Method for quantifying full cost of peak regulation of optical thermal unit considering life loss and related equipment

Abstract

The present application belongs to the technical field of economic evaluation of power system, and discloses a kind of photothermal unit peak shaving full cost quantification method and related equipment considering life consumption, method includes the start state determination result and rotor temperature rise rate input turbine life consumption prediction model, obtain rotor thermal fatigue damage factor, equivalent operating hour consumption is obtained by rotor thermal fatigue damage factor, temperature parameter and heat cycle number in heat cycle process are input molten salt heat storage system damage prediction model, obtain molten salt heat storage system cumulative damage degree, equivalent operating hour consumption, molten salt heat storage system cumulative damage degree are converted into monetization cost by start-stop number, load change rate, rotor thermal fatigue damage factor, based on monetization cost generation superimposed peak shaving full cost function, for realizing photothermal unit peak shaving full cost quantification;The present application can realize the accurate quantification of photothermal turbine start-stop life consumption, and the monetization evaluation of molten salt system life consumption.

Description

Technical Field

[0001] This invention relates to the field of power system economic evaluation technology, specifically to a method and related equipment for quantifying the full cost of peak shaving for solar thermal power units that takes into account lifespan losses. Background Technology

[0002] With the increasing proportion of new energy power generation, the demand for peak-shaving capacity in the power system is growing. Solar thermal power (CSP) units, with their flexible adjustment capabilities through molten salt thermal storage systems, have become a crucial support for peak-shaving in the power system. Molten salt thermal storage systems enable spatiotemporal decoupling of solar and thermal energy, allowing CSP units to generate electricity continuously at night or during cloudy / rainy weather, effectively addressing grid load fluctuations. However, to meet grid peak-shaving demands, CSP units require frequent start-up and shutdown operations and deep peak-shaving operation. This causes significant lifespan wear on key equipment such as turbine rotors, valves, heat exchangers, and molten salt thermal storage systems, leading to increased equipment maintenance costs and directly impacting the economic viability of CSP units for peak-shaving.

[0003] Among the existing peak-shaving economic assessment technologies, some schemes construct peak-shaving cost models based on the life cycle theory. For example, cost models, revenue models, and expenditure models are constructed for coal-fired power units, and the life cycle theory is combined to predict the cost of deep peak shaving. Some schemes optimize the peak-shaving strategy of thermal power units by establishing a carbon emission trading cost model and incorporating the carbon emission rights trading cost into the economic dispatch model. Other schemes establish a simplified calculation model of steam turbine thermal stress, calculate the life loss of steam turbines, and incorporate it into the maintenance cost for peak-shaving operation economic evaluation. However, the aforementioned existing technologies have at least the following problems: First, none of the existing methods have established differentiated life loss calculation models for the three startup states of solar thermal power units: cold, warm, and hot. The rotor temperature rise rate differs significantly under different startup states, and the degree of thermal fatigue damage varies by orders of magnitude. Using a single damage factor for calculation leads to serious distortion of start-up and shutdown costs. Second, the existing methods all ignore the life loss of molten salt thermal storage systems under high-temperature creep and thermal fatigue cumulative damage. The collector pipes and steam generators of molten salt thermal storage systems operate in high-temperature environments for a long time, and frequent thermal cycles cause the pipe materials to suffer double damage. However, the existing methods only consider the construction cost and operating energy consumption of molten salt thermal storage systems and do not convert the life loss unique to molten salt systems into monetized costs. This leads to a serious underestimation of the total peak-shaving cost of solar thermal power units. When participating in peak-shaving services in the electricity market, solar thermal power plants bid below the actual cost, and face the risk of economic losses in long-term operation.

[0004] Therefore, existing technologies have several problems that urgently need to be addressed: they do not establish differentiated life loss models for the three startup states of solar thermal power units (cold, warm, and hot), resulting in distorted calculations of start-up and shutdown costs; and they do not convert the cumulative damage from high-temperature creep and thermal fatigue of molten salt thermal storage systems into monetary costs, leading to a serious underestimation of the total cost of peak shaving. Summary of the Invention

[0005] The purpose of this invention is to provide a method and related equipment for quantifying the full cost of peak shaving for solar thermal power units that takes into account life loss, in order to overcome the problems existing in the prior art. This invention can achieve accurate quantification of the life loss of solar thermal power turbines during start-up and shutdown by establishing differentiated life loss calculation models for three start-up states of solar thermal power turbines: cold state, warm state, and hot state. Furthermore, by establishing a thermal cycle damage model for molten salt thermal storage systems and introducing high-temperature creep coefficient and thermal fatigue cumulative damage factor, the invention can achieve a monetized assessment of the life loss of molten salt systems.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifetime losses, comprising the following steps: Acquire the operating data of the solar thermal power unit, including the number of start-ups and shutdowns, the downtime before start-up, the rotor temperature rise rate, the load change rate, the number of thermal cycles, and the temperature parameters during the thermal cycle process; The downtime before startup is input into the trained startup state determination model, and the startup state determination result is obtained through the startup state determination model; wherein, the startup state determination model is internally configured with classification logic based on threshold comparison; The startup status determination result and the rotor temperature rise rate are input into the trained turbine life loss prediction model. The rotor thermal fatigue damage factor is obtained through the turbine life loss prediction model. The equivalent operating hours consumption is obtained through the rotor thermal fatigue damage factor. The temperature parameters and number of thermal cycles during the thermal cycling process are input into the trained molten salt thermal storage system damage prediction model. The cumulative damage level of the molten salt thermal storage system is obtained through the molten salt thermal storage system damage prediction model. The molten salt thermal storage system damage prediction model includes a damage level assessment module, which is connected to a high-temperature creep calculation module and a thermal fatigue accumulation calculation module. The equivalent operating hours consumption and the cumulative damage of the molten salt thermal storage system are converted into monetized costs by the number of start-stop cycles, load change rate, and rotor thermal fatigue damage factor. Based on the generation of a peak-shaving full cost function using monetized costs, this method is used to quantify the full cost of peak shaving for solar thermal power units.

[0007] In some embodiments, obtaining the startup status determination result through the startup status determination model specifically includes: The classification logic of the startup status determination model compares the input pre-start downtime with the preset cold start determination threshold and warm start determination threshold in turn: If the downtime before startup is greater than the cold start determination threshold, then output the cold start status. If the cold start determination threshold is less than or equal to the shutdown time before startup and less than the warm start determination threshold, then output the warm start status. If the shutdown time before startup is less than or equal to the warm-state startup threshold, then output the hot-state startup status. Output the startup status as either cold, warm, or hot.

[0008] In some embodiments, obtaining the rotor thermal fatigue damage factor and equivalent operating hours consumption through the turbine life loss prediction model specifically includes: The turbine life loss prediction model identifies the current start-up type based on the start-up status determination result, calls the damage factor calculation parameters corresponding to the current start-up type, and then combines the rotor temperature rise rate to calculate the rotor thermal fatigue damage factor that matches the current start-up type. The turbine life loss prediction model further calls the maximum number of start-stop cycles corresponding to the current start type, and outputs the equivalent operating hours consumption after obtaining the rotor thermal fatigue damage factor, the preset turbine design life, and the maximum number of start-stop cycles.

[0009] In some embodiments, obtaining the cumulative damage level of the molten salt thermal storage system through a damage prediction model specifically includes: The high-temperature creep coefficient is obtained by the high-temperature creep calculation module of the molten salt thermal storage system damage prediction model based on the temperature parameters during the thermal cycle and the preset material constants. The thermal fatigue cumulative damage factor is obtained by accumulating the ratio of the number of thermal cycles to the preset corresponding fatigue life number through the thermal fatigue cumulative calculation module of the molten salt thermal storage system damage prediction model. The damage assessment module evaluates the cumulative damage level of the molten salt thermal storage system based on the high-temperature creep coefficient and the thermal fatigue cumulative damage factor.

[0010] In some embodiments, the assessment of the cumulative damage degree of the molten salt thermal storage system by the damage degree assessment module based on the high-temperature creep coefficient and the thermal fatigue cumulative damage factor specifically includes: The high-temperature creep coefficient is compared with the preset high-temperature creep safety threshold to obtain the high-temperature creep exceedance. The thermal fatigue cumulative damage factor is compared with the preset thermal fatigue cumulative damage limit threshold to obtain the thermal fatigue cumulative damage exceedance amount. By weighted and fused high-temperature creep exceedance and thermal fatigue cumulative damage exceedance, a comprehensive index of cumulative damage for molten salt thermal storage system is obtained. The cumulative damage comprehensive index of the molten salt thermal storage system is used as the output of the cumulative damage level of the molten salt thermal storage system.

[0011] In some embodiments, the conversion of equivalent operating hours consumption and cumulative damage to the molten salt thermal storage system into monetized costs using start-stop frequency, load change rate, and rotor thermal fatigue damage factor specifically includes: The start-stop cost is obtained by the number of start-stop cycles, equivalent operating hours consumed, and preset unit operating hour cost. The load change rate is accumulated according to the peak shaving period to obtain the cumulative load change. The variable load cost is obtained by adding the cumulative load change to the preset unit variable load cost. Accelerated depreciation cost of equipment is obtained by using the rotor thermal fatigue damage factor corresponding to the equivalent operating hours consumption, the thermal fatigue cumulative damage factor corresponding to the cumulative damage degree of the molten salt thermal storage system, the total investment cost of equipment, and the design life in months. Start-up and shutdown costs, variable load costs, and accelerated equipment depreciation costs are output as monetized costs.

[0012] In some embodiments, the generation of the superimposed peak-shaving total cost function based on monetization costs specifically includes: By superimposing start-up and shutdown costs with variable load costs, a first-level variable cost function is generated to reflect the direct consumption of peak-shaving operation. Treat the accelerated depreciation cost of equipment as the second-level fixed cost function; The first-level variable cost function and the second-level fixed cost function are added together time-by-time to generate the third-level total cost function, which serves as the superimposed peak-shaving total cost function.

[0013] Secondly, the present invention provides a system for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, comprising: The data acquisition module is used to acquire the operating data of the solar thermal unit. The operating data includes the number of start-ups and shutdowns, the downtime before start-up, the rotor temperature rise rate, the load change rate, the number of thermal cycles, and the temperature parameters during the thermal cycle process. The startup status determination module is used to input the shutdown duration before startup into the trained startup status determination model and obtain the startup status determination result through the startup status determination model; wherein, the startup status determination model is internally configured with classification logic based on threshold comparison. The turbine life loss calculation module is used to input the start-up status judgment result and the rotor temperature rise rate into the trained turbine life loss prediction model, obtain the rotor thermal fatigue damage factor through the turbine life loss prediction model, and obtain the equivalent operating hours consumption through the rotor thermal fatigue damage factor. The molten salt thermal storage system damage calculation module is used to input the temperature parameters and number of thermal cycles during the thermal cycle process into the trained molten salt thermal storage system damage prediction model, and obtain the cumulative damage degree of the molten salt thermal storage system through the molten salt thermal storage system damage prediction model; wherein, the molten salt thermal storage system damage prediction model includes a damage degree assessment module, which is connected to a high temperature creep calculation module and a thermal fatigue cumulative calculation module respectively. The cost quantification module is used to convert the equivalent operating hours consumption and the cumulative damage degree of the molten salt thermal storage system into monetized costs by means of start-stop times, load change rate, and rotor thermal fatigue damage factor. The pricing optimization module is used to generate a superimposed peak-shaving full cost function based on monetized costs, which is used to quantify the full cost of peak shaving for solar thermal power units.

[0014] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for quantifying the full cost of peak shaving for solar thermal power units taking into account lifetime losses.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for quantifying the full cost of peak shaving for solar thermal power units, taking into account lifetime losses.

[0016] The above technical solution has the following advantages or beneficial effects: Firstly, this invention provides a method for quantifying the full cost of peak shaving for solar thermal power units, taking into account lifespan losses. Firstly, by establishing differentiated lifespan loss calculation models for three startup states of solar thermal turbines (cold, warm, and hot), the rotor thermal fatigue damage factor is calculated separately for the temperature rise rate characteristics of different startup states. This solves the problem of inaccurate start-up and shutdown cost calculations caused by existing methods failing to distinguish startup states, achieving accurate quantification of the lifespan losses during start-up and shutdown of solar thermal turbines. Secondly, by establishing a thermal cycle damage model for molten salt thermal storage systems, and introducing high-temperature creep coefficients and thermal fatigue cumulative damage factors, the high-temperature creep and thermal fatigue cumulative damage experienced by the collector pipes and steam generator during thermal cycling are converted into the remaining lifespan reduction cost of the equipment. This solves the problem of existing methods failing to consider the unique high-temperature creep and thermal fatigue damage of molten salt systems, achieving a monetized assessment of the lifespan losses of molten salt systems. In addition, by constructing a superimposed peak-shaving full cost function, the combined life loss of the steam turbine and molten salt system is incorporated into a unified accounting framework, which fully reflects the true cost of peak shaving, ensures that the peak-shaving price covers the true cost, effectively avoids long-term operating losses of solar thermal power plants, and improves the economic sustainability of peak shaving of solar thermal power units.

[0017] Secondly, this invention provides a full-cost quantification system for peak shaving of concentrated solar power (CSP) units that takes into account lifespan losses. Through the coordinated operation of various functional modules, a complete closed loop is achieved, from data acquisition, status determination, and lifespan loss calculation to cost quantification and pricing optimization. This solution solves the technical problems in existing CSP unit peak shaving cost accounting, such as cost distortion due to failure to distinguish startup status and underestimation of total cost due to neglecting thermal cycle damage in the molten salt system. It can automatically execute the full-cost quantification method for peak shaving, providing a scientific decision-making basis for CSP plants to participate in peak shaving services in the electricity market, effectively avoiding long-term operating losses, and improving the economic sustainability of peak shaving for CSP units.

[0018] Thirdly, the present invention provides a computer device that, through a processor executing a specific computer program, can efficiently implement the steps of the method of the present invention. When performing data processing tasks, the computer device can accurately perform numerical calculations and logical judgments, avoiding errors caused by human factors. At the same time, since the computer program has high stability and reliability, it can ensure the accuracy and consistency of the data processing results.

[0019] Fourthly, the present invention provides a computer-readable storage medium. By programming the steps of the method of the present invention into a computer program and storing it on the computer-readable storage medium, users can easily load these programs onto any compatible computer device and execute them without rewriting or converting the code, which greatly improves the convenience and flexibility of program execution. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, as shown in an embodiment of the present invention. Figure 2 This is a schematic diagram of the full-cost quantification system for peak shaving of a solar thermal power unit, taking into account lifespan losses, as shown in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the peak-shaving pricing optimization method according to an embodiment of the present invention. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] With the increasing proportion of new energy power generation, the demand for peak-shaving capacity in the power system is growing. Solar thermal power (CSP) units, with their flexible adjustment capabilities through molten salt thermal storage systems, have become a crucial support for peak-shaving in the power system. Molten salt thermal storage systems enable spatiotemporal decoupling of solar and thermal energy, allowing CSP units to generate electricity continuously at night or during cloudy / rainy weather, effectively addressing grid load fluctuations. However, to meet grid peak-shaving demands, CSP units require frequent start-up and shutdown operations and deep peak-shaving operation, causing significant lifespan losses to key equipment such as turbine rotors, valves, heat exchangers, and molten salt thermal storage systems. The degree of thermal shock experienced by the turbine rotor varies significantly under cold, warm, and hot start-up conditions; during frequent thermal cycles, the collector pipes and steam generator of the molten salt thermal storage system suffer cumulative damage from high-temperature creep and thermal fatigue. If these equipment lifespan losses are not accurately quantified and converted into economic costs, the peak-shaving pricing of CSP units will fail to reflect the true cost, leading to a long-term risk of economic losses.

[0025] Existing peak-shaving economic assessment technologies mainly focus on three directions. The first category is peak-shaving cost models based on the life-cycle theory. For example, one technical solution constructs a cost model, revenue model, and expenditure model for coal-fired power units, combining life-cycle theory and environmental compensation factors to predict the cost of deep peak shaving. However, this solution does not model the joint operation characteristics of the molten salt thermal storage system and steam turbine of solar thermal power units, making it unsuitable for peak-shaving scenarios of solar thermal power units. The second category is peak-shaving economic dispatch models based on carbon emission trading. Some technical solutions establish carbon emission trading cost models, incorporating carbon emission rights trading costs into the economic dispatch model to optimize the peak-shaving strategy of thermal power units. However, these solutions still focus on carbon cost accounting and do not involve the monetization assessment of equipment lifespan losses. The third category is a flexible assessment method that considers the thermal stress of the steam turbine. One technical solution establishes a simplified calculation model of the thermal stress of the steam turbine, calculates the life loss of the steam turbine and incorporates it into the maintenance cost for peak-shaving operation economic evaluation. However, this solution only applies to thermal power units, does not distinguish the differences in life loss between the three start-up states of cold, warm and hot, and does not cover the high-temperature creep and thermal fatigue damage of the molten salt thermal storage system.

[0026] A comprehensive analysis of existing technologies reveals three core technological gaps in the field of peak-shaving cost quantification. First, existing methods lack differentiated lifespan loss calculation models for the three startup states of solar thermal turbines: cold, warm, and hot. The rotor temperature rise rate differs significantly under different startup states: it can reach 80 Kelvin per minute during cold startup, while it is only 30 Kelvin per minute during hot startup. These different temperature rise rates lead to orders-of-magnitude differences in the degree of rotor thermal fatigue damage, but existing methods fail to distinguish the damage characteristics of different startup states, uniformly using a single damage factor for calculation. This results in distorted start-up and shutdown cost calculations, failing to accurately reflect the severe lifespan loss caused by cold startup. Second, existing methods neglect the lifespan loss of molten salt thermal storage systems under the cumulative damage of high-temperature creep and thermal fatigue. The collector pipes and steam generators of molten salt thermal storage systems operate in high-temperature environments ranging from 290°C to 565°C for extended periods. Frequent thermal cycling causes the pipe materials to suffer dual damage from high-temperature creep and thermal fatigue. Current methods only consider the construction cost and operating energy consumption of molten salt thermal energy storage systems, failing to convert the unique high-temperature creep coefficient and thermal fatigue cumulative damage factor of molten salt systems into the remaining lifespan cost reduction. This leads to a significant underestimation of the lifespan loss cost of molten salt thermal energy storage systems. Third, the aforementioned two technological gaps result in a significant underestimation of the total peak-shaving cost of solar thermal power units. Current methods only calculate operating costs and thermal energy storage construction costs, failing to incorporate the lifespan loss of key components such as turbine rotors, valves, and heat exchangers caused by frequent start-stop operations, as well as the high-temperature creep and thermal fatigue damage of molten salt thermal energy storage systems, into the basic calculation of peak-shaving pricing. This neglect of equipment lifespan loss costs causes solar thermal power plants to bid below their actual costs when participating in peak-shaving services in the electricity market, facing the risk of long-term economic losses.

[0027] Therefore, it is necessary to provide a method for quantifying the equipment lifespan loss of solar thermal power units due to frequent start-ups and variable load operation, and converting it into economic costs, so as to achieve a comprehensive coverage of the true cost in peak-shaving pricing.

[0028] Against this backdrop, this application provides a method for quantifying the total cost of peak shaving for concentrated solar power (CSP) units that takes into account lifespan losses. By acquiring operational data and utilizing a trained startup status determination model, the method accurately identifies the startup type. Based on the model, it predicts the thermal fatigue damage of the turbine rotor and the cumulative damage of the molten salt thermal storage system. Finally, it converts equipment lifespan losses into monetized costs and generates a superimposed peak shaving total cost function. This method provides a realistic cost floor constraint for peak shaving pricing, effectively avoiding long-term operating losses due to cost underestimation and significantly improving the economic sustainability of CSP units participating in peak shaving services.

[0029] Example 1: This embodiment provides a method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses. (See also...) Figure 1 This includes the following steps: Step 1: Obtain the operating data of the solar thermal unit. The operating data includes the number of start-ups and shutdowns, the downtime before start-up, the rotor temperature rise rate, the load change rate, the number of thermal cycles, and the temperature parameters during the thermal cycle.

[0030] In some embodiments, this method can be applied to the operation and management system or cloud scheduling platform of a solar thermal power plant to accurately quantify the peak-shaving total cost of solar thermal units.

[0031] In some embodiments, operational data of the solar thermal power unit is first acquired. This operational data forms the basis for all subsequent calculations and evaluations, and specifically includes, but is not limited to: the number of start-ups and shutdowns of the unit, the downtime before each start-up, the rate of temperature rise of the turbine rotor during start-up, the load change rate of the unit during peak shaving, the number of thermal cycles experienced by the molten salt thermal storage system during operation, and the temperature parameters during the thermal cycles. This data can be acquired in real time through the distributed control system (DCS) or supervisory control system (SCADA) of the solar thermal power plant, or retrieved from a historical database.

[0032] Step 2: Input the shutdown duration before startup into the trained startup status determination model, and obtain the startup status determination result through the startup status determination model; wherein, the startup status determination model is internally configured with classification logic based on threshold comparison, which is used to automatically identify the startup status based on the shutdown duration before turbine startup, providing a premise for subsequent targeted life loss calculation.

[0033] In some embodiments, step 2 specifically includes: The startup status determination model uses classification logic to compare the input pre-start downtime with preset cold start and warm start thresholds in sequence: if the pre-start downtime > cold start threshold, output cold start status; if cold start threshold ≤ pre-start downtime < warm start threshold, output warm start status; if pre-start downtime ≤ warm start threshold, output hot start status; output cold start status, warm start status, or hot start status as the startup status determination result.

[0034] In some embodiments, the startup state determination model training step specifically includes: Historical start-up and shutdown records of the solar thermal power unit are collected as training samples. Each training sample includes the shutdown duration before startup and the corresponding actual startup state label. The actual startup state label is marked as cold startup, warm startup, or hot startup based on the threshold range to which the shutdown duration belongs. Then, the training samples are input into an initialized startup state determination model. The determination threshold parameters in the threshold comparison-based classification logic within the model are optimized using supervised learning to minimize the classification error. The threshold parameters are iteratively updated until the model converges. After training, the startup state determination model is used to classify the newly input shutdown duration before startup. The output startup state determination result is used as input to the turbine life loss prediction model for subsequent use in calculating damage factor parameters corresponding to the current startup type.

[0035] Step 3: Input the start-up status determination result and the rotor temperature rise rate into the trained turbine life loss prediction model. Obtain the rotor thermal fatigue damage factor through the turbine life loss prediction model. Obtain the equivalent operating hours consumption through the rotor thermal fatigue damage factor, which is used to quantify the life loss of the turbine rotor caused by a single start-up.

[0036] In some embodiments, step 3 specifically includes: Step 3.1: The turbine life loss prediction model identifies the current start-up type based on the start-up status determination result, calls the damage factor calculation parameters corresponding to the current start-up type, and then calculates the rotor thermal fatigue damage factor that matches the current start-up type by combining the rotor temperature rise rate.

[0037] Step 3.2: The turbine life loss prediction model further calls the maximum number of start-stop cycles corresponding to the current start-up type, and outputs the equivalent operating hours consumption after obtaining the rotor thermal fatigue damage factor, the preset turbine design life, and the maximum number of start-stop cycles.

[0038] In some embodiments, the training steps of the turbine life loss prediction model specifically include: Historical operating data of the solar thermal power unit is collected. Each start-up and shutdown record includes the start-up state determination result, rotor temperature rise rate, and corresponding rotor thermal fatigue damage factor label. The damage factor label is calibrated based on rotor material fatigue test data or finite element simulation results. Then, the sample data is grouped according to start-up state type, and the model is trained using supervised learning. The model is internally configured with damage factor calculation parameters corresponding to each start-up state, including a reference temperature rise rate and material fatigue index. During training, the model learns the mapping relationship between rotor temperature rise rate and damage factor under different start-up states, aiming to minimize the error between the predicted damage factor and the label value, and iteratively optimizes the calculation parameters corresponding to each start-up state until the model converges. After training, the turbine life loss prediction model processes the input start-up state determination result and rotor temperature rise rate, outputs the corresponding rotor thermal fatigue damage factor, and further calculates the equivalent operating hours consumption by combining the preset turbine design life and maximum start-up and shutdown count, for subsequent use by the cost quantification module.

[0039] Step 4: Input the temperature parameters and number of thermal cycles during the thermal cycling process into the trained molten salt thermal storage system damage prediction model, and obtain the cumulative damage degree of the molten salt thermal storage system through the molten salt thermal storage system damage prediction model; wherein, the molten salt thermal storage system damage prediction model includes a damage degree assessment module, and the damage degree assessment module is connected to a high temperature creep calculation module and a thermal fatigue cumulative calculation module respectively.

[0040] In some embodiments, step 4 specifically includes: Step 4.1: The high-temperature creep coefficient is obtained by using the high-temperature creep calculation module of the molten salt thermal storage system damage prediction model based on the temperature parameters during the thermal cycle and the preset material constants.

[0041] Step 4.2: The thermal fatigue cumulative damage factor is obtained by accumulating the thermal fatigue damage factor based on the ratio of the number of thermal cycles to the preset corresponding fatigue life number through the thermal fatigue cumulative calculation module of the molten salt thermal storage system damage prediction model.

[0042] Step 4.3: The cumulative damage degree of the molten salt thermal storage system is assessed by the damage degree assessment module based on the high-temperature creep coefficient and the thermal fatigue cumulative damage factor.

[0043] In some embodiments, step 4.3 specifically includes: The high-temperature creep coefficient is compared with the preset high-temperature creep safety threshold to obtain the high-temperature creep exceedance; the thermal fatigue cumulative damage factor is compared with the preset thermal fatigue cumulative damage limit threshold to obtain the thermal fatigue cumulative damage exceedance; the high-temperature creep exceedance and the thermal fatigue cumulative damage exceedance are weighted and fused to obtain the molten salt thermal storage system cumulative damage comprehensive index; the molten salt thermal storage system cumulative damage comprehensive index is used as the output of the molten salt thermal storage system cumulative damage degree.

[0044] In some embodiments, the training steps of the molten salt thermal storage system damage prediction model specifically include: Historical operating data of the molten salt thermal storage system of the solar thermal power unit is acquired. This data includes temperature parameters, cycle duration, number of cycles, and the actual cumulative damage level obtained through non-destructive testing during each maintenance cycle. The measured damage level corresponding to each thermal cycle record is labeled as a tag, and these tags are combined to form training samples. Then, a supervised learning method is used to train the model. The high-temperature creep calculation module learns the mapping relationship between temperature parameters and the high-temperature creep coefficient, and the thermal fatigue accumulation calculation module learns the mapping relationship between the number of thermal cycles and the thermal fatigue cumulative damage factor. Optimization is performed with the goal of minimizing the error between the predicted value and the labeled value. After model convergence, the molten salt thermal storage system damage prediction model can be used for online prediction.

[0045] Step 5: The equivalent operating hours consumption and the cumulative damage degree of the molten salt thermal storage system are converted into monetary costs by the number of start-stop cycles, load change rate, and rotor thermal fatigue damage factor. This is used to convert the physical equipment life loss into economic cost values, so that the peak-shaving cost can be directly quantified.

[0046] In some embodiments, step 5 specifically includes: Step 5.1: Obtain the start-stop cost by the number of start-stop cycles, equivalent operating hours consumed, and the preset unit operating hour cost.

[0047] Step 5.2: Accumulate the load change rate according to the peak shaving period to obtain the cumulative load change amount. Then, obtain the variable load cost by combining the cumulative load change amount with the preset unit variable load cost.

[0048] Step 5.3: Obtain the accelerated depreciation cost of the equipment by using the rotor thermal fatigue damage factor corresponding to the equivalent operating hours consumption, the thermal fatigue cumulative damage factor corresponding to the cumulative damage degree of the molten salt thermal storage system, the total investment cost of the equipment, and the design life in months.

[0049] Step 5.4 outputs start-up and shutdown costs, variable load costs, and accelerated equipment depreciation costs as monetized costs.

[0050] Step 6: Generate a superimposed peak-shaving full cost function based on the monetized cost to realize the quantification of the full cost of peak shaving for solar thermal power units.

[0051] In some embodiments, step 6 specifically includes: Start-up and shutdown costs are superimposed with variable load costs to generate a first-level variable cost function that reflects the direct consumption of peak-shaving operation; accelerated equipment depreciation costs are used as the second-level fixed cost function; the first-level variable cost function and the second-level fixed cost function are added to each time period to generate a third-level total cost function, which serves as the superimposed peak-shaving total cost function.

[0052] In this embodiment, through the above steps, the differentiated life losses caused by different startup states are accurately identified using the startup state determination model and the turbine life loss prediction model. Simultaneously, the cumulative damage caused by thermal cycling is quantified using the molten salt thermal storage system damage prediction model. These life losses are then uniformly converted into monetized costs, ultimately generating a superimposed peak-shaving total cost function. This ensures that the calculation of peak-shaving total costs accurately reflects the impact of equipment life losses, providing a reliable cost basis for subsequent peak-shaving pricing.

[0053] This invention acquires operational data from solar thermal power units (CSP) and determines the start-up state (cold, warm, or hot) based on the pre-start-up shutdown duration. It establishes a turbine start-up and shutdown lifespan loss model, calculating the rotor thermal fatigue damage factor and equivalent operating hours consumption for different start-up states. A molten salt thermal storage system thermal cycle damage model is also established, calculating the high-temperature creep coefficient and thermal fatigue cumulative damage factor based on temperature parameters and duration during the thermal cycle. The lifespan losses of the turbine and molten salt thermal storage system are converted into monetized costs, including start-up and shutdown costs, variable load costs, and accelerated equipment depreciation costs. A three-layer superimposed peak-shaving total cost function is constructed. This method achieves accurate quantification of the total peak-shaving cost of CSP units, providing a true cost lower limit constraint for peak-shaving pricing, effectively avoiding long-term operating losses, and improving the economic sustainability of peak-shaving for CSP units.

[0054] Example 2: This embodiment, based on Embodiment 1, uses a 50MW tower solar thermal power plant as an example to illustrate the peak-shaving full-cost quantification method of the present invention. The power plant is equipped with an 8-hour molten salt thermal storage system, and the turbine adopts a high- and medium-pressure combined cylinder structure with a design life of 200,000 hours. The power plant participates in grid peak-shaving services, with an average of 12 start-ups and shutdowns per month (4 cold starts, 6 warm starts, and 2 hot starts), and a daily average load variation rate of ±30%.

[0055] Step 1: Obtain the operating data of the solar thermal unit. The operating data includes the number of start-ups and shutdowns, the downtime before start-up, the rotor temperature rise rate, the load change rate, the number of thermal cycles, and the temperature parameters during the thermal cycle.

[0056] Specifically, the distributed control system (DCS) collects unit operation data, including: number of start-ups and shutdowns (12 times / month), downtime before start-up (the downtime before a cold start was 96 hours), rotor temperature rise rate (80K / min for cold start, 50K / min for warm start, and 30K / min for hot start), load change rate (average ±30% per day, corresponding to an average daily load change of ±15MW for a 50MW unit), number of thermal cycles (30 times per month, once per day), and temperature parameters during the thermal cycle (molten salt temperature rises from 290℃ to 565℃, then drops back to 290℃, with a single thermal cycle duration of 4 hours).

[0057] Step 2: Input the shutdown duration before startup into the trained startup state determination model, and obtain the startup state determination result through the startup state determination model; wherein, the startup state determination model is internally configured with classification logic based on threshold comparison.

[0058] Specifically, the startup status is determined by comparing the pre-start downtime with a preset threshold. When the pre-start downtime exceeds the first preset threshold of 72 hours, it is determined to be a cold start; when the pre-start downtime is between the second preset threshold of 8 hours and the first preset threshold of 72 hours, it is determined to be a warm start; when the pre-start downtime is less than or equal to the second preset threshold of 8 hours, it is determined to be a hot start. In this embodiment, a pre-start downtime of 96 hours, exceeding the first preset threshold of 72 hours, is determined to be a cold start. Of the 12 monthly start-stop cycles, there were 4 cold starts, 6 warm starts, and 2 hot starts.

[0059] Step 3: Input the start-up status determination result and rotor temperature rise rate into the trained turbine life loss prediction model, obtain the rotor thermal fatigue damage factor through the turbine life loss prediction model, and obtain the equivalent operating hours consumption through the rotor thermal fatigue damage factor.

[0060] Specifically, different damage factor parameters are used to calculate the rotor thermal fatigue damage factor for different startup states. The calculation method for the rotor thermal fatigue damage factor is as follows: the ratio of the rotor temperature rise rate to the reference temperature rise rate is raised to a power, where the power is the material fatigue index, which is determined based on the rotor material properties.

[0061] The rotor thermal fatigue damage factor is calculated using the following formula: ; In the formula, This represents the rotor thermal fatigue damage factor; This indicates the rotor temperature rise rate, in K / min. The reference temperature rise rate is 50 K / min; is the material fatigue index. Based on the characteristics of the rotor material (25Cr2Ni2MoV steel), 5.0 is selected.

[0062] For cold starts, rotor temperature rise rate ,but: ; For warm-state start-up, rotor temperature rise rate ,but: ; For hot start-up, rotor temperature rise rate ,but: ; The calculation method for equivalent operating hours consumption is as follows: multiply the rotor thermal fatigue damage factor by the turbine's design life, and divide by the maximum design start-stop count. The specific formula is: ; In the formula, This represents the equivalent operating hours consumed, in hours (h). This represents the design life of the steam turbine, taken as 200,000. This indicates the maximum number of start-stop cycles designed for, with different settings depending on the start-up state: 500 cycles for cold start, 1000 cycles for warm start, and 2000 cycles for hot start.

[0063] For cold starts, the equivalent operating hours consumption is: ; For warm-state start-up, the equivalent operating hours consumption is: ; For hot starts, the equivalent operating hours consumption is: ; That is, a single cold start is equivalent to consuming 1684 hours of the turbine's operating life, a single warm start is equivalent to consuming 200 hours of operating life, and a single hot start is equivalent to consuming 7.8 hours of operating life.

[0064] Step 4: Input the temperature parameters and number of thermal cycles during the thermal cycling process into the trained molten salt thermal storage system damage prediction model, and obtain the cumulative damage degree of the molten salt thermal storage system through the molten salt thermal storage system damage prediction model; wherein, the molten salt thermal storage system damage prediction model includes a damage degree assessment module, and the damage degree assessment module is connected to a high temperature creep calculation module and a thermal fatigue cumulative calculation module respectively.

[0065] Specifically, the high-temperature creep coefficient is calculated based on the Larson-Miller parameter, which is determined by multiplying the temperature parameters during thermal cycling with the sum of the material constant and the logarithm of the thermal cycle duration. The specific formula is: ; In the formula, LMP Indicates the Larson-Miller parameters; This indicates the temperature of the molten salt, expressed in Kelvin (K). C This represents the material constant, which is taken as 20 based on the characteristics of the heat storage medium material (316H stainless steel). This indicates the duration of a single thermal cycle, expressed in hours (h).

[0066] In this embodiment, the temperature of the molten salt system during a single thermal cycle rises from 290°C to 565°C, and the peak temperature is taken. Duration of a single thermal cycle ,but: ; Based on the high-temperature creep curve of 316H stainless steel, the material used in molten salt pipelines, the corresponding... fatigue life cycles Second-rate.

[0067] The thermal fatigue cumulative damage factor is calculated using the Miner linear cumulative damage criterion, which sums the ratios of various thermal cycle counts to the corresponding fatigue life counts. The specific formula is: ; In the formula, This represents the cumulative damage factor due to thermal fatigue. Indicates the number of the i-th type of thermal cycle; This indicates the corresponding number of fatigue life cycles; k indicates the number of thermal cycle types.

[0068] In this embodiment, the monthly thermal cycle count Times, corresponding to fatigue life cycles Then the monthly cumulative damage factor is: .

[0069] Step 5: Convert the equivalent operating hours consumption and the cumulative damage degree of the molten salt thermal storage system into monetized costs by using the number of start-stop cycles, load change rate, and rotor thermal fatigue damage factor.

[0070] Specifically, the calculation method for the lifespan loss cost of a molten salt thermal storage system is as follows: multiply the cumulative thermal fatigue damage factor by the sum of the replacement costs of key heat exchange equipment and the replacement costs of the thermal storage medium circulation pipeline. The specific formula is: ; In the formula, This represents the lifespan loss cost of a molten salt thermal storage system, expressed in ten thousand yuan. The cost of replacing the steam generator is represented by 5 million yuan. This represents the cost of replacing the pipelines in the solar collector, taken as 10 million yuan.

[0071] The monthly lifespan loss cost of the molten salt thermal storage system is: ; The calculation method for start-up and shutdown costs is as follows: multiply the number of start-ups and shutdowns, the equivalent operating hours consumption, and the unit operating hour cost. The specific formula is: ; In the formula, This indicates start-up and shutdown costs, in ten thousand yuan. This represents the number of starts and stops for the i-th startup state; This indicates the corresponding equivalent operating hours consumed, in hours (h). This represents the cost per unit operating hour, taken as 300 yuan / hour; m This indicates the number of startup state types, in this embodiment... m =3 (cold state, warm state, hot state).

[0072] In this embodiment, the monthly start-stop frequency is: 4 cold starts, 6 warm starts, and 2 hot starts. Therefore, the monthly start-stop cost is: ; The method for calculating variable load cost is to multiply the cumulative load change by the unit variable load cost. The specific formula is:

[0073] In the formula, This represents the cost of variable load, expressed in ten thousand yuan. Indicates the first t Load variation over a period of time, in MW; This represents the unit variable load cost, taken as 50 yuan / MW; T This indicates the total number of statistical periods.

[0074] In this embodiment, the monthly cumulative load change (30 days × daily average ± 30% × 50MW), then the monthly variable load cost is: ; The method for calculating accelerated depreciation costs is as follows: multiply the sum of the rotor thermal fatigue damage factor and the cumulative thermal fatigue damage factor by the total investment cost of the key equipment, and then divide by the design life in months for monthly amortization. The specific formula is: ; In the formula, This represents the accelerated depreciation cost of equipment, expressed in ten thousand yuan. This represents the monthly average rotor thermal fatigue damage factor. This represents the total investment cost of key equipment, taken as 200 million yuan; This indicates the design life in months, taken as 276 months.

[0075] The monthly average rotor thermal fatigue damage factor is: ; Monthly accelerated equipment depreciation cost is: .

[0076] Step 6: Generate a superimposed peak-shaving full cost function based on the monetized cost to realize the quantification of the full cost of peak shaving for solar thermal power units.

[0077] Specifically, the peak-shaving total cost function is the sum of start-up and shutdown costs, variable load costs, and accelerated equipment depreciation costs. The specific formula is as follows: ; In this embodiment, the total monthly peak shaving cost is: ; The method described in this embodiment achieves accurate quantification of the total cost of peak shaving for solar thermal power units. By introducing differentiated calculation of cold / warm / hot start-up life loss and modeling of thermal cycle damage in molten salt systems, it solves the problems of existing methods that do not distinguish between start-up states and do not consider high-temperature creep and thermal fatigue of molten salt. By constructing a three-layer superimposed cost function, it incorporates start-up, variable load, and equipment depreciation costs into a unified accounting framework, avoiding the cost underestimation caused by existing methods that only consider operating costs and thermal storage construction costs.

[0078] Example 3: Based on Example 2, this embodiment introduces a dynamic cost weight adjustment mechanism, which dynamically adjusts the accelerated depreciation cost weight of the equipment according to the actual operating years of the equipment, so that the cost quantification is closer to the actual wear and tear of the equipment.

[0079] Assuming the solar thermal power plant has been in operation for [number] years Design life is 5 years (60 months). (Approximately 23 years, or 276 months), then the operating life ratio is: ; The weighting of accelerated depreciation costs is dynamically adjusted based on the ratio of the actual service life to the design life of the equipment. The adjustment method involves exponentially calculating the ratio of service life to design life; the exponent is the weighting adjustment index, which is determined based on the accelerated depreciation characteristics of the equipment. The specific formula is as follows: ; In the formula, Indicates cost weight; This represents the weight adjustment index, which is set to 2.0.

[0080] The cost weights are then:

[0081] Revised equipment depreciation acceleration costs The product of the original equipment depreciation acceleration cost and the aforementioned weight is given by the following formula: ; Revised monthly peak shaving total cost for:

[0082] By dynamically adjusting cost weights, the accelerated depreciation cost increases non-linearly with the number of years of operation, more accurately reflecting the impact of equipment aging on costs. In the early stages of equipment life (less than 30% of the design life), the accelerated depreciation cost weight is low to avoid excessive cost accrual; in the later stages of equipment life (more than 70% of the design life), the accelerated depreciation cost weight increases significantly, providing early warning of equipment maintenance needs.

[0083] Example 4: This embodiment replaces the physical model-based lifetime loss calculation method with a data-driven lifetime loss prediction method based on LSTM neural networks, based on embodiment 2.

[0084] Data preparation: Operational data for the solar thermal power plant over the past three years was collected, including downtime for each start-up and shutdown, rotor temperature rise rate, load change rate, molten salt temperature, number of thermal cycles, and corresponding equipment maintenance records (serving as labels for actual lifespan loss). A total of 1080 sample data points were collected (3 years × 12 months × 30 days).

[0085] LSTM model construction: Input features include downtime before startup, rotor temperature rise rate, cumulative load change, molten salt temperature, and number of thermal cycles; output is the predicted cumulative damage factor. and The LSTM network structure consists of two LSTM layers (128 hidden units each) plus one fully connected layer (outputting two nodes). The loss function is mean squared error (MSE), and the optimizer is Adam. The training set comprises 70% training data, 15% validation data, and 15% test data. Training lasts for 100 epochs with a learning rate of 0.001.

[0086] After the model was trained, the mean squared error (MSE) on the test set was 0.012, and the coefficient of determination (R²) was 0.93, indicating that the prediction accuracy was high.

[0087] Cost quantification: LSTM predictions and Substituting the cost quantification formula of Example 1, we calculate the start-up and shutdown costs, the life loss cost of the molten salt thermal storage system, and the accelerated depreciation cost of the equipment to obtain the total cost of peak shaving.

[0088] This embodiment is similar to Embodiment 2, both achieving the quantification of the total cost of peak shaving for solar thermal power units. However, this embodiment adopts a data-driven approach, eliminating the need for complex physical model parameter calibration (such as material fatigue index and material constants), making it more suitable for scenarios lacking detailed equipment parameters. Embodiment 1 is suitable for newly built solar thermal power plants with complete equipment parameters and clear physical mechanisms; this embodiment is suitable for older solar thermal power plants with abundant operating data but incomplete equipment parameters, or for scenarios requiring rapid deployment of a cost quantification system.

[0089] Example 5: This embodiment is based on the peak-shaving total cost calculated in Embodiment 2, see [link / reference]. Figure 3 By combining electricity market price signals, a peak-shaving pricing optimization model is constructed to solve for the optimal peak-shaving pricing strategy.

[0090] Market Price Signal Prediction: An LSTM neural network is used to predict the electricity market price for the next 24 hours. Input features include historical prices (past 7 days), load curves (past 7 days), and renewable energy output forecasts (wind and solar power). Training data consists of market price data from the past 30 days. LSTM network structure: 3-layer LSTM (256 hidden units) plus 1 fully connected layer (outputting 24 nodes, corresponding to the price for the next 24 hours). The mean absolute percentage error (MAPE) on the test set is 8.5%, indicating high prediction accuracy.

[0091] Peak shaving pricing optimization model: An optimization model is constructed with the goal of maximizing peak shaving revenue. ; The constraints include: ; ; ; In the formula, Indicates the first t Peak-shaving pricing for specific time periods, in yuan / MWh; Indicates the first t Peak-shaving power during specific time periods, in MW; Indicates the first t The total cost of peak shaving during specific time periods, in ten thousand yuan; Indicates the first t Market price during the period; This indicates the upper limit of the unit's peak-shaving capacity, taken as 15MW (30% of the peak-shaving range for a 50MW unit).

[0092] A dynamic programming algorithm is used to solve the peak-shaving pricing optimization model. The time is discretized into 24 time periods, the state variable is the unit load rate, and the decision variable is the peak-shaving price. The optimal peak-shaving pricing strategy that maximizes the cumulative peak-shaving revenue is calculated iteratively through the state transition equation.

[0093] The optimization results are shown in the table below (for some time periods):

[0094] The daily cumulative peak-shaving revenue is 352,500 yuan. Compared with blind bidding without full cost constraints (which may result in losses due to prices falling below cost), the method of this invention ensures that the bid covers the true cost, achieving economically sustainable peak-shaving.

[0095] By introducing a full cost constraint (using the full cost of peak shaving as the lower limit of the bid), the situation where the peak shaving bid is lower than the actual cost is avoided. By combining market price forecasting and dynamic programming optimization algorithms, dynamic optimization of peak shaving bids is achieved, maximizing peak shaving revenue while meeting cost coverage constraints, and providing a scientific decision-making basis for solar thermal power plants to participate in the electricity market.

[0096] It is understood that the start-up status determination threshold in the above embodiments is not limited to a shutdown duration of more than 72 hours as cold state, 8 to 72 hours as warm state, and less than or equal to 8 hours as hot state. The threshold can also be adjusted according to the thermal inertia characteristics of different units. For example, for large-capacity units (greater than or equal to 200MW), a shutdown duration of more than 96 hours can be used as cold state, 12 to 96 hours as warm state, and less than or equal to 12 hours as hot state.

[0097] Obviously, the rotor thermal fatigue damage factor is not limited to the Manson-Coffin formula, and other fatigue damage models can also be used, such as the Paris formula (crack propagation model) or stress-life curves based on finite element analysis.

[0098] It is understandable that the thermal cycling damage factor of molten salt systems is not limited to the Miner linear cumulative damage criterion; nonlinear cumulative damage criteria (such as the Corten-Dolan criterion) can also be used to more accurately reflect the fatigue characteristics of molten salt materials under variable amplitude loads. Clearly, peak-shaving pricing optimization algorithms are not limited to dynamic programming; other optimization algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and model predictive control (MPC) can also be used.

[0099] It is understandable that the system deployment method is not limited to the cloud-edge collaborative architecture. It can also adopt pure local deployment (all modules are deployed on the power plant side server) or pure cloud deployment (all modules are deployed in the cloud, and only the data acquisition interface is retained on the power plant side).

[0100] Example 6: In one embodiment of the present invention, a full-cost quantification system for peak shaving of a solar thermal power unit considering lifetime loss is provided, for executing the method described in any one of embodiments 1-5 above, see below. Figure 2 It includes: a data acquisition module, a startup status determination module, a turbine life loss calculation module, a molten salt thermal storage system damage calculation module, a cost quantification module, and a quotation optimization module.

[0101] The data acquisition module is used to acquire operational data of the solar thermal power unit. This operational data includes the number of start-ups and shutdowns, the duration of shutdown before start-up, the rotor temperature rise rate, the load change rate, the number of thermal cycles, and temperature parameters during the thermal cycle process. The data acquisition module can communicate with the distributed control system or data acquisition and monitoring control system of the solar thermal power plant to acquire the above operational data in real time or read it at preset intervals.

[0102] The startup status determination module is connected to the data acquisition module and is used to receive the pre-startup downtime output by the data acquisition module. The startup status determination module inputs the pre-startup downtime into the trained startup status determination model and obtains the startup status determination result through the model. The startup status determination model is internally configured with threshold comparison-based classification logic, capable of outputting one of three determination results—cold startup, warm startup, or hot startup—based on the downtime.

[0103] The turbine life loss calculation module is connected to both the start-up status determination module and the data acquisition module, and is used to receive the start-up status determination results and the rotor temperature rise rate. The turbine life loss calculation module inputs the above data into the trained turbine life loss prediction model, obtains the rotor thermal fatigue damage factor through the model, and calculates the equivalent operating hours consumption based on the rotor thermal fatigue damage factor.

[0104] The molten salt thermal storage system damage calculation module is connected to the data acquisition module to receive temperature parameters and the number of thermal cycles during the thermal cycling process. This module inputs the data into a trained molten salt thermal storage system damage prediction model. The damage prediction model includes a damage severity assessment module, which is connected to both a high-temperature creep calculation module and a thermal fatigue accumulation calculation module. The high-temperature creep calculation module calculates the high-temperature creep coefficient based on the temperature parameters, and the thermal fatigue accumulation calculation module calculates the thermal fatigue accumulation damage factor based on the number of thermal cycles. The damage severity assessment module evaluates and outputs the cumulative damage severity of the molten salt thermal storage system based on the high-temperature creep coefficient and the thermal fatigue accumulation damage factor.

[0105] The cost quantification module is connected to the turbine life loss calculation module and the molten salt thermal storage system damage calculation module, and is also connected to the data acquisition module. The cost quantification module is used to convert the equivalent operating hours consumption and the cumulative damage level of the molten salt thermal storage system into monetized costs based on start-up and shutdown frequency, load change rate, and rotor thermal fatigue damage factor.

[0106] The pricing optimization module is connected to the cost quantification module. It is used to generate a superimposed peak-shaving full cost function based on the monetized cost, so as to realize the quantitative output of the peak-shaving full cost of the solar thermal unit.

[0107] This embodiment achieves a complete closed loop from data acquisition, status determination, life loss calculation to cost quantification and quotation optimization through the coordinated cooperation of various functional modules. The data flow between modules is clear, the system architecture is complete, and it can automatically execute the peak shaving full cost quantification method described in the aforementioned embodiments.

[0108] Comparative experiments verify the advantages of the method of the present invention over existing methods in the quantification of the total cost of peak shaving.

[0109] Test conditions: Three solar thermal power plants of different sizes (50MW, 100MW, and 150MW) were selected, and the monthly peak-shaving total cost was calculated using both the method of this invention and existing methods. The existing method (refer to CN117575658A) only considers operating costs and environmental compensation costs, and does not consider equipment lifespan depreciation costs.

[0110] The test results are shown in the table below:

[0111] Comparative analysis: The peak-shaving total cost calculated by the method of this invention is on average 155% higher than that of existing methods, with the main increase coming from equipment life-cycle depreciation costs (start-up and shutdown costs and accelerated equipment depreciation costs). Existing methods do not consider the life-cycle depreciation of turbine rotors, molten salt pipelines, and steam generators caused by frequent start-ups and shutdowns, resulting in a serious underestimation of peak-shaving costs.

[0112] This invention introduces differentiated calculation of life loss during cold / warm / hot start-up, thermal cycle damage modeling of molten salt systems, and a three-layer superimposed cost function to accurately convert equipment life loss into monetary cost. This improves the quantification of peak shaving full cost by 155% compared to existing methods, demonstrating the unexpected technical effects brought about by differentiated technical characteristics. It avoids long-term operating losses caused by underestimating the true cost in peak shaving pricing, and provides an economic sustainability guarantee for solar thermal power plants to participate in peak shaving services in the electricity market.

[0113] This invention establishes differentiated lifespan loss calculation models for solar thermal turbines under three startup states: cold, warm, and hot. It calculates rotor thermal fatigue damage factors for the temperature rise rate characteristics of different startup states, solving the problem of inaccurate start-up and shutdown cost calculations caused by existing methods failing to differentiate startup states. This achieves precise quantification of the start-up and shutdown lifespan loss of solar thermal turbines, effectively avoiding long-term operating losses due to underestimating true costs in peak-shaving quotations. Furthermore, by establishing a thermal cycle damage model for molten salt thermal storage systems and introducing high-temperature creep coefficients and thermal fatigue cumulative damage factors, the high-temperature creep and thermal fatigue cumulative damage experienced by the collector pipes and steam generator during thermal cycling are converted into equipment remaining lifespan loss costs. This solves the problem of existing methods failing to consider the unique high-temperature creep and thermal fatigue damage of molten salt systems, enabling the monetization of molten salt system lifespan loss assessment, improving the completeness of peak-shaving full-cost calculations, and filling a gap in cost accounting methods. By constructing a three-tiered peak-shaving full-cost function that superimposes start-up and shutdown costs, load variation costs, and accelerated equipment depreciation costs, the combined lifespan loss of the turbine and molten salt system is incorporated into a unified accounting framework. This addresses the problem of existing methods that only consider operating costs and thermal storage construction costs, failing to convert the lifespan loss of key components due to frequent start-ups and shutdowns into monetary costs. It achieves a comprehensive reflection of the true cost of peak shaving, providing a cost floor constraint for market quotations and ensuring that peak-shaving quotations cover the true cost. Furthermore, by introducing a peak-shaving quotation optimization method based on full-cost constraints, combined with market price forecasting and dynamic programming algorithms, peak-shaving revenue is maximized while meeting cost coverage constraints. This solves the problem of existing methods lacking a scientific decision-making basis for peak-shaving quotations, achieving dynamic optimization of peak-shaving quotations. This provides a scientific economic decision-making basis for solar thermal power plants to participate in electricity market peak-shaving services, improving the economic sustainability of peak-shaving for solar thermal units.

[0114] This embodiment provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor is the computing and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in a computer storage medium to realize a corresponding method flow or corresponding function. The processor described in this embodiment can be used to execute related operations of the full-cost quantification method for peak shaving of solar thermal power units that takes into account lifetime loss.

[0115] This embodiment provides a computer-readable storage medium, which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space containing the terminal's operating system; and this storage space also contains one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for quantifying the full cost of peak shaving for solar thermal power units considering lifetime depletion in this embodiment.

[0116] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0117] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, characterized in that... Includes the following steps: Acquire the operating data of the solar thermal power unit, including the number of start-ups and shutdowns, the downtime before start-up, the rotor temperature rise rate, the load change rate, the number of thermal cycles, and the temperature parameters during the thermal cycle process; The downtime before startup is input into the trained startup state determination model, and the startup state determination result is obtained through the startup state determination model; wherein, the startup state determination model is internally configured with classification logic based on threshold comparison; The startup status determination result and the rotor temperature rise rate are input into the trained turbine life loss prediction model. The rotor thermal fatigue damage factor is obtained through the turbine life loss prediction model. The equivalent operating hours consumption is obtained through the rotor thermal fatigue damage factor. The temperature parameters and number of thermal cycles during the thermal cycling process are input into the trained molten salt thermal storage system damage prediction model. The cumulative damage level of the molten salt thermal storage system is obtained through the molten salt thermal storage system damage prediction model. The molten salt thermal storage system damage prediction model includes a damage level assessment module, which is connected to a high-temperature creep calculation module and a thermal fatigue accumulation calculation module. The equivalent operating hours consumption and the cumulative damage of the molten salt thermal storage system are converted into monetized costs by the number of start-stop cycles, load change rate, and rotor thermal fatigue damage factor. Based on the generation of a peak-shaving full cost function using monetized costs, this method is used to quantify the full cost of peak shaving for solar thermal power units.

2. The method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, as described in claim 1, is characterized in that... The process of obtaining the startup status determination result through the startup status determination model specifically includes: The classification logic of the startup status determination model compares the input pre-start downtime with the preset cold start determination threshold and warm start determination threshold in turn: If the downtime before startup is greater than the cold start determination threshold, then output the cold start status. If the cold start determination threshold is less than or equal to the shutdown time before startup and less than the warm start determination threshold, then output the warm start status. If the shutdown time before startup is less than or equal to the warm-state startup threshold, then output the hot-state startup status. Output the startup status as either cold, warm, or hot.

3. The method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, as described in claim 1, is characterized in that... The process of obtaining the rotor thermal fatigue damage factor and equivalent operating hours consumption through the turbine life loss prediction model specifically includes: The turbine life loss prediction model identifies the current start-up type based on the start-up status determination result, calls the damage factor calculation parameters corresponding to the current start-up type, and then combines the rotor temperature rise rate to calculate the rotor thermal fatigue damage factor that matches the current start-up type. The turbine life loss prediction model further calls the maximum number of start-stop cycles corresponding to the current start type, and outputs the equivalent operating hours consumption after obtaining the rotor thermal fatigue damage factor, the preset turbine design life, and the maximum number of start-stop cycles.

4. The method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, as described in claim 1, is characterized in that... The method of obtaining the cumulative damage level of the molten salt thermal storage system through the molten salt thermal storage system damage prediction model specifically includes: The high-temperature creep coefficient is obtained by the high-temperature creep calculation module of the molten salt thermal storage system damage prediction model based on the temperature parameters during the thermal cycle and the preset material constants. The thermal fatigue cumulative damage factor is obtained by accumulating the ratio of the number of thermal cycles to the preset corresponding fatigue life number through the thermal fatigue cumulative calculation module of the molten salt thermal storage system damage prediction model. The damage assessment module evaluates the cumulative damage level of the molten salt thermal storage system based on the high-temperature creep coefficient and the thermal fatigue cumulative damage factor.

5. The method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, as described in claim 4, is characterized in that... The damage assessment module evaluates the cumulative damage level of the molten salt thermal storage system based on the high-temperature creep coefficient and the thermal fatigue cumulative damage factor, specifically including: The high-temperature creep coefficient is compared with the preset high-temperature creep safety threshold to obtain the high-temperature creep exceedance. The thermal fatigue cumulative damage factor is compared with the preset thermal fatigue cumulative damage limit threshold to obtain the thermal fatigue cumulative damage exceedance amount. By weighted and fused high-temperature creep exceedance and thermal fatigue cumulative damage exceedance, a comprehensive index of cumulative damage for molten salt thermal storage system is obtained. The cumulative damage comprehensive index of the molten salt thermal storage system is used as the output of the cumulative damage level of the molten salt thermal storage system.

6. The method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, as described in claim 1, is characterized in that... The process of converting the equivalent operating hours consumption and the cumulative damage level of the molten salt thermal storage system into monetized costs through start-stop frequency, load change rate, and rotor thermal fatigue damage factor specifically includes: The start-stop cost is obtained by the number of start-stop cycles, equivalent operating hours consumed, and preset unit operating hour cost. The load change rate is accumulated according to the peak shaving period to obtain the cumulative load change. The variable load cost is obtained by adding the cumulative load change to the preset unit variable load cost. Accelerated depreciation cost of equipment is obtained by using the rotor thermal fatigue damage factor corresponding to the equivalent operating hours consumption, the thermal fatigue cumulative damage factor corresponding to the cumulative damage degree of the molten salt thermal storage system, the total investment cost of equipment, and the design life in months. Start-up and shutdown costs, variable load costs, and accelerated equipment depreciation costs are output as monetized costs.

7. The method for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan losses, as described in claim 1, is characterized in that... The aforementioned peak-shaving total cost function generated based on monetization costs specifically includes: By superimposing start-up and shutdown costs with variable load costs, a first-level variable cost function is generated to reflect the direct consumption of peak-shaving operation. Treat the accelerated depreciation cost of equipment as the second-level fixed cost function; The first-level variable cost function and the second-level fixed cost function are added together time-by-time to generate the third-level total cost function, which serves as the superimposed peak-shaving total cost function.

8. A system for quantifying the total cost of peak shaving for solar thermal power units, taking into account lifespan depreciation, characterized in that, The method for quantifying the full cost of peak shaving for solar thermal power units, taking into account lifetime losses, as described in any one of claims 1-7, includes: The data acquisition module is used to acquire the operating data of the solar thermal unit. The operating data includes the number of start-ups and shutdowns, the downtime before start-up, the rotor temperature rise rate, the load change rate, the number of thermal cycles, and the temperature parameters during the thermal cycle process. The startup status determination module is used to input the shutdown duration before startup into the trained startup status determination model and obtain the startup status determination result through the startup status determination model; wherein, the startup status determination model is internally configured with classification logic based on threshold comparison. The turbine life loss calculation module is used to input the start-up status judgment result and the rotor temperature rise rate into the trained turbine life loss prediction model, obtain the rotor thermal fatigue damage factor through the turbine life loss prediction model, and obtain the equivalent operating hours consumption through the rotor thermal fatigue damage factor. The molten salt thermal storage system damage calculation module is used to input the temperature parameters and number of thermal cycles during the thermal cycle process into the trained molten salt thermal storage system damage prediction model, and obtain the cumulative damage degree of the molten salt thermal storage system through the molten salt thermal storage system damage prediction model; wherein, the molten salt thermal storage system damage prediction model includes a damage degree assessment module, which is connected to a high temperature creep calculation module and a thermal fatigue cumulative calculation module respectively. The cost quantification module is used to convert the equivalent operating hours consumption and the cumulative damage degree of the molten salt thermal storage system into monetized costs by means of start-stop times, load change rate, and rotor thermal fatigue damage factor. The pricing optimization module is used to generate a superimposed peak-shaving full cost function based on monetized costs, which is used to quantify the full cost of peak shaving for solar thermal power units.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for quantifying the full cost of peak shaving for solar thermal power units, taking into account lifespan losses, as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for quantifying the full cost of peak shaving for solar thermal power units, taking into account lifetime losses, as described in any one of claims 1-7.

Images