Evaluation method and storage medium for measuring the effect of user participation in quasi-linear demand response

Abstract

The present invention provides an evaluation method and storage medium for measuring the effect of user participation in quasi-linear demand response. The method includes acquiring large-scale user load data, analyzing user electricity consumption behavior, clustering users, and establishing a user clustering model; establishing a load quasi-line calculation model for large-scale users; considering the initiative and actual contribution to measure the evaluation method of user participation in quasi-linear demand response; and setting the penalty factor and tolerance under the load quasi-line to incentivize users to participate in quasi-linear demand response. The present invention designs an incentive mechanism to incentivize users to participate in quasi-linear demand response by setting the penalty factor and tolerance under the load quasi-line in terms of time scale and user category based on the user response behavior after clustering. The present invention defines the shape of the load curve for quasi-linear demand response, calculates the load quasi-line, and defines an evaluation method for measuring the effect of user participation in quasi-linear demand response, while considering the initiative and actual contribution of users.

Description

Technical Field

[0001] The present invention relates to the technical field of quasi-linear demand response, and in particular to an evaluation method and storage medium for measuring the effect of user participation in quasi-linear demand response. Background Art

[0002] In new power systems dominated by renewable energy, the regulation capacity of the generation side has significantly declined, and there is an urgent need to fully tap the regulation capacity of the demand side to compensate for the lack of flexibility caused by the integration of renewable energy. To address the problem that current demand response classification methods are crude and fail to reflect its essential characteristics, a classification principle based on the user contribution evaluation method in demand response is proposed. Existing studies have divided existing mechanisms into three categories: price-based, baseline-based, and quasi-linear. Traditional price-based and baseline-based mechanisms are difficult to adapt to the development of new power systems. Unlike these two types of mechanisms, which attempt to indirectly change user electricity consumption behavior through certain signals, the quasi-linear mechanism directly defines the user load curve shape expected by demand response implementers. This can fundamentally overcome the problems of other mechanisms such as small scale, unsustainability, lack of transparency, and susceptibility to rebound, and can well adapt to the development needs of new power systems.

[0003] The current scale and frequency of demand response implementation are relatively limited, and the results have not met expectations. Another widely studied demand response mechanism is leveraging price signals to tap into user regulation potential. The most common is peak-valley time-of-use pricing, but in practice, this has also led to phenomena such as load inversion and rebound.

[0004] Some scholars have proposed a demand response mechanism that uses a load baseline as an evaluation criterion within quasi-linear demand response. If the baseline measures how much a user has changed relative to their own needs, and there are as many baselines as there are users, then the baseline describes what kind of load is system-friendly, and uses this curve to guide diverse users to respond autonomously. Therefore, quasi-linear demand response is a goal-oriented mechanism. Summary of the Invention

[0005] The present invention proposes an evaluation method for measuring the effect of user participation in quasi-linear demand response, which can solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] An evaluation method for measuring the effect of user participation in quasi-linear demand response includes the following steps:

[0008] Step 1: Cluster large-scale users to obtain N user groups that take into account differences in energy consumption behavior and demand response characteristics;

[0009] Step 2: Based on the N user groups obtained after clustering in step 1, which take into account differences in energy consumption behavior and demand response characteristics, a load guideline calculation model for large-scale users is established;

[0010] Step 3: Based on the N user groups obtained in step 1 that take into account energy consumption differences and demand response characteristics, and combined with the load quasi-linear calculation model for large-scale users established in step 2, define a method for evaluating the effectiveness of user participation in quasi-linear demand response, which can take into account both user enthusiasm and actual contribution;

[0011] Step 4: Based on the N user groups obtained after clustering in step 1, the load quasi-linear calculation model for large-scale users established in step 2, and the evaluation method for measuring the effectiveness of large-scale users participating in quasi-linear demand response obtained in step 3, according to the demand response characteristics of the clustered user groups, inclusiveness and penalty factors are set in time scales and user categories to encourage users to participate in quasi-linear demand response.

[0012] Furthermore, the steps specifically include:

[0013] Step 1.1: Obtain the user's historical electricity consumption data, including: the user's historical electricity load curve, the user's historical demand response capability, and the user's historical demand response willingness; obtain historical electricity price changes, and calculate the user's electricity price sensitivity coefficient;

[0014] Step 1.2: Determine clustering characteristics, including historical user demand response capabilities, historical user demand response willingness, user electricity price sensitivity coefficient, historical user load proportions, and historical typical day electricity load curves.

[0015] The user's historical load proportions include basic load proportion, interruptible load proportion, shiftable load proportion and transferable load proportion;

[0016] Step 1.3: Based on the clustering feature values ​​selected in step 1.2, a fuzzy C-means clustering algorithm is used to perform clustering modeling on a large number of users to obtain N clustered user groups.

[0017] Furthermore, the step 2 specifically includes:

[0018] Step 2.1: The load line of large-scale users is composed of the basic load, interruptible load, shiftable load and transferable load of the N user groups obtained after clustering in step 1, that is,

[0019]

[0020] Where: P D (t), P B,i (t), P I,i (t), P L,i (t), PT,i (t) is the load line value of large-scale users in time period t, the basic load of N user groups, the interruptible load, the shiftable load and the transferable load, They are the basic load proportion, interruptible load proportion, shiftable load proportion and transferable load proportion of user group i in period t, respectively;

[0021] And there are the following constraints:

[0022]

[0023] Step 2.2: Calculate the ideal load criterion based on the system operating parameters of large-scale users, and establish a load criterion calculation model for large-scale users.

[0024] Furthermore, the load guideline calculation model for large-scale users is established in step 2.2 as follows:

[0025] Step 2.2.1. Construct an objective function, which is to minimize the operating costs of large-scale user systems while maximizing the consumption of new energy.

[0026]

[0027] Where: P G,j (t) is the active power output of the jth adjustable generator in the large-scale user system during period t; n G is the number of adjustable generators; P R (t) is the output of all renewable energy power generation including photovoltaic and wind power in the large-scale user system during period t; P R,max (t) is the theoretical maximum output of renewable energy power generation in period t, which is obtained by day-ahead prediction; a j 、b j 、c j are the power generation cost coefficients of the jth adjustable generator; C R is the penalty coefficient for curtailing wind and solar power; the first term of the objective function is the total operating cost of the adjustable generator set, and the second term of the objective function is the penalty cost for curtailing wind and solar power;

[0028] Step 2.2.2: Construct the constraint conditions and the corresponding power balance equation, namely

[0029]

[0030] Where: P D (t) is the demand response load baseline value of large-scale users in period t; P C (t) is the non-adjustable load power during period t.

[0031] Furthermore, the step 3 specifically includes:

[0032] Step 3.1 defines the load curve shape;

[0033] Let l(t) be a load curve. The total power in time period T is Based on W(t), l(t) is normalized to

[0034]

[0035] Where: l * (t) is the load curve l(t) after normalization function f u () after the per-unit value;

[0036] Step 3.2: Use cosine similarity to calculate the user's enthusiasm for participating in quasi-linear demand response;

[0037] Assume that the point on the load curve of user group i at time t is l i (x i (t), y i (t)), the point on the load line is l D (x D (t), y D (t)) After step 3.1, the function f is normalized. u The per-unit values ​​after () are Then the cosine of the angle between the two is calculated as follows:

[0038]

[0039] Where: ε i (t) is an indicator that measures the similarity of the curve shape at time t, indicating the degree of enthusiasm of user group i in participating in the quasi-linear demand response;

[0040] Step 3.3, calculating the actual contribution of users to the quasi-linear demand response based on a one-way distance measurement method;

[0041] The one-way distance metric is defined as follows:

[0042]

[0043] Where: point p is a point on the curve l1, |l1| represents the length of the curve l1, and d(p,l2) represents the distance from point p to the curve l2. Simplify the above formula

[0044]

[0045] Based on the basic idea of ​​OWD distance, an indicator is defined to measure the actual contribution of users to quasi-linear demand response, which is expressed as

[0046]

[0047] Where: is the OWD distance between the two curves after normalization; R i (t) is the measurement index of OWD distance, which is defined as the contribution of different load curves to quasi-linear demand response, and ψ is a given constant value.

[0048] Step 3.4: Define an evaluation method to measure the effectiveness of user participation in quasi-linear demand response.

[0049] Calculate the weighted sum of the activeness in step 3.2 and the contribution in step 3.3 to measure the effect of quasi-linear demand response. Define an evaluation method for measuring user participation in quasi-linear demand response. The expression is:

[0050] χ i (t) = ε i (t)·R i (t) (10)

[0051] Where: i (t) is the evaluation method for measuring the participation of user group i in quasi-linear demand response at time t.

[0052] Furthermore, the step 4 specifically includes:

[0053] Step 4.1: Set the inclusiveness α on the time scale for the N user groups obtained after clustering in step 1 i (t);

[0054]

[0055] Where: α i (t) is the inclusiveness of user group i at time t; P D,i (t) and P B,i (t) are the quasi-linear demand response load quasi-linear value and actual response value of user group i at time t; ρ i0 (t) and ρ i1 (t) are the compliance rate and the cap rate of inclusiveness; ε i (t) is the indicator of user group i participating in quasi-linear demand response at time t;

[0056] Step 4.2: Based on the tolerance α set in step 4.1 i (t), for the N user groups obtained after clustering in step 1, the penalty factor β is set on the time scale i (t);

[0057]

[0058] Where: β i(t) is the penalty factor of user group i at time t; δ i0 (t) and δ i1 (t) are the compliance ratio and cap ratio of the penalty factor respectively;

[0059] Step 4.3: Based on the tolerance set in step 4.1 and the penalty factor set in step 4.2, design a quasi-linear demand response incentive mechanism;

[0060] That is, the effectiveness of user response is evaluated according to the actual response amount of the user, and the penalty factor is set on the time scale and user category to set the relevant demand response subsidy Y according to the inclusiveness of different types of users. i (t):

[0061] Y i (t) = (1-β i (t))γ i (t) (13)

[0062] Where: γ i (t) is the maximum subsidy unit price for user group i participating in quasi-linear demand response at time t.

[0063] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the above method.

[0064] As can be seen from the above technical solutions, the present invention's evaluation method for measuring the effectiveness of user participation in quasi-linear demand response is based on obtaining large-scale user load data, analyzing user electricity consumption behavior, clustering users, and establishing a user clustering model; establishing a load baseline calculation model for large-scale users; considering the enthusiasm and actual contribution to measure the evaluation method for user participation in quasi-linear demand response; and setting a penalty factor and tolerance below the load baseline to incentivize users to participate in quasi-linear demand response. The present invention establishes a user clustering model, obtains user groups with similar demand response situations by selecting clustering characteristics such as user historical electricity load curves, user historical demand response capabilities, and user historical demand response willingness, and measures the effectiveness of user participation in quasi-linear demand response by considering the enthusiasm and actual contribution of users in participating in quasi-linear demand response. Based on the demand response characteristics of the clustered user group, the invention incentivizes users to participate in quasi-linear demand response by setting tolerance and penalty factors based on time scales and user categories.

[0065] Compared with the existing technology, the improvements of the present invention are embodied in:

[0066] 1. For quasi-linear demand response, the load curve shape is defined, the load criterion is calculated, and a method for measuring the effectiveness of user participation in quasi-linear demand response is defined, taking into account user enthusiasm and actual contribution. Compared with existing methods for measuring the effectiveness of user participation in quasi-linear demand response, the present invention considers user enthusiasm and actual contribution, which can more accurately measure the effectiveness of user participation in quasi-linear demand response, closer to actual operation, and improves the quality of the method for measuring the effectiveness of user participation in quasi-linear demand response.

[0067] 2. Compared with the existing incentive mechanism, the present invention adopts the fuzzy C-means clustering algorithm to divide large-scale users into N user groups. Based on the user response behavior after clustering, an incentive mechanism is designed for the N user groups after clustering by setting the load line download time scale and the penalty factor and tolerance on the user category to encourage users to participate in quasi-linear demand response. The incentive mechanism of the present invention can significantly motivate N user groups to participate in quasi-linear demand response, promote new energy consumption as much as possible, and improve economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a schematic diagram of a flow chart of an embodiment of the present invention;

[0069] Figure 2 Schematic diagram of user participation quasi-linear demand response effect evaluation of the present invention;

[0070] Figure 3 is a flow chart of the FCM clustering algorithm according to an embodiment of the present invention;

[0071] Figure 4 is a load curve of a residential user not participating in quasi-linear demand response on a typical summer day according to an embodiment of the present invention;

[0072] Figure 5 is a load curve for commercial users not participating in quasi-linear demand response on a typical summer day in an embodiment of the present invention;

[0073] Figure 6 is a load curve of an industrial user not participating in quasi-linear demand response on a typical summer day in an embodiment of the present invention;

[0074] Figure 7 is a load curve after residential users participate in quasi-linear demand response on a typical summer day in an embodiment of the present invention;

[0075] Figure 8 is a load curve after commercial users participate in quasi-linear demand response on a typical summer day in an embodiment of the present invention;

[0076] Figure 9 This is a load curve after industrial users participate in quasi-linear demand response on a typical summer day in an embodiment of the present invention;

[0077] Figure 10 The load curves for various types of users on a typical summer day without participating in quasi-linear demand response;

[0078] Figure 11 This is the load curve for various types of users after participating in quasi-linear demand response on a typical summer day. DETAILED DESCRIPTION

[0079] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0080] like Figure 1 The present invention provides an evaluation method and incentive mechanism for measuring the effect of user participation in quasi-linear demand response, including the following steps:

[0081] Step 1: Cluster large-scale users to obtain N user groups that take into account differences in energy consumption behavior and demand response characteristics;

[0082] Step 2: Based on the N user groups obtained after clustering in step 1, which take into account differences in energy consumption behavior and demand response characteristics, a load guideline calculation model for large-scale users is established;

[0083] Step 3: Based on the N user groups obtained in step 1 that take into account energy consumption differences and demand response characteristics, and combined with the load quasi-linear calculation model for large-scale users established in step 2, define a method for evaluating the effectiveness of user participation in quasi-linear demand response, which can take into account both user enthusiasm and actual contribution;

[0084] Step 4: Based on the N user groups obtained after clustering in step 1, the load quasi-linear calculation model for large-scale users established in step 2, and the evaluation method for measuring the effectiveness of large-scale users participating in quasi-linear demand response obtained in step 3, according to the demand response characteristics of the clustered user groups, inclusiveness and penalty factors are set in time scales and user categories to encourage users to participate in quasi-linear demand response.

[0085] The following are specific instructions:

[0086] Among them, clustering is performed on large-scale users to obtain N user groups that take into account differences in energy consumption behavior and demand response characteristics, including:

[0087] Step 1.1: Obtain historical electricity consumption data of large-scale users, including historical electricity load curves, historical demand response capabilities, and historical demand response willingness; obtain historical electricity price changes and calculate the user's electricity price sensitivity coefficient;

[0088] Step 1.2: Determine clustering characteristics, including historical user demand response capabilities, historical user demand response willingness, user electricity price sensitivity coefficient, historical user load proportions, and historical typical day electricity load curves.

[0089] The user's historical load proportions include basic load proportion, interruptible load proportion, shiftable load proportion and transferable load proportion;

[0090] Step 1.3: Based on the clustering feature values ​​selected in step 1.2, a fuzzy C-means algorithm (FCM) is used to perform clustering modeling on a large number of users to obtain N clustered user groups.

[0091] Based on the differences in energy consumption behavior and demand response characteristics of large-scale users, a load baseline calculation model for large-scale users is established, including:

[0092] Step 2.1: The load line of large-scale users is mainly composed of the basic load, interruptible load, shiftable load and transferable load of the N user groups obtained after clustering in step 1, that is,

[0093]

[0094] Where: PD(t), PB,i(t), PI,i(t), PL,i(t), and PT,i(t) are the load line value of large-scale users in time period t, the basic load of N user groups, the interruptible load, the shiftable load, and the transferable load, respectively. They are the basic load proportion, interruptible load proportion, shiftable load proportion and transferable load proportion of user group i in period t.

[0095] And there are the following constraints:

[0096]

[0097] Step 2.2: Calculate the ideal load criterion based on the system operating parameters of large-scale users, and establish a load criterion calculation model for large-scale users.

[0098] The step 2.2 establishes a load guideline calculation model for large-scale users as follows:

[0099] Step 2.2.1. Construct the objective function, which is to minimize the operating costs of large-scale user systems while promoting the consumption of new energy as much as possible.

[0100]

[0101] Where: PG,j(t) is the active power output of the jth adjustable generator in the large-scale user system during period t; nG is the number of adjustable generators; PR(t) is the total output of all renewable energy sources, including photovoltaics and wind power, in the large-scale user system during period t; PR,max(t) is the theoretical maximum output of renewable energy sources during period t, obtained from day-ahead forecasts; aj, bj, and cj are the generation cost coefficients for the jth adjustable generator; and CR is the penalty coefficient for wind and solar power curtailment. The first term of the objective function is the total operating cost of the adjustable generator set, and the second term is the penalty for wind and solar power curtailment. To fully absorb renewable energy, CR can be set sufficiently large to ensure that wind and solar power curtailment occurs only when the constraints are difficult to meet.

[0102] Step 2.2.2: Construct the constraint conditions and the corresponding power balance equation, namely

[0103]

[0104] Where: PD(t) is the demand response load criterion value of large-scale users in period t; PC(t) is the non-adjustable load power in period t.

[0105] In step 3, an evaluation method for measuring the effect of user participation in quasi-linear demand response is defined, which can take into account both user enthusiasm and actual contribution, including:

[0106] Step 3.1 defines the load curve shape;

[0107] Let l(t) be a load curve. The total power in time period T is Based on W(t), l(t) is normalized to

[0108]

[0109] Where l*(t) is the per-unit value of the load curve l(t) after being normalized using the per-unit function fu(). After normalization, the magnitude characteristics of the load curve are eliminated, leaving only the shape characteristics.

[0110] Step 3.2: Use cosine similarity to calculate the user's enthusiasm for participating in quasi-linear demand response;

[0111] The similarity of the two curves is determined by calculating the cosine value of the angle between the load curve and the load directrix. The cosine similarity is more about distinguishing differences in direction and is not sensitive to absolute values. The closer the cosine value is to 1, the more similar the two curves are, which is called "cosine similarity". Figure 2 shown.

[0112] Assume that the point on the load curve of user group i at time t is li(xi(t), yi(t)), and the point on the load criterion is lD(xD(t), yD(t)). The per-unit values ​​after the per-unit conversion function fu() in step 3.1 are Then the cosine of the angle between the two is calculated as follows:

[0113]

[0114] Where: εi(t) is an indicator that measures the similarity of the curve shape at time t, which can represent the enthusiasm of user group i to participate in the quasi-linear demand response.

[0115] Step 3.3, calculating the actual contribution of users to the quasi-linear demand response based on a one-way distance measurement method;

[0116] The definition based on the one-way distance metric (OWD) method is as follows:

[0117]

[0118] Where: point p is a point on the curve l1, |l1| represents the length of the curve l1, and d(p,l2) represents the distance from point p to the curve l2. Simplify the above formula

[0119]

[0120] The basic idea of ​​OWD distance is based on the area enclosed by the two curves. When the area is larger, the distance between the two curves is farther, and the similarity is lower. On the contrary, if the area is 0, the two curves overlap. Figure 2 shown.

[0121] Based on the basic idea of ​​OWD distance, an indicator is defined to measure the actual contribution of users to quasi-linear demand response, which is expressed as

[0122]

[0123] Where: is the OWD distance between the two curves after normalization; Ri(t) is the measurement index of the OWD distance, which is defined as the contribution of different load curves to the quasi-linear demand response, and ψ is a given constant value.

[0124] Step 3.4: Define an evaluation method to measure the effectiveness of user participation in quasi-linear demand response.

[0125] Calculate the weighted sum of the activeness in step 3.2 and the contribution in step 3.3 to measure the effect of quasi-linear demand response. Define an evaluation method for measuring user participation in quasi-linear demand response. The expression is:

[0126] χ i (t) = ε i (t)·R i (t) (10)

[0127] Where: χi(t) is the evaluation method for measuring the participation of user group i in quasi-linear demand response at time t.

[0128] In step 4, a quasi-linear demand response incentive mechanism is designed based on the clustered user response behavior by setting penalty factors and tolerance on the time scale and user category under the load quasi-line, including:

[0129] Step 4.1: Set the inclusiveness αi(t) on the time scale for the N user groups obtained after clustering in step 1;

[0130]

[0131] Where: αi(t) is the inclusiveness of user group i at time t; P D,i (t) and P B,i (t) are the quasi-linear demand response load quasi-linear value and actual response value of user group i at time t; ρi0(t) and ρi1(t) are the compliance ratio and capping ratio of inclusiveness, respectively; εi(t) is the indicator of user group i’s participation in quasi-linear demand response at time t.

[0132] Step 4.2: Based on the inclusiveness αi(t) set in step 4.1, set a penalty factor βi(t) on the time scale for the N user groups obtained after clustering in step 1;

[0133]

[0134] Where: βi(t) is the penalty factor of user group i at time t; δi0(t) and δi1(t) are the compliance ratio and cap ratio of the penalty factor, respectively.

[0135] Step 4.3: Based on the tolerance set in step 4.1 and the penalty factor set in step 4.2, design a quasi-linear demand response incentive mechanism;

[0136] The power grid company evaluates the effectiveness of user responses based on the actual amount of user responses. Here, the penalty factor is set on the time scale and user category to set the relevant demand response subsidy Yi(t) according to the tolerance of different types of users:

[0137] Y i (t) = (1-β i (t))γ i (t) (13)

[0138] Where: γi(t) is the maximum subsidy unit price for user group i participating in quasi-linear demand response at time t.

[0139] In summary, the present invention clusters large-scale residential users, establishes a load criterion calculation model for large-scale users, defines a method for evaluating the effectiveness of user participation in quasi-linear demand response, and designs an incentive mechanism for quasi-linear demand response. The load criterion calculation model for large-scale users takes minimizing the system operating costs of large-scale users while maximizing the consumption of renewable energy; the method for evaluating the effectiveness of user participation in quasi-linear demand response takes into account both user enthusiasm and actual contribution; and the incentive mechanism for quasi-linear demand response sets penalty factors and tolerance levels under the load criterion on a time scale and for user categories.

[0140] The following example illustrates

[0141] The clustering algorithm used in the present invention is the fuzzy C-means clustering algorithm (FCM) flow chart as shown in the following figure: Figure 3 As shown; in the present invention, the load curves of residential users, commercial users and industrial users who do not participate in the quasi-linear demand response on a typical summer day are obtained by clustering. Figure 4 、 Figure 5 and Figure 6 The load curves of residential users, commercial users and industrial users after participating in quasi-linear demand response on a typical day in summer are shown in Figure 2. Figure 7 、 Figure 8 and Figure 9 ; Figure 10 is the load curve of various users on a typical summer day without participating in quasi-linear demand response. Figure 11 Table 1 shows the load curves of various users after participating in quasi-linear demand response on a typical summer day; Table 1 shows the electricity prices in each time period.

[0142] Table 1 Electricity sales price

[0143]

[0144] It can be seen from the above-mentioned relevant charts and data that clustered users will participate in quasi-linear demand response according to the load standard and adjust their own energy demand. Compared with not participating in quasi-linear demand response, clustered users’ participation in quasi-linear demand response promotes the consumption of a high proportion of new energy; and after clustered users participate in demand response, energy consumption is reduced by 103,100 kilowatts, users’ energy expenditure is reduced, and 134,030.82 yuan is saved, achieving a win-win situation.

[0145] In another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of any of the above methods.

[0146] On the other hand, the present invention further discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any of the above methods.

[0147] In another embodiment provided by the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the steps of any one of the methods in the above embodiments.

[0148] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts of the above method.

[0149] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0150] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for evaluating the effect of user participation in quasi-linear demand response, characterized in that: The following steps are involved: Step 1: Cluster large-scale users to obtain N user groups that take into account differences in energy consumption behavior and demand response characteristics; Step 2: Based on the N user groups obtained after clustering in step 1, which take into account differences in energy consumption behavior and demand response characteristics, a load guideline calculation model for large-scale users is established; Step 3: Based on the N user groups obtained in step 1 that take into account energy consumption differences and demand response characteristics, and combined with the load quasi-linear calculation model for large-scale users established in step 2, define a method for evaluating the effectiveness of user participation in quasi-linear demand response, which can take into account both user enthusiasm and actual contribution; Step 4: Based on the N user groups obtained after clustering in step 1, the load quasi-linear calculation model for large-scale users established in step 2, and the evaluation method for measuring the effectiveness of large-scale users participating in quasi-linear demand response obtained in step 3, the inclusiveness and penalty factors are set at the time scale and user category based on the demand response characteristics of the clustered user groups to incentivize users to participate in quasi-linear demand response; The step 2 specifically includes: Step 2.1: The load line of large-scale users is composed of the basic load, interruptible load, shiftable load and transferable load of the N user groups obtained after clustering in step 1, that is, （1） Where: P D ( t ), P B,i ( t ), P I,i ( t ), P L,i ( t ), P T,i ( t ) are in order t The load guideline value of large-scale users in the time period, the basic load of N user groups, the interruptible load, the shiftable load and the transferable load, 、 、 、 User groups i exist t The proportion of base load, interruptible load, shiftable load and transferable load in the time period; And there are the following constraints: （2） Step 2.2: Calculate the ideal load criterion based on the system operating parameters of large-scale users and establish a load criterion calculation model for large-scale users; The step 2.2 establishes a load guideline calculation model for large-scale users as follows: Step 2.2.

1. Construct an objective function, which is to minimize the operating costs of large-scale user systems while maximizing the consumption of new energy. （3） Where: P G,j ( t ) is the first j An adjustable generator t Active power output during the time period; n G The number of adjustable generators; P R ( t ) for all new energy generation such as photovoltaic and wind power in large-scale user systems t Output during the time period; P R,max ( t )for t New energy generation during the period t The theoretical maximum output value for the time period is obtained from the day-ahead forecast; a j 、 b j 、 c j Respectively j The power generation cost coefficient of an adjustable generator; C R is the penalty coefficient for curtailing wind and solar power; the first term of the objective function is the total operating cost of the adjustable generator set, and the second term of the objective function is the penalty cost for curtailing wind and solar power; Step 2.2.2: Construct the constraint conditions and the corresponding power balance equation, namely ,t=1,2,…,T(4) Where: P D ( t )for t Demand response load guideline value of large-scale users during the period; P C ( t )for t The load power cannot be adjusted during the time period.

2. The evaluation method for measuring the effect of user participation in quasi-linear demand response according to claim 1, characterized in that: The step 1 specifically includes: Step 1.1: Obtain the user's historical electricity consumption data, including: the user's historical electricity load curve, the user's historical demand response capability, and the user's historical demand response willingness; obtain historical electricity price changes, and calculate the user's electricity price sensitivity coefficient; Step 1.2: Determine clustering characteristics, including historical user demand response capabilities, historical user demand response willingness, user electricity price sensitivity coefficient, historical user load proportions, and historical typical day electricity load curves. The user's historical load proportions include basic load proportion, interruptible load proportion, shiftable load proportion and transferable load proportion; Step 1.3: Based on the clustering feature values ​​selected in step 1.2, a fuzzy C-means clustering algorithm is used to perform clustering modeling on a large number of users to obtain N clustered user groups.

3. The evaluation method for measuring the effect of user participation in quasi-linear demand response according to claim 1, characterized in that: The step 3 specifically includes: Step 3.1 Define the load curve shape; remember l ( t ) is a load curve, and the total power in time period T is ,by W(t ) as the reference pair l ( t ) is normalized, that is, ,t=1,2…,T(5) Where: l * ( t ) is the load curve l（t ) after normalization function f u ( ) after the per-unit value; Step 3.2: Use cosine similarity to calculate the user's enthusiasm for participating in quasi-linear demand response; Assumed user group i exist t The point on the load curve at that moment is l i ( x i ( t ), y i ( t )), the point on the load line is l D ( x D ( t ), y D ( t )) After step 3.1, the unitary function f u The per-unit values ​​after ( ) are l ( x ( t ), y ( t ))、 l ( x ( t ), y ( t ), then the cosine of the angle between the two is calculated as follows: （6） Where: ε i ( t ) is in t An indicator of the similarity of the shape of the moment-to-moment measurement curve, representing the user group i the degree of active participation in quasi-linear demand response; Step 3.3, calculating the actual contribution of users to the quasi-linear demand response based on a one-way distance measurement method; The one-way distance metric is defined as follows: （7） Where: point p For curve l Point on 1, | l 1| represents the curve l The length of 1, d(p,l 2 ) Indicates a point p To the curve l 2; simplify formulas (5) to (7): （8） Based on the basic idea of ​​OWD distance, an indicator is defined to measure the actual contribution of users to quasi-linear demand response, which is expressed as （9） Where: is the OWD distance between the two curves after normalization; R i ( t ) is the measurement index of OWD distance, which is defined as the contribution of different load curves to quasi-linear demand response. ψ is a given constant value; Step 3.4: Define an evaluation method to measure the effectiveness of user participation in quasi-linear demand response. Calculate the weighted sum of the activeness in step 3.2 and the contribution in step 3.3 to measure the effect of quasi-linear demand response. Define an evaluation method for measuring user participation in quasi-linear demand response. The expression is: （10） Where: χ i ( t ) is in t Always measure user base i Evaluation methods for participating in quasi-linear demand response.

4. The evaluation method for measuring the effect of user participation in quasi-linear demand response according to claim 3, characterized in that: The step 4 specifically includes: Step 4.1: Set the inclusiveness on the time scale for the N user groups obtained after clustering in step 1 α i ( t ); （11） Where: α i ( t ) is the user group i exist t The inclusiveness of the moment; and User groups i exist t The quasi-linear demand response load quasi-linear value and actual response value at the moment; ρ i0 ( t )and ρ i1 ( t ) are the target and ceiling ratios of inclusiveness, respectively; ε i ( t ) is the user group i exist t Indicators for participating in quasi-linear demand response at all times; Step 4.2: Based on the tolerance set in step 4.1 α i ( t ), set the penalty factor for the N user groups obtained after clustering in step 1 on the time scale β i ( t ); （12） Where: β i ( t ) is the user group i exist t The penalty factor of the moment; δ i0 ( t )and δ i1 ( t ) are the compliance ratio and cap ratio of the penalty factor respectively; Step 4.3: Based on the tolerance set in step 4.1 and the penalty factor set in step 4.2, design a quasi-linear demand response incentive mechanism; That is, the effectiveness of user response is evaluated according to the actual response amount of the user, and the penalty factor is set on the time scale and user category to set the relevant demand response subsidy according to the inclusiveness of different types of users. Y i ( t ): （13） Where: γ i ( t ) is the user group i exist t The maximum subsidy unit price for participating in quasi-linear demand response at any time.

5. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 4.

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