Performance monitoring method, system and equipment of new energy load device and medium

Abstract

The embodiment of the invention relates to the field of production monitoring of new energy load devices, and discloses a performance monitoring method, system and equipment for a new energy load device and a medium. Particularly, the method reflects the power utilization rate index of a new energy load device to a new energy station, and evaluates the operation performance of the new energy load device in the period of time by using the operation index. According to the evaluation mode, the utilization rate of the new energy can be visually seen, the operation concept that the new energy load device needs to be flexibly coordinated with power generation of the new energy station and economic value maximization is achieved is better met, and the operation performance of the new energy load device can be evaluated more accurately.

Description

Technical Field

[0001] This invention relates to the field of production monitoring of new energy load devices, specifically to a method, system, equipment, and medium for performance monitoring of new energy load devices. Background Technology

[0002] Traditional chemical plants have a stable supply of raw materials and electricity. Their operation philosophy emphasizes stability, long-term operation, and maximizing full-load operation. The general evaluation indicators for such plants are energy consumption, efficiency, and producing as many chemical products as possible per unit of energy. However, new energy green chemical plants, which use water electrolysis to produce hydrogen as their main raw material, derive their electricity from renewable wind and solar power. Unlike the stable power supply of traditional chemical plants, the volatility and randomness of wind and solar power generation cause significant fluctuations in the power supply for green chemical plants. This results in a wide range of load fluctuations for water electrolysis hydrogen production. In other words, green chemical plants using hydrogen as their main raw material experience significant load fluctuations. Under large load fluctuations, the energy consumption and efficiency of chemical plants differ. For example, the net ammonia value of ammonia synthesis will inevitably decrease under low load, while the electricity consumption per unit of hydrogen will increase under high load in the electrolyzer. Therefore, evaluating the energy consumption and efficiency of chemical plants solely according to the methods used for traditional chemical plants would contradict the operational philosophy of new energy green chemical plants. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, device, and medium for monitoring the performance of new energy load devices, which provides an evaluation method that can more accurately assess the operational performance of new energy load devices.

[0004] To address the aforementioned technical problems, embodiments of the present invention provide a performance monitoring method for a new energy load device, comprising: determining the theoretical output characteristics of the new energy power station within a set time period based on historical power generation data of the new energy power station within a set time period; determining the ideal power consumption characteristics of the new energy load device within the set time period based on historical operating data and theoretical output characteristics of the new energy load device at the beginning of the set time period; determining the ideal operating indicators of the new energy load device within the set time period based on the ideal power consumption characteristics and theoretical output characteristics; wherein, the operating indicators include indicators reflecting the utilization rate of the new energy power station's power by the new energy load device; determining the actual operating indicators of the new energy load device within the set time period based on historical operating data and theoretical output curve of the new energy load device within the set time period; and evaluating the operational performance of the new energy load device within the set time period based on the ideal operating indicators and the actual operating indicators.

[0005] In one embodiment, the indicators reflecting the utilization rate of the renewable energy load device for the renewable energy power station include the amount of power curtailed and / or the curtailment rate.

[0006] In one embodiment, calculating the ideal power consumption characteristics of the new energy load device within the set time period includes: calculating the ideal power consumption characteristics of the new energy load device within the set time period using an optimization algorithm; wherein the optimization objective of the optimization algorithm further includes at least one of the following: minimizing the amount of power wasted, maximizing the product output of the new energy load device, minimizing the discharge of the energy storage device in the new energy power station, minimizing the load fluctuation amplitude of the new energy load device, and minimizing the number of start-stop cycles of the new energy load device.

[0007] In one embodiment, the constraints used in the optimization algorithm include at least one of the following: the equipment maintenance status and online / offline status of the new energy load device.

[0008] In one embodiment, the method is executed continuously in a rolling time window manner, wherein the length of the set time period is the window length of the time window.

[0009] In one embodiment, after evaluating the operational performance of the renewable energy load device within the set time period based on the ideal operating indicators and the actual operating indicators, the method further includes: providing guidance on the operational plan of the renewable energy load device in the next time period based on the operational performance.

[0010] In one embodiment, the new energy load device includes a water electrolysis hydrogen production device and a downstream chemical plant using hydrogen as a raw material.

[0011] Compared with the prior art, the above embodiments of the present invention can have at least one or more of the following beneficial effects: This application proposes a method for calculating the operational indicators of renewable energy load devices over a past period, particularly the power utilization rate of renewable energy load devices to renewable energy power plants, and using this power utilization rate to evaluate the operational performance of renewable energy load devices during that period. This evaluation method not only provides a clear view of renewable energy utilization but also aligns better with the operational philosophy that renewable energy load devices need to flexibly coordinate with renewable energy power plants to maximize economic value. It can more accurately assess the operational performance of renewable energy load devices; it focuses more on the proactive value creation orientation of renewable energy load devices as advanced electrical equipment or system integrations with active response, flexible adjustment, and intelligent coordination capabilities. Using the power utilization rate indicator can more accurately, fairly, and effectively drive technological progress and operational optimization. This evaluation method can better promote the transition of renewable energy from "grid connection" to "effective utilization." The amount of abandoned electricity and / or the abandonment rate can be selected as operational indicators for evaluation, because they not only directly reflect the level of "source-load matching" of the device or system, but also intuitively quantify the degree of loss of new energy, directly answering the core question of "how much green electricity that should have been used was wasted", enabling managers, investors and regulators to clearly perceive the system's inefficiencies in resource utilization. Attached Figure Description

[0012] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0013] Figure 1 This is a flowchart of a performance monitoring method for a new energy load device provided in an embodiment of the present invention; Figure 2 This is a flowchart of another performance monitoring method for a new energy load device provided in an embodiment of the present invention; Figure 3 This is a block diagram of a performance monitoring system for a new energy load device provided in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the various embodiments of the present invention to facilitate a better understanding of this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the various embodiments below is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0015] The term "new energy power station" in this application refers to a collection of facilities that generate electricity based on renewable energy sources, such as wind farms (consisting of multiple wind turbine generators), photovoltaic power stations (consisting of photovoltaic module arrays and inverter and booster systems), as well as solar thermal power stations and biomass power plants. These power stations convert natural energy into electrical energy, and their output is intermittent and fluctuates.

[0016] The new energy load devices involved in this application refer to advanced electrical equipment or system integrations with active response, flexible adjustment, and intelligent coordination capabilities under the background of new power systems. These devices, through digital and intelligent control, can dynamically adjust their power consumption and time periods according to the output fluctuations of renewable energy sources (such as wind power and photovoltaics), thereby efficiently absorbing green electricity and improving the overall economic efficiency and security of the energy system. The new energy load devices in this application can be water electrolysis hydrogen production (green hydrogen) devices and downstream chemical plants using hydrogen as raw material, such as at least one of ammonia synthesis plants and methanol synthesis plants. However, they are not limited to these; new energy load devices can also be flexible electric heating / electric thermal storage devices, such as electric boilers equipped with large thermal storage bodies and thermal storage electric heaters.

[0017] like Figure 1 As shown, an embodiment of the present invention relates to a performance monitoring method for a new energy load device, comprising the following steps: Step 101: Determine the theoretical power output characteristics of the new energy power station within the set time period based on the historical power generation data of the new energy power station within the set time period. Step 102: Based on the historical operating data and theoretical output characteristics of the new energy load device at the beginning of the set time period, determine the ideal power consumption characteristics of the new energy load device within the set time period. Step 103: Based on the ideal power consumption characteristics and theoretical power output characteristics, determine the ideal operating indicators of the new energy load device within a set time period; among which, the operating indicators include indicators that reflect the utilization rate of the new energy load device on the power of the new energy power station. Step 104: Determine the actual operating indicators of the new energy load device within the set time period based on the historical operating data and theoretical output characteristics of the new energy load device within the set time period. Step 105: Evaluate the operational performance of the new energy load device within the set time period based on the ideal operating indicators and the actual operating indicators.

[0018] This application proposes a method for calculating the power utilization rate of renewable energy load devices to renewable energy power plants over a past period, and using this power utilization rate as an indicator to evaluate the operational performance of the renewable energy load devices during that period. This evaluation method not only provides a clear view of renewable energy utilization but also aligns better with the operational philosophy that renewable energy load devices need to flexibly coordinate with renewable energy power plants to maximize economic value. It can more accurately assess the operational performance of renewable energy load devices; it focuses more on the proactive value creation orientation of renewable energy load devices as advanced electrical equipment or system integrations with proactive response, flexible adjustment, and intelligent coordination capabilities. Using the power utilization rate indicator can more accurately, fairly, and effectively drive technological progress and operational optimization. This evaluation method can better promote the transition of renewable energy from "grid connection" to "effective utilization."

[0019] In this application, data acquisition devices deployed at renewable energy power plants and renewable energy load devices can collect and store power generation data from renewable energy power plants and operational data from renewable energy load devices. Specifically, the data acquisition devices deployed at renewable energy power plants and renewable energy load devices include, but are not limited to, sensing and metering devices, which can periodically collect power generation data from renewable energy power plants and operational data from renewable energy load devices. The collected raw data can be transmitted to a data aggregation unit at the edge via an industrial communication network, where preprocessing, including timestamp alignment, format standardization, and preliminary filtering, can be performed. The processed data is then written into a database, and data integrity verification and logical rationality checks can be performed, ultimately forming a complete, well-organized, and high-quality historical time-series dataset, providing a data foundation for subsequent analysis. The data acquisition cycle can be, for example, 5 minutes or 15 minutes, and can be set as needed.

[0020] Historical power generation data from renewable energy power plants and historical operating data from renewable energy load devices can be pre-processed time-series datasets. Historical power generation data from renewable energy power plants can include renewable energy generation capacity and relevant environmental parameters such as wind speed, temperature, and real-time irradiance. Historical operating data from renewable energy load devices can include total power consumption, component power, key process parameters, load maintenance status, online status, and maximum allowable rate of change. The performance monitoring system can retrieve historical power generation data from renewable energy power plants and historical operating data from renewable energy load devices within a set time period from the database. This set time period can be a preset time cycle, such as one day, and can be set as needed.

[0021] The performance evaluation method in this application is executed by a performance monitoring system. In step 101, the performance monitoring system can obtain historical power generation data of new energy power plants within a preset time period from the database, and determine the theoretical output characteristics of the new energy power plants within the corresponding preset time period based on the obtained historical power generation data. Specifically, a preset model can be used to calculate the performance curve of the new energy power plants under relevant operating conditions. For example, for photovoltaic power plants, an irradiance-temperature-power mapping model can be established; for wind power plants, a wind speed-power conversion model can be established. The theoretical output characteristics not only include a complete theoretical power generation time series curve, but also further extract its statistical features, such as daily / monthly average output curves, maximum / minimum output, volatility, ramp rate, and other quantitative indicators. Furthermore, before using the model to obtain the theoretical output characteristics of the new energy power plants, the power series in the historical power generation data can be cleaned and reconstructed to remove abnormally low or zero value data segments caused by equipment failure, communication interruption, or human-induced power rationing, thereby restoring the power generation capacity that the new energy power plants should have under no external constraints. This set of theoretical output characteristic data will serve as a benchmark for evaluating actual operating efficiency, calculating power curtailment losses, and conducting future source-load matching analysis.

[0022] In addition, historical power generation data can also include the theoretical output characteristics of the new energy power station. In this case, the power generation of the new energy power station needs to be collected and recorded in real time on-site. Statistical characteristics can also be extracted based on the power generation in time series and stored in a database. In this case, the performance monitoring system only needs to extract the above-mentioned theoretical output characteristic data from the historical power generation data to use it directly.

[0023] In step 102, based on the theoretical output characteristics of the new energy power station and combined with the historical operating status of the new energy load device at the start of the set time period, the ideal power consumption characteristics of the new energy load device during the entire set time period are calculated.

[0024] This step aims to construct an operational baseline under a hypothetical scenario. Essentially, it involves building a model with theoretical power output characteristics as the primary input and operational constraints of renewable energy load units as boundary conditions. The historical operational states of the renewable energy load units at the start of a set time period, such as the unit status and process parameters at that start, are used to initialize the model's initial conditions and ensure feasibility. Based on this model, the scheduling strategy corresponding to the renewable energy load units under the theoretical power output characteristics of the renewable energy power plant can be calculated.

[0025] In one example, the model can be implemented using an optimization algorithm to construct an optimal operating benchmark. The optimization objective of this algorithm can be set as one or any combination of the following: minimizing abandoned power, maximizing the output of renewable energy load devices, minimizing the discharge of energy storage devices in renewable energy power plants, minimizing the load fluctuation range of renewable energy load devices, minimizing the start-up and shutdown frequency of renewable energy load devices, maximizing renewable energy absorption, and minimizing the overall system operating cost. The constraints used in this optimization algorithm include at least one of the following: the equipment maintenance status and online / offline status of the renewable energy load devices. After solving the model, a power consumption curve that highly matches the theoretical output characteristics in time sequence and strictly satisfies the process and safety constraints of the load devices themselves can be obtained; this is the ideal power consumption characteristic. This characteristic curve and the accompanying device state sequence (such as start-up and shutdown plans) together constitute an "ideal reference system" for evaluating the actual operating economy of the devices and formulating subsequent optimized scheduling instructions. Using the equipment maintenance status and online / offline status of the renewable energy load devices as constraints of the optimization algorithm makes the model closer to physical reality, and the ideal power consumption characteristics it generates are more consistent with the objective conditions of equipment operation, more accurate, and in line with the actual production process. The constraints can also include other types of constraints, such as upper and lower power limits, minimum continuous operation / downtime constraints, and start-stop frequency constraints to ensure equipment safety. They can also include process flow and material balance constraints, such as material / energy balance constraints, production continuity constraints, and product quality / specification constraints. Furthermore, they can include physical constraints such as upper and lower limits of energy storage SOC, upper and lower limits of hydrogen production load, and upper limits of hydrogen production fluctuations. This embodiment does not impose any restrictions and can be set according to actual needs.

[0026] In this example, the optimization algorithm integrates the theoretical output curve, the physical and operational constraints of the load devices (such as power limits, ramp rate, minimum start-up and shutdown time, and process continuity), and economic objectives (such as minimizing electricity costs and maximizing revenue) to obtain the theoretically optimal electricity consumption curve under given conditions. Its fundamental purpose is to transform the complex problem of renewable energy consumption and load management into a calculable, verifiable, and executable engineering science problem. It not only generates a curve but also provides a digital decision support core, systematically improving the operational economy, green contribution, equipment safety, and management intelligence of renewable energy load devices. It is an indispensable key technology link in building "active loads" in new power systems.

[0027] In step 103, based on the ideal power consumption characteristic curve and the theoretical power output characteristic curve, the ideal operating indicators that the load device should achieve within a set time period are calculated using a quantitative analysis model. During the calculation process, the theoretical power output characteristic curve and the ideal power consumption characteristic curve need to be aligned and compared on a time scale. The core of these operating indicators is to quantitatively evaluate the utilization efficiency and synergistic performance of new energy load devices on new energy power. The ideal operating indicators can include various types; examples of generating different ideal operating indicators are given below.

[0028] Indicators reflecting the power utilization rate of load devices include, for example: obtaining the theoretical absorption rate by calculating the ratio of the area under the ideal power consumption characteristic curve (i.e., ideal total power consumption) to the area under the theoretical output characteristic curve (i.e., theoretical total power generation); simultaneously, calculating the matching degree of power of the two curves for each time period to generate a theoretical time matching coefficient, which is used to quantify the tracking effect of theoretical power consumption behavior on the fluctuation of new energy sources; combining the above two curves, calculating the theoretical power curtailment (the difference between theoretical power generation and ideal total power consumption), theoretical power curtailment rate (the proportion of theoretical power generation not absorbed by the ideal load), and theoretical net load volatility (the smoothness index of the theoretical net load curve after subtracting ideal power consumption from theoretical output).

[0029] Indicators reflecting the economic and technical performance of load devices include, for example, directly calculating their statistical characteristics based on ideal power consumption characteristic curves to obtain ideal load volatility (such as standard deviation or peak-valley difference rate) and ideal average load rate (the ratio of average load to maximum load). Simultaneously, from the optimization model that generates ideal power consumption characteristics, the ideal start-stop frequency plan for the load device and the operating condition sequence of key equipment in the load device are extracted.

[0030] Through the above calculations, a complete and quantifiable set of ideal operating indicators is ultimately formed. This set not only defines the optimal performance benchmark under given resources and constraints, but also provides a clear benchmark for the next step of comparing and analyzing with actual operating data, thereby diagnosing performance deviations and guiding operational optimization.

[0031] In step 104, based on the historical operating data of the renewable energy load device within the set time period and the theoretical output characteristic curve of the renewable energy power station, the actual operating indicators achieved by the renewable energy load device within the set time period are determined through calculation and statistical analysis. Specifically, this calculation process is directly based on the time sequence of the recorded historical operating data. The actual operating indicators can include various types; examples of generating different actual operating indicators are given below.

[0032] Indicators reflecting the power utilization rate of load devices include, for example, integrating the actual power consumption curve in the historical operating data to obtain the actual total power consumption, comparing this actual total power consumption with the theoretical total power generation under the theoretical output characteristic curve, and calculating the actual renewable energy absorption rate. Simultaneously, the matching degree of the power of the two curves over time periods is calculated to obtain the actual time matching coefficient, which is used to quantify the tracking effect of actual operation on renewable energy fluctuations. Combining the above two curves, the actual power curtailment of the system under actual operating conditions (the difference between theoretical power generation and actual total power consumption) is calculated. By comparing the theoretical power generation not absorbed by the actual load over time periods, the actual power curtailment rate (the proportion of theoretical power generation not absorbed by the actual load) and the actual net load volatility (the smoothness index of the actual net load curve after subtracting actual power consumption from theoretical output) are obtained.

[0033] Indicators reflecting the economic and technical performance of load devices include, for example, statistical analysis of the actual power consumption curves in historical operating data to calculate the actual load fluctuation rate (such as standard deviation) and the actual average load rate (the ratio of average load to maximum load). Furthermore, by analyzing the historical sequence of load device status signals, the actual number of start-ups and shutdowns of key equipment within the load device during that time period can be statistically analyzed.

[0034] Through the above calculations, a complete set of actual operating indicators reflecting the true operating performance of the load unit during this historical period was obtained. These actual operating indicators correspond completely to the ideal operating indicators obtained from the aforementioned calculations in terms of definition and dimensions, thus laying a comparable data foundation for the next step of accurate performance gap diagnosis and root cause analysis.

[0035] In step 105, the operational performance of the new energy load device during the set time period is comprehensively evaluated and diagnosed by systematically comparing the ideal operating indicator set with the actual operating indicator set. This evaluation process may include one or more combinations of the following methods.

[0036] Method 1: Indicator Benchmarking and Gap Quantification. The ideal and actual indicators for each dimension are compared and calculated one by one to obtain a quantified performance deviation value. Core performance indicators include at least one of the following: the difference between theoretical and actual absorption rates, the difference between ideal and actual curtailment rates, the difference between ideal and actual curtailment rates, the difference between ideal and actual load fluctuation rates, and the difference between planned and actual start / stop times.

[0037] Method Two: Performance Rating Assessment. Based on pre-set performance benchmark thresholds, key deviation values ​​(especially those related to power utilization) are graded. For example, the achievement rate of the difference between the ideal and actual power curtailment can be rated as "Excellent," "Good," "Qualified," or "Needs Improvement," thus intuitively reflecting the core performance level of the device in terms of renewable energy consumption.

[0038] Method three involves multidimensional correlation and root cause diagnosis. This goes beyond numerical comparison and goes further to analyze the correlation between deviations in different indicators. For example, it correlates the spatiotemporal distribution of actual power curtailment with the operating status of load devices during the same period (e.g., shutdown, load reduction), grid dispatch instructions, or the theoretical peak output of power plants. This allows for the diagnosis of the main causes of performance loss—whether it stems from insufficient regulation capacity and response delays of the load devices themselves, limitations in the absorption capacity of the external power grid, or errors in previous prediction models.

[0039] In this embodiment, indicators such as abandoned power and abandoned power rate can be selected as core performance indicators for evaluation, because they not only directly reflect the level of "source-load matching" of the device or system, but also intuitively quantify the degree of loss of new energy, directly answering the core question of "how much green electricity that should have been used was wasted", so that managers, investors and regulators can clearly perceive the system's inefficiencies in resource utilization.

[0040] Furthermore, the benchmarking results, performance levels, and diagnostic analyses described above can be compiled into a structured operational performance assessment report.

[0041] In another embodiment, reference Figure 2 As shown, steps 201 to 206 are included, as detailed below.

[0042] Step 201: Determine the theoretical output characteristics of the new energy power station within the set time period based on the historical power generation data of the new energy power station within the set time period. Step 202: Based on the historical operating data and theoretical output characteristics of the new energy load device at the beginning of the set time period, determine the ideal power consumption characteristics of the new energy load device within the set time period. Step 203: Based on the ideal power consumption characteristics and theoretical power output characteristics, determine the ideal operating indicators of the new energy load device within a set time period; among which, the operating indicators include indicators reflecting the utilization rate of the new energy load device on the power of the new energy power station. Step 204: Determine the actual operating indicators of the new energy load device within the set time period based on the historical operating data and theoretical output characteristics of the new energy load device within the set time period. Step 205: Evaluate the operational performance of the new energy load device within the set time period based on ideal and actual operating indicators; Step 206: Based on the evaluated operating performance, obtain the operation improvement plan for the new energy load device in the next time period.

[0043] Figure 2 In the illustrated embodiment, steps 201-205 are... Figure 1 Steps 101-105 in the embodiments are the same and will not be repeated here. The difference is that... Figure 2 The illustrated embodiment may also include step 206, which involves obtaining an operational improvement plan for the new energy load device in the next time period based on the evaluated operational performance.

[0044] Specifically, this operational improvement plan can be formulated based on actual conditions, as illustrated below. System or professional personnel can take corresponding measures based on excessive deviations in key operational indicators diagnosed during performance evaluation. For example, when the difference between actual and theoretical power curtailment is too large, it is necessary to analyze, in conjunction with other relevant parameters, whether the cause is the error in renewable energy power prediction or insufficient regulation performance of the load device itself, and make targeted adjustments based on the analyzed causes. For instance, it may be found that the load device is unable to promptly increase its power to absorb additional renewable energy due to physical constraints (such as minimum operating time, ramp-up rate limits, and process continuity requirements), resulting in excessive actual power curtailment, or that the response to price signals has a higher priority than the response to renewable energy absorption. If the load device has excessively restrictive physical constraints, the following measures can be taken: within a safe range, cautiously relax the ramp-up rate limits or power regulation dead zones of the device to improve rapid response capabilities. If the reason is that the response to price signals has a higher priority than the response to renewable energy absorption, the following measures can be taken: significantly increase the weight of the "maximizing renewable energy absorption" objective in the scheduling model for the next time period, even making it superior to purely economic objectives in specific periods.

[0045] The method described in this embodiment can be executed continuously using a rolling time window, where the length of the set time period is the window length of the time window. This set time period can be set according to actual needs, such as the aforementioned setting of 1 day.

[0046] The steps described above are for clarity only. In practice, they can be combined into one step or some steps can be broken down into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0047] Furthermore, the examples mentioned in the above embodiments can be freely combined, and any combination can be understood as an embodiment. The terms "embodiment" or "example" appearing in various locations in the specification do not necessarily refer to the same embodiment, nor are they independent or alternative embodiments mutually exclusive with other embodiments. Those skilled in the art will understand that the embodiments described herein can be combined with other embodiments.

[0048] like Figure 3 As shown, this embodiment of the invention relates to a performance monitoring system for a new energy load device, comprising: The first processing module is used to determine the theoretical output characteristics of the new energy power station within a set time period based on the historical power generation data of the new energy power station within a set time period. The second processing module is used to determine the ideal power consumption characteristics of the new energy load device within the set time period based on the historical operating data and theoretical output characteristics of the new energy load device at the beginning of the set time period. The third processing module is used to determine the ideal operating indicators of the new energy load device within a set time period based on the ideal power consumption characteristics and the theoretical power output characteristics; wherein, the operating indicators include indicators that reflect the utilization rate of the new energy load device on the power of the new energy power station. The fourth processing module is used to determine the actual operating indicators of the new energy load device within a set time period based on the historical operating data and theoretical output curve of the new energy load device within a set time period. The fifth processing module is used to evaluate the operational performance of new energy load devices within a set time period based on ideal and actual operating indicators.

[0049] A data storage module is used to store at least a portion of the data from the first processing module, the second processing module, the third processing module, the fourth processing module, and the fifth processing module.

[0050] The first to fifth processing modules respectively execute steps 101 to 105 of the above method embodiments; the data storage module is used to store at least a portion of the data in the above method embodiments.

[0051] In one example, the performance monitoring system may also include a sixth processing module for guiding the operation plan of the new energy load device in the next time period based on the evaluated operating performance; that is, executing step 106 in the above method embodiment.

[0052] It is not difficult to see that this embodiment is a system embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments, and this embodiment has the same beneficial effects as the above method embodiments.

[0053] Additionally, embodiments of the present invention relate to an electronic device, including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method embodiments described above.

[0054] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0055] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0056] Furthermore, embodiments of the present invention relate to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the above-described method embodiments.

[0057] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0058] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for monitoring the performance of a new energy load device, characterized in that, include: Based on the historical power generation data of the new energy power station within a set time period, the theoretical output characteristics of the new energy power station within the set time period are determined. Based on the historical operating data of the new energy load device at the beginning of the set time period and the theoretical output characteristics, the ideal power consumption characteristics of the new energy load device within the set time period are determined. Based on the ideal power consumption characteristics and the theoretical power output characteristics, the ideal operating indicators of the new energy load device are determined within the set time period; wherein, the operating indicators include indicators reflecting the utilization rate of the new energy load device on the power of the new energy power station; Based on the historical operating data of the new energy load device within the set time period and the theoretical output characteristics, the actual operating indicators of the new energy load device within the set time period are determined. The operational performance of the new energy load device during the set time period is evaluated based on the ideal operating indicators and the actual operating indicators.

2. The performance monitoring method for new energy load devices according to claim 1, characterized in that, The indicators reflecting the utilization rate of the new energy load device for the power of the new energy power station include the amount of power abandoned and / or the power abandonment rate.

3. The performance monitoring method for new energy load devices according to claim 1, characterized in that, Determining the ideal power consumption characteristics of the new energy load device during the set time period includes: The ideal power consumption characteristics of the new energy load device during the set time period are calculated by optimizing the algorithm. The optimization objectives of the optimization algorithm include at least one of the following: minimizing the abandoned electricity, maximizing the product output of the new energy load device, minimizing the discharge of the energy storage device in the new energy power station, minimizing the load fluctuation range of the new energy load device, and minimizing the start-up and shutdown frequency of the new energy load device.

4. The performance monitoring method for new energy load devices according to claim 3, characterized in that, The constraints used in the optimization algorithm include at least one of the following: the equipment maintenance status and online / offline status of the new energy load device.

5. The performance monitoring method for a new energy load device according to any one of claims 1 to 4, characterized in that, The method is executed continuously in a rolling time window manner, and the length of the set time period is the window length of the time window.

6. The performance monitoring method for a new energy load device according to any one of claims 1 to 4, characterized in that, After evaluating the operational performance of the new energy load device within the set time period based on the ideal operating indicators and the actual operating indicators, the method further includes: Based on the assessed operational performance, an operational improvement plan for the new energy load device in the next time period is obtained.

7. The performance monitoring method for a new energy load device according to any one of claims 1 to 4, characterized in that, The new energy load device includes a water electrolysis hydrogen production device and a downstream chemical plant using hydrogen as raw material.

8. A performance monitoring system for a new energy load device, characterized in that, include: The first processing module is used to determine the theoretical output characteristics of the new energy power station within the set time period based on the historical power generation data of the new energy power station within the set time period. The second processing module is used to determine the ideal power consumption characteristics of the new energy load device within the set time period based on the historical operating data of the new energy load device at the initial moment of the set time period and the theoretical output characteristics. The third processing module is used to determine the ideal operating indicators of the new energy load device within the set time period based on the ideal power consumption characteristics and the theoretical power output characteristics; wherein, the operating indicators include indicators reflecting the utilization rate of the new energy load device on the power of the new energy power station; The fourth processing module is used to determine the actual operating indicators of the new energy load device within the set time period based on the historical operating data of the new energy load device within the set time period and the theoretical output characteristics. The fifth processing module is used to evaluate the operational performance of the new energy load device within the set time period based on the ideal operating indicators and the actual operating indicators; A data storage module is used to store at least a portion of the data from the first module, the second module, the third module, the fourth module, and the fifth module.

9. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the performance monitoring method for the new energy load device according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the performance monitoring method for the new energy load device according to any one of claims 1 to 7.

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