Method and system for processing abandoned energy of off-grid power station

Abstract

The embodiment of the invention provides an energy abandoning processing method and system for an off-grid power station, and relates to the technical field of power grid energy abandoning processing technologies. The method comprises the following steps: acquiring real-time state data in an off-grid power station; determining an energy abandoning event and target energy abandoning power corresponding to the energy abandoning event based on the state data under the condition that the state data meets a preset energy abandoning identification condition; in response to the energy abandoning event, acquiring real-time operation parameters of a plurality of available energy abandoning absorption paths in the off-grid power station; based on the target energy abandoning power and the real-time operation parameters, constructing an energy abandoning distribution optimization model, and solving the energy abandoning distribution optimization model to generate an optimal power distribution instruction; and according to the optimal power distribution instruction, controlling the available abandoned energy consumption path to perform consumption processing on the target abandoned energy power. According to the invention, the problem of low abandoned energy processing precision is solved, and the effect of improving the abandoned energy processing precision is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of power grid curtailment technology, and more specifically, to a method and system for handling curtailment from off-grid power plants. Background Technology

[0002] With the rapid development of renewable energy technologies, off-grid power plants, primarily powered by photovoltaic and wind power, have been widely used in remote areas and islands. However, due to the significant intermittency and fluctuations in the power output of photovoltaic and wind power, a large amount of excess energy is generated when the power generation capacity far exceeds local load demand at certain times and the energy storage system is close to saturation. This portion of energy that cannot be consumed or stored locally is called "waste energy."

[0003] Existing off-grid power plant operation and management schemes handle energy curtailment in a rather crude and simplistic manner. Most systems directly disconnect some generator units or dissipate excess energy as heat through unloading resistors, which is essentially a direct waste of energy. While some systems are equipped with controllable loads, they lack effective monitoring and coordinated control mechanisms, making it impossible to perform precise and dynamic energy dispatch based on real-time curtailment scale and power plant operating status. Therefore, existing technologies generally suffer from inaccurate curtailment monitoring, simplistic handling methods, low energy conversion and utilization efficiency, and poor system flexibility, leading to significant waste of valuable renewable energy and reducing the overall economic efficiency and energy efficiency of off-grid power plants. How to effectively monitor, accurately assess, and optimize the allocation of curtailment from off-grid power plants to achieve efficient recovery and rational utilization is a pressing technical problem that needs to be solved in the current off-grid power plant technology field. Summary of the Invention

[0004] This invention provides a method and system for handling energy curtailment from off-grid power plants, which at least solves the problem of energy curtailment accuracy in related technologies.

[0005] According to one embodiment of the present invention, a method for handling energy curtailment at off-grid power plants is provided, comprising: The real-time status data of the off-grid power station is obtained, wherein the status data includes the total output power of the power generation unit, the total power consumption of the load unit, and the operating status data of the energy storage unit; If the status data meets the preset energy curtailment identification conditions, the energy curtailment event and the target energy curtailment power corresponding to the energy curtailment event are determined based on the status data. In response to the energy curtailment event, real-time operating parameters of multiple available curtailment absorption paths within the off-grid power plant are obtained, wherein the real-time operating parameters of each path include the maximum absorbable power, energy conversion efficiency, and operating cost factor. Based on the target curtailment power and the real-time operating parameters, a curtailment allocation optimization model is constructed, and the curtailment allocation optimization model is solved to generate an optimal power allocation instruction. The optimal power allocation instruction is used to allocate the target curtailment power to the available curtailment absorption path. According to the optimal power allocation instruction, the available curtailment absorption path is controlled to absorb the target curtailed power.

[0006] In an exemplary embodiment, determining the energy curtailment event and the target energy curtailment power corresponding to the energy curtailment event based on the state data when the state data satisfies a preset energy curtailment identification condition includes: Based on the operating status data of the energy storage unit, the current maximum rechargeable power of the energy storage unit is determined; The power balance difference is calculated based on the total output power of the power generation unit and the total power consumption of the load unit; When the power balance difference is greater than the current maximum rechargeable power, the energy curtailment identification condition is triggered, and the difference between the power balance difference and the current maximum rechargeable power is determined as the target energy curtailment power.

[0007] In an exemplary embodiment, the step of constructing an energy curtailment allocation optimization model based on the target curtailment power and the real-time operating parameters, and solving the energy curtailment allocation optimization model to generate an optimal power allocation instruction includes: Obtain the absorption utility value per unit power for each available curtailment absorption path, wherein the absorption utility value is generated based on the energy conversion efficiency and the operating cost factor of the available curtailment absorption path; An objective function is constructed based on the power of all available energy curtailment absorption paths and the absorption utility value corresponding to each available energy curtailment absorption path; The objective function is solved under preset constraints to obtain the optimal power allocation instruction, wherein the optimal power allocation instruction includes a specific power value allocated to each available curtailment and absorption path, and the constraint is that the sum of the power allocated to all available curtailment and absorption paths is equal to the target curtailment power, and the power allocated to each available curtailment and absorption path is not greater than its corresponding maximum absorbable power.

[0008] In one exemplary embodiment, solving the objective function includes: The power allocated to all available energy waste absorption paths is initialized to zero; Under the condition that the power to be allocated is greater than zero, perform the following operations in a loop: among all available curtailment absorption paths that have not yet reached the maximum absorbable power, select the first path with the largest absorption utility value, allocate the preset power increment to the first path, and update the power to be allocated.

[0009] In an exemplary embodiment, after controlling the available energy curtailment absorption path to absorb the target energy curtailment power according to the optimal power allocation instruction, the method further includes: Real-time monitoring of the actual power absorption capacity of each available energy waste absorption path; Calculate the deviation between the actual absorbed power and the corresponding power in the optimal power allocation command; When the deviation exceeds a preset deviation threshold within multiple consecutive time periods, the real-time operating parameters of the available energy curtailment and consumption path corresponding to the deviation are updated.

[0010] In one exemplary embodiment, updating the real-time operating parameters of the available curtailment and absorption path corresponding to the deviation includes: Based on the deviation, a preset attenuation factor is used to correct the energy conversion efficiency of the available waste energy absorption path, so as to generate a corrected energy conversion efficiency.

[0011] In an exemplary embodiment, after controlling the available energy curtailment absorption path to absorb the target energy curtailment power according to the optimal power allocation instruction, the method further includes: Record the total output power of the power generation unit, the total power consumption of the load unit, the operating status data of the energy storage unit, the real-time operating parameters of the available curtailment absorption path, and the optimal power allocation instruction during the curtailment event to form a curtailment event log; When the curtailment rate of the off-grid power station exceeds the alarm threshold within a preset time period, the curtailment processing event log is retrieved. A retrospective analysis was performed on the data in the energy curtailment event log to pinpoint the cause of the abnormal energy curtailment rate.

[0012] According to another embodiment of the present invention, an off-grid power plant curtailment treatment system is provided, comprising: The data acquisition module is used to acquire real-time status data within the off-grid power station, wherein the status data includes the total output power of the power generation unit, the total power consumption of the load unit, and the operating status data of the energy storage unit. The energy curtailment identification module is used to determine an energy curtailment event and the target energy curtailment power corresponding to the energy curtailment event based on the state data when the state data meets the preset energy curtailment identification conditions. The path evaluation module is used to respond to the energy curtailment event and obtain real-time operating parameters of multiple available energy curtailment absorption paths within the off-grid power plant. The real-time operating parameters of each path include the maximum absorbable power, energy conversion efficiency, and operating cost factor. The allocation decision module is used to construct an energy curtailment allocation optimization model based on the target curtailment power and the real-time operating parameters, and solve the energy curtailment allocation optimization model to generate an optimal power allocation instruction. The optimal power allocation instruction is used to allocate the target curtailment power to the available energy curtailment absorption path. The execution control module is used to control the available curtailment absorption path to absorb the target curtailed power according to the optimal power allocation instruction.

[0013] According to yet another embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0014] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0015] This invention automatically identifies energy curtailment events by monitoring the power plant's operating status in real time, and uses an optimized allocation strategy to efficiently and rationally distribute the curtailed power to multiple available consumption paths. Therefore, it can solve the problem of low accuracy in energy curtailment handling and achieve the effect of improving the accuracy of energy curtailment handling and energy utilization. Attached Figure Description

[0016] Figure 1 This is a structural block diagram of an off-grid power plant curtailment treatment system according to an embodiment of the present invention; Figure 2 This is a flowchart of a method for handling energy curtailment at an off-grid power plant according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the efficiency allocation of the absorption path according to an embodiment of the present invention; Figure 4 This is a comparison chart of energy waste disposal before and after optimization according to Embodiment 4 of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0018] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0019] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.

[0020] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "coupled" can refer to an electrical connection that enables signal transmission.

[0021] As used herein, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from the given value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement system).

[0022] Example 1 This embodiment provides an off-grid power plant curtailment handling system. The system monitors the power plant's operating status in real time through a central controller or edge computing gateway deployed locally at the off-grid power plant, automatically identifies curtailment events, and adopts an optimized allocation strategy to efficiently and rationally distribute the curtailed power to multiple available absorption paths. This solves the technical problems of extensive curtailment handling methods and low energy utilization in the prior art, and achieves the beneficial effects of improving the accuracy of off-grid power plant curtailment handling, enhancing energy self-sufficiency, and improving economic benefits.

[0023] like Figure 1 As shown, the system may include a data acquisition module 110, a wasted energy identification module 120, a path evaluation module 130, an allocation decision module 140, an execution control module 150, and a parameter adaptive correction module 160, wherein: The data acquisition module 110 is used to construct a high-frequency real-time data acquisition network. This module communicates with various devices within the power station through multiple industrial communication protocols (such as Modbus RTU / TCP, CAN bus, IEC 61850). These devices mainly include power generation units, load units, and energy storage units. The power generation unit can consist of multiple photovoltaic arrays and several wind turbines. The data acquisition module 110 collects the DC-side voltage, current, and power data of each photovoltaic inverter, as well as the active and reactive power data output from the AC side. The sampling period can be set to 1 second. Simultaneously, it collects the wind speed, wind direction, rotor speed, and grid connection point output power data of each wind turbine. By aggregating and summing these collected data, the total output power of the power generation units of the entire power station is obtained. The load units may include production equipment, lighting systems, and residential electricity consumption. The data acquisition module 110 collects the total power consumption of the load units at the same 1-second cycle through smart meters or power quality analyzers installed on the main feeder or each branch circuit. For the energy storage unit, the data acquisition module 110 communicates with the battery management system (BMS) and the energy storage converter (PCS) to acquire key operating status data, including the state of charge (SOC) of the battery cluster, the terminal voltage and current of the battery cluster, the maximum allowable charging current and discharging current, the current operating power of the PCS and its maximum charging and discharging power limit, and so on, without being limited here.

[0024] The energy curtailment identification module 120 is used to determine the occurrence of energy curtailment events and quantify their scale based on real-time data collected by the data acquisition module 110. This module has an embedded set of energy curtailment identification logic. First, the energy curtailment identification module 120 determines the current maximum rechargeable power of the energy storage unit based on the operating status data of the energy storage unit (especially the SOC value and the maximum allowable charging current reported by the BMS). For example, when the SOC approaches 100%, the BMS will significantly reduce the allowable charging current to protect the battery, thus leading to... The reduction is calculated as follows: in, This is the rated charging power of the PCS. It is the battery's rated capacity. It is a time constant used to prevent overcharging. This is the maximum charging current allowed by the BMS. It measures the battery's real-time voltage; next, the module calculates the current power balance difference. This difference represents the surplus power available for charging energy storage; the preset energy curtailment identification condition is... When this condition is met, it means that the power generation not only meets all load demands but also exceeds the maximum capacity that the energy storage system can currently absorb, at which point the energy curtailment event is confirmed. Simultaneously, the energy curtailment identification module 120 identifies the target curtailment power. It is determined to be the difference between the two, that is ,this This refers to the power that needs to be processed by subsequent modules, otherwise it will be wasted.

[0025] The path evaluation module 130 is immediately activated after the curtailment identification module 120 confirms the curtailment event. This module evaluates all available paths within the power plant that can absorb the curtailed energy and generates a standardized set of real-time operating parameters for each path. In a typical off-grid power plant configuration, these paths may include multiple types, such as: Although the charging path of an energy storage unit primarily utilizes its charging capacity for power balance calculations, if its charging power falls below the equipment's rated limit, it can still be considered part of the energy curtailment absorption. Its maximum absorbable power is as described above. ; A controllable electric heating load path, such as a high-power electric boiler, is used to provide hot water for domestic or industrial purposes. Its availability is determined by its equipment status (whether it is idle, whether the water temperature is below the upper limit), and its maximum absorbable power is its rated power. ; The controlled electrolysis hydrogen production pathway, if the power plant is equipped with an electrolyzer, can convert waste energy into hydrogen for storage. Its availability depends on the operating status of the electrolyzer and the pressure of the hydrogen storage tank, and its maximum absorbable power is its rated power. And so on.

[0026] Then the path evaluation module 130 evaluates each available path. Generate a data structure containing three core parameters: maximum absorbable power. Energy conversion efficiency and operating cost factors For energy storage pathways, This refers to charging efficiency (approximately 0.9-0.95). This could be the equivalent cost of quantifying battery cycle life loss (e.g., 0.05 yuan / kWh); for the electrothermal path, Close to 1 (approximately 0.98). Typically, it is 0; for hydrogen production pathways, This is the conversion efficiency from electricity to hydrogen (approximately 0.6-0.7). This can represent the cost of electrode wear and pure water power consumption, and so on.

[0027] The allocation decision module 140 is used to receive the target energy curtailment power determined by the energy curtailment identification module 120. The path evaluation module 130 generates a set of parameters for each path, with the goal of constructing and solving an optimal power allocation model to generate the optimal power allocation command. First, the allocation decision module 140 assigns parameters to each available absorption path. Define a unit power absorption utility value This utility value is designed to comprehensively reflect the overall value that can be generated by allocating 1kW of power to this path: in These are weighting coefficients, representing the strategic value of different energy products; for example, electrical energy (storage) has the highest value. Hydrogen energy is second only to hydrogen energy. ), lowest thermal energy ( (This is not limited to any specific function; next, construct the objective function:) in, The assignment to the path to be solved The power; solving this objective function requires satisfying two core constraints: 1) (Total power constraint) 2) (Path capacity constraint) To solve this problem quickly, the allocation decision module 140 employs a heuristic algorithm based on marginal utility iterative allocation. This algorithm will... Viewed as a resource pool, with a very small power increment. Using units such as 1kW, in each iteration, this 1kW of power is allocated to the marginal utility (i.e., ...) of all currently available paths. The highest one, until the resource pool is fully allocated, the final output is a set of optimal power allocation instructions. .

[0028] The execution control module 150 is used to receive the optimal power allocation instruction from the allocation decision module 140 and translate it into physical control commands that the underlying devices can recognize and execute; for example, if the power allocated to the energy storage path is... The module will send a power setting command to the energy storage PCS via the CAN bus to set its charging power to [value missing]. If the power allocated to the electric boiler is The module will send commands to the PLC controlling the electric boiler via Modbus TCP to adjust its heating power to... In particular, the execution control module 150 is not only responsible for issuing instructions, but also continuously monitors the actual power feedback of each absorption path through a closed-loop control logic, and compares the actual power feedback with the instruction value to ensure that the instructions are executed accurately.

[0029] The parameter adaptive correction module 160 is used to realize the self-learning and self-optimization of the model. This module continuously monitors the power of the commands issued by the execution control module 150. and the actual absorption power obtained from the data acquisition module 110 The module then calculates the deviation between the two. If it finds that for a certain path (e.g., the electrolysis hydrogen production path), the actual power consumption is consistently significantly lower than the commanded power over multiple consecutive decision cycles (e.g., 1 hour) (e.g., the deviation consistently exceeds 5%), the module will determine the energy conversion efficiency model parameters for that path. The values ​​may be too high and fail to accurately reflect the actual aging or changes in operating conditions of the equipment. In this case, the module updates the parameters using a moving average with a forgetting factor or a simple decay correction, for example: in It is a decay factor close to 1 (e.g., 0.95). The power set in the corresponding instruction; the updated parameters. This information will be stored for use by the path evaluation module 130 and the allocation decision module 140 in the next decision. In this way, the system can continuously adapt to the evolution of equipment performance and maintain the accuracy of its decisions.

[0030] Example 2 Reference Figure 2 This embodiment provides a method for handling energy curtailment at off-grid power plants, specifically including the following steps: Step S100: Acquire real-time status data within the off-grid power station. The status data includes the total output power of the power generation unit, the total power consumption of the load unit, and the operating status data of the energy storage unit.

[0031] In this embodiment, the processing unit sends a data polling command to the communication gateway of the photovoltaic inverter cluster. This command follows the SunSpec Modbus protocol and requests the total active power output of all inverters. The gateway aggregates the data and returns a floating-point value. Simultaneously, the processing unit sends a request conforming to the IEC 61400-25 standard to the monitoring system of the wind turbine generator set to obtain the current total active power output of all wind turbines. Subsequently, the processing unit sends a Modbus TCP request to the multi-function power meter installed on the main power supply bus of the power station to read the current three-phase total active power, which is regarded as the total power consumption of the load unit. Finally, the processing unit communicates with the battery management system (BMS) and the power storage converter (PCS) of the energy storage system to obtain the average state of charge (SOC) of the battery cluster and the maximum allowable charging current calculated by the BMS based on the current battery temperature, voltage, and other conditions. In addition, the processing unit requests the device status and rated parameters from the PCS to obtain its maximum charging power. .

[0032] For example, at 12:00:00:00 on a sunny day, the total photovoltaic output power obtained is... The total wind power output obtained is The processing unit then adds these two figures together to obtain the total output power of the power generation unit at that moment. The total power consumption of the load obtained is The BMS return value is Maximum allowable charging current , And it is currently in standby mode. This data... , The complete status data of the energy storage unit (SOC, maximum charging current, PCS parameters, etc.) is encapsulated into a data frame with a timestamp of 12:00:00.000 and stored in a real-time database for use in subsequent steps.

[0033] Step S200: Based on the total output power of the power generation unit, the total power consumption of the load unit, and the operating status data of the energy storage unit, determine the energy curtailment event and the target energy curtailment power corresponding to the energy curtailment event when the preset energy curtailment identification conditions are met.

[0034] In this embodiment, after acquiring the real-time data frame, the processing unit immediately enters the calculation process: First, based on the operating status data of the energy storage unit, the current maximum rechargeable power of the energy storage unit is determined. : in, The maximum charging power is determined by the PCS's hardware. It is the maximum charging power allowed by the BMS based on cell safety considerations (such as preventing overcurrent and overtemperature). It is a soft limit based on SOC to prevent battery overcharging, wherein: in (e.g., 99%) and (For example, 95%) represents a range in which the charging power decreases linearly.

[0035] For example, when , At that time, assuming the current battery voltage ,but The current SOC is 98.2%, and assuming the linear decrease range is [95%, 99%], then... .

[0036] therefore, This indicates that although the PCS is very powerful, the BMS strictly limits the charging power because the battery is close to full charge, and the energy storage system can currently only absorb a maximum of 60.0 kW of power.

[0037] Next, the power balance difference is calculated. The instantaneous power imbalance between power generation and load: For example, This represents the remaining power in the power plant that needs to be processed after all loads have been met.

[0038] Then, when the power balance difference exceeds the current maximum rechargeable power, the energy curtailment identification condition is triggered, and the difference between the power balance difference and the current maximum rechargeable power is determined as the target energy curtailment power. The preset energy curtailment identification condition is... .

[0039] For example, currently , ;because Once this condition is met, the system confirms that an energy curtailment event has occurred.

[0040] Target curtailment power Calculated as: For example, This 660.7 kW power is the energy that would otherwise be discarded by photovoltaic or wind turbines through reduced power operation without additional measures.

[0041] Step S300: In response to the determination of the curtailment event, obtain the real-time operating parameters of multiple available curtailment absorption paths within the off-grid power plant. The real-time operating parameters of each available curtailment absorption path include the maximum absorbable power, energy conversion efficiency, and operating cost factor.

[0042] After confirming Then, an inventory of all potential energy absorption and storage points within the power plant is conducted; at this time, the processing unit will query the pre-configured list of available absorption paths and perform real-time status assessments on each path in the list.

[0043] For example, suppose an off-grid power plant is configured with the following three potential absorption paths: Path 1: Energy storage unit charging path.

[0044] Although its primary charging capacity has been used for calculations, it remains the first choice for energy waste disposal. The processing unit utilizes its maximum absorbable power. Set as calculated ,Right now Its energy conversion efficiency Read from the energy storage system technical documentation, for This means that 95% of the charged electrical energy can be stored, and its operating cost factor is... It is set to an equivalent battery aging cost, for example Yuan / kWh.

[0045] Path 2: Controllable electric heating load path.

[0046] This is a 250 kW electric boiler providing hot water to the factory area. The processing unit queries the boiler's controller via Modbus to check its current status. The controller returns the following information: current water temperature 65℃, target water temperature upper limit 90℃, equipment has no faults, and is in standby mode. Therefore, this path is determined to be available. Its maximum absorbable power... That is, its rated power. Its energy conversion efficiency (electricity to heat) Very high, set to Its operating cost factor Because it utilizes waste energy and equipment wear is negligible, it was set as... Yuan / kWh.

[0047] Pathway 3: Controlled electrolysis hydrogen production path.

[0048] This is a 500 kW proton exchange membrane (PEM) electrolyzer. The processing unit queries its control system for status and receives the following information: the equipment is fault-free and in hot standby mode; the hydrogen outlet pressure is 1.5 MPa, far below the upper limit pressure of the hydrogen storage tank (3.0 MPa), therefore, this path is also deemed usable; its maximum absorbable power... For its rated power, Its energy conversion efficiency (electricity to hydrogen, based on low calorific value) Based on the equipment factory test report, it is set as follows: Its operating cost factor Taking into account the trace losses of electrode catalysts and the consumption of deionized water, it is equivalent to Yuan / kWh.

[0049] Finally, the output of this step is a structured dataset containing real-time parameters for all available paths, as shown in the table below. This dataset fully describes the current power plant's energy curtailment absorption capacity: Table 1 Step S400: Based on the target curtailed power and the real-time operating parameters of multiple available curtailment and absorption paths, construct and solve the curtailment allocation optimization model to generate the optimal power allocation instruction to allocate the target curtailed power to the multiple available curtailment and absorption paths.

[0050] Specifically, the processing unit uses the calculated target power of abandoned energy. Combined with the generated path parameter table, perform optimization calculations.

[0051] First, a unit power absorption utility value is defined for each available energy curtailment absorption path. The processing unit uses a predefined utility function to calculate the marginal utility of each path. This aims to normalize paths with different physical dimensions (efficiency, cost) and different strategic values ​​onto a unified evaluation dimension: in, It is an equivalent number of hours, which is taken as 1 hour here to make the cost factor unit match; This represents the weighting of the value of energy products. Assuming the power plant operator sets the value of stored electrical energy as the highest, Hydrogen energy is an important product. Hot water is a byproduct. .

[0052] Calculate the utility value of each path accordingly: These utility values ​​indicate that, in terms of overall value, energy storage charging is the preferred method, followed by hydrogen electrolysis, and lastly, electric boilers. Specifically, for example... Figure 3 As shown.

[0053] Next, the objective function and constraints are constructed, where: Objective function: Constraints: Then, the optimization problem is solved. This embodiment employs a method based on marginal utility (i.e., The greedy allocation strategy is equivalent to iterative allocation; because In this model, it is a constant, and the allocation logic is as follows: 1. Press Sort the paths from largest to smallest: Path 1 > Path 3 > Path 2.

[0054] 2. Target curtailment power These paths are allocated sequentially until their capacity limits are met.

[0055] 3. Assign to path 1: .

[0056] 4. Remaining power to be allocated: .

[0057] 5. Assigned to path 3: .

[0058] 6. Remaining power to be allocated: .

[0059] 7. Assign to path 2: .

[0060] 8. Remaining power to be allocated: .

[0061] 9. Allocation complete.

[0062] Ultimately, the generated optimal power allocation command is a vector: All units are in kW.

[0063] Step S500: Based on the optimal power allocation instruction, control multiple available energy curtailment absorption paths to absorb the target energy curtailment power.

[0064] The processing unit distributes the generated optimal power allocation command to the corresponding device controller: Send the command to the energy storage PCS: Set_Charge_Power(60.0).

[0065] Send the command to the electric boiler controller: Set_Heating_Power(100.7).

[0066] Send the command to the electrolyzer controller: Set_Electrolysis_Power(500.0).

[0067] These instructions are sent via a communication protocol and address agreed upon with the equipment. Upon receiving the instructions, the equipment controller adjusts its internal power electronic converters (such as the PWM wave of IGBTs) to ensure that the input power of the equipment accurately tracks the instruction value. At the same time, the program executing the control starts a monitoring loop, reading the actual operating power of these devices every second to ensure that they successfully respond to the control instructions and achieve the absorption of the target curtailment power of 660.7 kW.

[0068] Example 3 The difference from Examples 1-2 is that, in order to ensure the accuracy of the instructions, adaptive correction of model parameters and diagnosis of abnormal events are also possible.

[0069] For example, after the command has been executed for a period of time, the system detects that the actual input power of the electrolyzer hovers around 480 kW instead of the commanded 500 kW, a deviation of 4%. When this situation persists for more than 5 minutes, the parameter adaptive correction module is triggered, which considers the model to be in a state of flux. The initial settings may be too idealistic, causing the system to overestimate its absorption capacity under current operating conditions; in this case, the module will... Make a correction, for example, lower it to This will allow the power allocated to the electrolyzer to be more closely aligned with its actual capacity in future decision-making.

[0070] In addition, the system continuously records a complete log for each energy curtailment event, including source, load, and storage data at the time, evaluation parameters for each path, and the final allocation decision. If maintenance personnel find an abnormally high energy curtailment rate report for the month, they can enable the backtracking analysis function. Specifically, the system retrieves the logs for the days with the highest energy curtailment rates, and subsequent analysis reveals that during those days, the electric boiler path... It was always evaluated as 0, even when the energy curtailment was huge; at this point, by checking the original status message of the boiler controller, it was found that the "water temperature sensor fault" flag was set to 1. In this way, the system helped the operation and maintenance personnel quickly locate the root cause of the problem: it was not an error in the allocation algorithm, but because a sensor failure prevented an important consumption path from being utilized by the system, thus leading to an increase in the energy curtailment rate, and so on.

[0071] Example 4 Unlike Examples 1-3, this example no longer handles the current curtailed power in isolation. Instead, it integrates curtailment management into an overall energy dispatch strategy within a rolling time window (e.g., the next 24 hours). By predicting future power generation and load conditions, it proactively adjusts the operating status of energy storage units and controllable loads to prevent or minimize the generation of curtailment at its source through "peak shaving and valley filling." It also guides unavoidable curtailment to the most valuable absorption path in the future time scale, thereby solving the suboptimal decision-making problem caused by the lack of foresight in the prior art. For example, prematurely charging energy storage before the upcoming power generation peak results in the inefficient absorption of curtailment during the peak period.

[0072] The specific process may include the following steps: Step S600: Generate a power generation prediction sequence and a load power prediction sequence that cover the preset optimized time domain.

[0073] In this embodiment, a rolling time window (i.e., optimization of the time domain) is first set. ),For example Hours, time step If it is 15 minutes, then this time domain includes A discrete time step.

[0074] First, a power generation prediction sequence is then generated: The forecasting process can be accomplished by a deep learning forecasting model that uses numerical weather prediction (NWP) data as its core input.

[0075] For example, the system obtains NWP data for the next 24 hours at a resolution of 15 minutes from an external meteorological service interface, including total horizontal irradiance (GHI), wind speed, wind direction, and ambient temperature. This data constitutes a multidimensional input time series. This series is then fed into a pre-trained Long Short-Term Memory (LSTM) network model. This LSTM model is designed to capture the complex nonlinear temporal relationship between meteorological data and photovoltaic and wind power output.

[0076] The macroscopic architecture of this LSTM model consists of an input layer, two stacked LSTM layers, and a fully connected output layer: a. Input tensor definition: The shape of the tensor input to the model is... The dimensions correspond to batch size (Batch Size=1), time step (96 15-minute steps), and feature dimensions (5 meteorological features: GHI, wind speed, wind direction, temperature, and humidity).

[0077] b. Core Processing and Parameterization: The first LSTM layer contains 128 hidden units, used to extract preliminary temporal features from the input sequence. The shape of the output tensor of this layer is... The second LSTM layer also contains 128 hidden units, receiving the output of the first layer for deeper feature abstraction; this layer also outputs a shape... The tensor is then used; finally, a time-distributed fully connected layer is applied to each time step of the output sequence of the second LSTM layer; thus, the fully connected layer linearly transforms the 128-dimensional feature vector into a 2-dimensional output vector, corresponding to the predicted photovoltaic power and wind power, respectively.

[0078] c. Output Tensor Definition: The final output tensor of the model has the following shape: By summing the two feature dimensions of the output tensor at each time step, the final power generation prediction sequence is obtained. .

[0079] For example, in At the time step (i.e., 5 hours later), the model predicts that GHI reaches its peak, wind speed stabilizes, and the predicted output power may be... .

[0080] Next, the load power prediction sequence is generated: This forecast is based on historical load data and calendar features. For example, the system extracts load data for the same day (e.g., all Wednesdays) from the past four weeks and combines this with features such as whether the current date is a holiday and the current season. It then uses a seasonal autoregressive integral moving average (SARIMA) model to generate a 24-hour load forecast sequence. The model may then predict the load for the next 24 hours. (That is, 8 hours later), as the production shifts begin work, the load will experience a shift from... leap to The steps.

[0081] Step S700: Based on the power generation prediction sequence and the load power prediction sequence, generate a dynamic energy opportunity cost factor for each time step in the optimization time domain.

[0082] In this embodiment, the energy opportunity cost factor Aimed at quantifying time steps The relative value of storing or consuming 1 kWh of electrical energy is determined by the fact that during periods of predicted extreme energy surplus, the opportunity cost of energy is extremely low, so energy storage or energy conversion is carried out; while during periods of predicted energy shortage, the opportunity cost of energy is extremely high, and priority should be given to ensuring supply to avoid unnecessary consumption.

[0083] First, calculate the predicted net power sequence. ,in This sequence visually reflects the energy surplus and shortage situation in various future periods.

[0084] Then, through a nonlinear mapping function Convert net power into opportunity cost factor: in, It is a baseline cost, which can be set to 1; It is a positive sensitivity coefficient used to adjust how drastically the cost factor responds to changes in net power.

[0085] For example, suppose : exist At that time, predict net power (With a large surplus), the opportunity cost factor is: This extremely low value indicates that electricity is very "cheap" at this time.

[0086] exist At that time, it was predicted that photovoltaic output would decrease while the load was at its peak. (Severe shortage), then the opportunity cost factor is This value indicates that electrical energy is extremely precious at this time.

[0087] Finally, this step generates an opportunity cost factor sequence of the same length as the predicted sequence: .

[0088] S800: Construct and solve a rolling optimization model with the goal of maximizing the total operating value of the system in the optimization time domain, so as to generate a power scheduling scheme covering the optimization time domain and for energy storage units and all available curtailment and consumption paths.

[0089] First, we construct the second objective function of the optimization model, which aims to maximize the total operating value of the system over the next 24 hours. : in: In time step The value of the energy products produced, for the hydrogen production route via electrolysis, is [value missing]. Note that the value here is modulated by the opportunity cost factor: when energy is cheap ( (Small), hydrogen production has a higher relative value; The hydrogen production power to be optimized is similar for the electrothermal pathway, and will not be elaborated here.

[0090] It is the time step of each device The operating and aging costs, for example, the aging costs of energy storage charging and discharging, can be modeled as follows: In the formula, It is the punitive cost of abandoning energy. ,in Is the model at time step The calculated power that must be discarded, and It is a very large penalty coefficient to drive the optimizer to minimize energy wastage.

[0091] It is the terminal value of the remaining electricity of the energy storage unit at the end of the optimization time domain, used to prevent the model from running out of energy storage at the end of the period.

[0092] Next, the constraints of the model are determined, which ensure that the scheduling plan is physically feasible: Power balance constraints (for each time step) ): Energy storage state transition constraints (for each time step) ): Equipment operating constraints (for each time step) and each device ): More detailed constraints, such as equipment start-up and shutdown, and ramp rate, will not be elaborated here.

[0093] Then, the optimization model is solved to obtain a detailed power scheduling plan, that is, at what power all controllable units such as energy storage units, electrolyzers, and electric boilers should operate at 96 time points in the next 24 hours.

[0094] For example, such as Figure 4 As shown, the optimization results may display: Between 2:00 AM and 4:00 AM ( to Although wind power output resulted in a slight surplus of power, the model predicted a huge photovoltaic peak at noon. Therefore, the energy storage system was instructed to maintain a low SOC level (e.g., only slowly charging from 20% to 30%), reserving a large amount of storage space to absorb the energy wastage during the midday peak.

[0095] From 11:00 AM to 2:00 PM ( to When the photovoltaic output reaches its peak, it is predicted that a large amount of surplus energy will be generated. At this time, the dispatch plan instructs the energy storage system to charge at full power, and at the same time instructs the electrolyzer and electric boiler to operate at rated power to jointly absorb this part of the predicted "waste energy".

[0096] At 18:00 in the evening ( When the load reaches the evening peak, the photovoltaic system has disappeared; the dispatch plan instructs the energy storage system to start discharging to meet the load demand and avoid starting the backup diesel generator.

[0097] S900: Executes and updates the power scheduling scheme based on the principle of rolling optimization.

[0098] After obtaining the complete scheduling plan for the next 24 hours, the system will not execute it in its entirety. Instead, it will only extract the control instructions for the first time step (i.e. the next 15 minutes) of the plan and send them to each device through the execution control module (the same as module 150 in Embodiment 1).

[0099] For example, if the current time is 10:45 AM, the optimization model has been solved; in the generated plan, the instruction for the first time step (10:45-11:00 AM) is: energy storage charging power. Electrolytic cell power The system then sends a 200kW charging command to the PCS.

[0100] Fifteen minutes later, at 11:00, the system will not continue executing the instructions originally planned for 11:00-11:15, but will instead restart from step S600: 1. Obtain the latest actual status of the power plant (e.g., the actual SOC of energy storage is 35%, instead of the predicted 34.8%).

[0101] 2. Obtain the updated NWP data and regenerate the generation and load forecast sequence for the next 24 hours (from 11:00 to 11:00 the next day).

[0102] 3. Recalculate the cost factor sequence.

[0103] 4. Based on the new initial state and new predictions, re-solve the optimization model.

[0104] 5. Extract the instructions for the first time step (11:00-11:15) in the new plan and execute them.

[0105] This "prediction-optimization-execution-feedback" cycle continues, enabling the system to continuously integrate the latest actual and predicted information into decision-making. This results in strong robustness against prediction errors and unexpected disturbances, ensuring the continuous optimality of decision-making in uncertain environments.

[0106] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0107] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0108] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0109] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0110] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0111] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of 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.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for handling energy curtailment at off-grid power plants, characterized in that, include: The real-time status data of the off-grid power station is obtained, wherein the status data includes the total output power of the power generation unit, the total power consumption of the load unit, and the operating status data of the energy storage unit; If the status data meets the preset energy curtailment identification conditions, the energy curtailment event and the target energy curtailment power corresponding to the energy curtailment event are determined based on the status data. In response to the energy curtailment event, real-time operating parameters of multiple available curtailment absorption paths within the off-grid power plant are obtained, wherein the real-time operating parameters of each path include the maximum absorbable power, energy conversion efficiency, and operating cost factor. Based on the target curtailment power and the real-time operating parameters, a curtailment allocation optimization model is constructed, and the curtailment allocation optimization model is solved to generate an optimal power allocation instruction. The optimal power allocation instruction is used to allocate the target curtailment power to the available curtailment absorption path. According to the optimal power allocation instruction, the available curtailment absorption path is controlled to absorb the target curtailed power.

2. The method according to claim 1, characterized in that, When the state data meets the preset energy curtailment identification conditions, determining the energy curtailment event and the target energy curtailment power corresponding to the energy curtailment event based on the state data includes: Based on the operating status data of the energy storage unit, the current maximum rechargeable power of the energy storage unit is determined; The power balance difference is calculated based on the total output power of the power generation unit and the total power consumption of the load unit; When the power balance difference is greater than the current maximum rechargeable power, the energy curtailment identification condition is triggered, and the difference between the power balance difference and the current maximum rechargeable power is determined as the target energy curtailment power.

3. The method according to claim 1, characterized in that, The step of constructing an energy curtailment allocation optimization model based on the target curtailment power and the real-time operating parameters, and solving the energy curtailment allocation optimization model to generate the optimal power allocation instruction includes: Obtain the absorption utility value per unit power for each available curtailment absorption path, wherein the absorption utility value is generated based on the energy conversion efficiency and the operating cost factor of the available curtailment absorption path; An objective function is constructed based on the power of all available energy curtailment absorption paths and the absorption utility value corresponding to each available energy curtailment absorption path; The objective function is solved under preset constraints to obtain the optimal power allocation instruction, wherein the optimal power allocation instruction includes a specific power value allocated to each available curtailment and absorption path, and the constraint is that the sum of the power allocated to all available curtailment and absorption paths is equal to the target curtailment power, and the power allocated to each available curtailment and absorption path is not greater than its corresponding maximum absorbable power.

4. The method according to claim 3, characterized in that, Solving the objective function includes: The power allocated to all available energy waste absorption paths is initialized to zero; Under the condition that the power to be allocated is greater than zero, perform the following operations in a loop: among all available curtailment absorption paths that have not yet reached the maximum absorbable power, select the first path with the largest absorption utility value, allocate the preset power increment to the first path, and update the power to be allocated.

5. The method according to claim 1, characterized in that, After controlling the available energy curtailment absorption path to absorb the target energy curtailment power according to the optimal power allocation instruction, the method further includes: Real-time monitoring of the actual power absorption capacity of each available energy waste absorption path; Calculate the deviation between the actual absorbed power and the corresponding power in the optimal power allocation command; When the deviation exceeds a preset deviation threshold within multiple consecutive time periods, the real-time operating parameters of the available energy curtailment and consumption path corresponding to the deviation are updated.

6. The method according to claim 5, characterized in that, The real-time operating parameters for updating the available curtailment and energy consumption paths corresponding to the deviation include: Based on the deviation, a preset attenuation factor is used to correct the energy conversion efficiency of the available waste energy absorption path, so as to generate a corrected energy conversion efficiency.

7. The method according to claim 1, characterized in that, After controlling the available energy curtailment absorption path to absorb the target energy curtailment power according to the optimal power allocation instruction, the method further includes: Record the total output power of the power generation unit, the total power consumption of the load unit, the operating status data of the energy storage unit, the real-time operating parameters of the available curtailment absorption path, and the optimal power allocation instruction during the curtailment event to form a curtailment event log; When the curtailment rate of the off-grid power station exceeds the alarm threshold within a preset time period, the curtailment processing event log is retrieved. A retrospective analysis was performed on the data in the energy curtailment event log to pinpoint the cause of the abnormal energy curtailment rate.

8. A system for handling energy curtailment at an off-grid power plant, characterized in that, include: The data acquisition module is used to acquire real-time status data within the off-grid power station, wherein the status data includes the total output power of the power generation unit, the total power consumption of the load unit, and the operating status data of the energy storage unit. The energy curtailment identification module is used to determine an energy curtailment event and the target energy curtailment power corresponding to the energy curtailment event based on the state data when the state data meets the preset energy curtailment identification conditions. The path evaluation module is used to respond to the energy curtailment event and obtain real-time operating parameters of multiple available energy curtailment absorption paths within the off-grid power plant. The real-time operating parameters of each path include the maximum absorbable power, energy conversion efficiency, and operating cost factor. The allocation decision module is used to construct an energy curtailment allocation optimization model based on the target curtailment power and the real-time operating parameters, and solve the energy curtailment allocation optimization model to generate an optimal power allocation instruction. The optimal power allocation instruction is used to allocate the target curtailment power to the available energy curtailment absorption path. The execution control module is used to control the available curtailment absorption path to absorb the target curtailed power according to the optimal power allocation instruction.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method as described in any one of claims 1 to 7.

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