <f Lmin &f>f Hmax It is the grid frequency collapse interval; Where, f represents the real-time grid frequency; f Lmax It represents the first frequency below the rated grid frequency, which is a frequency less than the rated grid frequency; f Hmin It represents the first frequency above the rated grid frequency, which is a frequency greater than the rated grid frequency; f LM Indicates the second frequency below the rated grid frequency, f HM Indicates the second frequency above the rated grid frequency; f Lmin Indicates the third frequency below the rated grid frequency, f Hmax Indicates the third frequency above the rated grid frequency; If the real-time grid frequency is within the grid frequency safe operation range, one or more of smooth output control, economic optimization control and operation mode optimization control of the power generation equipment are performed; If the real-time grid frequency is within the grid frequency warning operation range, one or more of energy storage system output adjustment, power generation equipment output adjustment and flexible adjustable load adjustment are performed to return the grid frequency to the safe operation range; If the real-time grid frequency is within the grid frequency alarm operation range, the generator is cut off to reduce load; If the real-time grid frequency is in the grid frequency collapse interval, a power outage is performed for maintenance and a black start control is performed.

6. The park energy control method according to claim 4 or 5, characterized in that: Other ways to ensure voltage stability include: Setting U Lmax <U<U Hmin It is the voltage safe operating range; Setting U LM <U≤U Lmax &U Hmin ≤U HM It is the voltage warning operation range;​ Setting U Lmin ≤U≤U LM &U HM ≤U≤U Hmax It is the voltage alarm operation range; Setting U Lmin &U> Hmax is the voltage collapse interval;​ Where, U represents the real-time voltage; U Lmax Indicates the first voltage below the rated voltage, which is a voltage less than the rated voltage; U Hmin Indicates the first voltage above the rated voltage, which is a voltage greater than the rated voltage; U LM Indicates the second voltage below the rated voltage, U HM Indicates the second voltage above the rated voltage; U Lmin Indicates the third voltage below the rated voltage, U Hmax Indicates the third voltage above the rated voltage; If the real-time voltage is within the voltage safety operation range, one or more of smooth output control, economic optimization control and operation mode optimization control of the power generation equipment are performed; If the real-time voltage is within the voltage warning operation range, one or more of adjusting the reactive output of the energy storage system, the SVG compensation capacity, and the reactive output of the power generation equipment is adjusted to return the voltage to the safe operation range; If the real-time voltage is within the voltage alarm operation range, the generator is cut off to reduce load and / or a high-power reactive compensation device is switched on; If the real-time voltage is in the voltage collapse interval, a power outage is performed for maintenance and a black start control is performed.

7. The park energy control method according to claim 3, characterized in that: The cloud control platform dynamically adjusts and optimizes the collaborative optimization control strategy based on real-time data feedback.

8. The park energy control method according to claim 4, characterized in that: The edge control system dynamically adjusts and optimizes the energy storage charging and discharging power regulation target value based on real-time data feedback.

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