Industrial user demand response strategy optimization method, system and equipment

Abstract

The invention relates to the technical field of power demand response, in particular to an industrial user demand response strategy optimization method, system and equipment. The method comprises the following steps of: acquiring daily reduction capacity, daily transferable capacity, reduction rate, minimum reduction time length, maximum reduction time length, minimum transfer time length and maximum transfer time length of each industrial user in a response day based on a daily load curve of each industrial user in an industrial park for multiple continuous days before the response day, wherein the daily reduction capacity, the daily transferable capacity, the reduction rate, the minimum reduction time length, the maximum reduction time length, the minimum transfer time length and the maximum transfer time length of each industrial user in the response day; and based on the predicted electricity price of each time period of the response day, obtaining the reduction capacity of each industrial user participating in a reduction project and the transferable capacity of each industrial user participating in a transfer project in each time period of the response day, substituting the obtained data into the demand response strategy optimization model, and solving to obtain a demand response strategy of the target industrial user in the response day. The feasibility and the implementation effect of the industrial user demand response strategy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand response, and particularly to a method, a system and a device for optimizing an industrial user demand response strategy. Background Art

[0002] With the increasing requirements of the power system for the balance between supply and demand, industrial users, as an important part of the power system, have an important impact on the stable operation of the power system. As an effective means, the demand response strategy can guide industrial users to reasonably adjust their electricity loads and improve the balance ability between supply and demand of the power system. Demand response (DR) can be divided into price-based DR and incentive-based DR according to different operation mechanisms and implementation methods. Price-based DR mainly guides industrial users to adjust their electricity demands by implementing diversified electricity price strategies to achieve goals such as peak shaving, valley filling, and suppressing load fluctuations, such as real-time electricity prices, peak electricity prices, etc.; while incentive-based DR directly adopts incentive policies and compensation methods to guide industrial users to participate in load reduction projects and load transfer projects required by the system. Compared with price-based DR, incentive-based DR has higher reliability and response speed because the power grid company formulates refined incentive policies according to the supply and demand situation of the power system and the agreements signed with industrial users or aggregators.

[0003] At present, the research on industrial users' participation in incentive-based DR mainly includes two parts: solving adjustable potential variables and demand response strategies. Adjustable potential variables refer to reduction variables and transfer variables. The reduction variable is a variable that can represent the reduction adjustment potential of industrial users during the implementation period of the reduction project as stipulated in the signed reduction project agreement, and the transfer variable is a variable that can represent the transfer adjustment potential of industrial users during the implementation period of the transfer project as stipulated in the signed transfer project agreement. Most of the research on demand response strategies takes the maximization of the aggregator's net income or the minimization of cost as the optimization goal and constructs a model with the actual electricity demand and electricity consumption habits of industrial users as constraints. Some research divides flexible loads into shiftable loads, transferable loads, and reducible loads, defines them as virtual energy storage, then considers the response characteristics of various types of virtual energy storage, and constructs constraint conditions from the scheduling time period and total electricity consumption of virtual energy storage. Another research takes the load aggregator as an entity participating in the day-ahead power market, takes the minimization of the aggregator's incentive cost as the optimization goal, establishes a differential incentive mechanism considering the differences in the response flexibility of industrial users, and solves the proposed model using the particle swarm optimization algorithm. There is also research aiming at the uncertainty of distributed energy generation, generating wind-solar power generation scenarios, comprehensively considering the transfer-in and transfer-out time periods and the upper and lower limits of the regulation capacity, constructing a demand response strategy model with the maximization of the virtual power plant's net income as the goal, and solving the proposed model using an improved multi-universe algorithm.

[0004] However, for existing demand response strategies, there are still many defects that need to be urgently solved. The existing demand response strategies directly use the contract-specified value as the parameter value of the demand response strategy optimization model, which is simply set based on the contract-specified value. Although the contract-specified value has a certain degree of certainty, factors such as the supply-demand relationship in the power system and the production and operation activities of industrial users are complex and variable. It is difficult for the contract-specified value to accurately match the dynamic electricity consumption situation, resulting in a disconnection from the actual demand, affecting the accuracy and effectiveness of the demand response strategy. Moreover, the existing demand response strategies ignore the response speed constraints of regulation resources. The response speed of regulation resources plays a crucial role in the demand response effect and the safe and stable operation of the power system. As an important part of power consumption, the load reduction situation of industrial users directly affects the supply-demand matching effect of the power system. When a large number of industrial users only reduce their loads briefly and in small amounts, the total load reduction contributed by the entire industrial field is far from meeting the demand of the power system, and the power gap originally expected to be filled by the load reduction of industrial users still exists, resulting in the power system being unable to quickly and effectively achieve the matching of load and power supply, thereby triggering fluctuations or even faults in the power system. In addition, when considering the maximum duration constraints of reduction and transfer, the existing demand response strategies often simply stipulate that within the maximum transfer duration agreed in the contract for each industrial user, the cumulative number of times of completing the load reduction or transfer project should be greater than or equal to the benchmark value agreed in the contract. However, in real life, the production processes, equipment characteristics, and electricity consumption patterns of different industrial users vary greatly. The unified contract agreement method of the existing strategy does not take these differences into account. For chemical enterprises, the cumulative number of times required by the contract may be difficult to achieve, and forced implementation may lead to serious consequences such as production stagnation and product scrapping; for electronic manufacturing enterprises, it may be too loose to fully tap their load regulation potential, resulting in waste of resources, making the solved demand response strategy not match the actual production mode of industrial users and unable to effectively guide industrial users to reasonably adjust their electricity loads, weakening the power system's ability to balance supply and demand. Summary of the Invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the defects that the upper and lower limits of the constraint function of the existing demand response strategy rely on the contract-specified value, ignore the response speed of regulation resources, do not consider the differences of different industrial users when setting the maximum duration constraints of reduction and transfer, resulting in the solved demand response strategy not matching the actual production mode of industrial users and the instability of the power system.

[0006] To solve the above technical problems, the present invention provides an optimization method for the demand response strategy of industrial users, including the following steps:

[0007] Based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day, obtain the daily reduction capacity, daily transferable capacity, reduction rate, transfer rate, minimum reduction duration, maximum reduction duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day;

[0008] Based on the predicted electricity price for each time period on the response day and the daily reduction capacity of each industrial user, obtain the reduction capacity of each industrial user participating in the reduction project in each time period on the response day; based on the predicted electricity price for each time period on the response day and the daily transferable capacity of each industrial user, obtain the transferable capacity of each industrial user participating in the transfer project in each time period on the response day;

[0009] Substitute the daily reduction capacity, daily transferable capacity, reduction rate, transfer rate, minimum reduction duration, maximum reduction duration, minimum transfer duration, maximum transfer duration, reduction capacity of each industrial user participating in the reduction project in each time period, and transferable capacity of each industrial user participating in the transfer project in each time period into the demand response strategy optimization model, solve the demand response strategy optimization model, and obtain the demand response strategy of the target industrial user on the response day.

[0010] Preferably, the constraint conditions of the demand response strategy optimization model include: reduction rate constraint, minimum reduction duration constraint, maximum reduction duration constraint, initial price constraint of the reduction project, start-up completion status and participation status constraint of the reduction project, start-stop status constraint of the reduction project, transfer rate constraint, minimum transfer duration constraint, maximum transfer duration constraint, initial price constraint of the transfer project, start-up completion status and participation status constraint of the transfer project, start-stop status constraint of the transfer project, and time period constraint for participating in the transfer project.

[0011] Preferably, the reduction rate constraint is:

[0012]

[0013] The transfer rate constraint is:

[0014]

[0015] Among them, is the minimum reduction duration for industrial user i to participate in the reduction project, t is the time period index, and i is the industrial user index. is whether industrial user i participates in the reduction project in time period t. If industrial user i participates in the reduction project in time period t, then If industrial user i does not participate in the reduction project in time period t, then is whether industrial user i starts the reduction project in time period t. If industrial user i starts the reduction project in time period t, then If industrial user i does not start the reduction project in time period t, then is the daily curtailment capacity of industrial user i, is the curtailment rate of industrial user i, is the minimum transfer duration for industrial user i to participate in the transfer project, indicates whether industrial user i participates in the transfer project at time t. If industrial user i participates in the transfer project at time t, then If industrial user i does not participate in the transfer project at time t, then indicates whether industrial user i starts the transfer project at time t. If industrial user i starts the transfer project at time t, then If industrial user i does not start the transfer project at time t, then is the transfer rate of industrial user i, is the total transferable capacity of industrial user i.

[0016] Preferably, the maximum curtailment duration constraint is:

[0017]

[0018] The maximum transfer duration constraint is:

[0019]

[0020] where, is the maximum curtailment duration for industrial user i to participate in the curtailment project, is the maximum transfer duration for industrial user i to participate in the transfer project, t is the time period index, and i is the industrial user index, is the daily curtailment capacity of industrial user i, is the daily transferable capacity of industrial user i, is the curtailment rate of industrial user i, is the transfer rate of industrial user i, indicates whether industrial user i starts the curtailment project at time t. If industrial user i starts the curtailment project at time t, then If industrial user i does not start the curtailment project at time t, then indicates whether industrial user i completes the curtailment project at time t. If industrial user i completes the curtailment project at time t, then If industrial user i does not complete the curtailment project at time t, then indicates whether industrial user i starts the transfer project at time t. If industrial user i starts the transfer project at time t, then If industrial user i does not start the transfer project at time t, then indicates whether industrial user i completes the transfer project at time t. If industrial user i completes the transfer project at time t, then If industrial user i does not complete the transfer project at time t, then

[0021] Preferably, the process of constructing the objective function of the demand response strategy optimization model is as follows:

[0022] Taking the maximum net income of the park aggregator as the goal, construct a net income objective function; taking the minimum carbon emissions of industrial users as the goal, construct an environmental income objective function; taking the minimum deviation between the income of industrial users participating in the demand response project and the initial price as the goal, construct an industrial user satisfaction objective function; taking the maximum grid stability as the goal, construct a grid stability objective function.

[0023] Based on the net income objective function, the environmental income objective function, the industrial user satisfaction objective function, and the grid stability objective function, construct the total objective function.

[0024] Preferably, based on the net income objective function, the environmental income objective function, the industrial user satisfaction objective function, and the grid stability objective function, the total objective function is constructed as follows:

[0025] Max Total Income=maxIncome+Carbon Income+Reward-Penalty

[0026]

[0027] Reward=μ·(ΔP peak-valley +Δσ load )

[0028] where Max Total Income is the total objective function, maxIncome is the net income objective function, CarbonIncome is the environmental income objective function, Penalty is the industrial user satisfaction objective function, and Reward is the grid stability objective function. T is the total number of time periods for implementing the demand response project, and I is the number of industrial users participating in the demand response project. is the predicted electricity price for the park aggregator to sell the regulation capacity in time period t. is the capacity of industrial user i participating in the curtailment project in time period t. The capacity of industrial user i participating in the transfer project in time period. is whether industrial user i participates in the curtailment project in time period t. If industrial user i participates in the curtailment project in time period t, then If industrial user i does not participate in the curtailment project in time period t, then is whether industrial user i participates in the transfer project in time period t. If industrial user i participates in the transfer project in time period t, then If industrial user i does not participate in the transfer project in time period t, then i is the industrial user index, and t is the time period index. $R_{i,t}$ is the revenue obtained by industrial user $i$ from the park aggregator when participating in the curtailment project during period $t$. $P_{i,t}^c$ is the compensation electricity price for industrial user $i$ participating in the curtailment project during period $t$. $P_{i,t}^0$ is the actual initial price that industrial user $i$ can obtain from the park aggregator when participating in the curtailment project during period $t$. $R_{i,t}^t$ is the revenue obtained by industrial user $i$ from the park aggregator when participating in the transfer project during period $t$. $P_{i,t}^t$ is the compensation electricity price for industrial user $i$ participating in the transfer project during period $t$. $P_{i,t}^{t0}$ is the actual initial price that industrial user $i$ can obtain from the park aggregator when participating in the transfer project during period $t$, and $\gamma$ is the carbon trading market price. $E_{i,t}$ is the carbon emission reduction corresponding to industrial user $i$ participating in the curtailment project / transfer project during period $t$, $\lambda_1$ is the curtailment weight coefficient, $\lambda_2$ is the transfer weight index, $\mu$ is the power grid stability coefficient, $\Delta P$ peak-valley $\Delta P_{pv}$ is the change in the peak-valley difference before and after the implementation of demand response, $\Delta\sigma$ load $\Delta\sigma_{l}$ is the change in the load volatility.

[0029] Preferably, based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day, the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day are obtained, and the obtaining process is as follows:

[0030] Through the AP clustering method, the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day are classified to obtain the normal production load curve set, the curve set with curtailment characteristics, and the set with transfer characteristics corresponding to each industrial user;

[0031] The mean curve of all the curves in the normal production load curve set corresponding to each industrial user is used as the normal production load curve of each industrial user;

[0032] Through the k-means clustering algorithm, the curves in the curve set with curtailment characteristics and the curve set with transfer characteristics corresponding to each industrial user are further classified to obtain multiple curve subsets with curtailment characteristics and curve subsets with transfer characteristics corresponding to each industrial user;

[0033] The normal production load curve of each industrial user is subtracted from each curve in the curve subset with curtailment characteristics corresponding to the industrial user to obtain the curtailable curves of each curve in the multiple curve subsets with curtailment characteristics corresponding to each industrial user;

[0034] For each industrial user, calculate the average of the curtailment curves in each subset of curtailment characteristic curves, and use the obtained average curve as the curtailment curve for each subset of curtailment characteristic curves corresponding to the industrial user;

[0035] Based on the curtailment curves for each subset of curtailment characteristic curves corresponding to each industrial user, obtain the daily curtailment capacity, curtailment rate, minimum curtailment duration, and maximum curtailment duration of each industrial user on the response day;

[0036] Based on multiple subsets of transfer characteristic curves corresponding to each industrial user, obtain the daily transferable capacity, transfer rate, minimum transfer duration, and maximum transfer duration of each industrial user on the response day.

[0037] Preferably, solve the demand response strategy optimization model by calling GORUBI in MATLAB to obtain the demand response strategy of the target industrial user on the response day.

[0038] The present invention also provides an industrial user demand response strategy optimization system, including:

[0039] A data acquisition module for obtaining the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day;

[0040] A capacity prediction module for obtaining the curtailment capacity of each industrial user participating in the curtailment project in each period of the response day based on the predicted electricity price in each period of the response day and the daily curtailment capacity of each industrial user; obtaining the transferable capacity of each industrial user participating in the transfer project in each period of the response day based on the predicted electricity price in each period of the response day and the daily transferable capacity of each industrial user;

[0041] A solving module for substituting the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, maximum transfer duration, curtailment capacity of each industrial user participating in the curtailment project in each period, and transferable capacity of each industrial user participating in the transfer project in each period into the demand response strategy optimization model, solving the demand response strategy optimization model, and obtaining the demand response strategy of the target industrial user on the response day.

[0042] The present invention also provides an industrial user demand response strategy optimization device, including:

[0043] A memory for storing a computer program; a processor for implementing the steps of the above-mentioned industrial user demand response strategy optimization when executing the computer program.

[0044] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0045] For an industrial user demand response strategy optimization method, system and device according to the present invention, when determining each parameter of the demand response strategy optimization model, in-depth data mining is performed based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day. Since these daily load curves cover the electricity consumption of industrial users under different working conditions, parameters such as daily reduction capacity, minimum reduction duration, and maximum reduction duration that accurately reflect the electricity consumption patterns and potentials of each industrial user are obtained through their analysis and used as parameters for the response day. This enables the demand response strategy optimization model to closely fit the actual electricity consumption dynamics of industrial users, enhances the degree of fit with the electricity consumption conditions of each industrial user, and thus significantly improves the accuracy and effectiveness of the demand response strategy.

[0046] By setting the reduction rate constraint of the reduction variable and the transfer rate constraint of the transfer variable, the present invention stipulates that if industrial user i starts a reduction / transfer project at time period t, then within the next minimum reduction / transfer duration, the total number of time periods in which industrial user i participates in the reduction project should be greater than or equal to the theoretical duration required to complete the daily reduction capacity at the reduction rate, ensuring that after starting the reduction / transfer project, industrial users can complete the specified reduction / transfer tasks with a certain efficiency, and avoiding the situation where the overall effect of the demand response project and the stable operation of the power system are affected due to too few time periods for industrial users to participate in reduction / transfer and the requirement for the total reduction / transferable capacity not being met within a reasonable time.

[0047] When optimizing the demand response strategy for conventional industrial users, considering the maximum duration constraint of curtailment / transfer variables, it is often simply stipulated that within the maximum curtailment / transfer duration agreed upon in the contract, the cumulative number of times an industrial user completes the load curtailment / transfer project should be greater than or equal to the benchmark value agreed upon in the contract. However, based on the historical load data of each industrial user, the present invention obtains the theoretical time required for each industrial user to complete the daily curtailment capacity / daily transferable capacity according to its own curtailment / transfer rate, and takes the difference between the maximum curtailment / transfer duration and the curtailment / transfer theoretical time as the specified duration range. The maximum duration constraint of the curtailment / transfer variables proposed by the present invention requires that within the time period that conforms to the capabilities of industrial users, the number of times of completing the curtailment / transfer project should be greater than or equal to the number of times of initiating the curtailment / transfer project, comprehensively considering the actual curtailment / transfer capabilities of industrial users themselves. For industrial users with a fast curtailment rate and a small daily curtailment capacity, it will not be as loose as the conventional method for optimizing the demand response strategy of industrial users, nor will it overly restrict their curtailment time arrangement. For industrial users with a slow curtailment rate and a large daily curtailment capacity, it avoids unreasonable mandatory requirements and prevents situations such as industrial user production stagnation and product scrapping, enabling the solved demand response strategy of the target industrial users to closely fit the unique electricity consumption and production laws of each industrial user, improving the feasibility of the demand response strategy in actual implementation, and effectively guiding industrial users to reasonably adjust their electricity loads and enhancing the power system's supply-demand balance ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to make the content of the present invention easier to be clearly understood, the following further details the present invention according to specific embodiments of the present invention in combination with the drawings, wherein:

[0049] Figure 1 is a schematic diagram of the aggregated response operation mode of industrial users.

[0050] Figure 2 is a schematic flow diagram of a method for optimizing the demand response strategy of industrial users according to the present invention.

[0051] Figure 3 is a schematic diagram of the calculation interval definition, Figure 3 where (a) is the load curve to be defined in the interval, Figure 3 and (b) in it is the schematic diagram of the value of variable η i changing with time.

[0052] Figure 4 is a schematic diagram of the secondary clustering result of the load monitoring data of users in the cement industry, Figure 4 where (a) is the curtailment curve of users in the cement industry, Figure 4 and (b) in it is the transfer curve of users in the cement industry.

[0053] Figure 5It is the winter demand response of users in different industries. Figure 5 Among them, (a) is the winter curtailment response of users in different industries. Figure 5 Among them, (b) is the winter shift response of users in different industries.

[0054] Figure 6 It is the summer demand response of users in different industries. Figure 6 Among them, (a) is the summer curtailment response of users in different industries. Figure 6 Among them, (b) is the summer shift response of users in different industries.

[0055] Figure 7 It is the spring and autumn demand response of users in different industries. Figure 7 Among them, (a) is the spring and autumn curtailment response of users in different industries. Figure 7 Among them, (b) is the spring and autumn shift response of users in different industries.

[0056] Figure 8 It is a schematic diagram of the load change on a typical winter day.

[0057] Figure 9 It is a schematic diagram of the load comparison of industrial users in different industries before and after winter demand response. Specific implementation manner

[0058] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it, but the specific embodiments cited do not limit the present invention.

[0059] As Figure 1 shown, Figure 1 It is a schematic diagram of the operation mode of industrial user aggregation response. The industrial park load aggregator, as the carrier of industrial user aggregation response, aggregates industrial users through the industrial park load aggregator and represents them to participate in the operation of the power market. Therefore, the industrial park load aggregator can serve as a bridge between industrial users and the power grid. Facing industrial users, the park aggregator needs to evaluate their adjustable potential, represent industrial users to participate in the power market, and issue certain subsidies to industrial users; facing the power grid, the park aggregator is responsible for reporting the adjustable potential of the adjustable resources aggregated in the park, obtaining the adjustable capacity index allocated by the power grid, and selling the corresponding adjustable capacity to obtain benefits.

[0060] Incentive-based DR directly guides DR entities to complete load curtailment or load transfer projects in the form of incentive policies or compensation. Two main types of incentive-based DR are considered, namely curtailment-based DR and transfer-based DR.

[0061] Reduction-type DR refers to industrial users reducing their electricity load during peak hours or emergencies based on an agreement signed with a park aggregator. The content of the agreement should include reduction capacity, minimum reduction duration, maximum reduction duration, reduction rate, compensatory electricity price and initial price.

[0062] Transfer DR refers to industrial users transferring electricity load within a certain period of time to other time periods based on an agreement signed with a park aggregator, in order to reduce peak load during peak electricity consumption. The agreement should include transfer capacity, compensation electricity price, initial price, transfer-out period and transfer-in period.

[0063] However, existing methods generally directly use the contract value as the parameter value of the demand response strategy optimization model. Although this approach utilizes the certainty of the contract value to a certain extent and provides a basic framework for strategy formulation, it ignores the high complexity of the power system and the production and operation environment of industrial users. The supply and demand relationship of the power system is always in dynamic change. In different seasons and different time periods, the demand for electricity will fluctuate significantly due to factors such as residents' living electricity consumption habits and industrial production cycles. For example, during high temperatures in summer, the air conditioning load increases significantly, resulting in a sharp increase in electricity demand; and in the middle of the night, industrial production activities decrease, and electricity demand decreases accordingly. At the same time, the production and operation activities of industrial users are also full of variables. The production scale of industrial users may be adjusted due to changes in market demand, and new production orders may change the operating time and power of production equipment; sudden failures of equipment may also cause production interruptions or adjustments, thereby affecting the electricity consumption pattern.

[0064] If the demand response strategy is set based solely on the contract value, it will be difficult to adjust it as the power system and industrial users' production and operation conditions change. This deviation makes it impossible for the demand response strategy to accurately match the dynamic power consumption situation, and thus disconnect from the actual demand. The demand response strategy cannot accurately reflect the actual power consumption potential and demand of industrial users, which may lead to unreasonable load regulation requirements for industrial users. Excessive requirements will affect the normal production and operation of enterprises and increase enterprise costs; too low requirements will not fully tap the load regulation potential of industrial users, and will not effectively alleviate the contradiction between supply and demand in the power system. At the same time, it will also affect the dispatching and operation efficiency of the power system, reduce the utilization efficiency of power resources, and fail to achieve the optimal configuration of the power system.

[0065] For this purpose, refer to Figure 2 As shown, this embodiment 1 provides an industrial user demand response strategy optimization method, including the following steps:

[0066] Step S1: Based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day, obtain the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day.

[0067] In this embodiment, the adjustable potential variable set of industrial users includes curtailment variables and transfer variables. This adjustable potential variable set serves as the data basis for subsequent demand response strategy research. Through the method of secondary clustering combining AP clustering and k-means clustering, the normal production load curve, the curve with curtailment characteristics, and the curve with transfer characteristics of industrial users are extracted, and then the curtailment curve and the transfer curve are obtained. The calculation intervals of the two types of curves are defined, and considering the influence of three dimensions of capacity, time, and rate on the adjustable potential of industrial users, the adjustable potential variable set is obtained, as shown in Table 1. Table 1 is a schematic table of the adjustable potential variable set.

[0068] Table 1

[0069]

[0070] The adjustable potential variable set includes: the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day.

[0071] The specific steps to obtain the adjustable potential variable set are as follows:

[0072] Step S11: The daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day. The set of daily load curves of each industrial user is denoted as L i ={L i,d}, i = 1, 2…I, d = 1, 2…D, L i,d is the daily load curve of industrial user i on the dth day before the response day in the industrial park. Through the AP clustering method, the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day are classified to obtain three curve sets with different characteristics, including: the normal production load curve set Q i 、the curve set with curtailment characteristics W i 、the curve set with transfer characteristics E i ;

[0073] Classifying these daily load curves through the AP clustering method aims to gather curves with similar characteristics together, thereby obtaining three curve sets with different characteristics, which helps us distinguish the electricity consumption situations of industrial users in the normal production state, the curtailment load state, and the transferable load state.

[0074] Step S12: Take the mean curve of all the curves in the normal production load curve set Q corresponding to each industrial user as the normal production load curve of each industrial user i In it as the normal production load curve of each industrial user

[0075] To more accurately represent the normal production power consumption pattern of each industrial user, calculate the mean of all the curves in the normal production load curve set of each industrial user. The mean curve can comprehensively reflect the load characteristics of the industrial user under normal production conditions, avoiding the influence of accidental fluctuations that may exist in a single curve. Take the calculated mean curve as the normal production load curve of each industrial user as the basis for calculating the curtailment curve in the follow-up

[0076] Step S13: Through k-means clustering, further classify the curves in the curtailment characteristic curve set W i corresponding to each industrial user and the transfer characteristic curve set E i to obtain multiple curtailment characteristic curve subsets corresponding to each industrial user For the C i th curtailment characteristic curve subset corresponding to industrial user i, C i is the number of curtailment characteristic curve subsets corresponding to industrial user i, and multiple transfer characteristic curve subsets For the A i th transfer characteristic curve subset corresponding to industrial user i, A i is the number of transfer characteristic curve subsets corresponding to industrial user i;

[0077] Although the curtailment characteristic curve set W i and the transfer characteristic curve set E i have been obtained, there may still be more detailed differences among the curves in these sets. To further explore these differences, use the k-means clustering algorithm to classify these two sets again. In this way, curves with similar curtailment characteristics and transfer characteristics can be further grouped into different subsets

[0078] Step S14: Subtract each curve in the curtailment characteristic curve subset corresponding to each industrial user from the normal production load curve of each industrial user to obtain the curtailment curves of each curve in the multiple curtailment characteristic curve subsets corresponding to each industrial user;

[0079] Subtract the normal production load curve from each curve in each subset of the curves with curtailment characteristics to obtain the load that can be curtailed by industrial users relative to the normal production state under different curtailment modes, that is, obtain the curtailment curves of each curve in the multiple subsets of curves with curtailment characteristics corresponding to each industrial user. These curtailment curves intuitively show the load curtailment potential of industrial users under different conditions.

[0080] Step S15: Calculate the average value of the curtailment curves of each curve in each subset of the curves with curtailment characteristics corresponding to each industrial user, and use the obtained average curve as the curtailment curve of each subset of the curves with curtailment characteristics corresponding to the industrial user.

[0081] To more simply represent the curtailment characteristics of each subset of the curves with curtailment characteristics, calculate the average value of the curtailment curves of each curve in each subset of the curves with curtailment characteristics. The obtained average curve can comprehensively reflect the overall curtailment characteristics of the subset, and use it as the curtailment curve of each subset of the curves with curtailment characteristics corresponding to the industrial user. This can reduce the complexity of the data while retaining the main characteristics of each subset.

[0082] Step S16: Based on the curtailment curves of each subset of the curves with curtailment characteristics corresponding to each industrial user, obtain the daily curtailment capacity, curtailment rate, minimum curtailment duration, and maximum curtailment duration of each industrial user on the response day.

[0083] The formula for obtaining the daily curtailment capacity, curtailment rate, minimum curtailment duration, and maximum curtailment duration of each industrial user on the response day based on the curtailment curves of each subset of the curves with curtailment characteristics corresponding to each industrial user is as follows:

[0084] The calculation formula for the daily curtailment capacity of each industrial user is:

[0085]

[0086] where is the daily curtailment capacity of industrial user i, C i is the number of curtailment curves of each subset of the curves with curtailment characteristics corresponding to industrial user i, k is the curtailment curve index, P i,k is the k-th curtailment curve of industrial user i, α i,k is the number of curves in the subset of the curves with curtailment characteristics to which the k-th curtailment curve of industrial user i belongs, and the proportion in the set of curves with curtailment characteristics W i in.

[0087] The curtailment capacity is the part or all of the load that can be curtailed by industrial users, which may include production load or non-production load, and is a positive indicator.

[0088] The calculation formula for the reduction rate of each industrial user is as follows:

[0089]

[0090] Where, is the reduction rate of industrial user i, and K i,k is the slope of the curve at the start and end points of the reduction rate calculation interval in the k-th reducible curve of industrial user i.

[0091] The reduction rate is the amount of load reduced per unit time when an industrial user reduces part or all of its load. The greater the reducible rate, the faster the load shedding, which is more beneficial to the system. This indicator is a positive indicator.

[0092] The calculation formula for the minimum reduction duration of each industrial user is as follows:

[0093]

[0094] The calculation formula for the maximum reduction duration of each industrial user is as follows:

[0095]

[0096] Where, is the minimum reduction duration for industrial user i to participate in the reduction project, is the maximum reduction duration for industrial user i to participate in the reduction project, min{.} is the minimum value function, max{.} is the maximum value function, and t Ci is the reduction time of the C i -th reducible curve of industrial user i.

[0097] The minimum reduction time is the minimum continuous duration for an industrial user to partially reduce production during the peak electricity consumption period, and the maximum reduction time is the maximum continuous duration for an industrial user to partially reduce production during the peak electricity consumption period.

[0098] When calculating the above indicators, for each reducible curve, it is necessary to accurately determine the reduction rate calculation interval, reduction interval, and post-reduction recovery rate calculation interval. As Figure 3 shown, Figure 3 is a schematic diagram for defining the calculation interval. Figure 3 In (a) is the load curve to be defined for the interval, Figure 3 in (b) is a schematic diagram showing the change of the value of variable η i over time. The data sampling interval is 15 min, with a total of 96 points.

[0099] For each reducible curve, calculate the change in power ΔP i,k between adjacent moments. When ΔP i,k > 0, it indicates that the power is increasing. Set the variable η iAssign a value of 1 when ΔP i,k <0, it indicates that the power is decreasing. Assign the variable η i a value of -1, and set a limit threshold β. If the number of consecutive variables η i equal to 1 is greater than or equal to the limit threshold β, then for the consecutive variable η i corresponding interval is used as the reduction rate calculation interval. If the number of consecutive variables η i equal to -1 is greater than or equal to the limit threshold β, then for the consecutive variable η i corresponding interval is used as the recovery rate calculation interval after reduction. The interval between the reduction rate calculation interval and the recovery rate calculation interval after reduction is the reduction interval.

[0100] Step S17: Based on multiple subsets of transfer characteristic curves corresponding to each industrial user Obtain the daily transferable capacity, transfer rate, minimum transfer duration, and maximum transfer duration of each industrial user on the response day;

[0101] The formula for the daily transferable capacity of each industrial user is:

[0102]

[0103] where is the daily transferable capacity of the nth transferable curve in the multiple subsets of transfer characteristic curves corresponding to industrial user i, k n is the number of monitoring points in the transferable interval of the nth transferable curve, P i,n,j is the power value corresponding to the jth monitoring point in the transferable interval of the nth transferable curve of industrial user i, P i,n,min is the minimum power value in the transferable interval of the nth transferable curve of industrial user i, n f is the number of transferable curves in the multiple subsets of transfer characteristic curves corresponding to industrial user i.

[0104] The transferable capacity is the amount of load that an industrial user can transfer to other power consumption time periods during the peak power consumption period. This indicator is a positive indicator.

[0105] The formula for the transfer rate of each industrial user is:

[0106]

[0107] where is the transfer rate of industrial user i, P i,n,min is the minimum power value in the transferable interval of the nth transferable curve of industrial user i, P i,n,max is the maximum power value in the transferable interval of the nth transferable curve of industrial user i, T i,n,max is the moment corresponding to P i,n,max is the moment Ti,n,min For P i,n,min The corresponding moment.

[0108] The calculation formula for the minimum transfer duration of each industrial user is:

[0109]

[0110] The calculation formula for the maximum transfer duration of each industrial user is:

[0111]

[0112] Wherein, Is the minimum transfer duration for industrial user i to participate in the transfer project, Is the maximum transfer duration for industrial user i to participate in the transfer project, min{.} is the minimum value function, max{.} is the maximum value function, Is the nth f Cutting time of the cuttable curve of industrial user i.

[0113] For each transferable curve, calculate the change in power ΔP at adjacent moments i,k , when ΔP i,k > 0, it indicates that the power is increasing, and assign the variable η i To 1, when ΔP i,k < 0, it indicates that the power is decreasing, and assign the variable η i To -1, set the limit threshold β. If the number of consecutive variables η i That is 1 is greater than or equal to the limit threshold β, take the interval corresponding to the consecutive variable η i As the cut rate calculation interval. If the number of consecutive variables η i That is -1 is greater than or equal to the limit threshold β, take the interval corresponding to the consecutive variable η i As the post-cut recovery rate calculation interval. Take the interval between the start endpoint of the cut rate calculation interval and the end endpoint of the post-cut recovery rate calculation interval as the transferable interval.

[0114] Construct an adjustable potential variable set, which can comprehensively consider the production mode, adjustable characteristics and load volatility of industrial users, accurately describe the adjustable potential level and the adjustable potential at multiple time points on the response day, and provide a scientific basis for demand response strategies.

[0115] Step S2: Based on the predicted electricity price at each time period on the response day and the daily cut capacity of each industrial user, obtain the cut capacity of each industrial user participating in the cut project at each time period on the response day; based on the predicted electricity price at each time period on the response day and the daily transferable capacity of each industrial user, obtain the transferable capacity of each industrial user participating in the transfer project at each time period on the response day;

[0116] Step S3: Substitute the daily reduction capacity, daily transferable capacity, reduction rate, transfer rate, minimum reduction duration, maximum reduction duration, minimum transfer duration, maximum transfer duration of each industrial user, the reduction capacity of each time period participating in the reduction project, and the transferable capacity of each time period participating in the transfer project into the demand response strategy optimization model, solve the demand response strategy optimization model, and obtain the target industrial user's response day demand response strategy.

[0117] In this embodiment, preferably, the process of constructing the objective function of the demand response strategy optimization model includes:

[0118] Taking the maximum net income of the park aggregator as the goal, construct a net income objective function; taking the minimum carbon emissions of industrial users as the goal, construct an environmental income objective function; taking the minimum deviation between the income of users participating in the demand response project and the initial price as the goal, construct a user satisfaction objective function; taking the maximum grid stability as the goal, construct a grid stability objective function;

[0119] Based on the net income objective function, environmental income objective function, user satisfaction objective function and grid stability objective function, construct a total objective function.

[0120] The construction of the total objective function based on the net income objective function, environmental income objective function, user satisfaction objective function and grid stability objective function, the total objective function is:

[0121] Max Total Income=maxIncome+Carbon Income+Reward-Penalty

[0122]

[0123] Reward=μ·(ΔP peak-valley +Δσ load ),

[0124] where, Max Total Income is the total objective function, maxIncome is the net income objective function, CarbonIncome is the environmental income objective function, Penalty is the user satisfaction objective function, Reward is the grid stability objective function, T is the total number of time periods for implementing the demand response project, I is the number of industrial users participating in the demand response project, is the predicted electricity price for the park aggregator to sell the regulation capacity in time period t, is the capacity of user i participating in the reduction project in time period t, The capacity of user i participating in the transfer project in time period, is whether user i participates in the reduction project in time period t. If user i participates in the reduction project in time period t, then If user i does not participate in the curtailment project during period t, then is whether user i participates in the transfer project during period t. If user i participates in the transfer project during period t, then If user i does not participate in the transfer project during period t, then i is the user index and t is the time period index. is the revenue obtained by user i from the park aggregator when participating in the curtailment project during period t. is the compensation electricity price for user i to participate in the curtailment project during period t. is the actual initial price that user i can obtain from the park aggregator when participating in the curtailment project during period t. is the revenue obtained by user i from the park aggregator when participating in the transfer project during period t. is the compensation electricity price for user i to participate in the transfer project during period t. is the actual initial price that user i can obtain from the park aggregator when participating in the transfer project during period t. γ is the carbon trading market price. is the corresponding carbon emission reduction volume when user i participates in the curtailment project / transfer project during period t. λ1 is the curtailment weight coefficient, λ2 is the transfer weight index, μ is the power grid stability coefficient, ΔP peak-valley is the change in the peak-valley difference before and after the implementation of demand response, Δσ load is the change in the load volatility.

[0125] Traditional demand response strategies only take the maximization of the net revenue of park aggregators as a single goal, which has significant limitations. In the economic field, although it can improve the revenue of aggregators in the short term, in the long run, due to ignoring the deviation between the revenue of users participating in the project and the initial price, the enthusiasm of users to participate is frustrated, resulting in the coverage and implementation effect of the project being affected, hindering the sustainable growth of the overall economic benefits. From an environmental perspective, as industrial users are large electricity consumers, the single-goal strategy lacks the driving force for controlling their carbon emissions. With the increasing environmental protection requirements, the park will face potential economic losses and development bottlenecks. In terms of user experience, due to the lack of consideration of revenue deviation, user satisfaction is likely to decline, affecting the long-term stable implementation of the project. In terms of power grid stability, excessive pursuit of economic benefits may lead to unreasonable power allocation, threatening the safety of the power grid, and once a failure occurs, it will cause huge economic losses.

[0126] In view of this, the present invention innovatively constructs multiple objective functions. An environmental benefit objective function aiming at minimizing the carbon emissions of industrial users, by quantifying the carbon emission reduction amount and combining with the carbon trading market price, realizes a win-win situation for both the environment and the economy; a user satisfaction objective function aiming at minimizing the deviation between the revenue of user participation in the project and the initial price, with the help of reasonably setting the weight coefficient to quantify and regulate the revenue deviation, safeguards the interests of users and improves the participation degree; a power grid stability objective function aiming at maximizing the power grid stability, measures the degree of stability improvement according to the peak-valley difference, the change amount of load volatility and the power grid stability coefficient. Finally, these objective functions are integrated to construct a total objective function, comprehensively optimizing the demand response strategy, making up for the deficiencies of existing methods from multiple dimensions, being able to maximize the comprehensive benefits of the demand response strategy, and the solved demand response strategy can fully mobilize the enthusiasm of industrial users to participate in the demand response, improve the support of users for the regulation of the power system, and achieve a win-win situation for users and the power grid.

[0127] In this embodiment, preferably, rate constraints for the curtailment variable and the transfer variable are set in the demand response strategy optimization model, including:

[0128] The curtailment rate constraint for the curtailment variable is:

[0129]

[0130] The transfer rate constraint for the transfer variable is:

[0131]

[0132] Wherein, is the minimum curtailment duration for user i to participate in the curtailment project, t is the time period index, i is the user index, is whether user i participates in the curtailment project at time t. If user i participates in the curtailment project at time t, then If user i does not participate in the curtailment project at time t, then is whether user i starts the curtailment project at time t. If user i starts the curtailment project at time t, then If user i does not start the curtailment project at time t, then is the daily curtailment capacity of user i, is the curtailment rate of user i, is the minimum transfer duration for user i to participate in the transfer project, is whether user i participates in the transfer project at time t. If user i participates in the transfer project at time t, then If user i does not participate in the transfer project at time t, then is whether user i starts the transfer project at time t. If user i starts the transfer project at time t, then If user i does not start the transfer project at time t, then is the transfer rate for user i, is the total transferable capacity of user i.

[0133] In existing demand response strategies, due to the neglect of the regulation resource response speed constraint, especially for industrial users, which are an important power consumption group, their load reduction has a significant impact on the power system's supply-demand matching effect. When a large number of industrial users only reduce their loads briefly and in small amounts, the total load reduction contributed by the entire industrial sector far from meets the power system's demand, resulting in the power system being unable to quickly and effectively match load and power supply, and thus triggering fluctuations or even faults.

[0134] In the present invention, by setting the reduction rate constraint of the reduction variable and the transfer rate constraint of the transfer variable, it is stipulated that if industrial user i starts a reduction / transfer project in time period t, then within the next minimum reduction / transfer duration, the total number of time periods that industrial user i participates in the reduction project should be greater than or equal to the theoretical duration required to complete the daily reduction capacity according to the reduction rate, ensuring that after industrial users start a reduction / transfer project, they can complete the specified reduction / transfer tasks with a certain efficiency. Through the constraint setting of the present invention, the overall effect of the demand response project can be effectively guaranteed, enabling the load reduction and transfer of industrial users to truly meet the power system's demand, thereby maintaining the stable operation of the power system, avoiding fluctuations or even faults caused by supply-demand mismatch, and greatly improving the safety and reliability of the power system operation.

[0135] In this embodiment, preferably, the maximum reduction duration constraint in the demand response strategy optimization model is:

[0136]

[0137] The maximum transfer duration constraint is:

[0138]

[0139] Among them, is the maximum reduction duration for user i to participate in the reduction project, is the maximum transfer duration for user i to participate in the transfer project, t is the time period index, i is the user index, is the daily reduction capacity of user i, is the daily transferable capacity of user i, is the reduction rate of user i, is the transfer rate for user i, is whether user i starts a reduction project in time period t. If user i starts a reduction project in time period t, then If user i does not start a reduction project in time period t, then is whether user i completes the reduction project in time period t. If user i completes the reduction project in time period t, then If user i does not complete the curtailment project during period t, then Regarding whether user i starts the transfer project during period t, if user i starts the transfer project during period t, then If user i does not start the transfer project during period t, then Regarding whether user i completes the transfer project during period t, if user i completes the transfer project during period t, then If user i does not complete the transfer project during period t, then

[0140] Traditional methods simply require industrial users to reach a benchmark value for the cumulative number of load curtailment / transfer projects completed based solely on the maximum curtailment / transfer duration stipulated in the contract, without fully considering the actual curtailment / transfer capabilities of the users themselves. The present invention analyzes the historical load data of each industrial user to obtain the theoretical time required to complete the daily curtailment capacity / daily transferable capacity at its own curtailment / transfer rate, and takes the difference between the maximum curtailment / transfer duration and the curtailment / transfer theoretical time as the specified duration range, and requires that within the specified duration range, the number of completed curtailment / transfer projects be greater than or equal to the number of started curtailment / transfer projects. For example, for a certain electronics manufacturing enterprise, its equipment can start and stop quickly, with a fast curtailment rate and a relatively small daily curtailment capacity. The constraints of traditional methods may be too loose to effectively guide its rational electricity consumption. The present invention calculates the theoretical time and takes the difference between the maximum curtailment / transfer duration and the curtailment / transfer theoretical time as the specified duration range, which can more closely fit the actual curtailment capabilities of the industrial user and optimize its demand response strategy.

[0141] In this embodiment, specifically, the constraints of the demand response strategy optimization model further include:

[0142] The minimum curtailment duration constraint is:

[0143]

[0144] The initial price constraint of the curtailment project:

[0145]

[0146] The curtailment project start-completion status and participation status constraint:

[0147]

[0148] The curtailment project start-stop status constraint:

[0149]

[0150] The minimum transfer duration constraint is:

[0151]

[0152] The minimum transfer duration constraint is:

[0153]

[0154] Initial price constraint of transfer items:

[0155]

[0156] Constraints on the start completion status and participation status of transfer items:

[0157]

[0158] Constraints on the start-stop status of transfer items:

[0159]

[0160] Constraints on the time period of participating in transfer items:

[0161]

[0162] Among them, is the minimum reduction duration for user i to participate in the reduction project, t is the time period index, i is the user index, indicates whether user i participates in the reduction project in time period t. If user i participates in the reduction project in time period t, then If user i does not participate in the reduction project in time period t, then indicates whether user i starts the reduction project in time period t. If user i starts the reduction project in time period t, then If user i does not start the reduction project in time period t, then indicates whether user i completes the reduction project in time period t. If user i completes the reduction project in time period t, then If user i does not complete the reduction project in time period t, then is the minimum transfer duration for user i to participate in the transfer project, indicates whether user i participates in the transfer project in time period t. If user i participates in the transfer project in time period t, then If user i does not participate in the transfer project in time period t, then indicates whether user i starts the transfer project in time period t. If user i starts the transfer project in time period t, then If user i does not start the transfer project in time period t, then is the maximum transfer duration for user i to participate in the transfer project, indicates whether user i completes the transfer project in time period t. If user i completes the transfer project in time period t, then If user i does not complete the transfer project in time period t, then $p_{i}^{r,t}$ is the actual initial price that user $i$ can obtain from the park aggregator when participating in the curtailment project during period $t$. $p_{i}^{s,t}$ is the actual initial price that user $i$ can obtain from the park aggregator when participating in the transfer project during period $t$. $p_{i}^{r}$ is the initial price in the agreement when user $i$ participates in the curtailment project. $p_{i}^{s}$ is the initial price in the agreement when user $i$ participates in the transfer project. $T_{i}^{s}$ is the set of transfer time periods for user $i$ to participate in the transfer project.

[0163] In this embodiment, specifically, the demand response strategy optimization model is solved by calling GORUBI in MATLAB to obtain the demand response strategy of the target industrial user on the response day.

[0164] In this embodiment, optionally, the method for solving the demand response strategy optimization model is any one of the particle swarm algorithm, ant colony algorithm, and genetic algorithm.

[0165] Based on Embodiment 1, in this Embodiment 2, four types of users are set, namely mining users, set as the first type of users; cement industry users, set as the second type of users; daily-use ceramic industry users, set as the third type of users; and aluminum smelting industry users, set as the fourth type of users. The park aggregator is set to represent the above four industries to participate in DR, and 10 identical users in each industry are represented by the park aggregator. The curtailment variable and transfer variable are solved by the industrial user adjustable potential quantification evaluation method of quadratic clustering and modal decomposition. Since it is difficult to obtain the electricity market transaction price in this area, the industrial electricity price can be used as the predicted electricity price when the park aggregator participates in the day-ahead market for research.

[0166] Moreover, there are differences in the peak-valley-flat time period division in each season and the industrial electricity price in each month in this area. The adjustable potential variables are solved separately for summer, winter, and spring-autumn seasons, and the effect analysis of the demand response strategy is carried out. The influence of the adjustable potential on the effect and feasibility of the demand response strategy is compared and analyzed to verify the necessity of considering the adjustable potential when industrial users participate in DR.

[0167] Taking the cement industry users as an example, the adjustable potential variables in winter are solved. The solution processes for other seasons of other industry users are similar. The load monitoring data of this user in December and January are obtained, and the curtailment curve and transfer curve of this user are mined through the quadratic clustering method to solve the minimum curtailment duration, maximum curtailment duration, curtailment rate, and transfer capacity of the user. The result of quadratic clustering is as Figure 4 shown, Figure 4 which is a schematic diagram of the result of quadratic clustering of the load monitoring data of cement industry users, Figure 4 where (a) in it is the curtailment curve of cement industry users, Figure 4 and (b) in it is the transfer curve of cement industry users.

[0168] Since industrial users need to adjust their production equipment in advance according to the response period before participating in DR to ensure that the response capacity during the response period meets the requirements of the agreement, the calculation interval of the curtailment rate and the duration corresponding to the curtailment interval are defined as the curtailment duration. Figure 4 In (a) of, there are only two curtailment curves. Therefore, the curtailment time t1 of curtailment curve 1 is the maximum curtailment duration of 13 h, the curtailment time t2 of curtailment curve 2 is the minimum curtailment duration of 6 h, and the curtailment rate is 0.71 MW / h. Figure 4 In (b) of, there are two transfer curves. The daily transferable capacity of transfer curve 1 is smaller, and the daily transferable capacity is 0.71 MW.

[0169] As shown in Table 2, Table 2 is the adjustable potential variable set of different industrial users in winter.

[0170] Table 2

[0171]

[0172] As shown in Table 3, Table 3 is the adjustable potential variable set of different industrial users in summer.

[0173] Table 3

[0174]

[0175] As shown in Table 4, Table 4 is the adjustable potential variable set of different industrial users in spring and autumn.

[0176] Table 4

[0177]

[0178]

[0179] Assume that the predicted electricity price when the park aggregator participates in the day-ahead market is the electricity price of industrial users. The selected industrial users are all large industrial users and adopt the agent power purchase method. The following electricity price ratios are adopted for large industrial users in this area: peak period: flat period: valley period: super-peak period is 1.6:1:0.4:1.92. The transfer-in and transfer-out periods in the transfer variable constraints are set according to the local peak-valley-flat periods. The local peak period is 10:00 - 12:00, 15:00 - 21:00, the flat period is 7:00 - 10:00, 12:00 - 15:00, 21:00 - 23:00, and the valley period is 23:00 - 7:00 the next day, where the super-peak period in winter is 19:00 - 21:00 and the super-peak period in summer is 15:00 - 17:00. Set the initial price in the agreement to 1000 yuan, and analyze the DR effect of industrial users in each season respectively.

[0180] The results of industrial users in different industries participating in the curtailment project in each season are as Figure 5 , Figure 6 , Figure 7 shown. Figure 6 Figure Figure 6 shows the summer demand response of users in different industries. Figure 6 In Figure 6 (a), it is the summer curtailment response of users in different industries. Figure 6 In Figure 6 (b), it is the summer transfer response of users in different industries. Figure 7 Figure Figure 7 shows the spring and autumn demand response of users in different industries. Figure 7 In Figure 7 (a), it is the spring and autumn curtailment response of users in different industries. Figure 7 In Figure 7 (b), it is the spring and autumn transfer response of users in different industries.

[0181] Taking the winter demand response as an example, the analysis process in other seasons is the same. The results of these four types of industrial users participating in the winter curtailment / transfer project are Figure 5 shown. Figure 5 Figure Figure 5 shows the winter demand response of users in different industries. Figure 5 In Figure 5 (a), it is the winter curtailment response of users in different industries. Figure 5 In Figure 5 (b), it is the winter transfer response of users in different industries. As can be seen from the results of participating in the transfer project shown in Figure 5 Figure 5 (b), among the four types of industries agented by the park aggregator, the mining and cement industries mainly participate in the transfer project during the two peak hours in this area, and other industrial users mainly participate in the transfer project from 15:00 to 21:00.

[0182] The winter typical daily load curve of this area is obtained from the load monitoring data in December and January of this area. After each user participates in the curtailment and transfer projects, the change of the winter typical daily load is as Figure 8 shown. Figure 8 Figure Figure 8 is a schematic diagram of the change of the winter typical daily load. As can be seen from Figure 8 Figure 8 , after different industrial users participate in the curtailment project and the transfer project, the winter typical daily load curve has obvious peak shaving and valley filling behaviors.

[0183] As Figure 9 shown. Figure 9 Figure Figure 9 is a schematic diagram of the load comparison of industrial users in different industries before and after the winter demand response. As can be seen from Figure 9 Figure 9 , after the users agented by the park aggregator participate in the curtailment project and the transfer project, the load during the peak hours is reduced by 122.6MW and 319.7MW respectively; before the implementation of the DR project, the peak-valley difference is 5078.8MW, and after the implementation, the peak-valley difference is 4868.5MW. Therefore, the target industrial user response day demand response strategy obtained by the industrial user demand response strategy optimization method provided by the present invention can effectively reduce the peak-valley difference of the load curve and optimize the grid operation conditions.

[0184] Embodiment 3 provides an industrial user demand response strategy optimization system, including:

[0185] A data acquisition module, configured to obtain the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day;

[0186] A capacity prediction module, configured to obtain the curtailment capacity of each industrial user participating in the curtailment project in each period on the response day based on the predicted electricity price in each period on the response day and the daily curtailment capacity of each industrial user; and obtain the transferable capacity of each industrial user participating in the transfer project in each period on the response day based on the predicted electricity price in each period on the response day and the daily transferable capacity of each industrial user;

[0187] A solution module, configured to substitute the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, maximum transfer duration, the curtailment capacity of each industrial user participating in the curtailment project in each period, and the transferable capacity of each industrial user participating in the transfer project in each period into the demand response strategy optimization model, solve the demand response strategy optimization model, and obtain the demand response strategy of the target industrial user on the response day.

[0188] Embodiment 4 provides an industrial user demand response strategy optimization device, including:

[0189] A memory, configured to store a computer program; a processor, configured to implement the steps of the above-mentioned industrial user demand response strategy optimization when executing the computer program.

[0190] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0191] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 means for implementing the functions specified in one or more blocks or blocks.

[0192] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 means for implementing the functions specified in one or more blocks or blocks.

[0193] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operating steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 means for implementing the functions specified in one or more blocks or blocks.

[0194] Obviously, the above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to exhaustively list all implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. An optimization method for industrial user demand response strategies, characterized in that It includes the following steps: Based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day, obtain the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day; Based on the predicted electricity price for each time period on the response day and the daily curtailment capacity of each industrial user, obtain the curtailment capacity of each industrial user participating in the curtailment project for each time period on the response day; Based on the predicted electricity price for each time period on the response day and the daily transferable capacity of each industrial user, obtain the transferable capacity of each industrial user participating in the transfer project for each time period on the response day; Substitute the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, maximum transfer duration, curtailment capacity of each industrial user participating in the curtailment project for each time period, and transferable capacity of each industrial user participating in the transfer project for each time period into the demand response strategy optimization model, solve the demand response strategy optimization model, and obtain the demand response strategy of the target industrial user on the response day.

2. The optimization method for an industrial user demand response strategy according to claim 1, wherein The constraint conditions of the demand response strategy optimization model include: curtailment rate constraint, minimum curtailment duration constraint, maximum curtailment duration constraint, initial price constraint of the curtailment project, start-up completion status and participation status constraint of the curtailment project, start-stop status constraint of the curtailment project, transfer rate constraint, minimum transfer duration constraint, maximum transfer duration constraint, initial price constraint of the transfer project, start-up completion status and participation status constraint of the transfer project, start-stop status constraint of the transfer project, and participation time period constraint of the transfer project.

3. The optimization method for industrial user demand response strategy according to claim 2, wherein The curtailment rate constraint is: The transfer rate constraint is: Among them, is the minimum reduction duration for industrial user i to participate in the reduction project, t is the time period index, and i is the industrial user index. indicates whether industrial user i participates in the reduction project in period t. If industrial user i participates in the reduction project in period t, then If industrial user i does not participate in the reduction project in period t, then indicates whether industrial user i starts the reduction project in period t. If industrial user i starts the reduction project in period t, then If industrial user i does not start the reduction project in period t, then is the daily reduction capacity of industrial user i. is the reduction rate of industrial user i. is the minimum transfer duration for industrial user i to participate in the transfer project. indicates whether industrial user i participates in the transfer project in period t. If industrial user i participates in the transfer project in period t, then If industrial user i does not participate in the transfer project in period t, then indicates whether industrial user i starts the transfer project in period t. If industrial user i starts the transfer project in period t, then If industrial user i does not start the transfer project in period t, then is the transfer rate of industrial user i. is the total transferable capacity of industrial user i.

4. The optimization method for an industrial user demand response strategy according to claim 2, wherein The maximum curtailment duration constraint is: The maximum transfer duration constraint is: Among them, is the maximum reduction duration for industrial user i to participate in the reduction project, is the maximum transfer duration for industrial user i to participate in the transfer project, t is the time period index, and i is the industrial user index, is the daily reduction capacity of industrial user i, is the daily transferable capacity of industrial user i, is the reduction rate of industrial user i, is the transfer rate of industrial user i, indicates whether industrial user i starts the reduction project in period t. If industrial user i starts the reduction project in period t, then If industrial user i does not start the reduction project in period t, then indicates whether industrial user i completes the reduction project in period t. If industrial user i completes the reduction project in period t, then If industrial user i does not complete the reduction project in period t, then indicates whether industrial user i starts the transfer project in period t. If industrial user i starts the transfer project in period t, then If industrial user i does not start the transfer project in period t, then indicates whether industrial user i completes the transfer project in period t. If industrial user i completes the transfer project in period t, then If industrial user i does not complete the transfer project in period t, then 5. The optimization method for an industrial user demand response strategy according to claim 1, wherein The construction process of the objective function of the demand response strategy optimization model is: With the goal of maximizing the net income of the park aggregator, construct a net income objective function; with the goal of minimizing the carbon emissions of industrial users, construct an environmental income objective function; with the goal of minimizing the deviation between the income of industrial users participating in the demand response project and the initial price, construct an industrial user satisfaction objective function; with the goal of maximizing the power grid stability, construct a power grid stability objective function; Based on the net income objective function, environmental income objective function, industrial user satisfaction objective function, and power grid stability objective function, construct a total objective function.

6. The optimization method for an industrial user demand response strategy according to claim 5, wherein Regarding the construction of the total objective function based on the net income objective function, environmental income objective function, industrial user satisfaction objective function, and power grid stability objective function, the total objective function is: Max TotalIncome=maxIncome+CarbonIncome+Reward-Penalty Reward=μ·(ΔP peak-valley +Δσ load ), Among them, Max TotalIncome is the total objective function, maxIncome is the net income objective function, CarbonIncome is the environmental income objective function, Penalty is the industrial user satisfaction objective function, and Reward is the power grid stability objective function. T is the total number of time periods for implementing the demand response project, and I is the number of industrial users participating in the demand response project. is the predicted electricity price for the park aggregator to sell the regulation capacity in time period t. is the capacity of industrial user i participating in the curtailment project in time period t. The capacity of industrial user i participating in the transfer project in time period t. is whether industrial user i participates in the curtailment project in time period t. If industrial user i participates in the curtailment project in time period t, then If industrial user i does not participate in the curtailment project in time period t, then is whether industrial user i participates in the transfer project in time period t. If industrial user i participates in the transfer project in time period t, then If industrial user i does not participate in the transfer project in time period t, then i is the industrial user index, and t is the time period index. is the income obtained by industrial user i from the park aggregator when participating in the curtailment project in time period t. is the compensation electricity price for industrial user i participating in the curtailment project in time period t. is the actual initial price that industrial user i can obtain from the park aggregator when participating in the curtailment project in time period t. is the income obtained by industrial user i from the park aggregator when participating in the transfer project in time period t. is the compensation electricity price for industrial user i participating in the transfer project in time period t. is the actual initial price that industrial user i can obtain from the park aggregator when participating in the transfer project in time period t. γ is the carbon trading market price. is the corresponding carbon emission reduction amount when industrial user i participates in the curtailment project / transfer project in time period t. λ1 is the curtailment weight coefficient, λ2 is the transfer weight index, μ is the power grid stability coefficient, and ΔP peak-valley is the change in the peak-valley difference before and after the implementation of the demand response, and Δσ load is the change in the load volatility.

7. The optimization method for an industrial user demand response strategy according to claim 1, characterized in that Regarding the process of obtaining the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day, the obtaining process is: By using the AP clustering method, classify the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day to obtain the set of normal production load curves, the set of curves with curtailment characteristics, and the set of curves with transfer characteristics corresponding to each industrial user; Take the mean curve of all curves in the set of normal production load curves corresponding to each industrial user as the normal production load curve of each industrial user; Through the k-means clustering algorithm, further classify the curves in the set of curves with curtailment characteristics and the set of curves with transfer characteristics corresponding to each industrial user to obtain multiple subsets of curves with curtailment characteristics and subsets of curves with transfer characteristics corresponding to each industrial user; Subtract each curve in the subset of curves with curtailment characteristics corresponding to each industrial user from the normal production load curve of the industrial user to obtain the curtailment curves of each curve in the multiple subsets of curves with curtailment characteristics corresponding to each industrial user; Take the mean of the curtailment curves of each curve in each subset of curves with curtailment characteristics corresponding to each industrial user, and use the resulting mean curve as the curtailment curve of each subset of curves with curtailment characteristics corresponding to the industrial user; Based on the curtailment curves of each subset of curves with curtailment characteristics corresponding to each industrial user, obtain the daily curtailment capacity, curtailment rate, minimum curtailment duration, and maximum curtailment duration of each industrial user on the response day; Based on multiple subsets of curves with transfer characteristics corresponding to each industrial user, obtain the daily transferable capacity, transfer rate, minimum transfer duration, and maximum transfer duration of each industrial user on the response day.

8. The optimization method for an industrial user demand response strategy according to claim 1, characterized in that Solve the demand response strategy optimization model by calling GORUBI in MATLAB to obtain the demand response strategy of the target industrial user on the response day.

9. An industrial user demand response strategy optimization system, characterized in that, It includes: A data acquisition module for obtaining the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, and maximum transfer duration of each industrial user on the response day based on the daily load curves of each industrial user in the industrial park for multiple consecutive days before the response day; A capacity prediction module for obtaining the curtailment capacity of each industrial user participating in the curtailment project at each time period on the response day based on the predicted electricity price at each time period on the response day and the daily curtailment capacity of each industrial user; Based on the predicted electricity price at each time period on the response day and the daily transferable capacity of each industrial user, obtain the transferable capacity of each industrial user participating in the transfer project at each time period on the response day; A solution module for substituting the daily curtailment capacity, daily transferable capacity, curtailment rate, transfer rate, minimum curtailment duration, maximum curtailment duration, minimum transfer duration, maximum transfer duration, the curtailment capacity of each industrial user participating in the curtailment project at each time period, and the transferable capacity of each industrial user participating in the transfer project at each time period into the demand response strategy optimization model, solve the demand response strategy optimization model, and obtain the demand response strategy of the target industrial user on the response day.

10. An industrial user demand response strategy optimization device, characterized in that, It includes: A memory for storing computer programs; A processor, configured to implement the steps of optimizing an industrial user demand response strategy according to any one of claims 1 to 8 when executing the computer program.

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