High-energy-load industrial load adjustable potential assessment method, device and equipment

Abstract

The invention provides a high-energy-load industrial load adjustable potential assessment method, device and equipment, and relates to the technical field of power system operation. The method comprises the following steps: acquiring production data of a high-energy-load industrial user; the production data comprises load types of the users; for a user, based on production data, solving by combining an evaluation model to obtain theoretical adjustment potential and comprehensive power utilization cost before and after adjustment; the evaluation model comprises constraint conditions corresponding to a target function and a load type; based on the theoretical adjustment potential, combining the power utilization cost and a preset participation adjustment threshold value, and combining income analysis, screening to obtain effective users with participation willingness; for the effective user, correcting the target function based on the perception cost and the subjective probability to obtain a corrected evaluation model; and based on the production data, combining with the correction evaluation model for solving to obtain the actual adjustment potential of the user. According to the invention, the precision of adjustable potential evaluation can be improved.

Description

Technical Field

[0001] This invention relates to the field of power system operation technology, and in particular to a method, apparatus and equipment for assessing the adjustability potential of high energy-consuming industrial loads. Background Technology

[0002] With the continuous expansion of installed capacity of new energy sources (wind power, photovoltaics, etc.), the demand for load-side flexibility adjustment in the power system is becoming increasingly urgent. As the core means of load-side management, demand response guides users to actively adjust their electricity consumption behavior through price signals or incentive mechanisms. Its potential quantification results can provide core basis for power grid planning, dispatch optimization, and market mechanism design, and have become a research hotspot in the energy field.

[0003] High-energy-consuming industrial users (such as electrolytic aluminum and cement manufacturing) are the core entities of electricity consumption. They have a large scale of electricity consumption and relatively stable load characteristics, and have huge potential for demand response. The accurate assessment and exploration of their adjustable potential is a key support for promoting the progress of load regulation technology, promoting the consumption of new energy sources, and maintaining the real-time balance between power supply and demand.

[0004] Because high-energy-consuming industrial users have diverse production processes and complex, flexible electricity consumption behaviors, traditional methods for assessing the load adjustability potential of these users typically employ generic models that neglect the inherent differences in process characteristics among different load types. This leads to a disconnect between model constraints and actual production needs, resulting in potential assessment results that are out of touch with reality. Furthermore, the subjective willingness of users to participate in demand response is difficult to quantify effectively, failing to accurately reflect the actual decision-making logic regarding user willingness and degree of participation, further increasing the difficulty of potential assessment. Ultimately, this results in insufficient accuracy in assessing the load adjustability potential of high-energy-consuming industrial users. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for assessing the adjustable potential of high-energy-consuming industrial loads, thereby addressing the problem of insufficient accuracy in assessing the adjustable potential of high-energy-consuming industrial loads using existing methods.

[0006] In a first aspect, embodiments of the present invention provide a method for assessing the adjustable potential of high-energy-consuming industrial loads, comprising: acquiring production data of high-energy-consuming industrial users; the production data including the user's load type; for each user, based on the production data and combined with an assessment model, obtaining the theoretical adjustment potential and the comprehensive electricity cost before and after adjustment; the assessment model including an objective function and constraints corresponding to the load type; based on the theoretical adjustment potential, comprehensive electricity cost, and a preset participation threshold for adjustment, combined with revenue analysis, selecting effective users with the willingness to participate; for effective users, based on the perceived cost representing the user's uncertainty regarding the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, modifying the objective function to obtain a modified assessment model; and based on the production data and combined with the modified assessment model, obtaining the user's actual adjustment potential.

[0007] In one possible implementation, for users, based on production data and combined with an evaluation model, the theoretical regulation potential and the comprehensive electricity cost before and after regulation are obtained. This includes: determining the constraints of the evaluation model based on the load type in the production data; the constraints include general output constraints and heterogeneous constraints matching the load type; the objective function is to minimize the comprehensive electricity cost, which includes the change in electricity purchase cost caused by participation in regulation, the weighting coefficient of the change in electricity purchase cost, the change in regulation cost, the change in labor cost, and the compensation benefit obtained from participation in regulation; based on the production data and the corresponding electricity price and cost coefficient, the optimization solution is performed through the objective function and constraints to obtain the original electricity load and the corresponding cost of the original electricity load when the user does not participate in regulation, and the optimal electricity load and the corresponding cost of the optimal electricity load after participation in regulation; based on the original electricity load and the optimal electricity load after participation in regulation, the theoretical regulation potential is calculated; the cost corresponding to the original electricity load is used as the comprehensive electricity cost before regulation, and the cost corresponding to the optimal electricity load is used as the comprehensive electricity cost after regulation.

[0008] In one possible implementation, constraints on the evaluation model are determined based on the load type in the production data, including: if the load type is continuously adjustable, the constraints include temperature constraints, voltage ramp-up constraints, and general output constraints; if the load type is discretely adjustable, the constraints include material balance constraints, material storage constraints, rotary kiln continuous operation constraints, load power balance constraints, and general output constraints.

[0009] In one possible implementation, for effective users, the objective function is modified based on the perceived cost representing the uncertainty of the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, resulting in a modified evaluation model. This model includes: statistically obtaining the probability distribution of the incentive coefficient and the corresponding objective probability based on historical data of the incentive coefficient, where the incentive coefficient is the clearing price in the electricity trading market during user participation in regulation; calculating the perceived cost representing the user's perception of the uncertainty of the incentive coefficient based on a value function of prospect theory, combined with the probability distribution of the incentive coefficient and the actual benefits of effective users under different incentive coefficients; calculating the subjective probability representing the user's preference for the probability distribution of the incentive coefficient based on a weight function of prospect theory, combined with the objective probability corresponding to the incentive coefficient, where the weight function represents the user's fascination with low-probability, high-incentive events; and modifying the objective function based on the perceived cost and subjective probability to obtain the modified evaluation model.

[0010] In one possible implementation, the objective function is modified based on perceived cost and subjective probability to obtain a modified evaluation model, including: calculating the expected compensation benefit based on the compensation benefit corresponding to the incentive coefficient and the subjective probability; modifying the objective function based on perceived cost and expected compensation benefit to obtain the modified evaluation model, wherein the constraints of the modified evaluation model are consistent with those of the evaluation model.

[0011] In one possible implementation, the objective function is modified based on perceived cost and expected compensation benefit to obtain a modified evaluation model, including: determining the modified objective function based on perceived cost, stability weight factor and expected compensation benefit to obtain the modified evaluation model; the stability weight factor is positively correlated with the weight coefficient of the change in electricity purchase cost.

[0012] In one possible implementation, based on theoretical adjustment potential, comprehensive electricity cost, and preset participation adjustment threshold, combined with benefit analysis, effective users willing to participate are screened, including: for each user, if the theoretical adjustment potential is greater than the preset adjustment potential value, the difference between the user's comprehensive electricity cost before adjustment and the comprehensive electricity cost after adjustment is calculated, and the difference result is obtained; when the difference result is greater than the preset participation adjustment threshold, the user is determined as an effective user willing to participate.

[0013] In one possible implementation, the load type includes continuously adjustable loads and discretely adjustable loads; for each user, if the theoretical adjustment potential is greater than the preset adjustment potential value, the difference between the user's comprehensive electricity cost before adjustment and the comprehensive electricity cost after adjustment is calculated. Before obtaining the difference result, the following steps are also taken: determining the preset adjustment potential value based on the rated adjustment capacity of the user's production equipment and the grid dispatching demand; wherein, the preset adjustment potential value of continuously adjustable loads is lower than the preset adjustment potential value of discretely adjustable loads.

[0014] Secondly, embodiments of the present invention provide a device for assessing the adjustable potential of high-energy-consuming industrial loads, comprising: a communication module for acquiring production data of high-energy-consuming industrial users; the production data including the user's load type; a processing module for, for each user, based on the production data and combined with an assessment model, solving to obtain the theoretical adjustment potential and the comprehensive electricity cost before and after adjustment; the assessment model including an objective function and constraints corresponding to the load type; based on the theoretical adjustment potential, comprehensive electricity cost, and a preset participation threshold for adjustment, combined with benefit analysis, selecting effective users with the willingness to participate; for effective users, based on the perceived cost representing the user's uncertainty of the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, modifying the objective function to obtain a modified assessment model; and based on the production data and combined with the modified assessment model, solving to obtain the user's actual adjustment potential.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0016] This invention first adapts the constraints in the evaluation model to the production process requirements of different load types. Combined with the objective function, it solves for the user's theoretical adjustment potential, ensuring the solution process matches the user's production process characteristics and avoiding evaluation bias caused by generic data. Then, by combining theoretical adjustment potential, comprehensive electricity costs, and preset participation thresholds with benefit analysis, this invention screens out effective users with actual willingness to participate, avoiding ineffective model corrections and calculations and improving evaluation efficiency. Simultaneously, for effective users, this invention quantifies the user's subjective perception of the uncertainty of the incentive coefficient through perceived cost, and characterizes the user's preference for the probability distribution of the incentive coefficient through subjective probability, correcting the objective function to obtain a corrected evaluation model. This compensates for the shortcomings of traditional models that ignore user subjective willingness and the randomness of market incentives, making the model more closely reflect the user's actual decision-making logic. Finally, based on the corrected evaluation model, this invention obtains the user's actual adjustment potential, improving the accuracy of the evaluation results. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the method for assessing the adjustable load potential of high-energy-consuming industries provided in this embodiment of the invention. Figure 2 This is a schematic diagram illustrating the principle of judging the willingness of high-energy-consuming industrial users to participate in regulation, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of the high-energy-consuming industrial load adjustable potential assessment device provided in an embodiment of the present invention; Figure 4This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] See Figure 1 The document illustrates a flowchart of the implementation of the method for assessing the adjustable potential of high-energy-consuming industrial loads provided in an embodiment of the present invention, which is described in detail below: Step 101: Obtain production data from high-energy-consuming industrial users; the production data includes the user's load type.

[0020] In some embodiments, high-energy-consuming industrial users refer to enterprises or production units in industrial sectors with high energy consumption intensity and high energy consumption per unit of output value. Typical industries include metallurgy (electrolytic aluminum, steel), chemicals (calcium carbide, caustic soda), and building materials (cement, glass). High-energy-consuming industrial users are the assessment objects of this evaluation method. These users have large electricity loads and significantly different regulation characteristics, and are the core resources for grid demand-side response and load regulation.

[0021] In some embodiments, production data refers to a collection of various data generated by high-energy-consuming industrial users during the production process that are directly related to electricity load and production technology. Production data is the core input data of the entire assessment method, providing the basis for calculations in the assessment model and determining the accuracy of the theoretical and actual regulation potential calculations. Sources of production data include information collected from the user side, information collected from the grid side, and user-reported information.

[0022] For example, user-side information collected includes production equipment operation logs, process parameters recorded by the DCS system (distributed control system), and load data collected by electricity metering devices.

[0023] For example, the information collected on the power grid side includes time-of-use electricity data collected by smart meters.

[0024] For example, user-submitted information includes management data such as production plans, equipment rated parameters, and capacity targets.

[0025] In some embodiments, load types are categories classified according to the regulation characteristics of the electrical loads of high-energy-consuming industrial users. For example, load types include continuously adjustable loads and discretely adjustable loads. Load types serve as the basis for configuring differentiated constraints; different load types correspond to different production process constraints, directly determining the specific content of heterogeneous constraints in the evaluation model and avoiding evaluation biases caused by using a general model.

[0026] Step 102: For users, based on production data and combined with the evaluation model, the theoretical adjustment potential and the comprehensive electricity cost before and after adjustment are obtained; the evaluation model includes the objective function and the constraints corresponding to the load type.

[0027] In one possible implementation, for the user, based on production data and combined with an evaluation model, the theoretical regulation potential and the comprehensive electricity cost before and after regulation are obtained, including: Based on the load type in the production data, the constraints of the evaluation model are determined; the constraints include general output constraints and heterogeneous constraints that match the load type. The objective function is to minimize the overall electricity cost, which includes the change in electricity purchase cost caused by participation in regulation, the weighting coefficient of the change in electricity purchase cost, the change in regulation cost, the change in labor cost, and the compensation income obtained from participating in regulation. Based on production data and the corresponding electricity price and cost coefficient, the optimization solution is obtained through objective function and constraints to obtain the original electricity load and corresponding cost when the user does not participate in regulation, and the optimal electricity load and corresponding cost after the user participates in regulation. The theoretical regulation potential is calculated based on the original power load and the optimal power load after regulation. The cost corresponding to the original electricity load is used as the comprehensive electricity cost before adjustment, and the cost corresponding to the optimal electricity load is used as the comprehensive electricity cost after adjustment.

[0028] In some embodiments, the evaluation model is a quantitative analysis model based on mathematical optimization theory, used to calculate the load regulation potential and comprehensive electricity cost of high-energy-consuming industrial users. Its core components are the objective function and the constraints corresponding to the load type. The evaluation model is the core computational tool of this method. Using production data as input, it obtains the theoretical regulation potential and comprehensive electricity cost through optimization, serving as a key link between basic data and evaluation results. The evaluation model is artificially constructed based on the production process characteristics of high-energy-consuming industrial users, electricity market rules, and demand-side response principles, and is customized and developed in conjunction with the regulation patterns of continuous / discrete loads.

[0029] In some embodiments, the objective function is the optimization objective expression of the evaluation model. In this invention, it is explicitly defined as minimizing the overall electricity cost. Its core components include changes in electricity purchase cost, changes in regulation cost, changes in labor cost, and compensation revenue, with weighting coefficients configured to mitigate the impact of changes in electricity purchase cost. The objective function guides the optimization direction of the evaluation model. By minimizing the overall electricity cost, it solves for the optimal electricity load for users under constraints, providing a basis for calculating theoretical regulation potential. The objective function is designed based on cost-benefit analysis principles, combined with the production needs of high-energy-consuming industrial users for cost reduction and efficiency improvement, and the incentive mechanism of the electricity market.

[0030] In some embodiments, the constraints corresponding to the load type are limitations that ensure the evaluation results meet the actual production process requirements of the user. These constraints are divided into two categories: general output constraints (capacity requirements that all users must meet) and heterogeneous constraints (process limitations that match the load type). The load type is the feasibility boundary of the evaluation model, ensuring that the optimal power load obtained by solving will not exceed the production process bottom line (such as the temperature constraint of continuously adjustable loads and the material balance constraint of discrete adjustable loads), thus avoiding theoretical calculation results from deviating from reality.

[0031] In some embodiments, theoretical regulation potential is the maximum load regulation capacity that a user can achieve under the premise of meeting all production constraints and minimizing overall electricity costs. It is numerically equal to the difference between the initial electricity load and the optimal electricity load. Theoretical regulation potential is a fundamental indicator for evaluating a user's load regulation capacity, reflecting the user's regulation potential in both purely technical and objective cost dimensions, and providing a core basis for subsequent selection of effective users. Theoretical regulation potential is calculated by an evaluation model, with inputs including production data, electricity prices, and cost coefficients. The initial and optimal electricity loads are obtained through optimization of the objective function, and the difference is then calculated.

[0032] In some embodiments, the pre-regulation comprehensive electricity cost is the comprehensive electricity cost of maintaining the original production state when the user does not participate in load regulation. The post-regulation comprehensive electricity cost is the comprehensive electricity cost when the user participates in load regulation and operates at the optimal electricity load; both include core items such as electricity purchase cost, regulation cost, labor cost, and compensation revenue. The pre- and post-regulation comprehensive electricity costs are the core indicator for measuring the economic benefits of users participating in load regulation, and the difference is a key benefit basis for subsequent selection of effective users. The pre- and post-regulation comprehensive electricity costs are calculated by the evaluation model. The pre-regulation cost is calculated based on the original electricity load and conventional production parameters, while the post-regulation cost is calculated based on the optimal electricity load and regulation operating parameters.

[0033] In some embodiments, the general output constraint is a fundamental constraint applicable to all high-energy-consuming industrial users. It requires that after participating in load regulation, the user's production output must still meet the established capacity targets or order demands, and that load regulation does not cause a significant deviation from the planned output. The general output constraint ensures the production safety and feasibility of load regulation, avoids sacrificing production output to reduce electricity costs, and ensures that the assessment results align with the company's actual needs for maintaining production and adjusting load. It is determined based on the user's production plan, order contracts, and industry capacity standards, and the data can be obtained from the user's production management system or declared production ledgers.

[0034] In some embodiments, heterogeneous constraints are differentiated process constraints matched to load types, serving as the core constraints that distinguish the adjustment boundaries of different load types. Heterogeneous constraints set adjustment limits based on the production process characteristics of different loads, solving the problem that general constraints cannot adapt to differentiated loads and ensuring that the theoretical adjustment potential calculation does not exceed the safe operating boundaries of the equipment and process. These constraints are determined based on production process standards, equipment technical manuals, and historical operating data from different energy-intensive industries. For example, the voltage ramp-up parameters for electrolytic aluminum are derived from the rectifier equipment nameplate, and the continuous operation constraints for cement kilns are derived from the clinker calcination process specifications.

[0035] In some embodiments, the change in electricity purchase cost is the change in electricity purchase price caused by changes in electricity load (such as load reduction or increase) after a user participates in load regulation. Its calculation is directly related to time-of-use pricing and the magnitude of load regulation. The change in electricity purchase cost is a core component of the overall electricity cost, directly affecting the optimization direction of the objective function and reflecting the degree of impact of load regulation on users' electricity expenses.

[0036] In some embodiments, the weighting coefficient for changes in electricity purchase costs is a correction factor used to adjust the weight of changes in electricity purchase costs in the overall electricity cost. Its value is related to the degree to which the user's production process depends on the stability of electricity consumption. The weighting coefficient for changes in electricity purchase costs is used to mitigate the impact of these changes, preventing the evaluation model from excessively pursuing lower electricity prices, which could lead to excessive load adjustments and consequently cause fluctuations in production processes (such as electrode spacing fluctuations in electrolytic aluminum production or thermal imbalances in cement kilns).

[0037] In some embodiments, the change in regulation cost refers to the change in costs incurred by users when adjusting the operating status of production equipment (such as changing the rectifier output power, starting and stopping the mill) during load regulation, including additional equipment losses and maintenance expenses. This is an important supplement to the overall electricity cost, reflecting the implicit costs of load regulation and preventing the evaluation model from ignoring equipment losses and overestimating regulation benefits. Calculations are based on equipment operation and maintenance manuals, historical maintenance data, and industry statistical reports. For example, the additional loss cost corresponding to each 1% load adjustment can be derived by fitting long-term operating data.

[0038] In some embodiments, the change in labor costs refers to the change in additional labor hours incurred due to increased manual operations (such as adjusting process parameters or monitoring equipment status) when users participate in load regulation. The change in labor costs improves the composition of overall electricity costs, comprehensively measures the economic cost of load regulation, and ensures the accuracy of the assessment results. It is calculated based on the company's labor wage standards and job hour quotas, for example, the number of labor hours required for each load regulation multiplied by the unit hourly cost.

[0039] In some embodiments, compensation revenue is the incentive revenue that users receive from the grid company or the electricity market after participating in grid demand-side response or load regulation services. Its amount is related to the load regulation magnitude, regulation duration, and market incentive standards. Compensation revenue is the core revenue item for offsetting load regulation costs, directly affecting the calculation of comprehensive electricity costs, and is the core driving force for users to participate in load regulation.

[0040] In some embodiments, cost coefficients are key parameters for calculating control costs and labor costs, including equipment depreciation coefficients and labor hour coefficients. Cost coefficients are crucial input parameters for evaluating the optimization solution of the model, determining the accuracy of control and labor cost calculations.

[0041] In some embodiments, the original electrical load is the electrical load that maintains normal production status when the user does not participate in any load regulation.

[0042] In some embodiments, the optimal electricity load is the user's optimal electricity load calculated by the evaluation model under the condition of satisfying all constraints and achieving the goal of minimizing the overall electricity cost.

[0043] As one possible implementation, embodiments of the present invention can refine the constraints into general output constraints and heterogeneous constraints matched to load types. Simultaneously, the objective function's optimization goal is clearly defined as minimizing the overall electricity cost, and the complete composition of the overall electricity cost is defined (changes in electricity purchase cost, weighting coefficients, changes in regulation cost, changes in labor cost, and compensation revenue). A complete process is specified, from inputting production data, electricity price, and cost coefficients, to finding the original / optimal electricity load through optimization, and then to calculating the theoretical regulation potential and determining the overall electricity cost before and after regulation.

[0044] In one possible implementation, constraints on the evaluation model are determined based on the load type in the production data, including: If the load type is continuously adjustable, the constraints include temperature constraints, voltage ramp-up constraints, and general output constraints. If the load type is discrete and adjustable, the constraints include material balance constraints, material storage constraints, rotary kiln continuous operation constraints, load power balance constraints, and general output constraints.

[0045] In some embodiments, temperature constraints are boundary conditions that limit the temperature of core equipment or reaction systems during continuous adjustable load production, including upper and lower temperature limits and limits on the rate of temperature change. Temperature constraints ensure the stability of the production process and product quality, and prevent equipment failure (such as overheating of the electrolytic cell) or product scrap caused by excessive temperature due to load adjustment.

[0046] In some embodiments, voltage ramp constraints are constraints on the rate of voltage regulation and the magnitude of a single regulation for electrically driven continuous production equipment (such as electrolytic rectifiers). Voltage ramp constraints prevent sudden voltage rises and falls from impacting the equipment and avoid grid voltage fluctuations or equipment lifespan degradation caused by excessively rapid load regulation.

[0047] In some embodiments, material balance constraints are constraints that require the input, output, and loss of materials at each stage of the production process to maintain a dynamic balance, i.e., total material input = total material output + reasonable loss. Material balance constraints ensure smooth production processes, prevent material backlog or supply disruptions due to load adjustments, and prevent production interruptions.

[0048] In some embodiments, material storage constraints are restrictions on the storage capacity and duration of raw materials, semi-finished products, and finished products in a production system, including upper and lower storage thresholds. Material storage constraints provide a buffer for load adjustment, ensuring sufficient raw material supply or controllable finished product inventory during load adjustment, while preventing safety hazards caused by exceeding storage limits.

[0049] In some embodiments, the continuous operating state constraint of a rotary kiln is a constraint condition for the minimum continuous operating time and the shortest start-stop interval of rotary kilns, which are core equipment in industries such as cement and metallurgy. The continuous operating state constraint of a rotary kiln avoids frequent start-stop of the rotary kiln, prevents cracks in the kiln body due to thermal expansion and contraction, reduces equipment maintenance costs, and ensures production continuity.

[0050] In some embodiments, load power balance constraints are constraints that require the power consumption of each process equipment in a discrete production system to be balanced with the power supply capacity of the power grid and the load carrying capacity of the system. Load power balance constraints prevent system tripping caused by overload operation of single-process equipment and ensure the stability of the power supply of the entire production system during load regulation.

[0051] As one possible implementation, embodiments of the present invention can define corresponding heterogeneous constraint lists for the production process characteristics of continuously adjustable loads and discretely adjustable loads, respectively. Continuous loads are subject to temperature and voltage ramp-up constraints, while discrete loads are subject to material balance, material storage, continuous operation of the rotary kiln, and load power balance constraints. The defined constraints (such as the minimum continuous operating time of the rotary kiln and the upper limit of the electrolytic cell temperature) directly correspond to the production equipment and process baselines of high-energy-consuming industries. This avoids the evaluation model calculating theoretical adjustment potential that exceeds process limits and is impractical, ensuring the practicality of the evaluation results.

[0052] Step 103: Based on theoretical regulation potential, comprehensive electricity cost, and preset participation regulation threshold, combined with benefit analysis, select effective users who are willing to participate.

[0053] In one possible implementation, based on theoretical regulation potential, comprehensive electricity costs, and a preset participation threshold, combined with revenue analysis, effective users willing to participate are screened, including: For each user, if the theoretical adjustment potential is greater than the preset adjustment potential value, calculate the difference between the user's comprehensive electricity cost before adjustment and the comprehensive electricity cost after adjustment, and obtain the difference result; When the difference is greater than the preset participation threshold, the user is identified as a valid user with the intention to participate.

[0054] In some embodiments, the preset adjustment potential value is a pre-set minimum potential threshold for determining whether a user possesses the technical feasibility for load adjustment. Only when a user's theoretical adjustment potential exceeds this value will they proceed to the subsequent revenue analysis stage. The preset adjustment potential value serves as a technical feasibility screening threshold, filtering out users with excessively low theoretical adjustment potential who lack practical scheduling value, reducing unnecessary calculations in subsequent revenue analysis, and improving screening efficiency.

[0055] In some embodiments, the difference result is the difference between the comprehensive electricity cost before adjustment and the comprehensive electricity cost after adjustment, i.e., difference result = comprehensive electricity cost before adjustment - comprehensive electricity cost after adjustment. A positive value indicates that the user can reduce costs and gain benefits by participating in the adjustment. The difference result is the basis for judging economic benefits. Only when the difference result is positive and exceeds the preset participation threshold can it be determined that the user has the willingness to participate.

[0056] In some embodiments, the preset participation threshold is a pre-set minimum benefit threshold for determining whether a user has the economic incentive to participate in load regulation. Only when the difference exceeds this value will the user be considered a valid user. The preset participation threshold is an economic benefit screening threshold that filters out users who are theoretically feasible but whose benefits are too low to cover the implicit costs of regulation, ensuring that the selected valid users are both technically feasible and economically reasonable.

[0057] In some embodiments, a qualified user with the willingness to participate is a user who simultaneously meets both the conditions of theoretical regulation potential > preset regulation potential value and difference result > preset participation regulation threshold. Such a user is a target user possessing both technical regulation capability and economic motivation to participate. Qualified users with the willingness to participate are the core output of the entire evaluation method, providing the power grid dispatching department with a list of available load resources to support the formulation of demand-side response strategies.

[0058] As one possible implementation, embodiments of the present invention define two necessary conditions for valid users: first, a technical feasibility threshold (theoretical regulation potential > preset regulation potential value); and second, an economic rationality threshold (difference in comprehensive electricity costs before and after regulation > preset participation threshold for regulation). Both conditions are indispensable, ensuring that selected users possess both the technical capability for load regulation and the economic incentive to participate in regulation, thus avoiding invalid users who are technically feasible but have no benefit or whose benefits meet the standards but lack regulation capability. The screening order is specified: first verify technical feasibility, then calculate economic benefits. First, determine if the theoretical regulation potential meets the standard; if not, exclude users directly. If it does, calculate the cost difference to further screen users whose benefits meet the standards. This step-by-step screening logic reduces the amount of invalid benefit calculations and improves screening efficiency.

[0059] Step 104: For effective users, based on the perceived cost representing the user's uncertainty about the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, the objective function is modified to obtain the modified evaluation model.

[0060] In one possible implementation, for effective users, the objective function is modified based on the perceived cost representing the user's uncertainty regarding the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, resulting in a modified evaluation model, including: Based on historical data of the incentive coefficient, the probability distribution of the incentive coefficient and the objective probability corresponding to the incentive coefficient are statistically obtained. The incentive coefficient is the clearing price of the electricity trading market during the user participation in the regulation process. Based on the value function of prospect theory, combined with the probability distribution of incentive coefficients and the actual benefits of effective users under different incentive coefficients, the perceived cost characterizing the user's perception of the uncertainty of incentive coefficients is calculated. Based on prospect theory, the weight function, combined with the objective probability corresponding to the incentive coefficient, calculates the subjective probability that represents the user's preference for the probability distribution of the incentive coefficient. The weight function represents the user's fascination with low-probability, high-incentive events. Based on perceived cost and subjective probability, the objective function is modified to obtain a modified evaluation model.

[0061] In some embodiments, the incentive coefficient is the unit load regulation revenue standard given by the grid company or the electricity market when a user participates in grid load regulation or demand-side response services. In this invention, it is explicitly defined as the clearing price of the electricity trading market. The incentive coefficient is a core indicator for measuring the user's regulation revenue, and its uncertainty is a key factor affecting the user's willingness to participate, as well as the basis for subsequent calculations of perceived costs and subjective probabilities.

[0062] In some embodiments, historical data on incentive coefficients refers to historical transaction records of incentive coefficients in the electricity market within a certain period (e.g., 12-24 months), including incentive coefficient values ​​under different time periods and supply and demand scenarios. Historical data on incentive coefficients serves as a data source for statistically analyzing the probability distribution and objective probability of incentive coefficients, providing data support for quantifying the uncertainty of incentive coefficients.

[0063] In some embodiments, the probability distribution of the incentive coefficient describes the statistical distribution law (such as normal distribution or skewed distribution) of the probability of the incentive coefficient appearing in different numerical ranges, reflecting the fluctuation characteristics and uncertainty of the incentive coefficient. The probability distribution of the incentive coefficient is the core statistical basis for calculating perceived cost and subjective probability, quantifying the random variation characteristics of the incentive coefficient.

[0064] In some embodiments, the objective probability corresponding to the incentive coefficient is the actual probability of the incentive coefficient falling within a certain numerical range, derived from historical data statistics. It is an objective statistical result that does not consider user subjective preferences. The objective probability corresponding to the incentive coefficient is the benchmark value for calculating the subjective probability. Through the correction of the prospect theory weighting function, it is transformed into a subjective probability that reflects user preferences.

[0065] In some embodiments, prospect theory is a psychological theory describing decision-making behavior under uncertainty. Its core principle is that decision-makers make subjective value judgments about gains and losses based on reference points and exhibit a subjective weighting tendency towards probabilities. Prospect theory is the core theoretical basis for this invention in characterizing users' subjective intentions, achieving the transformation from objective data to subjective intentions through its value function and weighting function.

[0066] In some embodiments, the value function in prospect theory is a function used to quantify decision-makers' subjective perception of the value of gains / losses. It typically exhibits a concave function over a gain interval and a convex function over a loss interval, reflecting differences in users' sensitivities to gains and losses. The value function in prospect theory is a core tool for calculating perceived costs, transforming the objective fluctuations in gains caused by the uncertainty of incentive coefficients into changes in costs subjectively perceived by users.

[0067] In some embodiments, the actual benefit of an effective user under different incentive coefficients is a simulation of the net benefit (comprehensive electricity cost difference) that a specific effective user can obtain by participating in load regulation under different incentive coefficient values. The actual benefit of an effective user under different incentive coefficients is an input variable for calculating perceived cost, and combined with the value function, it can quantify the user's subjective perception of the uncertainty of the incentive coefficients.

[0068] In some embodiments, perceived cost is a subjective psychological cost to users due to the uncertainty of incentive coefficients in participating in load adjustment. It is a subjective quantification of the fluctuation of actual returns under different incentive coefficients based on the prospect theory value function. Perceived cost is one of the core parameters of the modified objective function, incorporating users' aversion to incentive uncertainty into the model, making the modified model more closely reflect users' actual decision-making logic.

[0069] In some embodiments, the weighting function of prospect theory is a function used in prospect theory to describe the decision-maker's subjective weighting of objective probabilities. Its core characteristic is overestimating low-probability events and underestimating high-probability events; in this invention, this is manifested as amplifying the weight of low-probability, high-incentive events. The weighting function of prospect theory is a core tool for calculating subjective probabilities, transforming the objective probability of the incentive coefficient into a probability reflecting the user's subjective preferences.

[0070] In some embodiments, subjective probability is the incentive coefficient probability that reflects the user's subjective preference after being modified based on the prospect theory weighting function. Its core feature is the amplification of the probability weight of low-probability, high-incentive events. Subjective probability is one of the core parameters of the modified objective function, reflecting the user's subjective emphasis on high-incentive rewards, making the modified model more in line with the user's decision-making preferences.

[0071] In some embodiments, the modified evaluation model incorporates perceived costs and subjective probabilities into the original evaluation model. It is a novel evaluation model obtained by modifying the objective function, taking into account user subjective intentions and incentive uncertainties, while maintaining the same constraints as the original model. The modified evaluation model is a core tool for calculating users' actual adjustment potential, overcoming the shortcomings of the original model which only considers objective technology and costs while ignoring user subjective intentions.

[0072] As one possible implementation, this invention clearly defines the complete technical path of historical data statistics on incentive coefficients, calculation of perceived costs, derivation of subjective probabilities, and correction of the objective function. By statistically analyzing the probability distribution and objective probabilities of incentive coefficients, the quantification problem of the uncertainty of incentive coefficients is solved. Furthermore, relying on the value function and weight function of prospect theory, the fluctuation of incentive returns is transformed into the user's subjective perceived costs, and the objective probabilities are corrected into subjective probabilities that reflect user preferences, thus filling the gap in the original evaluation model that only considers objective technology and costs while ignoring the influence of user subjective decisions.

[0073] In one possible implementation, the objective function is modified based on perceived cost and subjective probability to obtain a modified evaluation model, including: Based on the compensation benefits corresponding to the incentive coefficient and the subjective probability, the expected compensation benefits are calculated. Based on perceived costs and expected compensation benefits, the objective function is modified to obtain a modified evaluation model. The constraints of the modified evaluation model are consistent with those of the original evaluation model.

[0074] In some embodiments, the compensation benefit corresponding to the incentive coefficient is the grid / market incentive benefit calculated based on the adjustment range of an effective user and the incentive coefficient after the user participates in load regulation, for a specific incentive coefficient value. The compensation benefit corresponding to the incentive coefficient is the core input for calculating the expected compensation benefit, reflecting the direct benefit that the user can obtain under different incentive coefficient levels, and is a key parameter connecting the incentive coefficient and user benefits.

[0075] In some embodiments, the expected compensation benefit is the expected benefit value obtained by weighting the compensation benefits corresponding to different incentive coefficients with subjective probabilities as weights. It reflects the user's expected level of future compensation benefits under subjective preferences. The expected compensation benefit is the core benefit parameter of the modified objective function, replacing the fixed compensation benefit term in the original objective function. This allows the modified model to reflect the user's subjective expectation of incentive benefits, rather than a static calculation based on a single fixed incentive coefficient.

[0076] As one possible implementation, this embodiment of the invention first calculates the expected compensation benefit through subjective probability weighting, and then uses the perceived cost and expected compensation benefit as core parameters to modify the original objective function. This step transforms the abstract modification into a two-step, actionable process, turning the modification logic of the objective function from a framework into concrete actions. A key technical constraint is that the constraints of the modified evaluation model remain consistent with the original evaluation model. Its core purpose is to ensure that the model modification only targets the objective function (reflecting cost-benefit and user subjective intentions), while fully inheriting the constraints of the original model (general output constraints, heterogeneous process constraints). This constraint avoids disrupting the feasibility boundary of the production process due to model modification and ensures consistency between the modified model and the original model in terms of process compliance.

[0077] In one possible implementation, the objective function is modified based on perceived cost and expected compensation benefit to obtain a modified evaluation model, including: Based on perceived cost, stability weight factor, and expected compensation benefit, a modified objective function is determined to obtain a modified evaluation model; the stability weight factor is positively correlated with the weight coefficient of the change in electricity purchase cost.

[0078] In some embodiments, the stability weight factor is a dimensionless coefficient used to balance the influence of expected compensation benefits and perceived costs on the objective function. Its value range is typically [0,1], representing the model's preference for a trade-off between benefits and risks. The stability weight factor addresses the imbalance between benefit and cost weights and can be dynamically adjusted according to the risk preferences of the power grid / market: a weight approaching 1 emphasizes benefits, while a weight approaching 0 emphasizes risk aversion, allowing the model to adapt to decision preferences under different scenarios and improving the robustness and flexibility of the modified model.

[0079] In some embodiments, the modified objective function is the final objective function obtained by weighting the cost and benefit terms with a stability weight factor after adjusting for perceived costs and expected compensation benefits. The core is to achieve the dual constraints of subjective will and power supply stability through weighted correlation. The modified objective function is the core algorithm of the modified evaluation model, addressing the deficiency of the modified model in not considering the inheritance of power supply stability. This ensures that the final objective function reflects both user subjective decision-making preferences and meets the requirements of production processes for power supply stability.

[0080] As one possible implementation, this invention introduces a stability weighting factor and clarifies its positive correlation with the weighting coefficient of changes in electricity purchase costs. Essentially, this transfers the electricity stability constraints of the original model to the modified objective function, ensuring that model modification does not sacrifice production and electricity stability. By using the stability weighting factor to weight and adjust perceived costs and expected compensation benefits, the modified objective function simultaneously incorporates both user subjective desires and electricity stability constraints. This addresses the potential flaw in modified models that prioritize subjective benefits over objective stability, making the evaluation results more aligned with the actual needs of industrial production.

[0081] Step 105: Based on production data and combined with the modified evaluation model, the user's actual adjustment potential is obtained.

[0082] In some embodiments, actual regulation potential refers to the load regulation capacity that effective users truly possess the willingness and ability to participate in, under the premise of meeting production process constraints, taking into account their own subjective will and the uncertainty of incentive coefficients. This differs from theoretical regulation potential calculated solely based on objective conditions. It is the ultimate core output of the entire evaluation method, providing the power grid dispatching department with an accurate list of available load resources, directly supporting practical application scenarios such as demand-side response strategy formulation and power grid load regulation.

[0083] This invention first adapts the constraints in the evaluation model to the production process requirements of different load types. Combined with the objective function, it solves for the user's theoretical adjustment potential, ensuring the solution process matches the user's production process characteristics and avoiding evaluation bias caused by generic data. Then, by combining theoretical adjustment potential, comprehensive electricity costs, and preset participation thresholds with benefit analysis, this invention screens out effective users with actual willingness to participate, avoiding ineffective model corrections and calculations and improving evaluation efficiency. Simultaneously, for effective users, this invention quantifies the user's subjective perception of the uncertainty of the incentive coefficient through perceived cost, and characterizes the user's preference for the probability distribution of the incentive coefficient through subjective probability, correcting the objective function to obtain a corrected evaluation model. This compensates for the shortcomings of traditional models that ignore user subjective willingness and the randomness of market incentives, making the model more closely reflect the user's actual decision-making logic. Finally, based on the corrected evaluation model, this invention obtains the user's actual adjustment potential, improving the accuracy of the evaluation results.

[0084] In one possible implementation, the load type includes continuously adjustable loads and discretely adjustable loads.

[0085] In one possible implementation, before calculating the difference between the user's comprehensive electricity cost before and after adjustment when the theoretical adjustment potential is greater than the preset adjustment potential value for each user, and obtaining the difference result, the following steps are also included: Based on the rated regulation capacity of user production equipment and grid dispatching requirements, a preset regulation potential value is determined; among them, the preset regulation potential value of continuously adjustable loads is lower than that of discrete adjustable loads.

[0086] In some embodiments, rated adjustment capacity refers to the maximum allowable load adjustment range and rate (such as equipment rated power, maximum ramp rate, minimum operating power, etc.) within the technological limits of safe and stable operation of the user's production equipment. Rated adjustment capacity sets a technical upper limit for preset adjustment potential values ​​to avoid preset values ​​exceeding the physical and technological carrying capacity of the equipment, ensuring the technical rationality of the screening criteria.

[0087] In some embodiments, grid dispatch demand refers to the requirements of the grid side on user load regulation, including capacity, time period, and response speed, to ensure power supply security, absorb renewable energy, and peak shaving and valley filling (such as peak-valley load difference, renewable energy absorption gap, and reserve capacity requirements). Grid dispatch demand sets a lower limit for system demand based on preset regulation potential values, enabling the selected effective users to match the actual grid regulation needs, thus achieving a precise match between user regulation potential and grid demand.

[0088] As one possible implementation, this invention specifies that the preset adjustment potential value should be determined by combining the rated adjustment capacity of the user equipment and the grid dispatching requirements. The former defines the upper limit of the equipment's physical process, preventing the preset value from exceeding the actual adjustment capacity; the latter anchors the grid's control requirements, ensuring that the selected users can match the grid's dispatching targets, making the preset value both technically reasonable and system-practical. The preset adjustment potential value for continuously adjustable loads is specified to be lower than that for discretely adjustable loads. This differentiated setting aligns with the adjustment characteristics of the two types of loads: continuously adjustable loads (such as electrolytic aluminum) can have their power smoothly fine-tuned, and small adjustments are sufficient to participate in grid control; discretely adjustable loads (such as cement kilns) require start-up / shutdown or large-scale adjustments to produce an effect, thus requiring a higher preset threshold. This limitation solves the drawbacks of a one-size-fits-all screening standard and improves the accuracy of the screening results.

[0089] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0090] The above embodiments are in Figure 1 Based on the method shown, each step will be discussed in detail. To facilitate understanding of the complete execution process, the overall method flow will be discussed below with reference to an embodiment.

[0091] 1) Unified Model for Assessing the Demand Response Potential of High Energy-Consuming Industrial Users The demand response potential of high-energy-consuming industrial users is determined by the comprehensive electricity cost. That is, by adjusting the operating status of industrial production equipment to optimize the comprehensive electricity cost of the load after participating in demand response, the optimal demand response potential can be determined. This invention proposes a unified model for evaluating the demand response potential of high-energy-consuming industrial users. This model consists of three components: an objective function, general constraints, and heterogeneous constraints. The heterogeneous constraints consider the respective production processes and response requirements of different high-energy-consuming industrial users. The objective function is shown in equation (1): (1) In the formula, the subscript s represents a typical high-energy-consuming user. This invention includes continuously adjustable high-energy-consuming industrial loads, taking the aluminum smelting load (ASL) as an example, and discretely adjustable high-energy-consuming industrial loads, taking the cement manufacturing load (CML) as an example; C s It is the total electricity cost for user s, ΔCE s It is the change in electricity purchase costs resulting from participation in demand response, ΔCC s It is the change in regulation costs resulting from participation in demand response, ΔCS sIt is the change in labor costs resulting from participation in demand response, RE s This refers to the compensation gained from participating in demand response; the weighting coefficient λ s This approach mitigates the impact of changes in electricity purchase costs and prevents industrial users from consuming electricity during off-peak hours. Otherwise, the optimization result is only theoretically optimal and cannot meet the actual needs of industrial production.

[0092] The model input includes the electricity price, cost coefficient, incentive coefficient, historical data, etc., and the parameters in Equations (2) to (19) are output. The output is the user's lowest electricity cost and the corresponding electricity load. Essentially, it is an optimization process.

[0093] Heterogeneous constraints correspond to temperature constraints and voltage ramping constraints in the continuously adjustable high-energy-consuming industrial load response potential assessment model; and to material balance constraints, material storage constraints, rotary kiln continuous working state constraints, and load power balance constraints in the discrete adjustable high-energy-consuming industrial load response potential assessment model.

[0094] The general constraint is that all users must meet their respective production output constraints.

[0095] The objective function is the comprehensive electricity cost of the load after participating in regulation. This objective function has two uses: first, to screen industrial users who are willing to participate in demand response during the editing stage; second, to be modified during the evaluation stage, taking into account the randomness of the incentive coefficient based on equation (2), and using a weighting function to convert it into the user willingness modification equation (2).

[0096] a) Continuously Adjustable High-Energy-Consuming Industrial Load Response Potential Assessment Model (2) (3) (4) (5) (6) In the formula, T It is the scheduling cycle. P ASL,0 and P ASL,t These represent the power before and after the electrolytic aluminum load participates in demand response. EP t It is the electricity price at time t; K This refers to the number of converter transformers for electrolytic aluminum loads, C. ASL It is the converter transformer regulation cost coefficient, z t , k0 and z t, k These are the converter transformer tap positions before and after the electrolytic aluminum load participates in demand response; this invention only considers high-energy-consuming industrial loads in two time periods ( t 1 and t 2) Enjoy the incentive of demand response, where ω1 and ω2 are the incentive coefficients.

[0097] The production constraints of the electrolytic aluminum industry include: Temperature constraints: (7) In the formula, D ASL It is the electrolysis temperature, D ASL,min and D ASL,max These are the upper and lower boundaries of the electrolysis temperature. c and m These are the equivalent heat capacity and equivalent mass of electrolytic aluminum, respectively.

[0098] Voltage ramp-up constraints: (8) In the formula, V ASL It is the DC voltage on the secondary side of the converter transformer, V ASL,min and V ASL,max These are the upper and lower boundaries of the DC voltage. R V,down and R V,up These represent the up and down ramp rates of the DC voltage.

[0099] Production constraints: (9) In the formula, K ASL It is the electrolytic aluminum production coefficient. N cell It is the number of electrolytic cells. η t It's the electrolysis efficiency. I ASL,t It is the electrolysis current; M ASL,N This is the daily rated output. M ASL,t It is the rated output at time t.

[0100] b) Discrete Adjustable High-Energy-Consuming Industrial Load Response Potential Assessment Model (10) (11) (12) (13) (14) In the formula, P CML,0 and P CML,t These are the power levels before and after the cement manufacturing load participates in the demand response. I It refers to the cement manufacturing and production process, C CML It is the start-up and shutdown cost coefficient of electrical equipment used in various stages of cement manufacturing, s t,i and These are the equipment states before and after the cement manufacturing load participates in demand response; C SA It is the labor cost coefficient of a cement manufacturing plant.

[0101] The production constraints of the cement manufacturing industry include: Material balance constraints: (15) In the formula, the subscript m Indicates materials, M For material quality; n i For the production process i The total number of devices, μ i For the production process i Medium-sized production equipment t Rated power within the time period - material conversion factor P i For the production process i The rated power of a single production unit. Therefore μ i P i That is, the production process. i The mass of materials generated or consumed by a single production unit. It is in working status.

[0102] Material storage constraints: (16) In the formula, M m,min and M m,max Materials m The lower and upper limits of warehousing.

[0103] Constraints for continuous operation of rotary kiln: (17) In the formula, s CF This indicates the rotary kiln is in operation. n CF This refers to the number of rotary kiln platforms.

[0104] Load power balance constraints: (18) Production constraints: (19) In the formula, M CML,T and M CML,0 These represent the quality of the cement product at the beginning and end of the demand response period, respectively; M CML,N The daily production demand for cement is determined by the daily order volume.

[0105] 2) Modified model for assessing the demand response potential of high-energy-consuming industrial users This invention uses prospect theory to characterize the demand response intentions of high energy-consuming industrial users under stochastic incentive coefficients in the electricity market, and modifies the above-mentioned demand response potential assessment model for high energy-consuming industrial users.

[0106] a) Editing stage This phase uses a response threshold as a reference point to screen industrial users willing to participate in demand response. Unlike traditional residential and commercial loads, industrial users prioritize safe production and aim to complete production tasks with high quality and quantity. Therefore, high-energy-consuming industrial loads have extremely high demand response participation thresholds, such as... Figure 2 As shown.

[0107] Figure 2 C0 and C n These represent the combined electricity costs of high-energy-consuming industrial users before and after participating in demand response, respectively. C0 represents the electricity costs for users who do not participate in demand response, i.e., their usual electricity bills. n It is obtained through calculation using equation (1). Rays 1 and 2 divide the first quadrant into three parts: region A represents C0. <C n Users are unwilling to participate in demand response; region B indicates C0>C. n But C0-C n Less than the response threshold C TL Users are unwilling to participate in demand response; region C indicates that C0 > C0. n And C0-C n If the response threshold is exceeded, users are willing to participate in demand response. That is: (20) b) Evaluation Phase This stage considers the uncertainty of the incentive coefficient and the user's willingness to respond, and modifies the unified model for assessing the demand response potential of high-energy-consuming industrial users proposed in Section 1).

[0108] The perceived cost for high-energy-consuming industrial users is: (twenty one) In the formula, It is the perceived cost of high-energy-consuming industrial users, expressed as a value function. It is the probability of the incentive coefficient and , It is a complete set of incentive coefficients.

[0109] The incentive coefficient is the clearing price in the electricity trading market during the user participation in demand response. This price is uncertain, but its probability distribution can be known through historical data.

[0110] High-energy-consuming industrial users in terms of incentive coefficient The actual profit can be calculated using equation (22).

[0111] (twenty two) Using weight functions to Converted to the corresponding subjective probability (twenty three) (a) The weighting function is an inherent expression in prospect theory, reflecting the degree of decision-makers’ “fascination” with low-probability events (even though low-probability events rarely occur, many people are still keen to buy lottery tickets and insurance). In this invention, it represents the degree of industrial users’ “fascination” with incentive coefficients.

[0112] (b) Based on equation (1), the response cost is calculated after considering the probability distribution of the excitation coefficient. The load response potential can be obtained by comparing the industrial user electricity load obtained under the condition of minimum cost with the original load that does not participate in the response.

[0113] Based on equations (21) and (23), equation (1) is modified as follows: (twenty four) The revised demand response potential assessment model for high-energy-consuming industrial users is as follows: (25) Subjective will refers to an individual's intrinsic attitude or preference towards things, usually manifested as an active preference for specific behaviors, decisions, or outcomes. In this invention, it refers to the behavioral preference of high-energy-consuming industrial users to actively adjust their production processes and participate in the power system's demand response under the incentive of electricity prices. Demand response potential refers to the ability to guide users to actively adjust their electricity consumption behavior through price signals or incentive mechanisms, thereby optimizing grid load management. Its core lies in quantifying the amount of load that users can reduce or increase, providing a basis for grid planning, dispatching, and renewable energy consumption.

[0114] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0115] Figure 3 A schematic diagram of the structure of the adjustable potential assessment device for high-energy-consuming industrial loads provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 3 As shown, the high-energy-consuming industrial load adjustable potential assessment device 3 includes: Communication module 31 is used to acquire production data from high-energy-consuming industrial users; the production data includes the user's load type. Processing module 32 is used to, for users, based on production data and combined with an evaluation model, obtain the theoretical adjustment potential and the comprehensive electricity cost before and after adjustment; the evaluation model includes an objective function and constraints corresponding to the load type; based on the theoretical adjustment potential, comprehensive electricity cost, and preset participation threshold, combined with revenue analysis, select effective users willing to participate; for effective users, based on the perceived cost representing the user's uncertainty of the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, modify the objective function to obtain a modified evaluation model; based on production data and combined with the modified evaluation model, obtain the user's actual adjustment potential.

[0116] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 4 As shown, the electronic device 4 in this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, it implements the steps in the various method embodiments described above. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the various device embodiments described above.

[0117] For example, computer program 42 may be divided into one or more modules / units, which are stored in memory 41 and executed by processor 40 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 42 in electronic device 4.

[0118] Electronic device 4 may include, but is not limited to, processor 40 and memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 4 may also include input / output devices, network access devices, buses, etc.

[0119] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0120] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0121] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for assessing the adjustable load potential of high-energy-consuming industries, characterized in that, include: Acquire production data from high-energy-consuming industrial users; the production data includes the user's load type. For users, based on the production data and combined with the evaluation model, the theoretical adjustment potential and the comprehensive electricity cost before and after adjustment are obtained; the evaluation model includes the objective function and the constraints corresponding to the load type. Based on theoretical regulation potential, comprehensive electricity costs, and preset participation thresholds, combined with revenue analysis, effective users with the willingness to participate are selected. For effective users, the objective function is modified based on the perceived cost representing the uncertainty of the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, resulting in a modified evaluation model. Based on the production data, and by combining the modified evaluation model, the user's actual adjustment potential is obtained.

2. The method for assessing the adjustable load potential of high-energy-consuming industries according to claim 1, characterized in that, For users, based on the production data and combined with the evaluation model, the theoretical adjustment potential and the comprehensive electricity cost before and after adjustment are obtained, including: Based on the load type in the production data, the constraints of the evaluation model are determined; the constraints include general output constraints and heterogeneous constraints that match the load type. The objective function is to minimize the overall electricity cost, which includes the change in electricity purchase cost caused by participation in regulation, the weighting coefficient of the change in electricity purchase cost, the change in regulation cost, the change in labor cost, and the compensation income obtained from participating in regulation. Based on the production data and the corresponding electricity price and cost coefficient, the optimization solution is obtained through the objective function and constraints to obtain the original electricity load and corresponding cost when the user does not participate in the adjustment, and the optimal electricity load and corresponding cost after the user participates in the adjustment. Based on the original power load and the optimal power load after regulation, the theoretical regulation potential is calculated. The cost corresponding to the original electricity load is used as the comprehensive electricity cost before adjustment, and the cost corresponding to the optimal electricity load is used as the comprehensive electricity cost after adjustment.

3. The method for assessing the adjustable load potential of high-energy-consuming industries according to claim 2, characterized in that, The constraints for determining the evaluation model based on the load type in the production data include: If the load type is continuously adjustable, the constraints include temperature constraints, voltage ramp-up constraints, and general output constraints. If the load type is discrete and adjustable, the constraints include material balance constraints, material storage constraints, rotary kiln continuous operation constraints, load power balance constraints, and general output constraints.

4. The method for assessing the adjustable load potential of high-energy-consuming industries according to claim 1, characterized in that, For effective users, based on the perceived cost representing the user's uncertainty regarding the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, the objective function is modified to obtain a modified evaluation model, including: Based on historical data of the incentive coefficient, the probability distribution of the incentive coefficient and the objective probability corresponding to the incentive coefficient are statistically obtained. The incentive coefficient is the clearing price of the electricity trading market during the user participation in the regulation process. Based on the value function of prospect theory, combined with the probability distribution of the incentive coefficient and the actual benefits of effective users under different incentive coefficients, the perceived cost characterizing the user's perception of the uncertainty of the incentive coefficient is calculated. Based on the weighting function of prospect theory, combined with the objective probability corresponding to the incentive coefficient, the subjective probability representing the user's preference for the probability distribution of the incentive coefficient is calculated. The weighting function represents the user's fascination with low-probability, high-incentive events. Based on the perceived cost and the subjective probability, the objective function is modified to obtain a modified evaluation model.

5. The method for assessing the adjustable load potential of high-energy-consuming industries according to claim 4, characterized in that, The process of modifying the objective function based on the perceived cost and the subjective probability to obtain a modified evaluation model includes: Based on the compensation benefit corresponding to the incentive coefficient and the subjective probability, the expected compensation benefit is calculated. Based on the perceived cost and the expected compensation benefit, the objective function is modified to obtain a modified evaluation model, wherein the constraints of the modified evaluation model are consistent with those of the original evaluation model.

6. The method for assessing the adjustable load potential of high-energy-consuming industries according to claim 5, characterized in that, The objective function is modified based on the perceived cost and the expected compensation benefit to obtain a modified evaluation model, including: Based on the perceived cost, stability weight factor, and expected compensation benefit, a modified objective function is determined to obtain the modified evaluation model; the stability weight factor is positively correlated with the weight coefficient of the change in electricity purchase cost.

7. The method for assessing the adjustable load potential of high-energy-consuming industries according to claim 1, characterized in that, Based on theoretical adjustment potential, comprehensive electricity costs, and preset participation thresholds, combined with revenue analysis, the qualified users willing to participate are selected, including: For each user, if the theoretical adjustment potential is greater than the preset adjustment potential value, the difference between the user's comprehensive electricity cost before adjustment and the comprehensive electricity cost after adjustment is calculated to obtain the difference result; When the difference result is greater than the preset participation adjustment threshold, the user is determined as a valid user with the intention to participate.

8. The method for assessing the adjustable load potential of high-energy-consuming industries according to claim 7, characterized in that, The load types include continuously adjustable loads and discretely adjustable loads; Before calculating the difference between the user's comprehensive electricity cost before and after adjustment, and obtaining the difference result, if the theoretical adjustment potential is greater than the preset adjustment potential value for each user, the process also includes: The preset adjustment potential value is determined based on the rated adjustment capacity of the user's production equipment and the grid dispatching requirements; wherein, the preset adjustment potential value of continuously adjustable loads is lower than that of discrete adjustable loads.

9. A device for assessing the adjustable potential of high-energy-consuming industrial loads, characterized in that, include: The communication module is used to acquire production data from high-energy-consuming industrial users; the production data includes the user's load type. The processing module is used to obtain the theoretical adjustment potential and the comprehensive electricity cost before and after adjustment based on the production data and the evaluation model for users; the evaluation model includes the objective function and the constraints corresponding to the load type. Based on theoretical adjustment potential, comprehensive electricity cost, and preset participation adjustment threshold, combined with benefit analysis, effective users with the willingness to participate are screened out; for effective users, based on the perceived cost representing the user's uncertainty of the incentive coefficient and the subjective probability representing the user's preference for the probability distribution of the incentive coefficient, the objective function is modified to obtain the modified evaluation model. Based on the production data, and by combining the modified evaluation model, the user's actual adjustment potential is obtained.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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