Electric heating molten salt system regulation and control effect scoring system and method and computer readable medium

Abstract

The invention provides an electric heating molten salt system regulation and control effect scoring system and method and a computer program product, and belongs to the technical field of electric heating molten salt systems. Comprising the following steps: taking economic benefits, heat supply demand satisfaction degree and carbon emission reduction after the electric heating molten salt system participates in power grid regulation as indexes for evaluating a strategy for the electric heating molten salt system to participate in power grid regulation; a fuzzy analytic hierarchy process is adopted for the indexes, and weights of the indexes are obtained; clustering the operation data of the electric heating molten salt system participating in the power grid regulation and control in the plurality of time periods, and judging the operation scene of the electric heating molten salt system participating in the power grid regulation and control in the corresponding time period according to the values of a plurality of clustering centers in combination with a preset judgment standard; and performing weighted summation on each index of the strategy of the electric heating molten salt system participating in power grid regulation and control in each operation scene and the corresponding weight to obtain a comprehensive score of the strategy. According to the method, the economic benefit, the heat supply demand satisfaction degree and the carbon emission reduction amount are comprehensively considered.

Description

Technical Field

[0001] This invention relates to the field of electric heating molten salt systems, and more specifically to a scoring system, method, and computer-readable medium for evaluating the control effect of an electric heating molten salt system. Background Technology

[0002] With the profound transformation of the global energy structure, the proportion of clean energy sources, represented by wind and solar power, connected to the power grid is increasing daily. However, the intermittency, volatility, and randomness of clean energy bring enormous peak-shaving pressure and absorption challenges to the power grid, leading to severe "wind curtailment" and "solar curtailment." Traditional power dispatching models are unable to effectively address this challenge.

[0003] Meanwhile, the industrial heating sector still relies heavily on fossil fuels, which not only exacerbates carbon emissions but also makes industrial production costs susceptible to energy price fluctuations. Combining electric heating technology with thermal storage systems, especially systems using molten salt as the thermal storage medium, offers a new technological approach to solving these problems. Electric heating molten salt systems can convert electrical energy into heat energy for storage, absorbing excess electricity during off-peak hours or periods of high clean energy generation, and providing a stable and reliable heat source for industrial production when needed.

[0004] However, current comprehensive value assessments of such systems in complex power grid environments generally suffer from problems such as limited assessment dimensions, crude quantitative models, and a lack of dynamism and systematicity. Traditional assessment methods often focus solely on economic benefits, neglecting the multiple values ​​of electrically heated molten salt systems in ensuring power grid safety and stability, improving clean energy utilization, and achieving carbon emission reduction. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a scoring system for the control effect of an electrically heated molten salt system, comprising: The indicator construction module is used to evaluate the economic benefits, heating demand satisfaction, and carbon emission reduction of the electric heating molten salt system after it participates in grid regulation as indicators for assessing the strategy of the electric heating molten salt system participating in grid regulation. The indicator weight calculation module is used to obtain the weight of the indicator by applying fuzzy hierarchical analysis. The operation scenario identification module is used to cluster the operation data of the electric heating molten salt system after participating in grid regulation under multiple time periods, and obtain several cluster centers of the operation data of the electric heating molten salt system under each time period. Based on the values ​​of several cluster centers and combined with preset judgment criteria, the operation scenario of the electric heating molten salt system after participating in grid regulation under the corresponding time period is determined. The comprehensive scoring module is used to perform weighted summation of the index values ​​of the strategies for the electric heating molten salt system to participate in power grid regulation under each operating scenario, with their respective weights, to obtain a comprehensive score for the strategy.

[0006] Furthermore, in the indicator construction module, the economic benefits, the heating demand satisfaction rate, and the carbon emission reduction are specifically as follows: The spot peak-valley electricity price revenue is obtained based on the difference in electricity consumption before and after the electric heating molten salt system participates in grid regulation. The ancillary service and demand response revenue is obtained based on the ancillary service capacity and demand response volume when the electric heating molten salt system participates in grid regulation. The energy efficiency optimization revenue is obtained based on the energy saved by the electric heating molten salt system replacing fossil fuel heating. The economic benefits are the sum of the spot peak-valley electricity price revenue, the ancillary service and demand response revenue, and the energy efficiency optimization revenue. The heating demand satisfaction is obtained by comparing the maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation with the rated heating demand. The emission reduction from clean energy consumption is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation when absorbing clean energy. The carbon emission reduction from grid regulation is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation. The carbon emission reduction is the sum of the emission reduction from clean energy consumption and the carbon emission reduction from grid regulation.

[0007] Furthermore, spot peak-valley electricity price revenue The calculation formula is as follows: In the formula, The spot electricity price for time period t. The electrical power consumed by the electrically heated molten salt system in time period t, which is supplied only according to the rated heating demand before the system participates in grid regulation; ΔT represents the actual power consumption of the electrically heated molten salt system during time period t after it participates in grid regulation, where ΔT is the duration of time period t and T is the total number of time periods. Ancillary services and demand response revenue The calculation formula is as follows: In the formula, The capacity of auxiliary services for time period t; The clearing price for this service during time period t; The demand response amount for time period t; The demand response compensation price for time period t; Energy efficiency optimization benefits The calculation formula is as follows: In the formula, The price of fossil fuels in time period t; The amount of fossil fuel consumed for heating in time period t, provided only according to the rated heating demand through fossil fuel heating. The actual electrical power consumed by the electrically heated molten salt system during time period t to supply heat according to the rated heating demand when it is only used to replace fossil fuel heating. The economic benefits .

[0008] Furthermore, the degree of satisfaction of the heating demand The calculation formula is as follows: In the formula, The rated heating demand for time period t. The maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation during time period t.

[0009] Furthermore, clean energy consumption and emission reduction The calculation formula is as follows: In the formula, For the purpose of energy absorption index, when the electrically heated molten salt system absorbs clean energy... =1, otherwise =0, It is the average carbon emission factor of coal-fired power plants; Grid regulation carbon emission reduction The calculation formula is as follows: In the formula, The electricity consumption during time period t for an electrically heated molten salt system that supplies heat only according to its rated heating demand before participating in grid regulation. For the electrically heated molten salt system to participate in grid regulation during time period t ; The carbon emission reduction .

[0010] Furthermore, in the indicator weight calculation module, the specific method of using fuzzy hierarchical analysis to calculate the economic benefits, the heating demand satisfaction, and the carbon emission reduction is as follows: A fuzzy judgment matrix is ​​constructed for economic benefits, heating demand satisfaction, and carbon emission reduction, as follows: In the formula, for OK Fuzzy judgment matrix of columns, As an indicator and indicators The triangular fuzzy number between them (Economic benefits, satisfaction of heating demand, and carbon emission reduction) ; The geometric mean method is used to calculate the fuzzy weights of economic benefits, heating demand satisfaction, and carbon emission reduction, as shown below: In the formula, As an indicator Fuzzy weights, Separate indicators The minimum, most likely, and maximum values ​​of the weights. Fuzzy multiplication of triangular fuzzy numbers; Comparing the magnitude relationships among the various fuzzy weights, the probability function for measuring the magnitude relationship between two triangular fuzzy numbers is expressed as follows: In the formula, for The probability, for Any possible value of , for Any possible value of , For the upper bound, Used to take all values ​​when y≥x The maximum value in, The membership function of the triangular fuzzy number. for for The degree of subordination, for for The degree of membership; In the formula, As an indicator The initial unfuzzy weights; The initial unfuzzy weights are normalized as follows: In the formula, index The non-fuzzy weights.

[0011] Furthermore, the running scene recognition module includes: The first clustering unit is used to randomly select K data points from the operational dataset of the electrically heated molten salt system as initial cluster centers using the K-means clustering method. ; The data processing unit is used for processing each data point in the running dataset. Calculate data points With each initial cluster center The squared Euclidean distance of the data points. Assigned to the cluster containing the nearest cluster center In the middle, data points Belongs to cluster If and only if For all Established; The second clustering unit is used to recalculate the cluster center of each cluster. The new cluster center is the mean of all data points in that cluster. The iterative unit is used to repeatedly execute the actions of the data processing unit and the second clustering unit until the cluster centers no longer change or the amount of change is less than the threshold.

[0012] Furthermore, in the comprehensive scoring module, the specific method for weighted summation of each indicator value of the strategy for the electrically heated molten salt system to participate in power grid regulation under each operating scenario is as follows: The indicators of the strategies for the electric heating molten salt system to participate in power grid regulation under each operating scenario are normalized, and the normalized indicator values ​​are weighted and summed with their corresponding non-fuzzy weights.

[0013] A method for evaluating the control effect of an electrically heated molten salt system includes: The economic benefits, heating demand satisfaction, and carbon emission reduction of the electric heating molten salt system after participating in grid regulation will be used as indicators to evaluate the strategy of participating the electric heating molten salt system in grid regulation. The weights of the indicators are obtained by applying fuzzy hierarchical analysis. Clustering is performed on the operating data of the electrically heated molten salt system after participating in grid regulation under multiple time periods to obtain several cluster centers of the operating data of the electrically heated molten salt system under each time period. Based on the values ​​of several cluster centers and combined with preset judgment criteria, the operating scenario of the electrically heated molten salt system after participating in grid regulation under the corresponding time period is determined. The comprehensive score of the strategy for the electric heating molten salt system to participate in grid regulation under each operating scenario is obtained by weighting and summing the indicators of each strategy with their respective weights.

[0014] The specific economic benefits, the degree of heating demand satisfaction, and the carbon emission reduction are as follows: The spot peak-valley electricity price revenue is obtained based on the difference in electricity consumption before and after the electric heating molten salt system participates in grid regulation. The ancillary service and demand response revenue is obtained based on the ancillary service capacity and demand response volume when the electric heating molten salt system participates in grid regulation. The energy efficiency optimization revenue is obtained based on the energy saved by the electric heating molten salt system replacing fossil fuel heating. The economic benefits are the sum of the spot peak-valley electricity price revenue, the ancillary service and demand response revenue, and the energy efficiency optimization revenue. The heating demand satisfaction is obtained by comparing the maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation with the rated heating demand. The emission reduction from clean energy consumption is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation when absorbing clean energy. The carbon emission reduction from grid regulation is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation. The carbon emission reduction is the sum of the emission reduction from clean energy consumption and the carbon emission reduction from grid regulation.

[0015] Furthermore, spot peak-valley electricity price revenue The calculation formula is as follows: In the formula, The spot electricity price for time period t. The electrical power consumed by the electrically heated molten salt system in time period t, which is supplied only according to the rated heating demand before the system participates in grid regulation; ΔT represents the actual power consumption of the electrically heated molten salt system during time period t after it participates in grid regulation, where ΔT is the duration of time period t and T is the total number of time periods. Ancillary services and demand response revenue The calculation formula is as follows: In the formula, The capacity of auxiliary services for time period t; The clearing price for this service during time period t; The demand response amount for time period t; The demand response compensation price for time period t; Energy efficiency optimization benefits The calculation formula is as follows: In the formula, The price of fossil fuels in time period t; The amount of fossil fuel consumed for heating in time period t, provided only according to the rated heating demand through fossil fuel heating. The actual electrical power consumed by the electrically heated molten salt system during time period t to supply heat according to the rated heating demand when it is only used to replace fossil fuel heating. The economic benefits .

[0016] Furthermore, the degree of satisfaction of the heating demand The calculation formula is as follows: In the formula, The rated heating demand for time period t. The maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation during time period t.

[0017] Furthermore, clean energy consumption and emission reduction The calculation formula is as follows: In the formula, For the purpose of energy absorption index, when the electrically heated molten salt system absorbs clean energy... =1, otherwise =0, It is the average carbon emission factor of coal-fired power plants; Grid regulation carbon emission reduction The calculation formula is as follows: In the formula, The electricity consumption during time period t for an electrically heated molten salt system that supplies heat only according to its rated heating demand before participating in grid regulation. For the electrically heated molten salt system to participate in grid regulation during time period t ; The carbon emission reduction .

[0018] Furthermore, the specific method for applying fuzzy hierarchical analysis to the aforementioned economic benefits, the degree of satisfaction of heating demand, and the carbon emission reduction is as follows: A fuzzy judgment matrix is ​​constructed for economic benefits, heating demand satisfaction, and carbon emission reduction, as follows: In the formula, for OK Fuzzy judgment matrix of columns, As an indicator and indicators The triangular fuzzy number between them (Economic benefits, satisfaction of heating demand, and carbon emission reduction) ; The fuzzy weights for economic benefits, heating demand satisfaction, and carbon emission reduction are calculated using the geometric mean method, and are expressed as follows: In the formula, As an indicator Fuzzy weights, Separate indicators The minimum, most likely, and maximum values ​​of the weights. Fuzzy multiplication of triangular fuzzy numbers; Comparing the magnitude relationships among the various fuzzy weights, the probability function for measuring the magnitude relationship between two triangular fuzzy numbers is expressed as follows: In the formula, for The probability, for Any possible value of , for Any possible value of , For the upper bound, Used to take all values ​​when y≥x The maximum value in, The membership function of the triangular fuzzy number. for for The degree of subordination for for The degree of membership; In the formula, As an indicator The initial unfuzzy weights; The initial unfuzzy weights are normalized as follows: In the formula, index The non-fuzzy weights.

[0019] Furthermore, the specific method for clustering the operating data of the electrically heated molten salt system after participating in grid regulation across multiple time periods is as follows: Step 1: Using K-means clustering, randomly select K data points from the operational dataset of the electrically heated molten salt system as initial cluster centers. ; Step 2, for each data point in the running dataset Calculate data points With each initial cluster center The squared Euclidean distance of the data points. Assigned to the cluster containing the nearest cluster center In the middle, data points Belongs to cluster If and only if For all Established; Step 3: Recalculate the cluster center for each cluster. The new cluster center is the mean of all data points in that cluster. Step 4: Repeat steps 3 and 4 iteratively until the cluster centers no longer change or the change is less than the threshold.

[0020] A computer-readable medium storing a computer program / instructions, which, when executed, implements the above-described method for evaluating the control effect of an electrically heated molten salt system.

[0021] The beneficial effects of this invention are as follows: 1. This invention breaks through the limitations of traditional assessments that focus solely on economics, comprehensively encompassing the core value of electrically heated molten salt systems in new power systems, including economic benefits, heating demand satisfaction, and carbon emission reduction. This multi-dimensional perspective ensures the scientific rigor and comprehensiveness of the assessment.

[0022] 2. This invention establishes precise and computable mathematical quantitative models for each evaluation indicator. These models not only consider the peak-valley price difference in the electricity spot market, but also encompass ancillary service market subsidies, demand response compensation, and indirect benefits and carbon emission reductions resulting from fossil fuel substitution. The dynamic nature of the models is reflected in their ability to perform precise calculations based on real-time electricity prices, grid operating status, and industrial heat load data, ensuring the timeliness and accuracy of the evaluation results.

[0023] 3. This invention applies unsupervised machine learning, specifically K-means clustering, to the operational data analysis of electrically heated molten salt systems. Through clustering massive amounts of high-dimensional data, the system can automatically identify and summarize typical operational scenarios with clear physical meaning, such as priority grid arbitrage, green energy consumption-driven development, and production supply assurance. This technological breakthrough makes the evaluation results more targeted and representative, and can intuitively reveal the system performance under different control strategies.

[0024] 4. This invention employs the Fuzzy Analytic Hierarchy Process (FAHP) to assign weights to each indicator. This method not only retains the intuitiveness of FAHP but also, by introducing fuzzy mathematics theory, allows experts to express their judgments using fuzzy language, thereby better handling the uncertainty and subjectivity in the evaluation process. This combined subjective and objective weighting approach significantly improves the robustness and credibility of the evaluation results.

[0025] 5. This invention combines the aforementioned quantitative model, clustering results, and weighting, enabling the normalization of quantified index values ​​for each scenario to ultimately obtain a comprehensive evaluation score and ranking for each scenario. This result not only intuitively reveals the advantages and disadvantages of different control strategies but also provides a solid foundation for designing advanced, multi-objective intelligent control systems, allowing the evaluation system to transform from passive assessment into a proactive optimization decision-making tool. Attached Figure Description

[0026] Figure 1 This is a system block diagram of the present invention.

[0027] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0028] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0029] Example 1 A scoring system for the control effect of an electrically heated molten salt system, referenced Figure 1 ,include: The indicator construction module is used to evaluate the economic benefits, heating demand satisfaction, and carbon emission reduction of the electric heating molten salt system after it participates in grid regulation as indicators for assessing the strategy of the electric heating molten salt system participating in grid regulation. The indicator weight calculation module is used to obtain the weight of the indicator by applying fuzzy hierarchical analysis. The operation scenario identification module is used to cluster the operation data of the electric heating molten salt system after participating in grid regulation under multiple time periods, and obtain several cluster centers of the operation data of the electric heating molten salt system under each time period. Based on the values ​​of several cluster centers and combined with preset judgment criteria, the operation scenario of the electric heating molten salt system after participating in grid regulation under the corresponding time period is determined. The comprehensive scoring module is used to perform weighted summation of the index values ​​of the strategies for the electric heating molten salt system to participate in power grid regulation under each operating scenario, with their respective weights, to obtain a comprehensive score for the strategy.

[0030] Breaking away from the limitations of traditional assessments that focus solely on economic benefits, this approach comprehensively covers the core value of the system in terms of economic value, safety assurance, and low-carbon contribution through three-dimensional indicators: economic benefits, heating demand satisfaction, and carbon emission reduction, thus addressing the problem of a single assessment dimension. Employing fuzzy hierarchical analysis for weighting effectively addresses the subjectivity and uncertainty of expert judgment, making it more scientific than traditional subjective weighting and enhancing the credibility of weight allocation. Clustering algorithms automatically identify typical operating scenarios, avoiding the one-sidedness of manual scenario division and allowing the assessment to adapt to different power grids and heat load environments, overcoming the lack of dynamic assessment. A weighted summation-based comprehensive scoring method transforms multi-dimensional indicators into intuitive quantitative results, clearly revealing the advantages and disadvantages of different control strategies and providing direct evidence for decision-making.

[0031] In a preferred embodiment, the economic benefits, the heating demand satisfaction, and the carbon emission reduction in the indicator construction module are specifically as follows: The spot peak-valley electricity price revenue is obtained based on the difference in electricity consumption before and after the electric heating molten salt system participates in grid regulation. The ancillary service and demand response revenue is obtained based on the ancillary service capacity and demand response volume when the electric heating molten salt system participates in grid regulation. The energy efficiency optimization revenue is obtained based on the energy saved by the electric heating molten salt system replacing fossil fuel heating. The economic benefits are the sum of the spot peak-valley electricity price revenue, the ancillary service and demand response revenue, and the energy efficiency optimization revenue. The heating demand satisfaction is obtained by comparing the maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation with the rated heating demand. The emission reduction from clean energy consumption is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation when absorbing clean energy. The carbon emission reduction from grid regulation is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation. The carbon emission reduction is the sum of the emission reduction from clean energy consumption and the carbon emission reduction from grid regulation.

[0032] The economic benefits are broken down into spot peak-valley electricity price revenue, ancillary service and demand response revenue, and energy efficiency optimization revenue, covering the main economic sources of the system and solving the one-sidedness of traditional economic assessment that only considers a single revenue.

[0033] As a preferred specific implementation method, spot peak-valley electricity price revenue The calculation formula is as follows: In the formula, The spot electricity price for time period t. The electrical power consumed by the electrically heated molten salt system in time period t, which is supplied only according to the rated heating demand before the system participates in grid regulation; ΔT represents the actual power consumption of the electrically heated molten salt system during time period t after it participates in grid regulation, where ΔT is the duration of time period t and T is the total number of time periods. Low electricity price period ( > At this time, electricity prices are low, and the electrically heated molten salt system will consume more electricity (more than...). A portion of the electrical energy is directly converted into heat energy to meet current heating demand, while the excess electrical energy is converted into molten salt heat energy for storage. During this period, normal heating is provided, and the heat storage device simultaneously stores energy to prepare for periods of high electricity prices. (High electricity price periods...) < When electricity prices are high, the system reduces direct electric heating and instead draws on the molten salt heat energy stored during periods of low electricity prices to meet current heating needs. At this time, heating is still provided normally, and the heat source comes from the thermal storage device rather than real-time electricity consumption.

[0034] The formula for spot peak-valley electricity price revenue incorporates the difference in power consumption before and after regulation and the length of the time period, accurately reflecting the arbitrage logic of low storage and high utilization, and conforming to the thermal storage characteristics of electric heating molten salt systems.

[0035] Ancillary services and demand response revenue The calculation formula is as follows: In the formula, The capacity of auxiliary services for time period t; The clearing price for this service during time period t; The demand response amount for time period t; The demand response compensation price for time period t; Ancillary services are defined as various support services provided by the generation side, energy storage side, and user side, in addition to directly supplying electricity to users, to ensure the safe, stable, and high-quality operation of the power system. Examples of such services include peak shaving and frequency regulation. The ancillary service revenue formula distinguishes between ancillary service capacity and demand response, adapting to the diversified incentive mechanisms of the electricity market and ensuring that economic benefit assessment covers the full value of the system's participation in grid interaction.

[0036] Energy efficiency optimization benefits The calculation formula is as follows: In the formula, The price of fossil fuels in time period t; The amount of fossil fuel consumed for heating in time period t, provided only according to the rated heating demand through fossil fuel heating. The actual power consumption of an electrically heated molten salt system when it is used solely to replace fossil fuel heating, based on the rated heating demand during time period t; the energy efficiency optimization benefit formula focuses on fossil fuel substitution, quantifies the indirect economic value of the system replacing traditional heating methods, responds to the needs of energy structure transformation, and enhances the comprehensiveness of the assessment.

[0037] The economic benefits .

[0038] As a preferred embodiment, the heating demand satisfaction... The calculation formula is as follows: In the formula, The rated heating demand for time period t. The maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation during time period t.

[0039] As a preferred specific implementation method, clean energy consumption and emission reduction The calculation formula is as follows: In the formula, For the purpose of energy absorption index, when the electrically heated molten salt system absorbs clean energy... =1, otherwise =0, It is the average carbon emission factor of coal-fired power; the formula for emission reduction through clean energy consumption introduces a consumption index, which only counts the emission reduction during the period of consuming green electricity, avoiding misjudgment of emission reduction caused by conventional electricity consumption, and ensuring the accuracy of emission reduction calculation.

[0040] Grid regulation carbon emission reduction The calculation formula is as follows: In the formula, The electricity consumption during time period t for an electrically heated molten salt system that supplies heat only according to its rated heating demand before participating in grid regulation. For the electrically heated molten salt system to participate in grid regulation during time period t The power grid regulation and emission reduction formula focuses on the difference in electricity consumption before and after regulation, quantifies the indirect emission reduction brought about by electricity consumption optimization, covers the low-carbon value of the system on the power grid side, and solves the limitation of traditional emission reduction assessment that only focuses on direct substitution.

[0041] The carbon emission reduction The formula uniformly incorporates the average carbon emission factor from coal-fired power plants. This ensures that the assessment results are consistent with the industry's carbon emission reduction statistics, making them applicable to carbon trading and green policies and enhancing their practicality.

[0042] As a preferred embodiment, the specific method of using fuzzy hierarchical analysis to calculate the economic benefits, the heating demand satisfaction, and the carbon emission reduction in the indicator weight calculation module is as follows: To assess economic benefits, heating demand satisfaction, and carbon emission reduction, experts in the field were organized to conduct pairwise comparisons and construct a fuzzy judgment matrix, as shown below: In the formula, for OK Fuzzy judgment matrix of columns, As an indicator and indicators The triangular fuzzy number between them (Economic benefits, satisfaction of heating demand, and carbon emission reduction) ; The geometric mean method is used to calculate the fuzzy weights of economic benefits, heating demand satisfaction, and carbon emission reduction, as shown below: In the formula, As an indicator Fuzzy weights, Separate indicators The minimum, most likely, and maximum values ​​of the weights. Fuzzy multiplication of triangular fuzzy numbers; Comparing the magnitude relationships among the various fuzzy weights, the probability function for measuring the magnitude relationship between two triangular fuzzy numbers is expressed as follows: In the formula, for The probability, for Any possible value of , for Any possible value of , For the upper bound, Used to take all values ​​when y≥x The maximum value in, The membership function of the triangular fuzzy number. for for The degree of subordination for for The degree of membership; In the formula, As an indicator The initial unfuzzy weights; The initial unfuzzy weights are normalized as follows: In the formula, index The non-fuzzy weights.

[0043] In one preferred embodiment, the running scene recognition module includes: The first clustering unit is used to randomly select K data points from the operational dataset of the electrically heated molten salt system as initial cluster centers using the K-means clustering method. ; The data processing unit is used for processing each data point in the running dataset. Calculate data points With each initial cluster center The squared Euclidean distance of the data points. Assigned to the cluster containing the nearest cluster center In the middle, data points Belongs to cluster If and only if For all Established; The second clustering unit is used to recalculate the cluster center of each cluster. The new cluster center is the mean of all data points in that cluster. The iterative unit is used to repeatedly execute the actions of the data processing unit and the second clustering unit until the cluster centers no longer change or the amount of change is less than the threshold.

[0044] The actual operating data of electrically heated molten salt systems is massive, high-dimensional, and dynamically changing. In order to extract valuable information from the complex operating data and to objectively compare the system performance under different control strategies, this section uses clustering algorithms from unsupervised machine learning to analyze the long-term operating data of the system, automatically identifying and summarizing typical operating scenarios.

[0045] Data preprocessing and feature engineering: The effectiveness of cluster analysis is highly dependent on the quality of the input data. Therefore, rigorous preprocessing and feature engineering of the raw data are essential before applying clustering algorithms.

[0046] The required data includes: molten salt system charging power, system heating power, molten salt storage tank temperature, real-time grid electricity price, regional wind and solar power forecasts / actual output and curtailment, and industrial side planned / actual heat load time series data.

[0047] The preprocessing steps include: Data cleaning: Handling missing values ​​caused by sensor failure or communication interruption (e.g., filling with interpolation) and outliers (e.g., removing them using box plots or the 3σ principle).

[0048] Time alignment: Aligning data streams from different sources with potentially different sampling frequencies to a unified timestamp (such as 15 minutes or 1 hour).

[0049] Feature engineering: Extracting features from raw data that better reflect the operating mode, such as "average electricity price during charging periods", "heating power fluctuation rate", and "charging ratio during periods of high clean energy generation".

[0050] Data standardization: Due to the significant differences in the dimensions and numerical ranges of various features, to prevent certain features from dominating distance calculations, all features need to be Z-score standardized to ensure they follow a standard normal distribution with a mean of 0 and a variance of 1. The formula is as follows: Where x is the original data, μ is the mean of the feature, and σ is the standard deviation. The data is standardized using Z-score, which constitutes the running dataset.

[0051] After clustering, the centroid of each cluster represents a quantitative characteristic of a typical operating scenario. By analyzing these centroid vectors, abstract data clusters can be transformed into operating patterns with clear physical meaning.

[0052] Table 1 Based on the quantitative features in Table 1, a more in-depth interpretation of each scenario is provided: Scenario A: Grid Arbitrage Priority Mode: The key feature of this scenario is large-scale charging during periods of extremely low electricity prices, while electricity consumption is strictly limited during peak periods. Its operational logic is highly driven by electricity price signals. The high volatility of heating power indicates that its heating behavior serves arbitrage objectives and has a relatively low coupling with production demand. This mode aims to maximize the system's direct economic benefits.

[0053] Scenario B: Green Energy Consumption Driven Mode: In this scenario, charging behavior highly overlaps with the peak generation periods of clean energy (such as wind and solar power) in the region, even if the electricity price is not at its lowest point of the day. This indicates that the system's regulation strategy prioritizes the consumption of potentially obsolete clean energy to maximize environmental benefits. This mode directly addresses the project's core objective of supporting clean energy consumption.

[0054] Scenario C: Basic Production Supply Mode: In this mode, the system's heat charging and discharging behavior closely matches the planned heat load curve of industrial production, with an extremely high correlation coefficient and minimal fluctuations in heating power. This indicates that the system's primary task is to ensure the stability and reliability of heat used in production, with low sensitivity to electricity price signals. This is the essential basic operating mode that the system must possess as a key link in industrial production.

[0055] As a preferred embodiment, the specific method for weighted summation of the index values ​​of the strategies for the electric heating molten salt system to participate in power grid regulation under each operating scenario in the comprehensive scoring module is as follows: The indicators of the strategies for the electric heating molten salt system to participate in power grid regulation under each operating scenario are normalized, and the normalized indicator values ​​are weighted and summed with their corresponding non-fuzzy weights.

[0056] Normalization is performed using the following formula: , This refers to the normalized metric value under a specific operating scenario. These are the metric values ​​for this operating scenario. This represents the minimum metric value across all operating scenarios. This represents the maximum metric value across all operating scenarios.

[0057] Table 2 As shown in Table 2, the performance trade-offs between different control strategies are clearly revealed: Scenario A (arbitrage-first) scored highest in economic benefits, but its performance in safety, stability, and low-carbon environmental protection was unsatisfactory, resulting in the lowest overall score. This indicates that a regulatory strategy that solely pursues economic interests cannot fully realize the comprehensive value of the system and may even affect production safety.

[0058] Scenario C (production and supply assurance) received near-perfect scores in terms of safety and stability, validating its effectiveness as a basic guarantee model. However, its economic and environmental benefits were relatively mediocre.

[0059] Scenario B (green electricity consumption) demonstrates outstanding performance in low-carbon and environmental protection, while also maintaining a high level of safety and stability. Although its economic benefits are not optimal, it achieves the highest overall score. This indicates that a control strategy that balances environmental protection and production safety best aligns with the overall value orientation of this project.

[0060] At a deeper level, this entire evaluation framework is not merely a tool for post-event analysis; it provides a solid foundation for designing advanced, multi-objective intelligent control systems. The evaluation model itself, especially the formula for calculating the overall comprehensive evaluation score, can be directly transformed into an objective function for an optimization control problem. The core task of future intelligent control systems will no longer be to execute fixed, rule-based logic, but rather to dynamically adjust operating strategies based on short-term forecasts of electricity prices, renewable energy output, and heat load to maximize the expected comprehensive evaluation value in real time. For example, the system can automatically switch to green electricity consumption mode when wind curtailment is predicted and production load is stable; and switch to "grid arbitrage" mode when extreme peak-valley price differences are predicted and production plans allow for adjustment. This shift from passive assessment to proactive optimization internalizes the evaluation system as part of the system's intelligence, a key path to achieving the ultimate goal of optimized project control.

[0061] Example 2 A method for evaluating the control effect of an electrically heated molten salt system, referenced Figure 2 ,include: The economic benefits, heating demand satisfaction, and carbon emission reduction of the electric heating molten salt system after participating in grid regulation will be used as indicators to evaluate the strategy of participating the electric heating molten salt system in grid regulation. The weights of the indicators are obtained by applying fuzzy hierarchical analysis. Clustering is performed on the operating data of the electrically heated molten salt system after participating in grid regulation under multiple time periods to obtain several cluster centers of the operating data of the electrically heated molten salt system under each time period. Based on the values ​​of several cluster centers and combined with preset judgment criteria, the operating scenario of the electrically heated molten salt system after participating in grid regulation under the corresponding time period is determined. The comprehensive score of the strategy for the electric heating molten salt system to participate in grid regulation under each operating scenario is obtained by weighting and summing the indicators of each strategy with their respective weights.

[0062] Example 3 A computer-readable medium storing a computer program / instruction, which, when executed, implements the method for evaluating the control effect of the electrically heated molten salt system of Embodiment 2.

[0063] The contents not described in detail in this specification are prior art known to those skilled in the art. 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. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] 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 will 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.

[0065] 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.

[0066] 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 1The steps of the function specified in one or more boxes.

[0067] 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A scoring system for the control effect of an electrically heated molten salt system, characterized in that, include: The indicator construction module is used to evaluate the economic benefits, heating demand satisfaction, and carbon emission reduction of the electric heating molten salt system after it participates in grid regulation as indicators for assessing the strategy of the electric heating molten salt system participating in grid regulation. The indicator weight calculation module is used to obtain the weight of the indicator by applying fuzzy hierarchical analysis. The operation scenario identification module is used to cluster the operation data of the electric heating molten salt system after participating in grid regulation under multiple time periods, and obtain several cluster centers of the operation data of the electric heating molten salt system under each time period. Based on the values ​​of the several cluster centers and combined with the preset judgment criteria, the operation scenario of the electric heating molten salt system after participating in grid regulation under the corresponding time period is determined. The comprehensive scoring module is used to perform weighted summation of the index values ​​of the strategies for the electric heating molten salt system to participate in power grid regulation under each operating scenario, with their respective weights, to obtain a comprehensive score for the strategy.

2. The scoring system for the control effect of the electrically heated molten salt system according to claim 1, characterized in that, In the indicator construction module, the economic benefits, the heating demand satisfaction, and the carbon emission reduction are specifically as follows: The spot peak-valley electricity price revenue is obtained based on the difference in electricity consumption before and after the electric heating molten salt system participates in grid regulation. The ancillary service and demand response revenue is obtained based on the ancillary service capacity and demand response volume when the electric heating molten salt system participates in grid regulation. The energy efficiency optimization revenue is obtained based on the energy saved by the electric heating molten salt system replacing fossil fuel heating. The economic benefits are the sum of the spot peak-valley electricity price revenue, the ancillary service and demand response revenue, and the energy efficiency optimization revenue. The heating demand satisfaction is obtained by comparing the maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation with the rated heating demand. The emission reduction from clean energy consumption is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation when absorbing clean energy. The carbon emission reduction from grid regulation is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation. The carbon emission reduction is the sum of the emission reduction from clean energy consumption and the carbon emission reduction from grid regulation.

3. The scoring system for the control effect of the electrically heated molten salt system according to claim 2, characterized in that: Spot peak-valley electricity price revenue The calculation formula is as follows: In the formula, The spot electricity price for time period t. The electrical power consumed by the electrically heated molten salt system in time period t, which is supplied only according to the rated heating demand before the system participates in grid regulation; ΔT represents the actual power consumption of the electrically heated molten salt system during time period t after it participates in grid regulation, where ΔT is the duration of time period t and T is the total number of time periods. Ancillary services and demand response revenue The calculation formula is as follows: In the formula, The capacity of auxiliary services for time period t; The clearing price for this service during time period t; The demand response amount for time period t; The demand response compensation price for time period t; Energy efficiency optimization benefits The calculation formula is as follows: In the formula, The price of fossil fuels in time period t; The amount of fossil fuel consumed for heating in time period t, provided only according to the rated heating demand through fossil fuel heating. The actual electrical power consumed by the electrically heated molten salt system during time period t to supply heat according to the rated heating demand when it is only used to replace fossil fuel heating. The economic benefits .

4. The scoring system for the control effect of the electrically heated molten salt system according to claim 2, characterized in that: The degree of satisfaction of heating demand The calculation formula is as follows: In the formula, The rated heating demand for time period t. The maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation during time period t.

5. The scoring system for the control effect of the electrically heated molten salt system according to claim 2, characterized in that: Clean energy consumption and emission reduction The calculation formula is as follows: In the formula, For the purpose of energy absorption index, when the electrically heated molten salt system absorbs clean energy... =1, otherwise =0, It is the average carbon emission factor of coal-fired power plants; Grid regulation carbon emission reduction The calculation formula is as follows: In the formula, The electricity consumption during time period t for an electrically heated molten salt system that supplies heat only according to its rated heating demand before participating in grid regulation. For the electrically heated molten salt system to participate in grid regulation during time period t ; The carbon emission reduction .

6. The scoring system for the control effect of the electrically heated molten salt system according to claim 1, characterized in that, In the indicator weight calculation module, the specific method of using fuzzy hierarchical analysis to calculate the economic benefits, the heating demand satisfaction, and the carbon emission reduction is as follows: A fuzzy judgment matrix is ​​constructed for economic benefits, heating demand satisfaction, and carbon emission reduction, as follows: In the formula, for OK Fuzzy judgment matrix of columns, As an indicator and indicators The triangular fuzzy number between them (Economic benefits, satisfaction of heating demand, and carbon emission reduction) ; The geometric mean method is used to calculate the fuzzy weights of economic benefits, heating demand satisfaction, and carbon emission reduction, as shown below: In the formula, As an indicator Fuzzy weights, Separate indicators The minimum, most likely, and maximum values ​​of the weights. Fuzzy multiplication of triangular fuzzy numbers; Comparing the magnitude relationships among the various fuzzy weights, the probability function for measuring the magnitude relationship between two triangular fuzzy numbers is expressed as follows: In the formula, for The probability, for Any possible value of , for Any possible value of , For the upper bound, Used to take all values ​​when y≥x The maximum value in, The membership function of the triangular fuzzy number. for for The degree of subordination, for for The degree of membership; In the formula, As an indicator The initial unfuzzy weights; The initial unfuzzy weights are normalized as follows: In the formula, index The non-fuzzy weights.

7. The scoring system for the control effect of the electrically heated molten salt system according to claim 1, characterized in that, The operation scene recognition module includes: The first clustering unit is used to randomly select K data points from the operational dataset of the electrically heated molten salt system as initial cluster centers using the K-means clustering method. ; The data processing unit is used for processing each data point in the running dataset. Calculate data points With each initial cluster center The squared Euclidean distance of the data points. Assigned to the cluster containing the nearest cluster center In the middle, data points Belongs to cluster If and only if For all Established; The second clustering unit is used to recalculate the cluster center of each cluster. The new cluster center is the mean of all data points in that cluster. The iterative unit is used to repeatedly execute the actions of the data processing unit and the second clustering unit until the cluster centers no longer change or the amount of change is less than the threshold.

8. A method for evaluating the control effect of an electrically heated molten salt system, characterized in that, include: The economic benefits, heating demand satisfaction, and carbon emission reduction of the electric heating molten salt system after participating in grid regulation will be used as indicators to evaluate the strategy of participating the electric heating molten salt system in grid regulation. The weights of the indicators are obtained by applying fuzzy hierarchical analysis. Clustering is performed on the operating data of the electrically heated molten salt system after participating in grid regulation under multiple time periods to obtain several cluster centers of the operating data of the electrically heated molten salt system under each time period. Based on the values ​​of the several cluster centers and combined with the preset judgment criteria, the operating scenario of the electrically heated molten salt system after participating in grid regulation under the corresponding time period is determined. The comprehensive score of the strategy for the electric heating molten salt system to participate in grid regulation under each operating scenario is obtained by weighting and summing the indicators of each strategy with their respective weights.

9. The method for evaluating the control effect of the electrically heated molten salt system according to claim 8, characterized in that, The specific economic benefits, the degree of heating demand satisfaction, and the carbon emission reduction are as follows: The spot peak-valley electricity price revenue is obtained based on the difference in electricity consumption before and after the electric heating molten salt system participates in grid regulation. The ancillary service and demand response revenue is obtained based on the ancillary service capacity and demand response volume when the electric heating molten salt system participates in grid regulation. The energy efficiency optimization revenue is obtained based on the energy saved by the electric heating molten salt system replacing fossil fuel heating. The economic benefits are the sum of the spot peak-valley electricity price revenue, the ancillary service and demand response revenue, and the energy efficiency optimization revenue. The heating demand satisfaction is obtained by comparing the maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation with the rated heating demand. The emission reduction from clean energy consumption is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation when absorbing clean energy. The carbon emission reduction from grid regulation is obtained by comparing the difference in power consumption before and after the electric heating molten salt system participates in grid regulation. The carbon emission reduction is the sum of the emission reduction from clean energy consumption and the carbon emission reduction from grid regulation.

10. The method for evaluating the control effect of an electrically heated molten salt system according to claim 9, characterized in that: Spot peak-valley electricity price revenue The calculation formula is as follows: In the formula, The spot electricity price for time period t. The electrical power consumed by the electrically heated molten salt system in time period t, which is supplied only according to the rated heating demand before the system participates in grid regulation; ΔT represents the actual power consumption of the electrically heated molten salt system during time period t after it participates in grid regulation, where ΔT is the duration of time period t and T is the total number of time periods. Ancillary services and demand response revenue The calculation formula is as follows: In the formula, The capacity of auxiliary services for time period t; The clearing price for this service during time period t; The demand response amount for time period t; The demand response compensation price for time period t; Energy efficiency optimization benefits The calculation formula is as follows: In the formula, The price of fossil fuels in time period t; The amount of fossil fuel consumed for heating in time period t, provided only according to the rated heating demand through fossil fuel heating. The actual electrical power consumed by the electrically heated molten salt system during time period t to supply heat according to the rated heating demand when it is only used to replace fossil fuel heating. The economic benefits .

11. The method for evaluating the control effect of an electrically heated molten salt system according to claim 9, characterized in that: The degree of satisfaction of heating demand The calculation formula is as follows: In the formula, The rated heating demand for time period t. The maximum heat supply that the electric heating molten salt system can actually provide after participating in grid regulation during time period t.

12. The method for evaluating the control effect of an electrically heated molten salt system according to claim 9, characterized in that: Clean energy consumption and emission reduction The calculation formula is as follows: In the formula, For the purpose of energy absorption index, when the electrically heated molten salt system absorbs clean energy... =1, otherwise =0, It is the average carbon emission factor of coal-fired power plants; Grid regulation carbon emission reduction The calculation formula is as follows: In the formula, The electricity consumption during time period t for an electrically heated molten salt system that supplies heat only according to its rated heating demand before participating in grid regulation. For the electrically heated molten salt system to participate in grid regulation during time period t ; The carbon emission reduction .

13. The method for evaluating the control effect of the electrically heated molten salt system according to claim 8, characterized in that, The specific method of using fuzzy hierarchical analysis to evaluate the economic benefits, the satisfaction of heating demand, and the carbon emission reduction is as follows: A fuzzy judgment matrix is ​​constructed for economic benefits, heating demand satisfaction, and carbon emission reduction, as follows: In the formula, for OK Fuzzy judgment matrix of columns, As an indicator and indicators The triangular fuzzy number between them (Economic benefits, satisfaction of heating demand, and carbon emission reduction) ; The geometric mean method is used to calculate the fuzzy weights of economic benefits, heating demand satisfaction, and carbon emission reduction, as shown below: In the formula, As an indicator Fuzzy weights, Separate indicators The minimum, most likely, and maximum values ​​of the weights. Fuzzy multiplication of triangular fuzzy numbers; Comparing the magnitude relationships among the various fuzzy weights, the probability function for measuring the magnitude relationship between two triangular fuzzy numbers is expressed as follows: In the formula, for The probability, for Any possible value of , for Any possible value of , For the upper bound, Used to take all values ​​when y≥x The maximum value in, The membership function of the triangular fuzzy number. for for The degree of subordination, for for The degree of membership; In the formula, As an indicator The initial unfuzzy weights; The initial unfuzzy weights are normalized as follows: In the formula, index The non-fuzzy weights.

14. The method for evaluating the control effect of the electrically heated molten salt system according to claim 8, characterized in that, The specific method for clustering the operating data of the electrically heated molten salt system after participating in power grid regulation under multiple time periods is as follows: Step 1: Using K-means clustering, randomly select K data points from the operational dataset of the electrically heated molten salt system as initial cluster centers. ; Step 2, for each data point in the running dataset Calculate data points With each initial cluster center The squared Euclidean distance of the data points. Assigned to the cluster containing the nearest cluster center In the middle, data points Belongs to cluster If and only if For all Established; Step 3: Recalculate the cluster center for each cluster. The new cluster center is the mean of all data points in that cluster. Step 4: Repeat steps 3 and 4 iteratively until the cluster centers no longer change or the change is less than the threshold.

15. A computer-readable medium storing a computer program / instructions, characterized in that, The computer program / instruction implements the method for evaluating the control effect of the electrically heated molten salt system as described in claims 8-14 during runtime.

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