Industrial load adjustable potential assessment method and device considering security constraints

Abstract

The invention provides an industrial load adjustable potential assessment method and device considering security constraints, and relates to the technical field of potential assessment. The method comprises the following steps: constructing a high-energy-consumption industrial user side load model and establishing a load curve; clustering the load curve by using a black hole entropy fuzzy clustering algorithm and determining a load interruption interval and a transferable interval; corresponding indexes are obtained according to the corresponding intervals, and multiple indexes such as safety consideration are introduced; an improved G1 method based on Spearman rank correlation is used for carrying out weighting on each index and determining the weight, and then the adjustable potential is evaluated. In this way, data support is provided for power supply enterprises to reasonably dispatch user side resources. The device comprises a first construction module, a second construction module, a weight value determination module and a calculation module, all the modules cooperate with one another to achieve the industrial load adjustable potential evaluation method considering the security constraints, and quantitative evaluation of the adjustable capability of industrial users is greatly achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of potential assessment, and more particularly, to a method and device for assessing the adjustable potential of industrial loads taking safety constraints into consideration. Background Art

[0002] With the continuous advancement of industrialization, electricity demand has increased year by year, which has put forward higher requirements for the stability and security of the power grid. In this context, the evaluation of the adjustable potential of industrial loads has gradually become one of the important means to optimize the operation of power systems. Traditional industrial load management mainly relies on simple load reduction or peak shifting and valley filling methods, but these methods often lack refined control methods and are difficult to achieve the expected results in complex and changeable industrial environments. With the rapid development of smart grid and big data technology, it is possible to evaluate the adjustable potential of industrial loads through multi-source data fusion and intelligent algorithms. By real-time monitoring and analysis of multi-dimensional data such as the operating status of industrial equipment, power load data, and environmental parameters, the use of advanced algorithm models can more accurately evaluate the adjustable potential of industrial loads. Summary of the invention

[0003] In view of this, and in response to the technical difficulties existing in the current evaluation of the adjustable potential of industrial loads, the purpose of the present invention is to provide a method and device for evaluating the adjustable potential of industrial loads taking into account safety constraints, which can not only improve the operating efficiency and safety of the power grid, but also provide strong support for the energy consumption management and cost control of industrial enterprises, and promote the green and sustainable development of industrial production.

[0004] According to an embodiment of the present invention, an industrial load adjustable potential evaluation and device taking into account safety constraints is provided, which can effectively solve the problem that the adjustment capacity has not been fully tapped due to the unique characteristics of high-energy-consuming industrial loads in industrial parks, and the coordination between safety constraints and diversified adjustments is difficult.

[0005] In a first aspect of the present invention, a method for evaluating the adjustable potential of industrial loads considering safety constraints is provided, the method comprising:

[0006] Step 1: Construct a load model for high-energy-consuming industrial users and establish a load curve, including:

[0007] The high-energy-consuming industrial user-side load models constructed include: steel production load model, cement production load model and electrolytic aluminum load model.

[0008] The steel production load model is as follows:

[0009] P sz.i.t =P s.deal.i.t +P s.eaf.i.t

[0010] Where i represents the i-th steel production enterprise. t represents time t. zs.i.t Represents the total load of steel production by steel producer i at time t. s.deal.i.t Represents the power consumed by steel rolling. s.eaf.i.t Represents the power consumed by the electric arc furnace.

[0011] P s.deal.i.t The calculation method is as follows:

[0012]

[0013] Among them, k is the number of steel tires on the steel rolling production line per unit time. s.i is the quadratic coefficient of production consumption coefficient, b s.i is the first-order coefficient of production consumption coefficient. steel tire It is the unit power (Kw) consumed in producing a single steel tire. sk.deal.i.t is the fixed energy consumption (Kw) of the steel rolling production line in the hot start state.

[0014] P s.eaf.i.t The calculation is as follows:

[0015]

[0016] Where P s.eafs.i.t is the power output of the arc furnace when it is running stably (Kw), t s is the arc furnace start-up time (h), t e is the arc furnace stop time (h).

[0017] The cement production load model is as follows:

[0018]

[0019] Among them, P conz.i.t is the motor power (kW), Q is the raw material input (tons / h), E is the efficiency of the crushing equipment (%), η is the motor efficiency (%), V is the voltage (V), and cosφ is the power factor.

[0020] The electrolytic aluminum production load model is as follows:

[0021] M AL.i.t =[λ a1 +λ a2 (λ a3 +λ a4 P AL.i.t ) 0.5 ]Δt

[0022] Among them, P AL.i.tis the electrolytic cell power (Kw), M AL.i.t are the electrolytic cell output (g). a1 (g / h), λ a2 (unitless), λ a3 (g / h) 2 , a4 (g / Kw.h) 2 is the coefficient of relationship between electrolytic cell output and power.

[0023] Collect historical load data of high-energy-consuming industrial users, including power load and energy consumption data of various energy-consuming equipment. By collecting these power consumption data, we can understand the changes in energy demand of the system in different time periods.

[0024] Step 2: Use the black hole entropy fuzzy clustering algorithm to cluster the load curve and determine the load interruption interval and transferable interval, including:

[0025] Step 2.1: The black hole entropy fuzzy clustering algorithm imagines each cluster center as a black hole, so clustering can be reasonably regarded as finding black holes, and the black hole entropy (BHE) H in astronomy is introduced as follows:

[0026] H=A / 4+γln(A / 4)+β

[0027] Among them, A represents the field of view, γ and β are constants used for model calibration.

[0028] Step 2.2: Before clustering on the load curve dataset, use the S ij The sum is expressed as:

[0029]

[0030] Among them, x i is the i-th load curve; y j is the jth cluster center; λ is a normal constant defined by the user; u ij is the fuzzy membership of the i-th load curve to the j-th class; C is the number of clusters; N is the number of all load curves; S ij It is used to reflect the fuzzy membership size of the load curve formed by the jth surrounding cluster. The smaller it is, the more x i Belong to y j The greater the certainty;

[0031] The black hole entropy of the jth cluster is defined as follows:

[0032]

[0033] Step 2.3: According to steps 2.1 and 2.2, the final fuzzy objective function BHE(X, Y, U) of the black hole entropy fuzzy clustering algorithm is expressed as follows:

[0034]

[0035] Among them, X and Y represent a given load curve data set X with cluster center set Y; U is the membership matrix, and each u in the matrix ij Both are greater than 0.

[0036] Step 3: Obtain the corresponding indicators according to the corresponding intervals, including:

[0037] Step 3.1: The interruptible index includes the interruptible load capacity of index 1 and the interruptible time of index 2. The calculation formula is as follows:

[0038]

[0039] Among them, P int represents the interruptible load capacity; c represents the number of interruptible loads, a1 and a c Similar to a c As an example, it represents the proportion of load curves of the interruptible load c, P 11 and P 1c Similarly, P 1c As an example, it represents the average value of the load capacity in the interruption interval under different clustering results in the interruptible load c; t int Indicates the interruptible time; t1 and t c Similar, with t c Take as an example to illustrate the time of the interruptible load c under different clustering results.

[0040] Step 3.2: The transferable indicators include transferable capacity, transferable time and peak power consumption ratio. The transferable capacity of indicator 3 is as follows:

[0041]

[0042] Among them, P trans represents the transferable capacity, and its superscripts 1 and 2 correspond to different transferable interval numbers; a and b represent the amount of data in the transferable interval, respectively. 21 and P 2a Similar to P 21 For example, P 31 and P 3b Similar to P 31 As an example, they represent the load power values ​​corresponding to the monitoring points in the transferable interval 1 and the transferable interval 2 respectively; P 2min and P 3minRepresents the minimum power value within transferable interval 1 and transferable interval 2.

[0043] Step 3.3: Before calculating the transferable time, the peak moments need to be T max1 With T max2 Make a judgment, usually set a threshold based on expert experience, so indicator 4 can be transferred to time T trans as follows:

[0044] T trans =|T max1 -T max2 |

[0045] Step 3.4: The peak electricity consumption ratio of indicator 5 is as follows:

[0046] G r =W p / W d

[0047] Among them, G r Indicates the proportion of electricity consumption by users during peak hours; W p is the electricity consumption of industrial users during peak hours; W d A whole day's electricity consumption for industrial users.

[0048] Step 4: Introduce multiple indicators that consider safety constraints: load smoothness, peak-to-valley difference change rate, and responsiveness. Safety constraints are reflected through corresponding indicators. There are many indicators related to load safety. In order to improve computing performance, several of them are selected as safety indicators, including:

[0049] Step 4.1: The load smoothness index is used to characterize the smoothness of the load curve. When the system security is reduced and the curve fluctuates violently, the load smoothness index can well reflect this situation. Therefore, the root mean square value of the load curve is used as the index 6:

[0050]

[0051] Among them, P i represents the power value at the i-th point in a day, and N represents the number of user load sampling times in a day.

[0052] Step 4.2: The change rate of the peak-to-valley difference is defined as the ratio of the difference between the maximum load and the minimum load of industrial users in the industrial park in a day to the difference when they do not participate in demand response, and is used as an indicator7:

[0053]

[0054] Among them, H d Indicates the rate of change of the peak-to-valley difference; P p and Pv They represent the peak and valley values ​​of the system load of high-energy-consuming industrial users when they do not participate in DR; P p ′ and P v ′ respectively represent the peak and valley values ​​of the system load when high-energy-consuming industrial users participate in DR. Indicator 7 provides a basis for the power system to adjust load safety electricity use measures.

[0055] Step 4.3: Responsiveness is defined as the ratio of the actual response of the system load to its maximum response, which is selected as indicator 8:

[0056]

[0057] Among them, I i (t) is the responsiveness at time t; P i ′(t) is the system load at time t, indicating that there are flexible resources participating in demand response; P i,0 (t) is the system load at time t, representing no demand response participation; P i,max (t) is the maximum response mount of user load participating in time t, γ shift (t), γ trans (t) and γ cut (t) are the proportions of transferable, shifted, and reduced user loads, respectively; Indicator 8 describes the actual level of user load participating in security demand response at time t, which is closely related to factors such as the power consumption characteristics of the load and the interaction mechanism of the dispatch center.

[0058] Step 5: Use the improved G1 method based on Spearman rank correlation (SRC) to weight each indicator and determine the weight, including:

[0059] Step 5.1: To weight the eight indicators, we first need to use the rank sizes of the two variables for correlation analysis:

[0060]

[0061] Where d = (s i -s y ), s i and y is the rank of two variables sorted by size or quality, n is the sample size; the range of SRC is [-1,1], and the correlation is proportional to the absolute value of SRC. When the information between indicators is similar, their effect is smaller. When the information overlap between indicators is small, their effect is greater, so the set weight also increases. Therefore, the weight ratio of two adjacent indicators is calculated as follows:

[0062]

[0063] Step 5.2: Calculate the value of each indicator M and perform normalization. Normalization includes positive indicators and negative indicators, as follows:

[0064]

[0065] Where j represents the number of indicators, ranging from [1,8].

[0066] Step 5.3: Calculate the importance of the indicator by having the expert group provide information on the indicator;

[0067] Step 5.4: Use SRC to calculate the correlation between adjacent indicators;

[0068] Step 5.5: Calculate the indicator weight factor based on the correlation coefficient;

[0069] Step 5.6: Calculate the overall user score; the comprehensive score formula is as follows:

[0070]

[0071] Among them, W j Represents the weight assigned to each indicator.

[0072] Step 6: Evaluate the adjustable potential, specifically including: using a comprehensive evaluation method to obtain the adjustable potential score of user-side resources in the participating DR areas.

[0073] In a second aspect of the present invention, there is provided an industrial load adjustable potential assessment device considering safety constraints, the device comprising:

[0074] A first building block for establishing a load curve;

[0075] The second building module is used to build a load interruption interval and a transferable interval and multiple safety indicators;

[0076] A weight value determination module is used to determine the weight value of each indicator using the Spearman rank correlation method;

[0077] The calculation module is used to calculate the industrial load resource potential score using the improved G1 algorithm to obtain the demand response potential and the dispatchable capacity of the demand response area.

[0078] The above scheme can realize the evaluation of the adjustable potential of user-side resources in the regions participating in demand response, and provide data support for power supply companies to rationally dispatch user-side resources.

[0079] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0081] Figure 1 A flowchart of a method for evaluating the adjustable potential of industrial loads considering safety constraints according to an embodiment of the present invention is shown;

[0082] Figure 2 A schematic diagram of multiple calculation interval definitions according to an embodiment of the present invention is shown;

[0083] Figure 3 shows an elbow criterion diagram for determining an optimal number of primary clusters according to an embodiment of the present invention;

[0084] Figure 4 A clustering accuracy diagram of the first type of load regulation potential according to an embodiment of the present invention is shown;

[0085] Figure 5 A block diagram of an industrial load adjustable potential assessment device considering safety constraints according to an embodiment of the present invention is shown; DETAILED DESCRIPTION

[0086] To illustrate the purpose, technical solutions and benefits of the embodiments of the present invention, the following is a description of the attached drawings in conjunction with the embodiments of the present invention. Figure 1 The present invention clearly and comprehensively describes the technical solution. The clear description is for some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work belong to the protection scope of the present invention.

[0087] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0088] The high-energy-consuming industrial users selected in this embodiment are usually involved in energy-intensive production activities, and their energy consumption level is significantly higher than that of general industrial users. Their energy consumption is huge, usually accounting for more than half of the total energy consumption of the entire factory. The equipment needs to run continuously and has high power requirements.

[0089] Example 1

[0090] In the specific embodiment 1, 9 industrial users in a certain area are selected as the research objects for analysis. The above-mentioned index values ​​are obtained from the annual electricity consumption data of industrial users. In order to provide a clearer explanation, this embodiment is implemented with the data of only one user before calculating the potential score. The user-side resource adjustable potential score is calculated and analyzed for the improved G1 algorithm.

[0091] Figure 1 A flow chart of a method for evaluating the adjustable potential of industrial loads considering safety constraints according to an embodiment of the present invention is shown. The method comprises:

[0092] Step 1 is to build a load model for high-energy-consuming industrial users and establish a load curve, which includes:

[0093] Step 1.1: The constructed high-energy-consuming industrial user-side load models include: steel production load model, cement production load model and electrolytic aluminum load model.

[0094] The steel production load model is as follows:

[0095] P sz.i.t =P s.deal.i.t +P s.eaf.i.t

[0096] Where i represents the i-th steel production enterprise. t represents time t. zs.i.t Represents the total load of steel production by steel producer i at time t. s.deal.i.t Represents the power consumed by steel rolling. s.eaf.i.t Represents the power consumed by the electric arc furnace.

[0097] P s.deal.i.t The calculation method is as follows:

[0098]

[0099] Among them, k is the number of steel tires on the steel rolling production line per unit time. s.i is the quadratic coefficient of production consumption coefficient, b s.i is the first-order coefficient of production consumption coefficient. steel tire It is the unit power (Kw) consumed in producing a single steel tire. sk.deal.i.t is the fixed energy consumption (Kw) of the steel rolling production line in the hot start state.

[0100] P s.eaf.i.t The calculation is as follows:

[0101]

[0102] Where Ps.eafs.i.t is the power output of the arc furnace when it is running stably (Kw), t s is the arc furnace start-up time (h), t e is the arc furnace stop time (h).

[0103] The cement production load model is as follows:

[0104]

[0105] Among them, P conz.i.t is the motor power (kW), Q is the raw material input (tons / h), E is the efficiency of the crushing equipment (%), η is the motor efficiency (%), V is the voltage (V), and cosφ is the power factor.

[0106] The electrolytic aluminum production load model is as follows:

[0107] M AL.i.t =[λ a1 +λ a2 (λ a3 +λ a4 P AL.i.t ) 0.5 ]Δt

[0108] Among them, P AL.i.t is the electrolytic cell power (Kw), M AL.i.t are the electrolytic cell output (g). a1 (g / h), λ a2 (unitless), λ a3 (g / h) 2 , a4 (g / Kw.h) 2 is the coefficient of relationship between electrolytic cell output and power.

[0109] Step 1.2: Collect historical load data of high-energy-consuming industrial users, including power load and energy consumption data of various energy-consuming equipment. By collecting these power consumption data, we can understand the changes in energy demand of the system in different time periods.

[0110] Step 2 is to cluster the load curve using the black hole entropy fuzzy clustering algorithm and determine the load interruption interval and transferable interval, which specifically includes:

[0111] Step 2.1: Black hole entropy fuzzy clustering algorithm imagines each cluster center as a black hole, so clustering can be reasonably regarded as finding black holes, and the black hole entropy (BHE) H in astronomy is introduced as follows:

[0112] H=A / 4+γln(A / 4)+β

[0113] Among them, A represents the field of view, γ and β are constants used for model calibration.

[0114] Step 2.2: Before clustering the load curve data set, since the size of each cluster is unknown, the S between the data object and the cluster center is used. ij The sum is expressed as:

[0115]

[0116] Among them, x i is the i-th load curve; y j is the jth cluster center; λ is a normal constant defined by the user; u ij is the fuzzy membership of the i-th load curve to the j-th class; C is the number of clusters; N is the number of all load curves; S ij It is used to reflect the fuzzy membership size of the load curve formed by the jth surrounding cluster. The smaller it is, the more x i Belong to y j The greater the certainty.

[0117] The BHE of the jth cluster is defined as follows:

[0118]

[0119] Step 2.3: According to the introduction in steps 2.1 and 2.2, the final fuzzy objective function BHE(X, Y, U) of the black hole entropy fuzzy clustering algorithm can be expressed as follows:

[0120]

[0121] Among them, X and Y represent a given load curve data set X with cluster center set Y; U is the membership matrix, and each u in the matrix ij All are greater than 0. It should be noted that the idea of ​​the black hole entropy fuzzy clustering algorithm is to interpret clustering as a simulation of the black hole phenomenon by data objects. The clustering results obtained by this technology under the condition of providing a new and understandable explanation for the fuzzifier are used to determine the interrupt interval and the transfer interval. Figure 2 First, based on the clustering results, we take the interruptible curve. At this time, we set a variable η i and threshold β, where ΔP i To express the load capacity difference, η i It is used to determine the recorded ΔP i If it is greater than 0, it is 1, otherwise it is -1. Then record η iCurve, if the number of times 1 appears continuously exceeds β, this interval is defined as ①; if the number of times -1 appears continuously exceeds β, this interval is defined as ③; between the two intervals is defined as ②. At this time, the interruption interval can be obtained as ②; by repeatedly recording η i Curve, the transfer interval range is from ① to ③.

[0122] Step 3 is to obtain the corresponding indicators according to the corresponding intervals, which specifically include:

[0123] Step 3.1: The interruptible index includes the interruptible load capacity of index 1 and the interruptible time of index 2. The calculation formula is as follows:

[0124]

[0125] Among them, P int represents the interruptible load capacity; c represents the number of interruptible loads, a1 and a c Similar to a c As an example, it represents the proportion of load curves of the interruptible load c, P 11 and P 1c Similarly, P 1c As an example, it represents the average value of the load capacity in the interruption interval under different clustering results in the interruptible load c; t int Indicates the interruptible time; t1 and t c Similar, with t c Take as an example to illustrate the time of the interruptible load c under different clustering results.

[0126] Step 3.2: The transferable indicators include transferable capacity, transferable time and peak power consumption ratio. The transferable capacity of indicator 3 is as follows:

[0127]

[0128] Among them, P trans represents the transferable capacity, and its superscripts 1 and 2 correspond to different transferable interval numbers; a and b represent the amount of data in the transferable interval, respectively. 21 and P 2a Similar to P 21 For example, P 31 and P 3b Similar to P 31 For example, P 21 and P 31 Respectively represent the load power values ​​corresponding to the monitoring points in transferable interval 1 and transferable interval 2; P 2min and P 3min Represents the minimum power value within transferable interval 1 and transferable interval 2.

[0129] Step 3.3: Before calculating the transferable time, the peak moments need to be T max1 With T max2 Make a judgment, usually set a threshold based on expert experience, so indicator 4 can be transferred to time T trans as follows:

[0130] T trans =|T max1 -T max2 |

[0131] Step 3.4: The peak electricity consumption ratio of indicator 5 is as follows:

[0132] G r =W p / W d

[0133] Among them, G r Indicates the proportion of electricity consumption by users during peak hours; W p is the electricity consumption of industrial users during peak hours; W d A whole day's electricity consumption for industrial users.

[0134] Among them, step 4 introduces multiple indicators that consider safety constraints: load smoothness, peak-to-valley difference change rate, and responsiveness. Safety constraints are reflected through corresponding indicators. There are many indicators related to load safety. In order to improve computing performance, several of them are selected as safety indicators, including:

[0135] Step 4.1: The load smoothness index is used to characterize the smoothness of the load curve. When the system security is reduced and the curve fluctuates violently, the load smoothness index can well reflect this situation. Therefore, the root mean square value of the load curve is used as the index 6:

[0136]

[0137] Among them, P i represents the power value at the i-th point in a day, and N represents the number of user load sampling times in a day.

[0138] Step 4.2: The change rate of the peak-to-valley difference is defined as the ratio of the difference between the maximum load and the minimum load of industrial users in the high-energy consumption park in a day to the difference when they do not participate in demand response, and is used as an indicator7:

[0139]

[0140] Among them, H d Indicates the rate of change of the peak-to-valley difference; P p and P vThey represent the peak and valley values ​​of the system load of high-energy-consuming industrial users when they do not participate in DR; P p ′ and P v ′ respectively represent the peak and valley values ​​of the system load when high-energy-consuming industrial users participate in DR. Indicator 7 provides a basis for the power system to adjust load safety electricity use measures.

[0141] Step 4.3: Responsiveness is defined as the ratio of the actual response of the system load to its maximum response, which is selected as indicator 8:

[0142]

[0143] Among them, I i (t) is the responsiveness at time t; P i ′(t) is the system load at time t, indicating that there are flexible resources participating in demand response; P i,0 (t) is the system load at time t, representing no demand response participation; P i,max (t) is the maximum response mount of user load participating in time t, γ shift (t), γ trans (t ) and γ cut (t) are the proportions of user loads that can be transferred, transferred, and reduced, respectively. Indicator 8 describes the actual level of user load participating in security demand response at time t, which is closely related to factors such as the power consumption characteristics of the load and the interaction mechanism of the dispatch center. The final indicator system is shown in Table 1.

[0144] Table 1 Load adjustable potential index system

[0145]

[0146] Among them, step 5 is to use the improved G1 method based on Spearman rank correlation (SRC) to weight each indicator and determine the weight, which specifically includes:

[0147] Step 5.1: To weight the eight indicators, we first need to use the rank sizes of the two variables for correlation analysis:

[0148]

[0149] Where d = (s i -s y ), s i and yis the rank of two variables sorted by size or quality, and n is the sample size. The range of SRC is [-1,1], and the correlation is proportional to the absolute value of SRC. When the information between indicators is similar, their effect is smaller. When the information overlap between indicators is small, their effect is greater, so the set weight also increases. Therefore, the weight ratio of two adjacent indicators is calculated as follows:

[0150]

[0151] Step 5.2: Calculate the value of each indicator M and perform normalization. Normalization includes positive indicators and negative indicators, as follows:

[0152]

[0153] Where j represents the number of indicators, ranging from [1,8].

[0154] Step 5.3: Calculate the importance of the indicator by having the expert group provide information on the indicator;

[0155] Step 5.4: Use SRC to calculate the correlation between adjacent indicators;

[0156] Step 5.5: Calculate the indicator weight factor based on the correlation coefficient;

[0157] Step 5.6: Calculate the overall user score. The comprehensive score formula is as follows:

[0158]

[0159] Among them, W j Represents the weight assigned to each indicator. In this embodiment, the weights of each indicator are obtained as shown in Table 2.

[0160] Table 2 Weights of each indicator

[0161]

[0162]

[0163] Step 6 is to evaluate the adjustable potential, which specifically includes: using a comprehensive evaluation method to obtain the adjustable potential scores of user-side resources in the participating DR areas and sorting them. The results are shown in Table 3.

[0164] Table 3 Comprehensive adjustable potential and priority of each user load

[0165]

[0166] As can be seen from Table 3, user 4 has the highest adjustable potential, so it should be given priority in load regulation. User 2 has a higher transfer potential, but considering that the interruption potential and production potential ranking of this user are not optimal, its final comprehensive potential ranks fourth, and users 6 and 8 have the lowest adjustable potential. Compared with the actual situation, it can be found that the process of evaluating the adjustable potential of user loads using the proposed method is effective to a certain extent. The proposed method can formulate configuration plans for different industrial users and promote the safe and stable operation of the power grid in high-energy-consuming parks.

[0167] Example 2

[0168] In specific embodiment 2, 31 high-energy-consuming industrial users in a certain administrative region of a province are taken as the research objects. The electricity consumption data mainly comes from the electricity meter, and the sampling period of the monitoring data is 1 hour, so there are 24 data points every day. The user basic information collection data contains 8 indicators that affect the user's electricity consumption behavior and safety, which are uniformly expressed in digital form. Since the user's actual adjustable potential is different from the theoretical value, the confidence level of the user's adjustable potential should also be considered. The data is set as the basic data of the cluster. According to the combination of power consumption and participation adjustment value, the adjustable capacity and confidence are obtained.

[0169] By performing black hole entropy fuzzy clustering on the historical data of different high-energy-consuming industrial users, such as Figure 3 According to the elbow method, the optimal number of clusters in a single cluster is determined to be 3. Different users are divided into three subcategories, as shown in Table 4.

[0170] Table 4 Clustering results of load data of the first clustering

[0171]

[0172] The same category of users is clustered again, and each category is further divided into four subcategories, namely high reliability and high adjustment ability, high reliability and low adjustment ability, low reliability and high adjustment ability, and low reliability and low adjustment ability. The reliability level corresponds to the confidence level. Adjustment ability represents the user's adjustable potential. Users with high reliability and high adjustability represent users with high adjustability and high probability of participating in adjustability, according to their expected adjustability values. See Table 5.

[0173] Table 5. Results of clustering similar users again

[0174]

[0175]

[0176] From the classification of these industrial users and the actual situation, it can be seen that users with high adjustment capabilities are generally time-shift users with flexible adjustment, such as machine tool processing and heat storage boilers. Users with low adjustment capabilities are generally users of metal synthesis equipment such as electrolytic magnesium and silicon carbide. Reliability mainly depends on user capacity and incentive mechanism. When the user capacity is large or the product demand is large, the enthusiasm for participating in the adjustment is generally low, that is, the adjustment reliability is low.

[0177] The annual historical data of the first type of load involved in regulation are compared with the clustering results. Figure 4 . The regulation potential of load 17, load 8, load 9, load 1, load 15, and load 20 is relatively high. However, compared with load 17, load 8, and load 9, load 1, load 15, and load 20 have a large deviation in the actual amount of participation in supervision, which can be as high as 67%. The participation in supervision activities is not high and the reliability is low. Similarly, although the adjustment potential of load 6 and load 19 is relatively weak, the deviation between theory and practice is small, and the reliability of participation in adjustment is relatively high, which can meet the reliability demand response of emergency regulation of the power grid. The effectiveness of the industrial load adjustable potential evaluation method considering safety constraints is also proved through specific embodiment 2.

[0178] Example 3

[0179] In specific embodiment 3, the scheme of the present invention is further illustrated below by means of a device embodiment having the same inventive concept as the method in the aforementioned embodiment.

[0180] like Figure 5 As shown, the device comprises:

[0181] A first building block for establishing a load curve;

[0182] A second construction module is used to construct the load interruption interval and the transferable interval and multiple safety indicators;

[0183] A weight value determination module is used to determine the weight value of each indicator using the Spearman rank correlation method;

[0184] The calculation module is used to calculate the industrial load resource potential score using the improved G1 algorithm to obtain the demand response potential and the dispatchable capacity of the area.

[0185] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0186] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0187] According to an embodiment of the present invention, the present invention also provides a computer device and a storage medium, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the industrial load adjustable potential assessment method considering safety constraints in the above embodiment.

[0188] The storage medium is specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a method for evaluating the adjustable potential of industrial loads considering safety constraints in the above embodiment.

[0189] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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.

[0190] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0191] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0192] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0193] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the adjustable potential of industrial loads considering safety constraints, characterized in that: include: Step 1: Construct a load model for high-energy-consuming industrial users and establish a load curve; Step 2: Use the black hole entropy fuzzy clustering algorithm to cluster the load curve and determine the load interruption interval and transferable interval; Step 3: Obtain corresponding indicators according to the corresponding intervals, including interruptible load capacity as indicator 1, interruptible time as indicator 2, transferable capacity as indicator 3, transferable time as indicator 4, and peak power consumption proportion as indicator 5; Step 4: Introduce multiple indicators that consider safety constraints, including the RMS value of the load curve as indicator 6, the rate of change of the peak-to-valley difference as indicator 7, and the responsiveness as indicator 8; Step 5: Use the improved G1 method based on Spearman rank correlation to weight each indicator and determine the weight; Step 6: Use a comprehensive evaluation method to obtain the user-side resource adjustable potential score of the demand response area and evaluate the load adjustable potential.

2. The method for evaluating the adjustable potential of industrial loads considering safety constraints according to claim 1 is characterized in that: The high-energy-consuming industrial user-side load models constructed include: steel production load model, cement production load model and electrolytic aluminum load model; The steel production load model is as follows: P sz.i.t =P s.deal.i.t +P s.eaf.i.t Where i represents the i-th steel production enterprise; t represents time t; P zs.i.t represents the total load of steel production by steel producer i at time t; P s.deal.i.t Represents the power consumption of steel rolling; P s.eaf.i.t Represents the power consumed by the electric arc furnace; P s.deal.i.t The calculation method is as follows: Among them, k is the number of steel tires on the steel rolling production line per unit time; a s.i is the quadratic coefficient of production consumption coefficient, b s.i is the first-order coefficient of production consumption coefficient; P steel tire is the unit power consumed in producing a single steel tire; P sk.deal.i.t is the fixed energy consumption of the steel rolling production line in the hot start state; P s.eaf.i.t The calculation is as follows: Where P s.eafs.i.t is the output power of the arc furnace during stable operation, t s is the arc furnace start-up time, t e is the arc furnace stop time; The cement production load model is as follows: Among them, P conz.i.t is the motor power, Q is the raw material input, E is the efficiency of the crushing equipment, η is the motor efficiency, V is the voltage, and cosφ is the power factor; The electrolytic aluminum production load model is as follows: M AL.i.t =[λ a1 +λ a2 (l a3 +λ a4 P AL.i.t ) 0.5 ]Δt Among them, P AL.i.t is the electrolytic cell power, M AL.i.t are the electrolytic cell output, a1 , a2 , a3 , a4 is the coefficient of relationship between electrolytic cell output and power.

3. The method for evaluating the adjustable potential of industrial loads considering safety constraints according to claim 2 is characterized in that: The process of establishing a load curve includes: collecting historical load data of high-energy-consuming industrial users, including power load and energy consumption data of various energy-consuming equipment.

4. The method for evaluating the adjustable potential of industrial loads considering safety constraints according to claim 3 is characterized in that: Step 2 includes: Step 2.1: Determine the black hole entropy as follows: H=A / 4+γln(A / 4)+β Among them, H represents the black hole entropy, A represents the field of view, and γ and β are constants; Step 2.2: Before clustering on the load curve dataset, use the S ij The sum is expressed as: st1≤i≤N,1≤j≤C Among them, x i is the i-th load curve; y j is the jth cluster center; λ is a normal constant defined by the user; u ij is the fuzzy membership of the i-th load curve to the j-th class; C is the number of clusters; N is the number of all load curves; S ij It is used to reflect the fuzzy membership size of the load curve formed by the jth surrounding cluster. The smaller it is, the more x i Belong to y j The greater the certainty; The black hole entropy of the jth cluster is defined as follows: Step 2.3: According to steps 2.1 and 2.2, the final fuzzy objective function BHE(X, Y, U) of the black hole entropy fuzzy clustering algorithm is expressed as follows: Among them, X and Y represent a given load curve data set X with cluster center set Y; U is the membership matrix, and each u in the matrix ij Both are greater than 0.

5. The method for evaluating the adjustable potential of industrial loads considering safety constraints according to claim 4 is characterized in that: The process of obtaining the corresponding indicator according to the corresponding interval includes: Step 3.1: Determine the interruptibility index, which includes the interruptibility load capacity as index 1 and the interruptibility time as index 2. The calculation formula is as follows: Among them, P int represents the interruptible load capacity; c represents the number of interruptible loads, a1 and a c Similar to a c represents the proportion of load curves of the interruptible load c, P 11 and P 1c Similar, P 1c represents the average value of the load capacity in the interruption interval under different clustering results in the interruptible load c; t int Indicates the interruptible time; t1 and t c Similar, t c It represents the time of the interruption interval in the interruptible load c under different clustering results; Step 3.2: Determine the transferable indicators, which include transferable capacity, transferable time and peak power consumption ratio. The transferable capacity as indicator 3 is as follows: Among them, P trans represents the transferable capacity, and its superscripts 1 and 2 correspond to different transferable interval numbers; a and b represent the amount of data in the transferable interval, respectively. 21 and P 2a Similar, P 31 and P 3b Similar, P 21 and P 31 Respectively represent the load power values ​​corresponding to the monitoring points in transferable interval 1 and transferable interval 2; P 2min and P 3min represents the minimum power value in the transferable interval 1 and the transferable interval 2; Step 3.3: Before calculating the transferable time, the peak moments need to be T max1 With T max2 Make a judgment and set a threshold, so the transfer time T as indicator 4 trans as follows: T trans =|T max1 -T max2 | Step 3.4: The peak electricity consumption ratio as indicator 5 is as follows: G r =W p / W d Among them, G r Indicates the proportion of electricity consumption by users during peak hours; W p is the electricity consumption of industrial users during peak hours; W d A whole day's electricity consumption for industrial users.

6. The method for evaluating the adjustable potential of industrial loads considering safety constraints according to claim 5 is characterized in that: Step 4 specifically includes: Step 4.1: The load smoothness index is used to characterize the smoothness of the load curve. The root mean square value of the load curve is used as index 6: Among them, P i represents the power value at the i-th point in a day, and N represents the number of sampling times of the user load in a day; Step 4.2: The change rate of the peak-to-valley difference is defined as the ratio of the difference between the maximum load and the minimum load of industrial users in the industrial park in a day to the difference when they do not participate in demand response, and is used as an indicator7: Among them, H d Indicates the rate of change of the peak-to-valley difference; P p and P v They represent the peak and valley values ​​of the system load of high-energy-consuming industrial users when they do not participate in DR; P p ′ and P v ′ respectively represent the peak and valley values ​​of system load when high-energy-consuming industrial users participate in DR; Step 4.3: Responsiveness is defined as the ratio of the actual response of the system load to its maximum response, which is selected as indicator 8: Among them, I i (t) is the responsiveness at time t; P i ′(t) is the system load at time t, indicating that there are flexible resources participating in demand response; P i,0 (t) is the system load at time t, representing no demand response participation; P i,max (t) is the maximum response mount of user load participating in time t, γ shift (t), γ trans (t) and γ cut (t) are the proportions of user load that can be transferred, shifted, and reduced, respectively.

7. The method for evaluating the adjustable potential of industrial loads considering safety constraints according to claim 6 is characterized in that: Step 5 uses the improved G1 method based on Spearman rank correlation SRC, specifically including: Step 5.1: To weight the eight indicators, we first need to use the rank sizes of the two variables for correlation analysis: Where d = (s i -s y ), s i and y It is the rank of two variables sorted by size or quality, n is the sample size; the range of Spearman rank correlation SRC is [-1,1], and the correlation is proportional to the absolute value of SRC; the weight ratio of two adjacent indicators is calculated as follows: Step 5.2: Calculate the value of each indicator M and perform normalization; normalization includes positive indicators and negative indicators, as follows: Where j represents the number of indicators, ranging from [1,8]; Step 5.3: Calculate the importance of the indicator by having the expert group provide information on the indicator; Step 5.4: Use SRC to calculate the correlation between adjacent indicators; Step 5.5: Calculate the indicator weight factor based on the correlation coefficient; Step 5.6: Calculate the overall user score; the comprehensive score formula is as follows: Among them, W j Represents the weight assigned to each indicator.

8. An industrial load adjustable potential assessment device considering safety constraints, characterized in that: include: A first building block is used to establish a load power curve; The second building module is used to build a load interruption interval, a transferable interval and multiple safety indicators; A weight value determination module is used to determine the weight value of each indicator using the Spearman rank correlation method; The calculation module is used to calculate the industrial load resource potential score by using the improved G1 method based on Spearman rank correlation SRC to obtain the demand response potential and the dispatchable capacity of the demand response area.

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