A method and system for processing photovoltaic energy storage power data in industrial parks

Abstract

This invention relates to the technical field of energy management in industrial parks, and provides a method and system for processing photovoltaic energy storage power data in industrial parks. The method includes: receiving charging and discharging scheduling instructions from energy storage batteries within the industrial park; the charging and discharging scheduling instructions include a target power output; based on the charging and discharging scheduling instructions, real-time acquisition of operating parameters of the energy storage batteries; calculating the actual energy throughput of the energy storage batteries based on the operating parameters; comparing the actual energy throughput with the target power output to determine if there is an energy gap in the energy storage batteries; adjusting the charging and discharging power or duration of the energy storage batteries based on the energy gap; if an energy gap exists and the energy storage batteries' discharge does not meet load demand, supplementing energy from the external power grid; calculating the additional costs incurred in compensating for the energy gap, and feeding back the additional costs to the energy management system. This invention improves energy management decision support capabilities and park operational efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of energy management in industrial parks, specifically to a method and system for processing photovoltaic energy storage power data in industrial parks. Background Technology

[0002] In industrial parks, the coordinated management of photovoltaic power generation, energy storage systems, and electrical loads is crucial for achieving energy optimization and cost control. A modern industrial park's energy system is typically designed to be highly integrated to achieve energy self-sufficiency and cost optimization. Photovoltaic power generation arrays, advanced energy storage systems, and diverse industrial production loads constitute the core elements. The core responsibility of the park's Energy Management System (EMS) is to collect, process, and analyze massive amounts of power data from these multi-source, heterogeneous devices in real time, and then use this data for refined energy scheduling to address the intermittency of photovoltaic power generation, the fluctuations in the charging and discharging efficiency of energy storage devices, and the time-varying characteristics of industrial load demands.

[0003] When park managers discover a persistent discrepancy between actual electricity costs reported on financial statements and cost savings reported by the energy management system, they begin to seriously question the credibility of the entire system's decision-making. This mismatch not only causes the park's overall electricity costs to rise unnoticed, but more importantly, it undermines the reliability of long-term energy planning based on the system's data, predictive maintenance strategies for energy storage devices, and future expansion decisions. This ongoing operational predicament leaves the park in a passive position regarding energy management, unable to effectively cope with increasingly complex electricity market fluctuations and stringent carbon emission requirements, and may even impact the park's overall competitiveness.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a method and system for processing photovoltaic energy storage power data in industrial parks, aiming to solve the problems in the prior art, such as the gradual decay of the internal electrodes of energy storage batteries leading to a decrease in the accuracy of the remaining power estimation model, deviation in the execution of charging and discharging strategies, inaccurate assessment of the economic efficiency of energy dispatching, and a continuous increase in the overall operating cost of the park.

[0006] The technical solution of this application is as follows:

[0007] Firstly, this application discloses a method for processing photovoltaic energy storage power data in industrial parks, including:

[0008] Receive charging and discharging scheduling instructions for energy storage batteries within the industrial park;

[0009] The charge / discharge scheduling command includes the expected target charge level;

[0010] Based on charging and discharging scheduling commands, the operating parameters of the energy storage battery are collected in real time;

[0011] Calculate the actual energy throughput of the energy storage battery based on the operating parameters;

[0012] Compare the actual energy throughput with the expected target capacity to determine whether there is an energy gap in the energy storage battery;

[0013] If there is an energy gap, adjust the charging and discharging power or duration of the energy storage battery according to the energy gap;

[0014] If there is an energy gap and the energy storage battery discharge cannot meet the load demand, energy will be supplemented from the external power grid.

[0015] The system calculates the additional costs incurred in compensating for energy shortages and feeds these costs back to the energy management system. The energy management system then adjusts the corresponding energy dispatch strategy based on these additional costs, selecting the less costly emergency response path to improve the overall economic efficiency of the park's energy management.

[0016] This technical solution enables real-time monitoring and dynamic adjustment of the energy state of energy storage batteries, effectively addressing the energy gap caused by battery degradation. Through cost accounting and feedback mechanisms, it improves the economic efficiency of park energy management and solves the problems of economic losses and inaccurate decision-making caused by model bias in existing technologies.

[0017] Secondly, this application also discloses a photovoltaic energy storage power data processing system for industrial parks, comprising: a dispatch instruction receiving module for receiving charging and discharging dispatch instructions from energy storage batteries within the industrial park; the charging and discharging dispatch instructions include the expected target power; an operating parameter acquisition module for acquiring the operating parameters of the energy storage batteries in real time based on the charging and discharging dispatch instructions; a throughput calculation module for calculating the actual energy throughput of the energy storage batteries based on the operating parameters; an energy gap judgment module for comparing the actual energy throughput with the expected target power to determine whether there is an energy gap in the energy storage batteries; an energy gap compensation module for adjusting the charging and discharging power or duration of the energy storage batteries if an energy gap exists; an external charging execution module for supplementing energy from the external power grid if there is an energy gap and the energy storage batteries' discharge does not meet the load demand; and an additional cost feedback module for calculating the additional costs incurred in compensating for the energy gap and feeding the additional costs back to the energy management system; the energy management system adjusts the corresponding energy dispatch strategy based on the additional costs and selects the emergency response path with lower costs to improve the overall economic efficiency of energy management in the park.

[0018] This technical solution provides a system that integrates functions such as receiving dispatch instructions, collecting operating parameters, calculating energy throughput, determining energy gaps, compensating for energy gaps, executing external charging, and providing additional cost feedback. This enables comprehensive processing and management of photovoltaic energy storage power data in industrial parks, effectively improving the intelligence and economic efficiency of park energy management.

[0019] Beneficial Effects: The photovoltaic energy storage power data processing method for industrial parks disclosed in this application can accurately calculate the actual energy throughput by receiving charge and discharge dispatch instructions and collecting energy storage battery operating parameters in real time. By comparing the actual energy throughput with the expected target energy, the energy gap of the energy storage battery can be identified in a timely manner. For the energy gap, this method can dynamically adjust the charge and discharge power or duration of the energy storage battery and supplement energy from the external grid when necessary, thereby effectively compensating for the capacity shortage caused by energy storage battery degradation and ensuring the stability and reliability of the park's energy supply. More importantly, this method further calculates the additional costs incurred in compensating for the energy gap and feeds these costs back to the energy management system. This mechanism allows park managers to clearly understand the actual economic impact of battery degradation and compensation behavior, thereby optimizing energy dispatch strategies, avoiding potential economic losses, and improving the overall economic efficiency of park energy management. Compared with the problems of decreased accuracy of remaining power estimation models, deviations in charge and discharge strategy execution, and inaccurate assessment of energy dispatch economics caused by battery degradation in existing technologies, this application effectively solves these technical problems through closed-loop management of real-time monitoring, dynamic adjustment, and cost feedback, significantly improving the decision support capability of the energy management system and the overall operational efficiency of the park. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for processing photovoltaic energy storage power data in an industrial park, as described in one embodiment of the present invention.

[0021] Figure 2 This is a flowchart of a method for processing photovoltaic energy storage power data in an industrial park, according to another embodiment of the present invention.

[0022] Figure 3 This is a system block diagram of a photovoltaic energy storage power data processing system for an industrial park, according to another embodiment of the present invention.

[0023] Explanation of reference numerals in the attached figures:

[0024] 1. Industrial Park Photovoltaic Energy Storage Power Data Processing System; 11. Dispatch Instruction Receiving Module; 12. Operation Parameter Acquisition Module; 13. Throughput Calculation Module; 14. Energy Gap Judgment Module; 15. Energy Gap Compensation Module; 16. External Charging Execution Module; 17. Additional Cost Feedback Module. Detailed Implementation

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] This application proposes a method for processing photovoltaic energy storage power data in industrial parks, combining... Figure 1 As shown, it includes:

[0028] S1 receives charging and discharging scheduling instructions for energy storage batteries within the industrial park; the charging and discharging scheduling instructions include the expected target power.

[0029] S2 collects the operating parameters of the energy storage battery in real time based on charge and discharge scheduling commands;

[0030] S3, calculates the actual energy throughput of the energy storage battery based on the operating parameters;

[0031] S4 compares the actual energy throughput with the expected target capacity to determine whether there is an energy gap in the energy storage battery;

[0032] S5, if there is an energy gap, adjust the charging and discharging power or duration of the energy storage battery according to the energy gap;

[0033] S6, if there is an energy gap and the energy storage battery discharge does not meet the load demand, energy can be supplemented from the external power grid;

[0034] S7 calculates the additional costs incurred due to compensating for energy gaps and feeds these additional costs back to the energy management system. The energy management system adjusts the corresponding energy dispatch strategy based on the additional costs and selects the lowest-cost emergency response path to improve the overall economic efficiency of the park's energy management.

[0035] Specifically, a "charge and discharge scheduling instruction" refers to an instruction generated by the energy management system based on the park's electricity demand, photovoltaic power generation forecasts, and the current status of the energy storage batteries. This instruction guides the energy storage batteries in charging or discharging operations. It typically includes key parameters such as target capacity, charging / discharging power limits, and charging / discharging duration. The "expected target capacity" is the explicitly specified capacity value that the energy storage batteries should achieve or provide within a specific time period, as defined in the charge and discharge scheduling instruction.

[0036] "Operating parameters" refer to various physical quantities monitored in real time during the operation of an energy storage battery, such as battery voltage, current, temperature, and internal resistance. These parameters are key indicators for evaluating the battery's health and performance. "Actual energy throughput" refers to the actual amount of electricity charged or discharged by the energy storage battery within a specific time period, calculated based on the real-time collected operating parameters. This value reflects the battery's true energy exchange capacity.

[0037] "Energy gap" refers to the difference between the actual energy throughput of an energy storage battery and the expected target energy capacity. An energy gap exists when the actual energy throughput is lower than the expected target energy capacity, indicating that the energy storage battery has failed to provide sufficient energy according to dispatch instructions. "Additional costs" refer to the extra expenses incurred in compensating for the energy gap, such as the cost of purchasing electricity from the external grid, contract penalties, and carbon emission-related costs.

[0038] The implementation of this application may include the following steps:

[0039] First, the system receives charging and discharging scheduling instructions from the energy storage batteries within the industrial park. These instructions can be sent from the energy management system to the energy storage battery management system via wired or wireless communication. For example, the energy management system can generate an instruction based on the park's real-time load forecast and photovoltaic power generation forecast, requiring the energy storage battery to discharge 100 kWh within the next hour. This instruction will explicitly include 100 kWh as the expected target discharge amount.

[0040] Secondly, based on charge / discharge scheduling commands, the operating parameters of the energy storage battery are collected in real time. Upon receiving a charge / discharge scheduling command, the energy storage battery management system initiates real-time monitoring of the energy storage battery. For example, it can collect voltage, current, and temperature data of the energy storage battery once per second. This data is acquired through sensors and transmitted to the data processing unit.

[0041] Next, based on the operating parameters, the actual energy throughput of the energy storage battery is calculated. For example, by integrating the real-time collected current and voltage data, the actual amount of electricity charged or discharged by the energy storage battery within a specific time period can be calculated. If the average discharge current of the energy storage battery during command execution is 50A, the average voltage is 48V, and the discharge lasts for 2 hours, then the actual energy throughput is 50A * 48V * 2h = 4.8kWh.

[0042] Then, the actual energy throughput is compared with the expected target capacity to determine whether there is an energy gap in the energy storage battery. For example, if the expected target capacity is 100kWh, but the calculated actual energy throughput is 95kWh, then there is an energy gap of 5kWh.

[0043] If an energy deficit exists, the charging and discharging power or duration of the energy storage battery is adjusted accordingly. For example, if there is a 5kWh energy deficit, the system can instruct the energy storage battery to discharge at a higher power for the remaining time, or extend the discharge time, to make up for the 5kWh deficit.

[0044] If an energy gap exists and the energy storage battery discharge is insufficient to meet load demand, energy will be supplemented from the external power grid. For example, if the energy storage battery has already discharged at maximum power and extending the discharge time cannot meet the load demand, the system will send a power purchase instruction to the external power grid to obtain the required energy to make up for the gap.

[0045] Finally, the additional costs incurred in compensating for the energy gap are calculated and fed back to the energy management system to improve the overall economic efficiency of energy management in the park. For example, if 5 kWh of energy is supplemented from the external grid and the electricity price at the time is 1 yuan / kWh, an additional electricity purchase cost of 5 yuan will be incurred. This cost will be recorded and sent to the energy management system so that the system can take these economic factors into account in subsequent dispatch decisions.

[0046] In some implementations, the additional costs incurred to compensate for energy shortages refer to the operational costs of additional technical measures taken by the system to maintain microgrid stability in the event of a sudden drop in power generation. These additional costs typically include two categories.

[0047] The first category is costs related to the discharge of the energy storage system. In emergency situations, energy storage systems need to discharge at high power for short periods. This discharge accelerates the performance degradation of the energy storage units, increases the number of cycles, and causes additional operating costs. Therefore, the system assesses the usage costs associated with this emergency discharge based on the health status of the energy storage device, the depth of discharge, the duration of the discharge, and maintenance strategies, and considers this as part of the cost of compensating for energy shortages. In some preferred embodiments, the system performs the following steps when assessing the usage costs generated by the energy storage device during an emergency discharge: First, the system acquires the health status information of the energy storage device, including the current state of charge (SOC), state of health (SOH), the most recent deep discharge record, and the consistency between individual battery cells (including individual cell voltage deviation, temperature deviation, etc.). This information is continuously monitored and uploaded in real time by the battery management system inside the energy storage device, serving as the basis for assessing this discharge behavior. Second, the system records the depth of discharge of the energy storage device during the emergency discharge. By collecting the SOC values ​​at the start and end of the discharge and calculating the difference, the system determines the battery capacity occupancy of this discharge behavior. For example, when the SOC drops from 85% to 60%, the system records the depth of discharge as 25%. Simultaneously, the system records the energy storage device's output power, duration, maximum discharge power, average discharge power, and individual cell temperature changes during the entire discharge process at fixed time intervals, reflecting the impact of this discharge on the instantaneous performance of the energy storage device.

[0048] Furthermore, the system pre-stores a maintenance strategy table corresponding to different energy storage health states and operating conditions. This table specifies the discharge consumption level under different SOH levels, different discharge depths, different operating temperatures, and whether high-rate discharge occurs. For example, if the SOH is higher than 90% and the discharge depth is lower than 30%, it corresponds to "mild consumption"; if the SOH is between 70% and 90% and the discharge depth exceeds 40%, it corresponds to "moderate consumption"; if the SOH is lower than 70% and the discharge involves a rapid temperature rise or continuous high-rate discharge, it corresponds to "severe consumption". The system matches the health status data, discharge depth, discharge duration, and temperature changes collected during the discharge with the maintenance strategy table to determine the specific consumption level corresponding to the discharge behavior.

[0049] Finally, based on the matching results, the system generates a usage consumption assessment record for this emergency discharge. This record includes the impact of the discharge on the energy storage lifespan, whether it is necessary to enter the equalization calibration or maintenance cycle of replacing some energy storage units in advance, and whether there are any discharge risk markers (such as temperature approaching the threshold or discharge rate exceeding the limit). The system incorporates this usage consumption assessment as part of the cost incurred in compensating for the energy gap, and includes it in subsequent energy dispatch decisions or economic analyses, so that the performance loss of energy storage equipment after each emergency discharge can be quantified and managed.

[0050] The second category is costs related to load reduction. During emergencies, the system may need to reduce power consumption on some non-critical or adjustable loads. Different loads have varying degrees of impact on their normal functions, production rhythm, or service capabilities when power consumption is reduced. Some loads may not have a significant impact from power reduction in the short term, while others may experience decreased production efficiency, worsened user experience, or additional recovery time once power is reduced. Therefore, the system comprehensively assesses the potential losses based on the importance of various loads, the extent of reduction, the duration of reduction, and their impact on production or service, considering this as another part of the cost of compensating for energy shortages. In some preferred embodiments, to quantify the potential losses caused by load reduction, the system comprehensively assesses the importance of various loads, the extent of reduction, the duration of reduction, and their impact on production or service according to the following steps. First, the system pre-stores the importance levels of different load units within the park. These levels are divided into several categories based on the criticality of the load in the production process, the degree of impact on safe operation, and the requirements for business continuity, such as "non-reducible loads," "critical loads," "reducible but significantly impactful loads," and "generally reducible loads." The level of each load unit is configured by the park management system during the deployment phase and can be updated according to production plans or business adjustments.

[0051] Secondly, when formulating a reduction plan, the system combines the maximum reducible power and minimum stable operating power of each load unit to determine the expected reduction magnitude, and sets the reduction duration through scheduling strategies. Regarding the reduction magnitude, the system determines its adjustable range by analyzing real-time operating power and equipment operating parameters (such as operating conditions and work cycles); regarding the duration, it is set based on the current power deficit size, the energy storage system's buffer duration, and subsequent adjustment capabilities, and can be adjusted in real time according to load feedback.

[0052] Furthermore, the system quantifies the impact of load reduction on production processes, service quality, or staff comfort using a pre-defined impact assessment table. For example, for critical production equipment, the system considers potential output reductions, process interruption risks, and equipment restart costs as impact indicators; for office-related loads, such as lighting or general air conditioning, the system assesses their impact on staff comfort and service environment compliance rates. These impact indicators are set by the park management during the deployment phase and can be configured with different evaluation rules for different business scenarios.

[0053] Subsequently, the system weights and summarizes the load's importance level, reduction magnitude, reduction duration, and impact assessment items to form the potential loss assessment result of this reduction action. For example, high-importance loads will receive higher loss scores if the reduction magnitude is large and the duration is long; while generally reducible loads will receive relatively lower loss scores if the reduction magnitude is small. This weighting process can be implemented using rule tables or segmented scoring methods without the need for complex mathematical models.

[0054] Finally, the system incorporates the above-mentioned loss assessment results as another part of the cost of compensating for the energy gap, along with the energy storage discharge cost, into the overall energy allocation decision. This ensures that the energy dispatch process can minimize the impact on production or services while meeting the stability requirements of the microgrid, thereby ensuring the overall economic efficiency and reliability of the park's operation.

[0055] In the method of this application, the system dynamically evaluates the above two types of costs, using them as auxiliary basis in the energy allocation strategy formulation process. When a sudden drop in power generation occurs, the system not only focuses on the stability requirements of the microgrid but also considers the overall cost level of the current emergency response. Under the premise of ensuring safe operation, it prioritizes the lower-cost response path and adjusts the combination of energy storage discharge and load shedding to reduce unnecessary operating costs and economic impacts. In this way, this application optimizes the overall operating cost of the park while ensuring the stability of the microgrid, making the energy allocation control strategy take into account both safety and economy.

[0056] Optional, combined Figure 2 As shown, S4 is a step that compares the actual energy throughput with the expected target capacity to determine whether there is an energy gap in the energy storage battery. Specifically, it includes:

[0057] S41 identifies the charging condition of the energy storage battery and determines the timing of pulse injection;

[0058] S42, at the pulse injection timing, injects a multi-frequency current pulse sequence into the energy storage battery;

[0059] S43, during the injection of the multi-frequency current pulse sequence, simultaneously acquires the voltage and current data of the energy storage battery;

[0060] S44 performs spectral analysis on voltage and current data to extract the impedance characteristics of the energy storage battery;

[0061] S45 compares the degree of deviation of the impedance characteristics from the dynamic impedance baseline to determine whether there is an energy gap in the energy storage battery.

[0062] Specifically, identifying the charging condition of the energy storage battery and determining the pulse injection timing refers to the system intelligently selecting a suitable time to perform impedance measurement based on parameters such as the battery's current charging state (e.g., constant current charging stage, constant voltage charging stage, or late charging stage) and its state of charge (SOC). This timing is typically chosen during a period when the battery state is relatively stable and has minimal impact on normal charging and discharging operations. The purpose is to ensure the accuracy of the measurement during pulse injection and to avoid unnecessary interference with the normal operation of the battery.

[0063] In this context, injecting a multi-frequency current pulse sequence into the energy storage battery during pulse injection can be understood as employing electrochemical impedance spectroscopy (EIS) technology. A multi-frequency current pulse sequence refers to a small current perturbation signal containing multiple components of different frequencies, which is applied to the energy storage battery. The purpose is to excite the battery's response over a wide frequency range, thereby obtaining detailed information about different electrochemical processes within the battery (such as ohmic impedance, charge transfer impedance, diffusion impedance, etc.).

[0064] In practical applications, synchronously acquiring voltage and current data of the energy storage battery during the injection of a multi-frequency current pulse sequence refers to using high-precision data acquisition equipment to accurately record the voltage response across the battery and the current flowing through it while the multi-frequency current pulse sequence is being injected. The purpose of synchronous acquisition is to ensure a strict temporal correspondence between the voltage and current data, which is the foundation for subsequent accurate spectrum analysis.

[0065] Furthermore, spectral analysis of voltage and current data to extract the impedance characteristics of the energy storage battery involves converting the synchronously acquired time-domain voltage and current data into the frequency domain for analysis using signal processing techniques such as Fourier transform. By calculating the voltage-to-current ratio at each frequency point, the complex impedance value of the battery at different frequencies can be obtained, thereby constructing an impedance spectrum (such as a Nyquist plot or Bode plot). The shape, characteristic frequency points, and impedance values ​​of these impedance spectra constitute the impedance characteristics of the energy storage battery, which can reflect the physicochemical state inside the battery, such as the activity of electrode materials, the ion conductivity of the electrolyte, and interfacial reaction kinetics.

[0066] Therefore, comparing the deviation of the impedance characteristics from the dynamic impedance baseline to determine whether an energy storage battery has an energy gap involves comparing the real-time extracted impedance characteristics of the energy storage battery with a pre-established standard impedance curve (i.e., the dynamic impedance baseline) representing a healthy battery under different operating conditions. The dynamic impedance baseline can be dynamically adjusted based on factors such as battery type, lifespan, temperature, and state of charge. When a significant deviation occurs between the real-time impedance characteristics and the dynamic impedance baseline, such as a significant increase in ohmic impedance or charge transfer impedance, it indicates that there may be problems such as capacity decay, increased internal resistance, or loss of active material inside the battery, thus indicating that the energy storage battery has an energy gap.

[0067] In some preferred embodiments, it is assumed that an energy storage battery in an industrial park has a target capacity of 100 kWh when executing charge / discharge scheduling commands. Based on conventional energy throughput calculations, this battery appears capable of meeting the demand. However, to more accurately assess its actual usable energy, the solution of this application is applied. Specifically, when the energy storage battery is in the constant-voltage charging phase and its state of charge reaches 80%, the system identifies this as a pulse injection opportunity. At this time, a multi-frequency current pulse sequence containing frequencies ranging from 0.1 Hz to 10 kHz is injected into the energy storage battery, lasting approximately 30 seconds. During this period, high-precision sensors simultaneously acquire the battery's voltage and current data. Subsequently, this data is sent to a spectrum analysis module, where a Fast Fourier Transform (FFT) is used to extract the battery's impedance characteristics, such as ohmic impedance, charge transfer impedance, and diffusion impedance. For example, the analysis results show that the battery's ohmic impedance has increased by 15% compared to its dynamic impedance baseline in a healthy state, and its charge transfer impedance has increased by 25%. Based on preset deviation thresholds, the system determines that these deviations exceed the normal range, indicating significant battery degradation and that the actual usable energy is insufficient to reach the expected target capacity, i.e., an energy gap exists. Based on this determination, the energy management system immediately adjusts the discharge power of the energy storage battery and proactively initiates a strategy to supplement energy from the external power grid to ensure a stable power supply for the industrial park and avoid the risk of power outages due to battery performance degradation.

[0068] Optionally, the steps of calculating the additional costs incurred in compensating for the energy deficit and feeding these additional costs back to the energy management system may include the following:

[0069] Record the occurrence time, duration, compensation amount, and source of the energy deficit compensation event;

[0070] A pre-defined set of rules for assessing the impact of compensation actions based on electricity contract terms;

[0071] Based on the occurrence time, duration, compensation amount, and source of the energy gap compensation event, and in accordance with the rule set for assessing the impact of compensation behavior, the cumulative contract penalties or additional demand costs are calculated.

[0072] Cumulative contract penalties or additional demand charges will be combined with real-time electricity purchase costs to form corresponding additional costs;

[0073] The additional costs are fed back to the energy management system.

[0074] Specifically, an energy gap compensation event refers to an event that occurs when the energy storage battery discharges and fails to meet load demand, requiring supplemental energy from the external power grid. The event occurrence time is used to precisely record the start time of the compensation action, the duration records the duration of energy replenishment from the external power grid, the compensation amount refers to the actual amount of electricity replenished from the external power grid, and the compensation source specifies the specific source of the energy replenishment, such as mains power, a backup generator, or other emergency power supply channels. Recording this information aims to provide detailed basic data for subsequent cost accounting.

[0075] The pre-defined set of rules for assessing the impact of compensation activities based on electricity contract terms refers to a set of predefined assessment standards developed according to the contract terms signed between the industrial park and the electricity supplier. This set of rules may include specific clauses regarding overload electricity penalties, peak-valley electricity price differences, capacity fees, and emergency power supply service rates, used to quantify the potential economic impact of energy gap compensation activities.

[0076] In practical applications, based on the timing, duration, amount of compensation, and source of the energy deficit compensation event, and in accordance with the rule set for assessing the impact of compensation activities, cumulative contract penalties or additional demand costs can be calculated. Cumulative contract penalties refer to fines incurred for violating clauses in electricity contracts regarding electricity consumption, power factor, or peak load; additional demand costs refer to extra expenses incurred within a specific time period due to exceeding predetermined electricity demand. The calculation of these costs aims to accurately reflect the contractual breaches or additional service costs directly resulting from energy deficits.

[0077] Furthermore, cumulative contract penalties or additional demand charges are linked to real-time electricity purchase costs to form corresponding additional costs. Real-time electricity purchase costs refer to the actual cost of purchasing electricity from the external grid when an energy gap occurs; this cost is typically affected by factors such as market electricity price fluctuations and peak-valley pricing strategies. By aggregating these costs, a comprehensive additional cost indicator reflecting the economic burden of energy gap compensation can be formed.

[0078] Ultimately, the additional costs are fed back to the energy management system to provide it with accurate cost data, enabling the system to perform more refined energy scheduling optimization, cost analysis, and decision support, thereby improving the overall economic efficiency of energy management throughout the industrial park.

[0079] Optionally, the steps of calculating the additional costs incurred in compensating for the energy deficit and feeding these additional costs back to the energy management system include:

[0080] Record the occurrence time, duration, compensation amount, compensation source, and instantaneous cost parameters corresponding to the compensation source of the energy gap compensation event;

[0081] Pre-defined ruleset for long-term contracts and carbon emission impact assessment;

[0082] Based on the occurrence time, duration, compensation amount, compensation source, and instantaneous cost parameters of the energy gap compensation event, and in accordance with the long-term contract and carbon emission impact assessment rule set, the cumulative contract penalties, additional demand costs, and carbon emission-related costs are calculated.

[0083] The total additional cost is calculated by summing up direct electricity purchase costs or fuel costs, cumulative contract penalties, additional demand charges, and carbon emission-related costs.

[0084] The total additional costs are fed back to the energy management system in the form of multi-dimensional indicators.

[0085] Specifically, recording the occurrence time, duration, compensation amount, compensation source, and corresponding instantaneous cost parameters of energy gap compensation events means that when an energy gap occurs and external energy replenishment is needed, the system automatically records detailed information about the event. The instantaneous cost parameter can be understood as the immediate cost required to obtain a unit of electricity from a specific compensation source (such as the external power grid or backup generator sets) at a specific point in time. Its purpose is to capture energy cost fluctuations at different times and from different sources.

[0086] The pre-configured long-term contract and carbon emission impact assessment rule set refers to a series of rules pre-configured within the system to assess the impact of energy gap compensation behavior on long-term electricity purchase contracts and carbon emissions. Specifically, this rule set may include clauses in the contract regarding penalties for excessive electricity consumption, capacity fees, and demand-side response rewards, as well as carbon emission factors for different electricity sources (such as coal, gas, and renewable energy) and the pricing mechanism of the carbon trading market. Its purpose is to quantify the impact of energy gap compensation behavior on long-term financial and environmental responsibility.

[0087] In practical applications, based on the timing, duration, amount of compensation, source of compensation, and instantaneous cost parameters of the energy gap compensation event, and in accordance with long-term contracts and carbon emission impact assessment rule sets, cumulative contract penalties, additional demand charges, and carbon emission-related costs are calculated. Cumulative contract penalties refer to the accumulated penalties incurred for violating certain clauses of long-term power purchase contracts (e.g., exceeding the agreed-upon electricity consumption limit, failure to meet power factor requirements, etc.). Additional demand charges refer to the additional grid capacity fees or peak-hour surcharges triggered by a surge in electricity demand due to the energy gap within a specific time period. Carbon emission-related costs refer to the carbon emissions generated from obtaining supplementary energy from high-carbon emission sources (such as coal-fired power plants), calculated based on carbon trading market prices or internal carbon cost accounting mechanisms.

[0088] Furthermore, direct electricity purchase costs or fuel costs, cumulative contract penalties, additional demand charges, and carbon emission-related costs are aggregated into a total additional cost. Direct electricity purchase costs or fuel costs refer to the cost of fuel consumed in purchasing electricity directly from the grid or starting standby generators to make up for the energy gap. By aggregating these costs from different dimensions, a comprehensive "total additional cost" that reflects the true economic cost of the energy gap compensation event can be formed.

[0089] Therefore, the total additional costs are fed back to the energy management system in the form of multi-dimensional indicators to improve the overall economic efficiency of energy management in the park. The multi-dimensional indicators may include detailed cost composition (e.g., the proportion of direct electricity purchase cost, the proportion of contract penalty, and the proportion of carbon cost), the time distribution of cost occurrence, and deviation analysis from the expected target electricity volume, etc. The purpose is to provide the energy management system with richer and more insightful information to enable more refined decision-making.

[0090] Optionally, the steps of calculating the additional costs incurred in compensating for the energy deficit and feeding these additional costs back to the energy management system include:

[0091] Total additional cost of collecting and archiving;

[0092] Analyze the degradation trend of energy storage batteries to obtain a set of degradation characteristic parameters;

[0093] Construct several future operational scenarios;

[0094] For each future operating scenario, combined with the set of degradation characteristic parameters, the energy gap of the energy storage battery at different points in time is simulated to obtain the simulated energy gap.

[0095] A pre-defined set of rules for assessing the impact of compensation actions based on electricity contract terms;

[0096] Based on the simulated energy gap, and referring to the rule set for assessing the impact of compensation behavior and the rule set for assessing the impact of long-term contracts and carbon emissions, the real-time electricity purchase cost, contract penalties, additional demand costs, and carbon emission-related costs incurred under each future operating scenario are predicted and accumulated over time to obtain the cumulative additional cost prediction curves under different future operating scenarios.

[0097] Statistical analysis was performed on the cumulative additional cost prediction curve to calculate risk assessment indicators;

[0098] Risk assessment indicators are fed back to the energy management system in the form of multi-dimensional indicators.

[0099] Specifically, collecting and archiving total additional costs refers to the systematic collection, organization, and storage of total additional cost data calculated using the methods described above. This data typically includes the timing, duration, amount of compensation, source of compensation, instantaneous cost parameters, cumulative contract penalties, additional demand costs, and carbon emission-related costs of energy gap compensation events. Its purpose is to provide a historical data foundation for subsequent analysis of decline trends and future cost predictions.

[0100] Analyzing the degradation trend of energy storage batteries to obtain a set of degradation characteristic parameters involves analyzing historical operational data (such as charge / discharge cycle count, depth, temperature, voltage, and current) to establish mathematical models of how key performance parameters, such as battery capacity and internal resistance, change over time or under usage conditions. This set of degradation characteristic parameters can include capacity degradation rate, internal resistance growth rate, and cycle life prediction model parameters. Its purpose is to accurately predict the performance state of energy storage batteries at different points in the future, providing fundamental data for simulating future energy shortages.

[0101] In practical applications, constructing several future operating scenarios refers to setting a series of possible future operating scenarios based on various factors such as the industrial park's production plan, market electricity price forecasts, changes in carbon emission policies, and seasonal load fluctuations. Each scenario represents a specific combination of external environment and internal demand; for example, it may include high load and high electricity price scenarios, low load and low electricity price scenarios, and extreme weather scenarios. The purpose is to comprehensively cover various uncertainties that may be faced in the future, thereby improving the accuracy and robustness of forecasts.

[0102] Furthermore, for each future operating scenario, by combining the degradation characteristic parameter set, the energy gap that the energy storage battery will experience at different points in the future is simulated. The simulated energy gap refers to the actual available capacity and charge / discharge efficiency of the energy storage battery at specific points in the future (e.g., daily, weekly, monthly) under each preset future operating scenario, using the analyzed energy storage battery degradation characteristic parameter set. By comparing the simulated energy storage battery performance with the expected load demand and photovoltaic power generation under that scenario, it is possible to predict when and to what extent the energy storage battery will be unable to meet energy demand, thus obtaining the simulated energy gap.

[0103] The pre-defined set of rules for assessing the impact of compensation behavior based on electricity contract terms refers to establishing a set of rules based on electricity purchase and sale contracts signed between industrial parks and power grid companies, and operation and maintenance contracts signed with energy storage suppliers, to assess the impact of contract penalties, additional demand costs, etc., arising from energy gap compensation behavior. These rules may include overload penalty clauses, capacity fee clauses, peak-valley electricity pricing mechanisms, etc., with the aim of quantifying the direct economic losses caused by energy gaps.

[0104] Specifically, based on the simulated energy gap and referring to the rule sets for assessing the impact of compensation behavior and the rule sets for assessing the impact of long-term contracts and carbon emissions, the real-time electricity purchase cost, contract penalties, additional demand costs, and carbon emission-related costs incurred under each future operating scenario are predicted and accumulated over time. This results in a cumulative additional cost prediction curve for different future operating scenarios. This curve represents the real-time electricity purchase cost required to supplement energy from the external grid, potential contract penalties, costs incurred due to additional demand, and carbon emission-related costs resulting from electricity purchase or fuel consumption, calculated according to a pre-set rule set when the simulated energy gap occurs under each future operating scenario. These costs are accumulated chronologically to form a prediction curve reflecting the changes in cumulative additional costs under that scenario.

[0105] Furthermore, statistical analysis of the cumulative additional cost forecast curves and calculation of risk assessment indicators involves multi-dimensional statistical processing of the cumulative additional cost forecast curves obtained under different future operating scenarios. This may include calculating average costs, cost fluctuation ranges, maximum potential losses at a specific confidence level (e.g., Value at Risk (VaR) or Conditional Value at Risk (CVaR), skewness and kurtosis of cost distribution, etc. These statistics are used as risk assessment indicators to quantify the uncertainty and potential risks of future additional costs.

[0106] Optionally, the steps for statistically analyzing the cumulative additional cost forecast curve and calculating risk assessment indicators include:

[0107] Collect cumulative additional cost forecast curves;

[0108] Identify the nonlinear degradation characteristics of energy storage batteries;

[0109] Identify extreme electricity price events and carbon emission rights price fluctuation events in market volatility scenarios;

[0110] Based on the nonlinear decay characteristics, extreme electricity price events, and carbon emission rights price fluctuation events, the cumulative additional cost prediction curve is divided into segmented risk intervals to obtain segmented risk intervals.

[0111] Within each segmented risk interval, calculate the tail risk metric for additional costs;

[0112] Tail risk indicators are fed back to the energy management system as risk assessment indicators.

[0113] Specifically, collecting cumulative additional cost forecast curves refers to obtaining a series of cumulative additional cost data under different future operating scenarios from the aforementioned simulation and forecasting processes. These curves reflect the total additional costs that may arise over time under various assumptions.

[0114] Identifying the nonlinear degradation characteristics of energy storage batteries can be understood as establishing a nonlinear model of how key parameters such as battery capacity and internal resistance change with time, cycle count, and temperature by analyzing historical operating data, laboratory test data, or specifications provided by the manufacturer. For example, machine learning algorithms (such as support vector machines and neural networks) or empirical models can be used to fit the battery's degradation curve to more accurately predict its performance degradation under different operating conditions. The goal is to more accurately reflect the degradation pattern of the battery in actual operation and avoid errors introduced by linear models.

[0115] Identifying extreme electricity price events and carbon emission rights price fluctuations in market volatility scenarios involves statistically analyzing historical market data or utilizing expert systems and predictive models to identify abnormally high or low values ​​of electricity and carbon emission rights prices within a specific period, as well as their duration, frequency, and other characteristics. For example, Extreme Value Theory (EVT) can be used to model the probability distribution of these extreme events. Its purpose is to capture sudden risks in the market that may lead to a sharp increase or decrease in additional costs.

[0116] Based on nonlinear degradation characteristics, extreme electricity price events, and carbon emission rights price fluctuations, the cumulative additional cost prediction curve is segmented into risk intervals. Specifically, this involves logically dividing the cumulative additional cost prediction curve according to a time axis or cost interval. For example, the entire prediction period can be divided into several sub-intervals based on the acceleration point of battery degradation, the expected occurrence time window of extreme market events, or cost thresholds. Each segmented risk interval is considered to have relatively consistent risk characteristics or require a specific risk management strategy. The aim is to achieve refined management and assessment of different risk stages. In the photovoltaic energy storage power data processing method for industrial parks, segmenting the cumulative additional cost prediction curve into risk intervals and ensuring that each risk interval has relatively consistent risk characteristics or meets different risk management needs is a key step in achieving refined risk assessment. The specific implementation methods are as follows.

[0117] The implementation method of segmented risk interval division: This division is based on the nonlinear degradation characteristics of energy storage batteries, extreme electricity price events under market volatility scenarios, and carbon emission rights price fluctuation events, and logically segments the cumulative additional cost prediction curve.

[0118] Input data preparation includes the following: Cumulative additional cost prediction curve: Combining the energy storage battery degradation characteristic parameters, a curve is obtained by predicting and accumulating the real-time electricity purchase cost, contract performance penalty, demand response cost and carbon emission cost that may occur in the future, in order to reflect the trend of future cost changes over time.

[0119] Nonlinear degradation characteristics of energy storage batteries: Based on historical operating data (including cycle count, depth of charge / discharge, temperature, voltage, current, etc.), the nonlinear variation patterns of performance indicators such as battery capacity degradation and internal resistance changes are identified. For example, after reaching a certain number of cycles, the capacity degradation rate accelerates, which can serve as important risk information.

[0120] Information on extreme market events: By statistically analyzing historical electricity market and carbon trading market data, we identify extreme electricity price fluctuation events and drastic fluctuations in carbon emission rights prices, and extract their occurrence time windows, duration, and typical fluctuation amplitudes.

[0121] The determination of the classification criteria specifically includes: based on the battery degradation acceleration point: when the degradation rate of the energy storage battery increases significantly after a certain period, this point in time is used as the dividing point of the risk range to distinguish between the performance stability stage and the degradation acceleration stage.

[0122] Based on extreme market event time windows: Mark the time windows in which extreme high electricity prices or sharp fluctuations in carbon prices are expected as independent risk intervals. If necessary, a buffer period can be set before and after the event to further improve the rationality of the interval division.

[0123] Cost-based threshold: Set a cumulative additional cost threshold. When the forecast curve first reaches or exceeds this threshold, it is used as a dividing point of an interval, indicating that the risk level has entered a higher level.

[0124] The specific division of risk intervals is based on the above criteria, dividing the entire forecast period into multiple sub-intervals along the time axis, for example:

[0125] "Initial stable risk zone", "Mid-term accelerated decline risk zone", "Extreme market volatility risk zone", "Late stage high comprehensive risk zone".

[0126] In addition, it is important to ensure consistency in risk characteristics across different timeframes and to implement specific management strategies: the primary sources of risk should remain consistent within each timeframe. For example, in the initial stage, battery degradation and market volatility are relatively low; during periods of extreme market volatility, market price fluctuations become the primary source of risk.

[0127] The mechanisms underlying additional costs within the same timeframe are similar. For example, one timeframe may be primarily driven by contract penalty risks, while another may be primarily driven by real-time electricity purchase costs.

[0128] Once the intervals are determined, cost tail risk indicators, such as CVaR, can be calculated within each interval to quantify the magnitude of extreme losses.

[0129] Different management strategies are adopted for different zones, including but not limited to:

[0130] Initial stable risk range: Focus on energy storage dispatch optimization and routine cost monitoring;

[0131] Mid-term accelerated degradation risk zone: Adjust charging and discharging strategies, optimize battery health maintenance, and focus on monitoring the rate of cost increase;

[0132] Extreme market volatility risk zone: Introduce market hedging strategies, plan reserve capacity, and formulate contingency plans for external power purchases;

[0133] High-risk phase in the later stages: planning for expansion or upgrade of energy storage systems, reassessing long-term power purchase agreements;

[0134] The interval-based tail risk indicators and corresponding strategy recommendations are fed back to the energy management system as multi-dimensional risk indicators to provide decision support for departments such as finance, production, and procurement, thereby improving the forward-looking nature of the system operation.

[0135] Through the above implementation methods, long-term and complex risk exposures can be broken down into structured and manageable risk ranges, enabling various departments to implement targeted risk control measures within these ranges, thereby improving the economic efficiency and resilience of the park's energy system to uncertainties.

[0136] Within each segmented risk interval, tail risk metrics, which calculate additional costs, refer to the specific risk measurement methods used to quantify the probability and scale of extreme losses for each defined risk interval. Tail risk metrics, such as Conditional Value-at-Risk (CVaR) or Expected Shortfall (ES), measure the average loss exceeding a certain threshold at a given confidence level. Their purpose is to provide a more comprehensive assessment of potential losses under adverse scenarios, rather than focusing solely on averages.

[0137] In the data processing method for photovoltaic energy storage in industrial parks, the tail risk indicator, described in the phrase "calculating the tail risk indicator of additional costs within each segmented risk interval," is a statistical measure used to quantify the risk of additional costs under extremely adverse conditions. This tail risk indicator can be expressed in the form of Conditional Value-at-Risk (CVaR) or Expected Shortfall (ES), describing the expected value of additional costs exceeding a specific threshold at a given confidence level. Unlike average loss or standard deviation, this type of indicator reflects the degree of exposure to extreme losses within segmented risk intervals. For example, in high-risk intervals, the CVaR value may be significantly higher than the average expected loss, indicating a higher probability of extreme losses during that period. The tail risk indicator is fed back to the energy management system in a multi-dimensional structure for accurate assessment of potential losses under adverse operating conditions.

[0138] To achieve the goal of "reconstructing the decomposed tail risk indicators based on preset departmental responsibilities and decision-making needs," and to ensure that each department can generate actual technical effects based on the reconstructed indicators, the specific implementation method is as follows.

[0139] Decomposition of Tail Risk Indicators: The system decomposes the calculated comprehensive tail risk indicators to distinguish different sources of additional cost risk. These sources may include, but are not limited to: additional electricity purchase risk caused by nonlinear degradation of energy storage batteries, additional electricity purchase risk caused by extreme market electricity prices, and potential default costs arising from failure to meet long-term contract parameters. The decomposed sub-indicators are used to reveal the composition ratio and dominant factors of various risks. For example, when the comprehensive tail risk is 10 million yuan, it can be decomposed into: 6 million yuan for battery degradation, 3 million yuan for electricity price fluctuations, and 1 million yuan for contract default.

[0140] Restructuring and implementation effects based on departmental responsibilities: The decomposed tail risk sub-indicators are restructured according to the preset departmental responsibilities to form structured risk quantification information applicable to different business departments.

[0141] Among them, the restructuring method and technical effect of the finance department: the decomposed tail risk indicators are transformed into quantitative values ​​of financial risks related to cash flow, cash flow pressure and investment return fluctuations, including: additional electricity purchase expenditures in extreme cases, possible contract penalties, and potential negative impacts on the internal rate of return (IRR) of energy storage projects.

[0142] Based on this information, the finance department can formulate budget reserve and risk reserve strategies, and adjust investment return assessments. For example, if the restructuring results show that there may be a potential cash outflow of approximately 10 million yuan in the coming year, resulting in a 0.5% decrease in the investment return rate, the finance department can set up corresponding risk reserves in advance or adjust investment plans to enhance financial stability.

[0143] Restructuring methods and technological effects of the production department: Transforming tail risks into impact values ​​on production planning, power supply stability, critical equipment load levels, and potential downtime, such as the impact of long-term energy shortages in energy storage on the power continuity of production lines;

[0144] The production department can use this information to optimize production scheduling, dynamically adjust equipment uptime, or configure backup power strategies. For example, if the refactoring results show that there may be a cumulative power shortage of approximately 20 hours in extreme cases, affecting the utilization rate of critical equipment by 3%, the production department can arrange load shifting or implement emergency operation strategies in advance to reduce potential capacity losses.

[0145] The restructuring methods and technical effects of the procurement department: transforming tail risk indicators into risk premium assessments for long-term power purchase contracts, recommendations for reserve capacity requirements, and recommendations for adjusting market procurement strategies;

[0146] Based on this information, the procurement department can adjust the structure of long-term contracts, allocate additional reserve capacity, or restructure its procurement strategy. For example, if the restructuring indicates that existing long-term power purchase agreements may face a 2% risk premium in the coming year and recommends allocating approximately 10MW of reserve capacity, the procurement department can renegotiate the contracts or plan for additional backup power to reduce future procurement costs and supply uncertainty.

[0147] Through the above decomposition and restructuring based on departmental responsibilities, tail risk indicators are transformed into quantitative decision-making criteria with clear business implications that can be directly used by different departments. This enables the energy management system to provide targeted and actionable risk information to each department, allowing them to optimize budget planning, production scheduling, and procurement strategies, thereby improving the overall economy and operational resilience of the park.

[0148] In some preferred embodiments, it is assumed that the cumulative additional cost prediction curve for an energy storage battery in an industrial park has been generated. First, by analyzing the historical operating data of the energy storage battery, the nonlinear characteristic of its capacity degradation accelerating after reaching 80% of its total capacity is identified. Simultaneously, by analyzing electricity market data from the past five years, extreme electricity price events that may occur during the winter and summer peak seasons each year, as well as significant fluctuations in carbon emission rights prices that may occur during specific policy adjustment periods, are identified. Based on this information, the cumulative additional cost prediction curve is divided into three segmented risk intervals: an initial stable operating period (battery degradation is not significant, and market volatility is relatively small), a mid-term accelerated degradation period (battery degradation begins to accelerate, and market volatility is moderate), and a late high-risk period (battery degradation is severe, and the probability of extreme market events increases). Within each segmented risk interval, the 95% conditional value at risk (CVaR) of the additional cost is calculated using historical simulation or Monte Carlo simulation methods as a tail risk indicator for that interval. For example, in the late high-risk period, the calculated CVaR may be much higher than the average expected loss, indicating a significant increase in the risk of extreme losses during this period. These tail risk indicators are then fed back to the energy management system to guide the system in adopting different risk management strategies at different times, such as increasing reserve capacity or adjusting procurement strategies during later high-risk periods.

[0149] Optionally, the steps of feeding tail risk indicators as risk assessment indicators back to the energy management system include:

[0150] Decompose the tail risk indicators;

[0151] Based on the pre-defined departmental responsibilities and decision-making needs, the decomposed tail risk indicators are reconstructed;

[0152] For the finance department, the restructured tail risk indicators will be transformed into financial risk exposure and potential loss predictions related to cash flow and return on investment.

[0153] For the production department, the restructured tail risk indicators will be transformed into an impact assessment on production planning, equipment utilization, and downtime risk.

[0154] For the procurement department, the restructured tail risk indicators will be transformed into recommendations on risk premiums and reserve capacity requirements for long-term power procurement contracts.

[0155] Specifically, decomposing tail risk indicators involves breaking down a comprehensive risk indicator into multiple more detailed and granular sub-indicators. For example, a single tail risk indicator might encompass risks caused by various factors such as electricity price fluctuations, changes in carbon emission costs, and accelerated equipment degradation. The decomposition process separates these risks from different sources for targeted subsequent handling. Its purpose is to reveal the inherent structure and driving factors of the risk, providing foundational data for subsequent restructuring.

[0156] Reconstructing the decomposed tail risk indicators based on pre-defined departmental responsibilities and decision-making needs can be understood as recombining, aggregating, or transforming the decomposed risk sub-indicators according to the specific functions of different departments within the energy management system and the types of information they require in the decision-making process. For example, the finance department may be more concerned with risks directly related to monetary value, while the production department may be more concerned with risks related to physical operating status and reliability. The aim is to make risk information more targeted and actionable, ensuring that each department receives risk insights closely related to its business.

[0157] In practical applications, for the finance department, the reconstructed tail risk indicators are transformed into financial risk exposure and potential loss predictions related to cash flow and return on investment. For example, this can be specifically quantified in extreme market conditions, such as the impact of additional electricity purchase expenditures and contract penalties that may result from energy gap compensation on the company's cash flow, as well as the potential negative impact on the overall return on investment of photovoltaic energy storage projects. The aim is to provide the finance department with direct financial risk assessment, assisting them in budgeting, setting up risk reserves, and making investment decisions.

[0158] For the production department, the restructured tail risk indicators are transformed into impact assessments on production planning, equipment utilization, and downtime risk. For example, specific analyses can be conducted on the impact of persistent or frequent energy shortages in energy storage batteries on the stability of power supply to production lines in the industrial park, the operating load of critical equipment, and potential downtime. The aim is to help the production department optimize production scheduling, adjust equipment operation strategies, and develop contingency plans to minimize the impact of power supply risks on production efficiency.

[0159] For the procurement department, the restructured tail risk indicators will be translated into recommendations on risk premiums and reserve capacity requirements for long-term power purchase contracts. For example, specific assessments can be made of the additional cost risks that existing long-term power purchase contracts may face in the event of increased market volatility or unexpected performance degradation of energy storage systems, and recommendations can be made regarding whether to increase reserve capacity or adjust procurement strategies. The aim is to support the procurement department in making more informed decisions regarding the signing and management of long-term power contracts and the planning of backup power sources, thereby reducing future procurement costs and supply risks.

[0160] In some preferred embodiments, this application is implemented as follows: Suppose that in an industrial park, a comprehensive tail risk index is calculated using the above method. This index indicates that in the next year, due to the accelerated degradation of energy storage batteries and extreme fluctuations in market electricity prices, the park faces an additional cost risk of 10 million yuan.

[0161] First, the tail risk indicator will be broken down into: additional electricity purchase risk caused by battery degradation (e.g., 6 million yuan), additional electricity purchase risk caused by market electricity price fluctuations (e.g., 3 million yuan), and penalty risk that may arise from failure to meet contract requirements (e.g., 1 million yuan).

[0162] Next, this decomposed risk information will be reconstructed according to departmental responsibilities:

[0163] For the finance department: The restructured risk indicators will be transformed into financial risk exposure and potential loss forecasts such as "a potential increase of RMB 10 million in cash outflows within the next year, which may lead to a 0.5% decrease in the rate of return on investment".

[0164] For the production department: The restructured risk indicators will be transformed into an impact assessment of "In extreme cases, the production line may face a cumulative 20 hours of power supply shortage risk, resulting in a 3% decrease in the utilization rate of key equipment and potentially causing a production loss of 5 million yuan".

[0165] For the procurement department: The restructured risk indicator will translate into a recommendation that "existing long-term power purchase contracts may face a 2% risk premium in the next year, and it is recommended to consider increasing the reserve capacity by 10MW to cope with potential supply gaps".

[0166] In this way, each department can obtain actionable risk information that is directly related to its business, thereby enabling it to develop response strategies more effectively. For example, the finance department can set aside risk reserves, the production department can adjust production plans or arrange backup power, and the procurement department can re-evaluate procurement contracts or find new suppliers, thereby jointly improving the park's energy management level and economic benefits.

[0167] The steps involved in feeding back the total additional costs to the energy management system in the form of multi-dimensional metrics include:

[0168] The total additional costs are structured to obtain structured data;

[0169] Preset data mapping rules;

[0170] Based on the data mapping rules, the structured data is converted into the data fields, data types, and units of measurement required by the target system;

[0171] Encapsulate data fields, data types, and units of measurement into data packets that conform to the target system's interface protocol;

[0172] Send data packets through the target system's interface;

[0173] Receive the response from the target system.

[0174] Specifically, structuring the total additional cost refers to organizing and arranging the calculated total additional cost to conform to a predefined data structure, such as key-value pairs, tables, or JSON format. The purpose is to provide standardized input for subsequent data conversion and transmission.

[0175] The preset data mapping rules can be understood as a set of predefined conversion rules used to guide how to convert the structured data within this system into the specific data format required by the energy management system. These rules may include the correspondence between field names, the conversion of data types (e.g., from floating-point numbers to integers), and the conversion of units of measurement (e.g., from yuan to thousand yuan), with the aim of ensuring seamless data exchange between different systems.

[0176] In practical applications, according to data mapping rules, converting structured data into the data fields, data types, and units of measurement required by the target system involves converting each data item in the structured data one by one according to preset mapping rules. For example, if the "total cost" field in this system corresponds to "cumulative expenses" in the energy management system, and needs to be converted from "yuan" to "thousand yuan," then the corresponding field is renamed and the values ​​are converted according to the rules. The purpose is to ensure that the data fully conforms to the acceptance standards of the target system.

[0177] Furthermore, encapsulating data fields, data types, and units of measurement into data packets that conform to the target system's interface protocol refers to packaging the converted data according to the requirements of the energy management system's interface protocol. This may involve adding protocol headers, checksums, timestamps, and other information, and arranging them according to a specific byte order or data structure. For example, it can be encapsulated into an HTTP request body, an MQTT message, or a custom binary data packet. The purpose is to ensure that the data packet can be correctly parsed and processed by the target system.

[0178] Furthermore, sending data packets through the target system's interface refers to using the API interface or communication port provided by the energy management system to send out encapsulated data packets. This can employ various communication protocols, such as TCP / IP, UDP, HTTP / S, MQTT, etc., with the aim of achieving the physical transmission of data from this system to the energy management system.

[0179] Finally, receiving the target system's response refers to waiting for and receiving confirmation information or processing results from the energy management system after the data packet is sent. This response can indicate whether the data was successfully received, whether processing was completed, or whether any errors occurred. Its purpose is to verify the success of data transmission and obtain feedback status from the target system.

[0180] Optionally, the steps for receiving a response from the target system include:

[0181] Receive response data packets from the target system;

[0182] Perform a timing check on the response data packets to determine if the response data packets are out of order, and obtain the out-of-order judgment result;

[0183] Perform an integrity check on the response data packet to determine whether there are any missing or incomplete packets, and obtain the integrity judgment result;

[0184] Perform a checksum check on the response data packet to determine if there is an incorrect checksum, and obtain the checksum determination result;

[0185] If the out-of-order judgment result indicates that the response data packet is out of order, the integrity judgment result indicates that the response data packet is lost or incomplete, or the checksum judgment result indicates that the response data packet contains an incorrect checksum, then the exception handling process is triggered. The exception handling process includes: recording the exception type and the time of occurrence; initiating data retransmission or requesting the target system to resend the response according to the exception type; if no valid response is received within the specified time, it is marked as a communication failure, and an alarm is sent to the energy management system.

[0186] If the out-of-order judgment result, the integrity judgment result, and the check code judgment result indicate that the timing check, integrity check, and check code check have passed respectively, then the response data packet is parsed to extract the processing result or feedback information of the target system.

[0187] Specifically, receiving a response data packet from the target system refers to the data processing system receiving data units containing processing results or status updates from the energy management system through a pre-defined communication interface. The timing check of the response data packet aims to ensure that the packets are received and processed in the correct sending order. This is typically achieved by embedding sequence numbers or timestamps in the data packets; the receiving end uses these identifiers to determine if the packets are out of order. For example, if a data packet with sequence number N+2 is received, but the data packet with sequence number N+1 has not yet been received, out-of-order delivery can be determined. Furthermore, an integrity check is performed on the response data packet to confirm whether the received packet contains all expected data content and whether there is any data loss or incompleteness. This can be achieved by checking the packet length, number of fields, or specific end markers. For example, if a response is expected to contain five data fields, but only four are actually received, the packet can be determined to be incomplete. Additionally, a checksum check is performed on the response data packet to verify whether the data has been corrupted or tampered with during transmission. The data packet usually includes a checksum (such as CRC checksum, MD5 hash, etc.), and the receiving end recalculates the checksum of the received data content and compares it with the checksum carried in the data packet. If the two are inconsistent, it indicates that the data contains an incorrect checksum, meaning the data is corrupted.

[0188] When any of the above checks indicates a problem with the response data packet—that is, out-of-order delivery indicates out-of-order delivery, integrity delivery indicates loss or incompleteness, or checksum delivery indicates an incorrect checksum—the system will immediately trigger an exception handling procedure. This procedure first records the specific type of exception (e.g., "out-of-order data," "data loss," "checksum failure") and the precise time of occurrence for subsequent troubleshooting and system optimization. Based on the identified exception type, the system will intelligently activate the corresponding recovery mechanism. For example, for data loss or out-of-order delivery, a data retransmission mechanism can be initiated to request the target system to resend the missing or incorrect data packet; for checksum failure, the target system can be requested to resend the response. To prevent the system from waiting indefinitely, this application sets a specified time. If no valid response is received within this specified time, the communication will be marked as a communication failure, and an alarm will be immediately sent to the energy management system. This ensures that the energy management system can promptly detect communication failures and avoid making decisions based on expired or erroneous information. Conversely, if all checks pass successfully—that is, if the out-of-order judgment result, integrity judgment result, and checksum judgment result all indicate that the timing check, integrity check, and checksum check have all passed—then the response data packet is considered valid and reliable. At this point, the system will parse the response data packet to extract the target system's processing results or feedback information, such as confirming whether the additional cost feedback was successful, whether there are further scheduling suggestions, etc., and will use this information for subsequent energy management decisions.

[0189] This application proposes a photovoltaic energy storage power data processing system for industrial parks, combining... Figure 3 As shown, the photovoltaic energy storage power data processing system 1 in the industrial park includes:

[0190] The scheduling instruction receiving module 11 is used to receive the charging and discharging scheduling instructions of the energy storage batteries in the industrial park; the charging and discharging scheduling instructions include the expected target power.

[0191] The operating parameter acquisition module 12 is used to acquire the operating parameters of the energy storage battery in real time based on the charge and discharge scheduling instructions.

[0192] Throughput calculation module 13 is used to calculate the actual energy throughput of the energy storage battery based on the operating parameters;

[0193] The energy gap judgment module 14 is used to compare the actual energy throughput with the expected target power to determine whether there is an energy gap in the energy storage battery.

[0194] The energy gap compensation module 15 is used to adjust the charging and discharging power or duration of the energy storage battery according to the energy gap if an energy gap exists.

[0195] The external charging execution module 16 is used to supplement energy from the external power grid when there is an energy gap and the energy storage battery discharge does not meet the load demand;

[0196] The additional cost feedback module 17 is used to calculate the additional costs incurred due to the energy gap and feed these additional costs back to the energy management system. The energy management system adjusts the corresponding energy dispatch strategy based on the additional costs and selects the emergency response path with lower costs to improve the overall economic efficiency of the park's energy management.

[0197] Specifically, a "charge and discharge scheduling instruction" refers to an instruction generated by the energy management system based on the park's electricity demand, photovoltaic power generation forecasts, and the current status of the energy storage batteries. This instruction guides the energy storage batteries in charging or discharging operations. It typically includes key parameters such as target capacity, charging / discharging power limits, and charging / discharging duration. The "expected target capacity" is the explicitly specified capacity value that the energy storage batteries should achieve or provide within a specific time period, as defined in the charge and discharge scheduling instruction.

[0198] "Operating parameters" refer to various physical quantities monitored in real time during the operation of an energy storage battery, such as battery voltage, current, temperature, and internal resistance. These parameters are key indicators for evaluating the battery's health and performance. "Actual energy throughput" refers to the actual amount of electricity charged or discharged by the energy storage battery within a specific time period, calculated based on the real-time collected operating parameters. This value reflects the battery's true energy exchange capacity.

[0199] "Energy gap" refers to the difference between the actual energy throughput of an energy storage battery and the expected target energy capacity. An energy gap exists when the actual energy throughput is lower than the expected target energy capacity, indicating that the energy storage battery has failed to provide sufficient energy according to dispatch instructions. "Additional costs" refer to the extra expenses incurred in compensating for the energy gap, such as the cost of purchasing electricity from the external grid, contract penalties, and carbon emission-related costs.

[0200] The specific steps of the photovoltaic energy storage power data processing method for industrial parks have been described in the above embodiments and will not be repeated here. It should be emphasized that the photovoltaic energy storage power data processing system for industrial parks proposed in this application can effectively execute the above method through the implementation of its various functional modules.

[0201] Specifically, the dispatch instruction receiving module can be a communication interface unit, such as an Ethernet interface, a Wi-Fi module, or an RS485 interface, used to receive charging and discharging dispatch instructions from the energy management system. This module can be configured to periodically poll the instruction server or passively listen to a specific communication port to receive instructions. The expected target energy level included in the dispatch instruction can be a preset value, such as the amount of energy that needs to be charged or discharged within a specific time period.

[0202] The operating parameter acquisition module can consist of a series of sensors and data acquisition units, such as voltage sensors, current sensors, and temperature sensors, which are deployed at key locations within the energy storage battery pack. This module can transmit the acquired operating parameters, such as voltage, current, and temperature, to the central processing unit via wired or wireless means. For example, data sampling can be performed every few seconds or shorter intervals to ensure real-time data accuracy.

[0203] The throughput calculation module can be an embedded processor or a software module on a server, configured to receive data from the operating parameter acquisition module. This module calculates the actual energy throughput of the energy storage battery over a specific time period by integrating the real-time acquired current and voltage data. For example, a discrete integration algorithm can be used to multiply the power in each sampling period by the time interval and then sum them up to obtain the total energy.

[0204] The energy gap assessment module can be a logical judgment unit configured to receive the actual energy throughput output by the throughput calculation module and the expected target energy level provided by the scheduling instruction receiving module. This module directly compares these two values ​​to determine whether the actual energy throughput reaches or exceeds the expected target energy level. If the actual value is lower than the expected value, an energy gap is determined to exist. For example, an allowable error range can be set; when the difference between the actual energy throughput and the expected target energy level exceeds this range, an energy gap is considered to exist.

[0205] The energy gap compensation module can be a control unit configured to send an adjustment command to the energy storage battery management system when the energy gap detection module determines that an energy gap exists. This module can dynamically adjust the charging and discharging power or duration of the energy storage battery based on the size of the energy gap. For example, a discharge gap can be compensated by increasing the discharge current or extending the discharge time, or a charging gap can be compensated by increasing the charging current or extending the charging time.

[0206] An external charging execution module can be a control unit connected to an external power grid interface. It is configured to initiate a process of supplementing energy from the external power grid when the energy gap compensation module cannot fully compensate for the energy gap and the energy storage battery discharge fails to meet load demand. This module can send a power purchase request to the power grid dispatch center or control the grid-connected inverter to draw power from the grid. For example, this module will be activated when the energy storage battery has reached its maximum discharge power or minimum depth of discharge and still cannot meet load demand.

[0207] The additional cost feedback module can be a data processing and communication unit configured to calculate the additional costs incurred due to energy gap compensation activities (including adjusting the energy storage battery's own strategy and supplementing energy from the external grid). This module can calculate specific economic losses based on preset electricity prices, penalty rules, etc., and send this cost data to the energy management system via a communication interface. For example, it can record the amount of electricity purchased from the external grid and the current electricity price, calculate the purchase cost, and feed it back as part of the additional costs.

[0208] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An industrial park photovoltaic energy storage power data processing method, characterized in that, The method comprises the following steps: receiving a charge-discharge scheduling instruction of an energy storage battery in an industrial park; the charge-discharge scheduling instruction contains an expected target power; based on the charge-discharge scheduling instruction, collecting the operating parameters of the energy storage battery in real time; according to the operating parameters, calculating the actual energy throughput of the energy storage battery; comparing the actual energy throughput with the expected target power to determine whether there is an energy gap in the energy storage battery; if there is an energy gap, adjusting the charge-discharge power or duration of the energy storage battery according to the energy gap; if there is an energy gap and the discharge of the energy storage battery does not meet the load demand, supplementing energy from an external power grid; calculating the additional cost generated by compensating for the energy gap and feeding back the additional cost to an energy management system; the energy management system adjusts the corresponding energy scheduling strategy according to the additional cost, selects a lower-cost emergency disposal path, and improves the overall economic benefit of the park energy management; the step of comparing the actual energy throughput with the expected target power to determine whether there is an energy gap in the energy storage battery comprises: identifying the charging working condition of the energy storage battery to determine the pulse injection opportunity; at the pulse injection opportunity, injecting a multi-frequency current pulse sequence into the energy storage battery; synchronously collecting voltage and current data of the energy storage battery during the injection of the multi-frequency current pulse sequence; performing frequency spectrum analysis on the voltage and current data to extract the impedance characteristics of the energy storage battery; comparing the deviation of the impedance characteristics from the dynamic impedance baseline to determine whether there is an energy gap in the energy storage battery.

2. The industrial park photovoltaic energy storage power data processing method according to claim 1, characterized in that, The step of calculating the additional cost generated by compensating for the energy gap and feeding back the additional cost to the energy management system comprises: recording the occurrence time, duration, compensation power and compensation source of the energy gap compensation event; presetting a compensation behavior impact evaluation rule set based on power contract terms; according to the occurrence time, duration, compensation power and compensation source of the energy gap compensation event, comparing the compensation behavior impact evaluation rule set to calculate the cumulative contract penalty or additional demand cost; constituting the additional cost corresponding to the real-time power purchase cost by the cumulative contract penalty or additional demand cost; feeding back the additional cost to the energy management system.

3. The industrial park photovoltaic energy storage power data processing method according to claim 1, characterized in that, The step of calculating the additional cost generated by compensating for the energy gap and feeding back the additional cost to the energy management system comprises: recording the occurrence time, duration, compensation power, compensation source and corresponding instantaneous cost parameters of the compensation source of the energy gap compensation event; presetting a long-term contract and carbon emission impact evaluation rule set; according to the occurrence time, duration, compensation power, compensation source and corresponding instantaneous cost parameters of the compensation source of the energy gap compensation event, comparing the long-term contract and carbon emission impact evaluation rule set to calculate the cumulative contract penalty, additional demand cost and carbon emission related cost; summing up the direct power purchase cost or fuel cost, the cumulative contract penalty, the additional demand cost and the carbon emission related cost as the total additional cost; feeding back the total additional cost to the energy management system in the form of multi-dimensional indicators.

4. The industrial park photovoltaic energy storage power data processing method according to claim 3, characterized in that, The step of accounting for the additional cost resulting from compensating for the energy gap and feeding back the additional cost to the energy management system comprises: collecting and archiving the total additional cost; analyzing the attenuation trend of the energy storage battery to obtain a set of attenuation characteristic parameters; constructing a plurality of future operation scenarios; for each of the future operation scenarios, simulating the energy gap occurring at different time points in the future in combination with the set of attenuation characteristic parameters to obtain simulated energy gaps; presetting a set of compensation behavior impact evaluation rules based on power contract terms; according to the simulated energy gaps, referring to the set of compensation behavior impact evaluation rules and the set of long-term contract and carbon emission impact evaluation rules, predicting real-time power purchase costs, contract penalties, additional demand fees, and carbon emission related costs occurring in each of the future operation scenarios, and accumulating them by time to obtain cumulative additional cost prediction curves under different future operation scenarios; statistically analyzing the cumulative additional cost prediction curves to calculate risk assessment indicators; feeding back the risk assessment indicators to the energy management system in the form of multi-dimensional indicators.

5. The industrial park photovoltaic energy storage power data processing method according to claim 4, characterized in that, The step of statistically analyzing the cumulative additional cost prediction curves to calculate risk assessment indicators comprises: collecting the cumulative additional cost prediction curves; identifying the nonlinear attenuation characteristics of the energy storage battery; identifying extreme electricity price events and carbon emission right price fluctuation events in market fluctuation scenarios; based on the nonlinear attenuation characteristics, the extreme electricity price events, and the carbon emission right price fluctuation events, dividing the cumulative additional cost prediction curves into segmented risk intervals to obtain segmented risk intervals; in each of the segmented risk intervals, calculating tail risk indicators of the additional cost; feeding back the tail risk indicators as risk assessment indicators to the energy management system.

6. The industrial park photovoltaic energy storage power data processing method according to claim 5, characterized in that, The step of feeding back the tail risk indicators as risk assessment indicators to the energy management system comprises: decomposing the tail risk indicators; reconstructing the decomposed tail risk indicators according to preset department responsibilities and decision-making needs; for the finance department, converting the reconstructed tail risk indicators into financial risk exposure and potential loss prediction related to capital flow and return on investment; for the production department, converting the reconstructed tail risk indicators into impact assessment on production planning, equipment utilization, and downtime risk; for the procurement department, converting the reconstructed tail risk indicators into risk premium for long-term power purchase contracts and suggestions for backup capacity demand.

7. The industrial park photovoltaic energy storage power data processing method according to claim 3, characterized in that, The step of feeding back the total additional cost to the energy management system in the form of multi-dimensional indicators comprises: structuring the total additional cost to obtain structured data; presetting data mapping rules; according to the data mapping rules, converting the structured data into data fields, data types, and units of measurement required by the target system; packaging the data fields, data types, and units of measurement into data packets conforming to the interface protocol of the target system; sending the data packets through the interface of the target system; receiving the response of the target system.

8. The industrial park photovoltaic energy storage power data processing method according to claim 7, characterized in that, The step of receiving the response of the target system comprises: receiving the response data packet of the target system; The response data packet is subjected to a timing check to determine whether the response data packet is out of order, and an out-of-order determination result is obtained; The response data packet is subjected to an integrity check to determine whether the response data packet is lost or incomplete, and an integrity determination result is obtained; The response data packet is subjected to a check code check to determine whether the response data packet has an error check code, and a check code determination result is obtained; If the out-of-order determination result indicates that the response data packet is out of order, the integrity determination result indicates that the response data packet is lost or incomplete, or the check code determination result indicates that the response data packet has an error check code, an abnormal processing procedure is triggered; the abnormal processing procedure includes: recording the type of abnormality and the time of occurrence; starting data retransmission or requesting the target system to resend the response according to the type of abnormality; if no valid response is received within a specified time, marking as communication failure and issuing an alarm to the energy management system; If the out-of-order determination result, the integrity determination result, and the check code determination result respectively indicate that the timing check, the integrity check, and the check code check are passed, the response data packet is parsed to extract the processing result or feedback information of the target system.

9. An industrial park photovoltaic energy storage power data processing system for performing the industrial park photovoltaic energy storage power data processing method according to any one of claims 1 to 8, characterized in that, Comprise: A scheduling instruction receiving module is configured to receive a charge-discharge scheduling instruction of an energy storage battery in an industrial park; the charge-discharge scheduling instruction contains an expected target power; An operating parameter collecting module is configured to collect operating parameters of the energy storage battery in real time based on the charge-discharge scheduling instruction; A throughput calculating module is configured to calculate an actual energy throughput of the energy storage battery according to the operating parameters; An energy gap determining module is configured to compare the actual energy throughput with the expected target power to determine whether the energy storage battery has an energy gap; The step of comparing the actual energy throughput with the expected target power to determine whether the energy storage battery has an energy gap comprises: identifying a charging working condition of the energy storage battery to determine a pulse injection opportunity; injecting a multi-frequency current pulse sequence into the energy storage battery at the pulse injection opportunity; synchronously collecting voltage and current data of the energy storage battery during the injection of the multi-frequency current pulse sequence; performing spectrum analysis on the voltage and current data to extract impedance characteristics of the energy storage battery; and comparing the deviation degree of the impedance characteristics from a dynamic impedance baseline to determine whether the energy storage battery has an energy gap; An energy gap compensating module is configured to adjust the charge-discharge power or duration of the energy storage battery according to the energy gap if the energy gap exists; An external energy charging executing module is configured to supplement energy from an external power grid if the energy gap exists and the discharge of the energy storage battery does not meet the load demand; An additional cost feedback module is configured to calculate an additional cost generated by compensating the energy gap and feed back the additional cost to an energy management system; the energy management system adjusts a corresponding energy scheduling strategy according to the additional cost to select a lower-cost emergency disposal path, thereby improving the overall economic benefit of park energy management.

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