Method for visualizing complementary characteristics of photovoltaic storage hydrogen multi-energy load and related device

Abstract

This application relates to the field of monitoring technology for new energy power systems, and discloses a method and related equipment for visually monitoring the complementary characteristics of photovoltaic, energy storage, and hydrogen charging multi-energy loads. The method includes: resampling time-series data with different sampling periods; adaptively selecting zero-order hold interpolation or linear interpolation by detecting the power change rate, preserving step change characteristics and unifying the time base; then selecting photovoltaic output periods to calculate the photovoltaic-energy storage complementarity index, and calculating the complementarity contribution index based on the actual peak-valley difference and the benchmark peak-valley difference; finally generating a load complementarity map that simultaneously displays the power curves of photovoltaic, energy storage, charging, and hydrogen production, and annotates the above indicators, and dynamically refreshing the display through a preset terminal. This invention can accurately quantify the net contribution of multi-energy complementarity to peak shaving and valley filling, achieve low-latency real-time visualization, and provide intuitive and accurate data support for the operation optimization and capacity expansion decisions of integrated photovoltaic, energy storage, and hydrogen charging systems.

Description

Technical Field

[0001] This invention relates to the field of monitoring technology for new energy power systems, specifically to a visual monitoring method and related equipment for the complementary characteristics of photovoltaic, energy storage, hydrogen charging, and multi-energy loads. Background Technology

[0002] Integrated photovoltaic-storage-hydrogen charging power stations, as a core component of new power systems, achieve efficient consumption of new energy sources and stable grid operation through multi-energy synergy and complementarity, becoming an important technological path for the green and low-carbon transformation of energy. Current mainstream energy management systems mostly focus on basic operation monitoring of single equipment or single energy types, exhibiting significant shortcomings in the comprehensive analysis of multi-energy load complementarity characteristics. Differences in clock sources and sampling periods among multi-source heterogeneous equipment lead to poor consistency in time-series data. The lack of a dedicated complementary quantitative evaluation system for the quaternary photovoltaic-storage-hydrogen charging system makes it impossible to accurately reflect the unique synergistic relationships such as energy storage mitigating charging impacts and photovoltaic supporting hydrogen production. Visualization methods are limited and lack comprehensive presentation across multiple time scales, making it difficult to intuitively quantify the peak-shaving and valley-filling effects of multi-energy synergy. Maintenance personnel cannot quickly grasp the overall system operating status, and it is also difficult to provide accurate data support for site operation optimization and planning decisions.

[0003] Therefore, it is urgent to solve the problem of how to provide a visual monitoring method for the complementary characteristics of photovoltaic, energy storage, and hydrogen charging multi-energy loads that can achieve accurate synchronization of multi-source data, quantitative evaluation of multi-dimensional complementary characteristics, and intuitive display at multiple time scales. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention provide a method and related equipment for visual monitoring of the complementary characteristics of photovoltaic, energy storage, and hydrogen charging multi-energy loads, in order to solve the problem of how to provide a method for visual monitoring of the complementary characteristics of photovoltaic, energy storage, and hydrogen charging multi-energy loads that can achieve accurate synchronization of multi-source data, quantitative evaluation of multi-dimensional complementary characteristics, and intuitive display at multiple time scales.

[0005] According to one aspect of the present invention, a method for visually monitoring the complementary characteristics of photoelectric storage and hydrogen charging multi-energy loads is provided, the method comprising: Time-series data are collected according to a preset sampling period. The time-series data includes photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power. Time series data with different sampling periods are resampled to be unified to a target sampling period. The power change rate between adjacent sampling points is detected. If the power change rate exceeds a first change threshold, the time series data is determined to be in a step change scenario. Resampled data is generated by zero-order hold-behind interpolation that keeps the value of the previous sampling point unchanged. Otherwise, resampled data is generated by linear interpolation. Select a period when the photovoltaic power generation is greater than zero, accumulate the product of the photovoltaic power generation and the energy storage charging and discharging power during the period, and determine the ratio of the accumulated product value to the product of the absolute values ​​of the photovoltaic rated power and the energy storage rated power as the photovoltaic-energy storage complementarity index. Based on the resampled time-series data, the difference between the maximum and minimum values ​​of the actual total load power is calculated as the actual peak-valley difference. The actual total load power is the algebraic sum of the photovoltaic power generation power, energy storage charging and discharging power, charging load power, and hydrogen production load power. The sum of the absolute values ​​of each power at each sampling time is calculated, and the difference between the maximum and minimum values ​​of the sum of absolute values ​​is taken as the benchmark peak-valley difference. The ratio of the difference between the benchmark peak-valley difference and the actual peak-valley difference to the benchmark peak-valley difference is determined as the complementary contribution index. Based on the resampled time-series data, a load complementarity map is generated. The load complementarity map includes power curves displaying the photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power within a preset time range. The photovoltaic-energy storage complementarity index and the complementarity contribution index are marked on the load complementarity map. The load complementarity map is displayed through a preset terminal and updated based on a preset update command.

[0006] Furthermore, prior to the step of resampling time-series data from different sampling periods to unify them to the target sampling period, the method further includes: Determine whether the current data acquisition device supports network time synchronization protocol; If supported, the clock offset is obtained through network synchronization, and the timing data is timestamped based on the clock offset. If not supported, the pre-configured static clock offset value is read, and the timing data is timestamped based on the static clock offset value.

[0007] Furthermore, the content marked on the load complementarity map also includes a multi-energy comprehensive complementarity index, and the steps for obtaining the multi-energy comprehensive complementarity index include: Calculate the second complementarity index between energy storage charging and discharging power and charging load power; Calculate the third complementarity index between photovoltaic power generation and hydrogen production load; The photovoltaic-energy storage complementarity index, the second complementarity index, and the third complementarity index are multiplied by their respective weighting coefficients and then summed to obtain the multi-energy comprehensive complementarity index. Among them, if a grid demand restriction command is received, the weight of the second complementarity index is increased; if the curtailment rate of solar power or wind power exceeds the second preset threshold, the weight of the photovoltaic-energy storage complementarity index is increased; otherwise, the default weight is maintained.

[0008] Furthermore, after the step of displaying the load complementarity map through a preset terminal and updating it based on a preset update command, the method further includes: Continuously monitor the multi-energy comprehensive complementarity index; Determine whether the multi-energy comprehensive complementarity index is lower than a preset alarm threshold within a preset number of consecutive sampling periods: If so, an anomaly alarm is triggered, and the corresponding time period is highlighted on the load complementarity graph; the ratio of the average value of the photovoltaic-energy storage complementarity index, the second complementarity index, and the third complementarity index to the corresponding rated value during the time period is calculated respectively, and the equipment combination corresponding to the index with the lowest ratio is identified as the suspected equipment that causes the reduction of complementarity characteristics; and the corresponding conclusion is marked on the load complementarity graph. If not, continue monitoring.

[0009] Furthermore, the content marked on the load complementarity map also includes a load smoothness index, and the steps for obtaining the load smoothness index include: Calculate the average value and standard deviation of the actual total load power; The ratio of the average value to the standard deviation is determined as the load smoothness index; Determine whether the absolute value of the average is less than a preset threshold: If so, the load smoothness evaluation index will be automatically switched to the standard deviation of the actual total load power or the actual peak-valley difference. If not, then the ratio of the mean to the standard deviation remains the load smoothness index.

[0010] Furthermore, after the step of generating a load complementarity map based on the resampled time-series data, the method further includes: A weekly load complementarity map is generated, which uses a heat map format to display the average complementarity characteristics of each time period within a week. A monthly load complementarity map is generated, which uses a line graph to show the changing trend of daily complementarity characteristics within a month. Furthermore, in response to a click operation on any moment in the load complementarity map, detailed values ​​of the photovoltaic-energy storage complementarity index, complementarity contribution index, and load smoothness index corresponding to that moment are generated, along with the power values ​​of the photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power, as well as the actual peak-valley difference and benchmark peak-valley difference for the corresponding time period. An analysis pop-up window is then displayed in the analysis pop-up window.

[0011] Furthermore, after generating the load complementarity map, the process further includes: Based on pre-stored historical time-series data, calculate the average value of the photovoltaic-energy storage complementarity index within a preset long period or the duration of time it is below a third preset threshold; Determine whether the photovoltaic-energy storage complementarity index remains below a preset threshold for a preset long period: If so, analyze the energy storage state of charge data and curtailment rate data within the corresponding time period to determine whether the number of times the energy storage state of charge reaches the upper limit exceeds a preset threshold and the curtailment rate increases simultaneously: If so, the root cause is determined to be insufficient energy storage capacity, which prevents the effective absorption of photovoltaic power. A targeted expansion suggestion is generated, which includes at least prioritizing the increase of the capacity of the energy storage system, and is accompanied by quantitative supporting data, including the amount of curtailed or limited power generation caused by insufficient energy storage capacity. If not, the process ends.

[0012] According to another aspect of the present invention, a visualization and monitoring device for the complementary characteristics of photoelectric storage and hydrogen charging multi-energy loads is provided, comprising: The acquisition module is used to acquire time-series data according to a preset sampling period. The time-series data includes photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power. The resampling module is used to resample time-series data from different sampling periods to unify them to a target sampling period. It detects the power change rate between adjacent sampling points. If the power change rate exceeds a first change threshold, the time-series data is determined to be in a step-change scenario, and resampled data is generated using zero-order hold-behind interpolation that keeps the value of the previous sampling point unchanged; otherwise, linear interpolation is used to generate resampled data. The first indicator calculation module is used to select the period when the photovoltaic power generation is greater than zero, accumulate the product of the photovoltaic power generation and the energy storage charging and discharging power during the period, and determine the ratio of the accumulated product value to the product of the absolute values ​​of the photovoltaic rated power and the energy storage rated power as the photovoltaic-energy storage complementarity index. The second indicator calculation module is used to calculate the difference between the maximum and minimum values ​​of the actual total load power as the actual peak-valley difference based on the resampled time-series data. The actual total load power is the algebraic sum of the photovoltaic power generation power, energy storage charging and discharging power, charging load power, and hydrogen production load power. The module calculates the sum of the absolute values ​​of each power at each sampling time and takes the difference between the maximum and minimum values ​​of the sum of absolute values ​​as the benchmark peak-valley difference. The module determines the complementary contribution index by taking the ratio of the difference between the benchmark peak-valley difference and the actual peak-valley difference to the benchmark peak-valley difference. The load complementarity module is used to generate a load complementarity map based on the resampled time series data. The load complementarity map includes power curves displaying the photovoltaic power generation, energy storage charging and discharging power, charging load power and hydrogen production load power within a preset time range, and the photovoltaic-energy storage complementarity index and the complementarity contribution index are marked on the load complementarity map. The display update module is used to display and update the load complementarity map based on a preset update command via a preset terminal.

[0013] According to another aspect of the present invention, a computer device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, which causes the processor to perform the operation of the visualization monitoring method for the complementary characteristics of photo-storage-hydrogen-charging multi-energy loads described above.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein at least one executable instruction is stored therein, the executable instruction causing a computer device to perform the operation of the visualization monitoring method for the complementary characteristics of photo-storage-hydrogen-charging multi-energy loads described in any one of the preceding embodiments.

[0015] According to another aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the visualization and monitoring method for the complementary characteristics of photo-storage-hydrogen-charging multi-energy loads as described in any one of the above embodiments.

[0016] This invention, by detecting the power change rate between adjacent sampling points and adaptively selecting zero-order hold interpolation or linear interpolation, fully preserves the power jump characteristics in abrupt change scenarios such as energy storage charging and discharging switching, while ensuring data smoothness under normal operating conditions, effectively solving the problem of time-series asynchrony in multi-source heterogeneous data. Furthermore, the calculation of the photovoltaic-energy storage complementarity index is strictly limited to the photovoltaic output period, eliminating interference from invalid nighttime periods. Simultaneously, a complementarity contribution index based on the actual peak-valley difference and the benchmark peak-valley difference is introduced, accurately quantifying the net contribution of multi-energy complementarity behavior to peak shaving and valley filling, overcoming the difficulty of accurately extracting and quantitatively evaluating complementary characteristics. The generated load complementarity map simultaneously displays four types of power curves and directly labels the aforementioned quantitative indicators, transforming abstract data into an intuitive visual form, significantly reducing the cognitive burden on operation and maintenance personnel. Combined with the dynamic refresh mechanism of the preset terminal, low-latency real-time updates of monitoring data are achieved, fully meeting the needs of real-time operation and maintenance decision-making.

[0017] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating the visualization and monitoring method for the complementary characteristics of photovoltaic, hydrogen storage, and multi-energy loads provided in an embodiment of the present invention is shown. Figure 2 This diagram illustrates the structure of a visualization and monitoring device for the complementary characteristics of photovoltaic storage and hydrogen charging multi-energy loads provided in an embodiment of the present invention. Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention is shown. Detailed Implementation

[0019] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0020] Figure 1 A flowchart of the visualization and monitoring method for the complementary characteristics of photovoltaic-storage-hydrogen-charging multi-energy loads provided in an embodiment of the present invention is shown, such as... Figure 1 As shown, the method includes the following steps: S110. Collect time-series data according to a preset sampling period. The time-series data includes photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power. Specifically, this step collects the following time-series data according to a preset sampling period (the default value is 1 second, which can be configured by the user within the range of 1 to 60 seconds depending on the amount of data on site and network bandwidth): photovoltaic power generation P_pv(t), obtained by reading the real-time active power of the photovoltaic inverter; energy storage charging and discharging power P_pcs(t) and energy storage state of charge (SOC), where the power value is positive when the energy storage is charging and negative when it is discharging; charging load power P_ev(t), obtained by accumulating the real-time output power of each charging pile; hydrogen production load power P_h2(t), obtained by reading the input power of the water electrolysis hydrogen production equipment; optionally, the total meter power P_total(t) can also be collected as a verification reference. This step employs a multi-protocol compatible design (supporting Modbus-TCP, OPCUA, IEC61850, DL / T645, etc.), allowing access to energy equipment from different brands without replacing existing devices, thus reducing the difficulty and cost of system modification. The configurable sampling period ensures real-time data (1-second sampling can capture rapid fluctuations in photovoltaic power and instantaneous impacts on charging load) while avoiding the pressure on storage and computing caused by excessive data volume. Simultaneously, power and energy storage SOC status are collected, providing a data foundation for the "insufficient energy storage capacity determination" in subsequent complementary feature extraction.

[0021] S120. Resample time-series data with different sampling periods to unify them to the target sampling period. The power change rate between adjacent sampling points is detected. If the power change rate exceeds a first change threshold, the time-series data is determined to be in a step change scenario. Resampled data is generated by zero-order hold-alive interpolation that keeps the value of the previous sampling point unchanged. Otherwise, resampled data is generated by linear interpolation.

[0022] Specifically, because the internal sampling periods of photovoltaic inverters, energy storage PCS, charging piles, and hydrogen production equipment are different, directly performing joint analysis on data from these different periods will result in severe timing misalignment. Therefore, this step unifies all data to the same target sampling period ΔT (e.g., 1 minute). Traditional resampling methods typically use linear interpolation, but in a photovoltaic-energy storage-hydrogen charging scenario, the charging and discharging power of the energy storage PCS exhibits abrupt changes (e.g., the power may jump from -100kW to +80kW within tens of milliseconds when switching from a discharging state to a charging state). Linear interpolation will generate a sloping line between these two sampling points, "smoothing" the original abrupt change into a gradually rising curve, causing subsequent complementary feature extraction to incorrectly assume that the energy storage power has undergone a slow transition "from negative to positive".

[0023] To address this issue, this step first reads two adjacent sampling points (t_a, Di(t_a)) and (t_b, Di(t_b)) from the original time-series data and calculates the power change rate r, where r = |D_i(t_b) - Di(t_a)| / (t_b - t_a). Then, r is compared with a preset first change threshold R_th. This first change threshold can be pre-configured according to the equipment type; for example, it can be set to 80% / second of the absolute value of the rated power for energy storage PCS, 100% / second for charging piles, and 30% / second for photovoltaic and hydrogen production equipment. All of these thresholds can be adjusted as needed in the system configuration interface.

[0024] If r > R_th, the current data is determined to be in a step-change scenario. In this case, zero-order preserve interpolation is used: D_unified(T_k) = Di(t_a), which directly uses the value of the last sampling point before the change until the next sampling point arrives, thus completely preserving the original characteristics of the power change. If r ≤ R_th, the data is determined to be in a stationary or slowly changing scenario. Linear interpolation is used: D_unified(T_k) = Di(t_a) + [D_i(t_b) - Di(t_a)] × (T_k - t_a) / (t_b - t_a) to ensure the continuity and smoothness of the data curve.

[0025] This step adaptively selects the interpolation method, balancing data smoothness under normal operating conditions with feature fidelity under abrupt changes. It sets different rate of change thresholds for different equipment types, reflecting a refined modeling of the physical characteristics of various equipment. The introduction of zero-order hold interpolation ensures that the power jump at the moment of energy storage charging and discharging is fully preserved, ensuring the accuracy of subsequent photovoltaic-energy storage complementarity coefficient calculations and avoiding misjudgments of the complementary effect caused by smoothing steps.

[0026] S130. Select a period when the photovoltaic power generation is greater than zero, accumulate the product of the photovoltaic power generation and the energy storage charging and discharging power during the period, and determine the ratio of the accumulated product value to the product of the absolute values ​​of the photovoltaic rated power and the energy storage rated power as the photovoltaic-energy storage complementarity index.

[0027] Specifically, the photovoltaic-energy storage complementarity index α_pv_pcs is used to quantitatively assess the degree of coordination between the energy storage system and the photovoltaic power fluctuations in charging and discharging when the photovoltaic system is generating power. Its calculation formula is as follows: α_pv_pcs=(1 / T)∫[P_pv(t)×P_pcs(t)]dt / [P_pv_max×|P_pcs_max|] Where T is the length of the analysis period (e.g., one day), P_pv_max is the rated power of photovoltaics, and |P_pcs_max| is the absolute value of the rated power of energy storage. It should be noted that since P_pv(t) = 0 at night or on cloudy days when photovoltaic output is zero, the product term is zero and contributes nothing to the integral. Therefore, this indicator is essentially determined by the integral contribution during the photovoltaic output period, quantifying the energy storage's ability to follow and mitigate photovoltaic fluctuations when photovoltaic output is present, rather than a comprehensive evaluation of the energy storage's behavior throughout the entire period. When α_pv_pcs is close to 1, it indicates that the energy storage charges during periods of high photovoltaic power generation and discharges when photovoltaic power is insufficient, forming a good peak-shaving and valley-filling effect; when it is close to -1, it indicates that the energy storage is competing with photovoltaic power generation; and when it is close to 0, it indicates that the energy storage basically does not respond to photovoltaic fluctuations.

[0028] The indicators defined in this step closely align with the physical essence of photovoltaic-storage complementarity, avoiding interference from invalid nighttime periods on the evaluation results and enabling operation and maintenance personnel to accurately determine whether the energy storage configuration is reasonable. Through normalization, this indicator is not affected by the scale of the site and can be compared horizontally between different sites. At the same time, it provides a basic component for subsequent multi-energy comprehensive complementarity indicators and provides a quantitative basis for "energy storage capacity insufficiency judgment" in planning decision support.

[0029] S140. Based on the resampled time-series data, calculate the difference between the maximum and minimum values ​​of the actual total load power as the actual peak-valley difference. The actual total load power is the algebraic sum of the photovoltaic power generation power, energy storage charging and discharging power, charging load power, and hydrogen production load power. Calculate the sum of the absolute values ​​of each power at each sampling time, and take the difference between the maximum and minimum values ​​of the sum of the absolute values ​​as the benchmark peak-valley difference. Determine the proportion of the difference between the benchmark peak-valley difference and the actual peak-valley difference to the benchmark peak-valley difference as the complementary contribution index. Specifically, the core of this step is to propose a new indicator, complementary contribution, to quantitatively assess the net contribution of multi-energy complementary behavior itself to peak shaving and valley filling, which is different from simply observing the change in the total load peak-valley difference.

[0030] First, the actual total load power P_net(t) is defined as the algebraic sum of the power of all equipment: P_net(t) = P_pv(t) + P_pcs(t) + P_ev(t) + P_h2(t). Here, photovoltaic power is positive (power generation), charging load and hydrogen production load are positive (power consumption), and energy storage is positive during charging and negative during discharging. Therefore, P_net(t) is actually the exchange power between the power station and the external power grid (positive values ​​indicate power taken from the grid, and negative values ​​indicate power sent to the grid). Then, the actual peak-to-valley difference ΔP_actual = max(P_net(t)) - min(P_net(t)) is calculated, which reflects the load fluctuation amplitude felt by the grid side after multi-energy complementary regulation.

[0031] Secondly, we define the baseline total load power P_gross(t) = |P_pv(t)| + |P_pcs(t)| + |P_ev(t)| + |P_h2(t)|. Here, we take the sum of absolute values, which is equivalent to assuming that all power is non-cancellable (i.e., we do not consider complementary behaviors such as local consumption of photovoltaic power generation and cancellation of energy storage charging and discharging), and treat them all as independent power components. Then, we calculate the baseline peak-valley difference ΔP_baseline = max(P_gross(t)) - min(P_gross(t)), which reflects the theoretical upper limit of the load fluctuation amplitude without multi-energy complementarity.

[0032] Finally, the complementary contribution C_complement is defined as: C_complement = (ΔP_baseline - ΔP_actual) / ΔP_baseline × 100%. The denominator of this indicator is the baseline peak-to-valley difference, and the numerator is "the peak-to-valley difference eliminated through complementary behavior". Therefore, C_complement intuitively represents the percentage contribution of multi-energy complementarity to reducing load fluctuations.

[0033] Traditional peak shaving and valley filling assessments typically only observe changes in the peak-valley difference of the actual total load, but cannot distinguish whether this change is due to complementary behavior or simply a decrease in total electricity consumption. This step, by introducing a benchmark peak-valley difference as a control, eliminates the influence of changes in total electricity consumption and accurately quantifies the contribution of complementary behavior itself. It can be directly used for horizontal comparisons between different time periods and different sites, and can serve as one of the objective functions for system optimization, guiding the adjustment of energy storage charging and discharging strategies—when C_complement is low, it indicates that complementary behavior is not fully utilized, and the equipment operation strategy needs to be adjusted.

[0034] S150. Based on the resampled time-series data, a load complementarity map is generated. The load complementarity map includes power curves displaying the photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power within a preset time range. The photovoltaic-energy storage complementarity index and the complementarity contribution index are marked on the load complementarity map.

[0035] Specifically, the load complementarity chart is a visualization chart specifically designed for the multi-energy complementary characteristics of photovoltaic, energy storage, and hydrogen charging. Its core design concept is the deep integration of "quantitative indicators" and "trend curves." This step uses a stacked area chart format by default: the horizontal axis represents time (the preset time range can be 24 hours, one week, or one month, selected by the user), and the vertical axis represents power (unit: kW). In the same coordinate system, four filled curves are plotted: photovoltaic power generation (e.g., yellow or orange area, located at the bottom, representing the energy source), charging load power (e.g., red area, representing electricity demand), hydrogen production load power (blue area, representing continuous base load), and energy storage charging and discharging power (e.g., green area, stacked upwards during charging and extending downwards during discharging, represented by a bidirectional stacking method). This stacked area chart can intuitively show the power magnitude and trend of various devices, and the height of the total area can indicate the peak and valley conditions of the total load. At the same time, the bidirectional representation of energy storage clearly shows the charging and discharging behavior.

[0036] Regarding indicator labeling, this step displays key indicators in card format in the upper right or upper left corner of the graph (the specific area can be set according to actual needs): the photovoltaic-energy storage complementarity indicator α_pv_pcs (displayed as a numerical value with an accompanying circular progress bar, e.g., red indicates <0.3, yellow indicates 0.3-0.7, and green indicates >0.7), and the complementarity contribution C_complement (also displayed as a numerical value and a progress bar). Furthermore, when the user hovers the mouse over a point on the curve, the power values ​​of each device at that moment and the current instantaneous complementarity status will be dynamically displayed.

[0037] Traditional energy monitoring interfaces often only display a single power curve, lacking quantitative evaluation indicators. Operation and maintenance personnel need to analyze the complementary effects from the curve shape themselves. This step directly marks the calculated indicators on the graph, reducing the cognitive burden. The choice of stacked area map fully considers the characteristics of the photovoltaic-storage-hydrogen-charging scenario—photovoltaics, as the energy supply side, are located at the bottom, charging and hydrogen production, as the load side, are stacked upwards, and energy storage, as the regulation side, is stacked bidirectionally, which conforms to the physical intuition of energy flow. Through dynamic interaction (hovering to display details), it takes into account both the needs of macroscopic review and microscopic exploration.

[0038] S160. Display the load complementarity map through a preset terminal and update it based on a preset update command.

[0039] Specifically, the pre-installed terminals include two types: one is a web-based terminal, which allows maintenance personnel to view and interact with the system using a computer in the monitoring room; the other is a large-screen terminal, deployed on an LED video wall in the monitoring center, for remote global monitoring by monitoring personnel. Both types of terminals share the same backend data service, but their front-end interfaces differ: the web-based terminal provides complete interactive functions (time period selection, device filtering, indicator comparison, historical playback, etc.), while the large-screen terminal uses high-contrast color schemes, large fonts, and automatically hides unnecessary operation buttons to ensure optimal viewing from a distance.

[0040] In terms of data update mechanism, this step adopts WebSocket full-duplex communication technology. Traditional HTTP polling requires the front-end to send requests to the back-end at fixed intervals, resulting in high latency and server load. WebSocket technology, on the other hand, establishes a persistent connection after the initial connection, allowing the back-end to proactively push the latest data to the front-end as it changes. In this method, data collection continues continuously in 1-second cycles. Time-series synchronization and complementary feature extraction are used to calculate updated metrics in real time. When a new one-minute data block is generated, the back-end pushes the incremental data to all connected terminals via WebSocket. Upon receiving the push, the front-end dynamically updates the load complementarity graph—the curve extends to the right by one time point, the metric values ​​are recalculated, and the display is refreshed. The entire process requires no manual page refresh by the user.

[0041] Update commands can come from two trigger sources: one is automatic timed triggering, where users can set the refresh cycle of the graph in the configuration interface (default 5 seconds, adjustable from 1 to 60 seconds); the other is manual triggering, where the front end actively requests historical data or resets the time range when the user clicks the "refresh" button on the graph or drags the time axis slider.

[0042] The WebSocket push mechanism controls the monitoring data latency to within a specified time (e.g., 5 seconds), meeting the needs of real-time operation and maintenance decision-making (e.g., when photovoltaic power drops sharply, operation and maintenance personnel can quickly determine whether to adjust the hydrogen production load); the differentiated design of the web terminal and the large screen terminal takes into account both the depth of interaction and long-distance readability; it supports dragging the historical timeline, allowing operation and maintenance personnel to review the complementary status at any previous moment, which is convenient for fault analysis and operation review.

[0043] In one embodiment, before the step of resampling time-series data with different sampling periods to unify them to a target sampling period, the method further includes: S1100: Determine whether the current data acquisition device supports the network time synchronization protocol; S1101. If supported, obtain the clock offset through network time synchronization, and perform timestamp correction on the timing data based on the clock offset. S1102. If not supported, read the pre-configured static clock deviation value and perform timestamp correction on the timing data based on the static clock deviation value.

[0044] The process involves resampling time-series data from different sampling periods to align it to the target sampling period, followed by a clock alignment preprocessing step. Specifically, it first determines whether the current data acquisition device supports network time synchronization protocols (such as NTP or PTP). This can be achieved by checking the device firmware configuration, attempting to connect to a preset time server, or reading the system time synchronization service status. If network time synchronization is supported, the device actively synchronizes with the time server, obtaining the real-time offset Δt_offset between the local clock and the standard time, and correcting the timestamp of each acquired time-series data entry: T_corrected = T_original + Δt_offset. Here, T_original refers to the original timestamp recorded by the device, i.e., the time value recorded by the local clock of the data acquisition device. T_corrected is the corrected unified timestamp. This method eliminates clock drift accumulated over long-term operation and is particularly suitable for photovoltaic power fluctuation analysis scenarios with high time accuracy requirements. If network time synchronization is not supported, the static clock offset value δ (pre-stored in the device's non-volatile memory, this value is factory-calibrated or configured by the user based on historical experience) is read, and fixed offset correction is performed using the same formula: T_corrected = T_original + δ. Static correction is suitable for closed systems that cannot be networked and can eliminate systematic fixed errors. Through the above layered compatibility strategy, regardless of whether the device has network time synchronization capability, the time base of each time series data can be unified before resampling, thereby avoiding the complementary feature extraction deviation caused by clock asynchrony. The technical effect of this step is: to expand the device compatibility of the system, ensure that older devices can also be included in the unified time base, and at the same time provide a time-aligned data foundation for subsequent adaptive resampling.

[0045] In one embodiment, the content marked on the load complementarity map further includes a multi-energy comprehensive complementarity index, and the step of obtaining the multi-energy comprehensive complementarity index includes: S1201, Calculate the second complementarity index between energy storage charging and discharging power and charging load power; S1202. Calculate the third complementarity index between photovoltaic power generation and hydrogen production load power; S1203. Multiply the photovoltaic-energy storage complementarity index, the second complementarity index, and the third complementarity index by their respective weight coefficients and then sum them to obtain the multi-energy comprehensive complementarity index; wherein, if a grid demand restriction command is received, the weight of the second complementarity index is increased; if the curtailment rate of solar power or wind power exceeds the second preset threshold, the weight of the photovoltaic-energy storage complementarity index is increased; otherwise, the default weight is maintained.

[0046] The load complementarity map also includes a multi-energy comprehensive complementarity index. The steps for obtaining this index are as follows: First, using an integral normalization method similar to the photovoltaic-energy storage complementarity index, the second complementarity index between energy storage charging / discharging power and charging load power is calculated as follows: α_pcs_ev=(1 / T)∫[P_pcs(t)×P_ev(t)]dt / [|P_pcs_max|×P_ev_max], and the third complementarity index between photovoltaic power generation and hydrogen production load power is calculated as follows: α_pv_h2=(1 / T)∫[P_pv(t)×P_h2(t)]dt / [P_pv_max×P_h2_max]. The former is used to quantify the ability of energy storage to mitigate charging load fluctuations, while the latter is used to assess the level at which hydrogen production load follows photovoltaic power generation to absorb curtailed solar power. Then, the photovoltaic-energy storage complementarity index α_pv_pcs, the second complementarity index α_pcs_ev, and the third complementarity index α_pv_h2 are multiplied by their respective weighting coefficients and summed to obtain the multi-energy comprehensive complementarity index β = w1·α_pv_pcs + w2·α_pcs_ev + w3·α_pv_h2, where w1 + w2 + w3 = 1. The weighting coefficients are not fixed but dynamically adjusted according to the actual operating status: if a grid demand restriction command is received, the weight of the second complementarity index is automatically increased (e.g., w2 is adjusted to 0.5, and w1 and w3 are each adjusted to 0.25), shifting the evaluation focus to the ability of energy storage to smooth out charging loads, thereby helping to reduce demand charges; if the curtailment rate of solar or wind exceeds the second preset threshold (e.g., 5%), the weight of the photovoltaic-energy storage complementarity index is automatically increased (e.g., w1 is adjusted to 0.5), guiding the system to prioritize energy storage charging to reduce solar curtailment; in other cases, the default weights (all 1 / 3) are maintained. This dynamic weighting mechanism enables the comprehensive indicators to objectively reflect the core contradictions of multi-energy synergy under the current operating conditions, avoiding evaluation distortion caused by fixed weights; at the same time, it integrates the three sub-indicators into a single value, making it easier for operation and maintenance personnel to quickly judge the overall complementarity level, and can be combined with radar charts to locate weak links.

[0047] In one embodiment, after the step of displaying and updating the load complementarity map via a preset terminal based on a preset update command, the method further includes: S1031. Continuously monitor the multi-energy comprehensive complementarity index; S1032. Determine whether the multi-energy comprehensive complementarity index is lower than a preset alarm threshold within a preset number of consecutive sampling periods: S1033. If so, trigger an abnormal alarm and highlight the corresponding time period on the load complementarity graph; calculate the ratio of the average value of the photovoltaic-energy storage complementarity index, the second complementarity index, and the third complementarity index to the corresponding rated value during the time period, and identify the equipment combination corresponding to the index with the lowest ratio as the suspected equipment that causes the reduction of complementary characteristics; and mark the corresponding conclusion on the load complementarity graph. S1034. If not, continue monitoring.

[0048] Following the step of displaying the load complementarity map via a preset terminal and updating it based on a preset update command, an active diagnostic step is also performed. Specifically, the multi-energy comprehensive complementarity index β is continuously monitored, and it is determined whether the index is lower than a preset alarm threshold (e.g., β < 0.5, which can be adjusted according to the station's operating requirements) within a preset number of consecutive sampling periods (e.g., N = 3, where N can be configured by the user). If so, an anomaly alarm is triggered, and the corresponding time period is highlighted on the load complementarity chart (e.g., using a bright red background or shaded area to make the anomaly period immediately apparent). Simultaneously, the ratio of the average value of the photovoltaic-energy storage complementarity index α_pv_pcs, the second complementarity index α_pcs_ev, and the third complementarity index α_pv_h2 during that time period to their respective rated values ​​(rated values ​​are the theoretical maximum values ​​of each index, 1) is calculated. The equipment combination corresponding to the index with the lowest ratio is identified as the suspected equipment causing the reduced complementarity characteristics—for example, if the ratio of α_pcs_ev is the lowest, it is determined to be "an anomaly in the complementarity between energy storage and charging loads," and the corresponding suspected equipment combination is energy storage PCS and charging piles; if the ratio of α_pv_h2 is the lowest, it is determined to be "an anomaly in the complementarity between photovoltaic and hydrogen production loads," and the corresponding suspected equipment combination is photovoltaic inverters and hydrogen production equipment. Then, this diagnostic conclusion is directly labeled on the load complementarity chart in the form of a text label (e.g., displaying "Suspicious: Energy Storage-Charging Complementarity Anomaly" next to the anomaly period). If the multi-energy complementarity index is not lower than the alarm threshold, normal monitoring continues. This embodiment enables real-time automatic monitoring and anomaly location of multi-energy complementarity performance. Maintenance personnel can quickly identify the specific time period and responsible equipment where the system's complementarity effect declines without manual curve analysis, thereby significantly shortening troubleshooting time and improving the operational reliability of the photovoltaic-storage-hydrogen-charging system.

[0049] In one embodiment, the content marked on the load complementarity map further includes a load smoothness index, and the step of obtaining the load smoothness index includes: S1041. Calculate the average value and standard deviation of the actual total load power; S1042. The ratio of the average value to the standard deviation is determined as the load smoothness index; S1043. Determine whether the absolute value of the average value is less than a preset threshold: S1044. If so, the load smoothness evaluation index will be automatically switched to the standard deviation of the actual total load power or the actual peak-valley difference. S1045. If not, then the ratio of the mean to the standard deviation shall remain the load smoothness index.

[0050] The load complementarity map also includes a load smoothness index. The steps to obtain this index are as follows: First, calculate the average value μ and standard deviation σ of the actual total load power P_net(t), where μ = (1 / T)∫P_net(t)dt and σ = sqrt((1 / T)∫(P_net(t)-μ)^2dt). Then, define the ratio of the average value to the standard deviation as the load smoothness index γ = μ / σ. The larger this ratio, the smaller the fluctuation of the total load relative to its average value, indicating a better smoothing effect of multi-energy complementarity on the load. However, it is necessary to determine whether the absolute value of the average value μ is less than a preset threshold ε (e.g., ε = 1kW, which can be adjusted according to the rated power scale of the power station). When |μ| < ε, it indicates that the system is close to zero exchange power (i.e., photovoltaic power generation and local load are basically balanced). At this time, the denominator σ approaches a certain positive value while the numerator μ approaches zero, causing γ to be abnormally amplified (tending towards zero) and losing its physical meaning. To avoid this situation, the system automatically switches to using either the standard deviation σ of the actual total load power or the actual peak-to-valley difference ΔP_actual as an alternative load smoothness evaluation index. The standard deviation σ directly reflects the absolute fluctuation range of the load, while the peak-to-valley difference ΔP_actual directly reflects the extreme drop in load value. Both have clear physical meaning under zero exchange power conditions. If |μ|≥ε, then γ=μ / σ is retained as the load smoothness index. This embodiment comprehensively reflects the relative fluctuation of the total load through the ratio of the mean to the standard deviation, enabling horizontal comparisons between stations of different sizes. Simultaneously, an alternative index is designed for special operating conditions approaching zero exchange power, avoiding evaluation distortion and ensuring that the load smoothness index has stable physical meaning and numerical reliability under all operating conditions.

[0051] In one embodiment, after the step of generating a load complementarity map based on the resampled time-series data, the method further includes: S1051. Generate a weekly load complementarity map, wherein the weekly load complementarity map is presented in the form of a heat map to show the average complementarity characteristics of each time period within a week. S1052. Generate a monthly load complementarity map, wherein the monthly load complementarity map is displayed in the form of a line graph showing the changing trend of daily complementarity characteristics within a month; S1053. Furthermore, in response to a click operation on any moment in the load complementarity map, detailed values ​​of the photovoltaic-energy storage complementarity index, complementarity contribution index, and load smoothness index corresponding to that moment are generated, as well as the power values ​​of the photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power, and the actual peak-valley difference and benchmark peak-valley difference for the corresponding time period are generated, and an analysis pop-up window is displayed in the analysis pop-up window.

[0052] Following the step of generating a load complementarity map based on the resampled time-series data, a multi-time-scale load complementarity map is also generated and interactive analysis functions are provided. Specifically, a weekly load complementarity map is generated: this map adopts a heatmap format, with the horizontal axis representing the week (Monday to Sunday) and the vertical axis representing the hour (00:00-23:00). The color intensity of each cell represents the average complementarity characteristics within that time period (for example, using the complementarity contribution C_complement as the color mapping value, the darker the color, the greater the complementarity contribution), which helps maintenance personnel identify the periods and dates with better complementarity effects within a week and discover regular fluctuation patterns. Simultaneously, a monthly load complementarity chart is generated. This chart uses a line graph format, with the horizontal axis representing the date (1st to 31st) and the vertical axis representing complementarity characteristic indicators (such as the photovoltaic-energy storage complementarity index α_pv_pcs, complementarity contribution C_complement, and multi-energy comprehensive complementarity index β, etc.). It displays the daily trends of complementarity characteristics within a month and can also overlay cumulative data such as photovoltaic power generation, energy storage cycle count, charging volume, and hydrogen production, facilitating monthly operation analysis and performance evaluation. Furthermore, in response to user clicks on any point in the load complementarity chart (e.g., clicking a specific time point on the daily load complementarity chart), detailed data for that moment is automatically generated. This includes detailed values ​​for the photovoltaic-energy storage complementarity index, complementarity contribution index, and load smoothness index, as well as the power values ​​for photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power. It also displays the actual peak-to-valley difference ΔP_actual and the baseline peak-to-valley difference ΔP_baseline for the corresponding period, presented in an analysis pop-up window. The technical benefits of this step are as follows: it provides complementary characteristic analysis views at multiple time scales (daily, weekly, and monthly) through heatmaps and line charts, making it easier for operations and maintenance personnel to discover regular fluctuations and abnormal trends at different time granularities; the click-to-interact and detailed floating window design allows users to quickly obtain detailed data at any time without switching between multiple interfaces, greatly improving the efficiency of troubleshooting and operational optimization.

[0053] In one embodiment, after generating the load complementarity map, the method further includes: S1061. Based on pre-stored historical time-series data, calculate the average value of the photovoltaic-energy storage complementarity index within a preset long period or the duration of time it is below a third preset threshold. S1062. Determine whether the photovoltaic-energy storage complementarity index remains below a preset index threshold within a preset long period: S1063. If so, analyze the energy storage state of charge data and curtailment rate data within the corresponding time period to determine whether the number of times the energy storage state of charge reaches the upper limit exceeds a preset threshold and the curtailment rate increases synchronously: S1064. If so, it is determined that the root cause is insufficient energy storage capacity, which leads to the inability of photovoltaic power to be effectively absorbed. A targeted expansion suggestion is generated. The expansion suggestion includes at least prioritizing the increase of the capacity of the energy storage system, and is accompanied by quantitative support data. The quantitative support data includes the amount of curtailed or limited power generation caused by insufficient energy storage capacity. S1065. If not, the process ends.

[0054] After generating the load complementarity map, a planning decision support step is executed to provide data support for site expansion and equipment capacity increase. Specifically, based on pre-stored historical time-series data (such as data from the past month, quarter, or year), the duration for which the average value of the photovoltaic-energy storage complementarity index α_pv_pcs is below a third preset threshold (e.g., α_pv_pcs < 0.3) over a preset long period is calculated, and it is determined whether the index has been consistently below the preset threshold for a long period (e.g., an average value less than 0.4, or a cumulative duration below 0.3 exceeding 30% of the total duration). If so, the energy storage state of charge (SOC) data and curtailment rate data for the corresponding period are further analyzed to determine whether the number of times the energy storage SOC reaches the upper limit (e.g., SOC ≥ 95%) exceeds a preset threshold (e.g., more than 3 times per day) and the curtailment rate increases simultaneously (e.g., curtailment rate > 5% and showing an upward trend). If both conditions are met, it is determined that the root cause is insufficient energy storage capacity, resulting in ineffective absorption of photovoltaic power, and targeted expansion suggestions are generated. The quantitative calculation formula for capacity expansion recommendations is as follows: Let the historical peak load (or the peak power of curtailed photovoltaic power during the statistical period) be P_peak_max (unit: kW), the existing total rated power or capacity of energy storage be P_rated (unit: kW or kWh, selected according to the type of expansion container), and the safety margin factor be η_safe (usually taken as 1.2-1.5, configured by the user according to the importance of the site and the characteristics of load fluctuations). Then, the recommended expansion capacity ΔP_expand = P_peak_max × η_safe - P_rated. When the calculation result is positive, it indicates that there is a capacity gap; when it is negative or zero, it indicates that the existing capacity is sufficient and no expansion is needed. In scenarios where the root cause is "insufficient energy storage capacity," P_rated in the above formula should be replaced with the rated capacity of the existing energy storage system, and P_peak_max should be replaced with the photovoltaic output value or peak power of curtailed photovoltaic power when the energy storage SOC reaches its upper limit during the statistical period, so that the capacity expansion recommendation accurately points to the energy storage capacity gap. The expansion recommendation should include at least "prioritize increasing the capacity of the energy storage system," along with quantitative supporting data, such as "this month, due to insufficient energy storage capacity, a total of xxx kWh of solar power has been curtailed, equivalent to a loss of xxx yuan in electricity costs," and "it is recommended to increase the energy storage capacity by ΔP_expandkWh." If the above conditions are not met (i.e., the α_pv_pcs index is normal, or although it is low, the energy storage SOC has not frequently reached its upper limit, and the curtailment rate has not increased synchronously), the process will end, and no expansion recommendation will be generated.The technical benefits of this step are: it enables automatic diagnosis and quantitative analysis of a typical fault that leads to a decline in the complementary effect of photovoltaic and energy storage due to insufficient energy storage capacity. It not only informs operation and maintenance personnel that "there is a problem," but also clearly provides "what is the cause" (insufficient energy storage capacity), "how much energy storage should be added" (specific quantitative value), and "the economic loss of not expanding capacity" (wasted photovoltaic power and corresponding electricity costs). This provides an objective basis for technical transformation and investment decisions, avoids blind expansion or ineffective operation and maintenance, and reflects the causal closed loop from "characteristic analysis" to "planning decision".

[0055] Figure 2 A schematic diagram of the structure of a visualization monitoring device for the complementary characteristics of photovoltaic storage and hydrogen charging multi-energy loads provided in an embodiment of the present invention is shown. This device, as... Figure 2 As shown, it includes: The acquisition module 310 is used to acquire time-series data according to a preset sampling period. The time-series data includes photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power. The resampling module 320 is used to resample time-series data from different sampling periods to unify them to a target sampling period. It detects the power change rate between adjacent sampling points. If the power change rate exceeds a first change threshold, it determines that the time-series data is in a step change scenario and generates resampled data using a zero-order hold-behind interpolation method that keeps the value of the previous sampling point unchanged; otherwise, it generates resampled data using a linear interpolation method. The first indicator calculation module 330 is used to select a period when the photovoltaic power generation is greater than zero, accumulate the product of the photovoltaic power generation and the energy storage charging and discharging power during the period, and determine the ratio of the accumulated product value to the product of the absolute values ​​of the photovoltaic rated power and the energy storage rated power as the photovoltaic-energy storage complementarity index. The second indicator calculation module 340 is used to calculate the difference between the maximum and minimum values ​​of the actual total load power as the actual peak-valley difference based on the resampled time-series data. The actual total load power is the algebraic sum of the photovoltaic power generation power, energy storage charging and discharging power, charging load power, and hydrogen production load power. The module calculates the sum of the absolute values ​​of each power at each sampling time and takes the difference between the maximum and minimum values ​​of the sum of absolute values ​​as the benchmark peak-valley difference. The module determines the complementary contribution index by taking the ratio of the difference between the benchmark peak-valley difference and the actual peak-valley difference to the benchmark peak-valley difference. The graph generation module 350 is used to generate a load complementarity graph based on the resampled time series data. The load complementarity graph includes power curves displaying the photovoltaic power generation, energy storage charging and discharging power, charging load power and hydrogen production load power within a preset time range, and the photovoltaic-energy storage complementarity index and the complementarity contribution index are marked on the load complementarity graph. The display update module 360 ​​is used to display and update the load complementarity map based on a preset update command through a preset terminal.

[0056] Figure 3 The diagram shows a structural schematic of a computer device provided in an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computer device.

[0057] like Figure 3 As shown, the computer device may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0058] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other network elements, such as clients or other servers. Processor 402 executes program 410, specifically performing the relevant steps in the above-described embodiment of the method for visually monitoring the complementary characteristics of photovoltaic-storage-hydrogen-charging multi-energy loads.

[0059] Specifically, program 410 may include program code, which includes computer-executable instructions.

[0060] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computer device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0061] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0062] Specifically, program 410 can be called by processor 402 to enable computer equipment to perform the relevant steps in the embodiment of the method for visually monitoring the complementary characteristics of photoelectric storage and hydrogen charging multi-energy loads.

[0063] This invention provides a computer-readable storage medium storing at least one executable instruction that, when executed on a computer device, causes the computer device to perform the visualization and monitoring method for the complementary characteristics of photo-storage-hydrogen-charging multi-energy loads in any of the above method embodiments.

[0064] This invention provides a computer program that can be called by a processor to enable a computer device to execute the visualization and monitoring method for the complementary characteristics of photo-storage-hydrogen-charging multi-energy loads in any of the above method embodiments.

[0065] This invention provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed on a computer, cause the computer to perform the visualization and monitoring method for the complementary characteristics of photo-storage-hydrogen-charging multi-energy loads in any of the above method embodiments.

[0066] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0067] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0068] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0069] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0070] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for visually monitoring the complementary characteristics of photovoltaic, hydrogen storage, and multi-energy loads, characterized in that: The method includes: Time-series data are collected according to a preset sampling period. The time-series data includes photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power. Time series data with different sampling periods are resampled to be unified to a target sampling period. The power change rate between adjacent sampling points is detected. If the power change rate exceeds a first change threshold, the time series data is determined to be in a step change scenario. Resampled data is generated by zero-order hold-behind interpolation that keeps the value of the previous sampling point unchanged. Otherwise, resampled data is generated by linear interpolation. Select a period when the photovoltaic power generation is greater than zero, accumulate the product of the photovoltaic power generation and the energy storage charging and discharging power during the period, and determine the ratio of the accumulated product value to the product of the absolute values ​​of the photovoltaic rated power and the energy storage rated power as the photovoltaic-energy storage complementarity index. Based on the resampled time-series data, the difference between the maximum and minimum values ​​of the actual total load power is calculated as the actual peak-valley difference. The actual total load power is the algebraic sum of the photovoltaic power generation power, energy storage charging and discharging power, charging load power, and hydrogen production load power. The sum of the absolute values ​​of each power at each sampling time is calculated, and the difference between the maximum and minimum values ​​of the sum of absolute values ​​is taken as the benchmark peak-valley difference. The ratio of the difference between the benchmark peak-valley difference and the actual peak-valley difference to the benchmark peak-valley difference is determined as the complementary contribution index. Based on the resampled time-series data, a load complementarity map is generated. The load complementarity map includes power curves displaying the photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power within a preset time range. The photovoltaic-energy storage complementarity index and the complementarity contribution index are marked on the load complementarity map. The load complementarity map is displayed through a preset terminal and updated based on a preset update command.

2. The method according to claim 1, characterized in that, Before the step of resampling time-series data from different sampling periods to unify them to the target sampling period, the method further includes: Determine whether the current data acquisition device supports network time synchronization protocol; If supported, the clock offset is obtained through network synchronization, and the timing data is timestamped based on the clock offset. If not supported, the pre-configured static clock offset value is read, and the timing data is timestamped based on the static clock offset value.

3. The method according to claim 1, characterized in that, The load complementarity map also includes a multi-energy comprehensive complementarity index, and the steps for obtaining the multi-energy comprehensive complementarity index include: Calculate the second complementarity index between energy storage charging and discharging power and charging load power; Calculate the third complementarity index between photovoltaic power generation and hydrogen production load; The photovoltaic-energy storage complementarity index, the second complementarity index, and the third complementarity index are multiplied by their respective weighting coefficients and then summed to obtain the multi-energy comprehensive complementarity index. Among them, if a grid demand restriction command is received, the weight of the second complementarity index is increased; if the curtailment rate of solar power or wind power exceeds the second preset threshold, the weight of the photovoltaic-energy storage complementarity index is increased; otherwise, the default weight is maintained.

4. The method according to claim 3, characterized in that, After the step of displaying the load complementarity map through a preset terminal and updating it based on a preset update command, the method further includes: Continuously monitor the multi-energy comprehensive complementarity index; Determine whether the multi-energy comprehensive complementarity index is lower than a preset alarm threshold within a preset number of consecutive sampling periods: If so, an anomaly alarm is triggered, and the corresponding time period is highlighted on the load complementarity graph; the ratio of the average value of the photovoltaic-energy storage complementarity index, the second complementarity index, and the third complementarity index to the corresponding rated value during the time period is calculated respectively, and the equipment combination corresponding to the index with the lowest ratio is identified as the suspected equipment that causes the reduction of complementarity characteristics; and the corresponding conclusion is marked on the load complementarity graph. If not, continue monitoring.

5. The method according to claim 1, characterized in that, The load complementarity map also includes a load smoothness index, and the steps for obtaining the load smoothness index include: Calculate the average value and standard deviation of the actual total load power; The ratio of the average value to the standard deviation is determined as the load smoothness index; Determine whether the absolute value of the average is less than a preset threshold: If so, the load smoothness evaluation index will be automatically switched to the standard deviation of the actual total load power or the actual peak-valley difference. If not, then the ratio of the mean to the standard deviation remains the load smoothness index.

6. The method according to claim 5, characterized in that, After the step of generating a load complementarity map based on the resampled time-series data, the method further includes: A weekly load complementarity map is generated, which uses a heat map format to display the average complementarity characteristics of each time period within a week. A monthly load complementarity map is generated, which uses a line graph to show the changing trend of daily complementarity characteristics within a month. Furthermore, in response to a click operation on any moment in the load complementarity map, detailed values ​​of the photovoltaic-energy storage complementarity index, complementarity contribution index, and load smoothness index corresponding to that moment are generated, along with the power values ​​of the photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power, as well as the actual peak-valley difference and benchmark peak-valley difference for the corresponding time period. An analysis pop-up window is then displayed in the analysis pop-up window.

7. The method according to claim 1, characterized in that, After generating the load complementarity map, the method further includes: Based on pre-stored historical time-series data, calculate the average value of the photovoltaic-energy storage complementarity index within a preset long period or the duration of time it is below a third preset threshold; Determine whether the photovoltaic-energy storage complementarity index remains below a preset threshold for a preset long period: If so, analyze the energy storage state of charge data and curtailment rate data within the corresponding time period to determine whether the number of times the energy storage state of charge reaches the upper limit exceeds a preset threshold and the curtailment rate increases simultaneously: If so, the root cause is determined to be insufficient energy storage capacity, which prevents the effective absorption of photovoltaic power. A targeted expansion suggestion is generated, which includes at least prioritizing the increase of the capacity of the energy storage system, and is accompanied by quantitative supporting data, including the amount of curtailed or limited power generation caused by insufficient energy storage capacity. If not, the process ends.

8. A visual monitoring device for the complementary characteristics of photovoltaic, hydrogen storage, and multi-energy loads, characterized in that, include: The acquisition module is used to acquire time-series data according to a preset sampling period. The time-series data includes photovoltaic power generation, energy storage charging and discharging power, charging load power, and hydrogen production load power. The resampling module is used to resample time-series data from different sampling periods to unify them to a target sampling period. It detects the power change rate between adjacent sampling points. If the power change rate exceeds a first change threshold, the time-series data is determined to be in a step-change scenario, and resampled data is generated using zero-order hold-behind interpolation that keeps the value of the previous sampling point unchanged; otherwise, linear interpolation is used to generate resampled data. The first indicator calculation module is used to select the period when the photovoltaic power generation is greater than zero, accumulate the product of the photovoltaic power generation and the energy storage charging and discharging power during the period, and determine the ratio of the accumulated product value to the product of the absolute values ​​of the photovoltaic rated power and the energy storage rated power as the photovoltaic-energy storage complementarity index. The second indicator calculation module is used to calculate the difference between the maximum and minimum values ​​of the actual total load power as the actual peak-valley difference based on the resampled time-series data. The actual total load power is the algebraic sum of the photovoltaic power generation power, energy storage charging and discharging power, charging load power, and hydrogen production load power. The module calculates the sum of the absolute values ​​of each power at each sampling time and takes the difference between the maximum and minimum values ​​of the sum of absolute values ​​as the benchmark peak-valley difference. The module determines the complementary contribution index by taking the ratio of the difference between the benchmark peak-valley difference and the actual peak-valley difference to the benchmark peak-valley difference. The load complementarity module is used to generate a load complementarity map based on the resampled time series data. The load complementarity map includes power curves displaying the photovoltaic power generation, energy storage charging and discharging power, charging load power and hydrogen production load power within a preset time range, and the photovoltaic-energy storage complementarity index and the complementarity contribution index are marked on the load complementarity map. The display update module is used to display and update the load complementarity map based on a preset update command via a preset terminal.

9. A computer device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the visualization monitoring method for the complementary characteristics of photoelectric storage and hydrogen charging multi-energy loads as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on a computer device, causes the computer device to perform the operation of the visualization monitoring method for the complementary characteristics of photovoltaic storage and hydrogen charging multi-energy loads as described in any one of claims 1-7.

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