Method and system for analyzing and evaluating flexible load response potential of low-voltage transformer area

Abstract

The invention discloses a low-voltage transformer area flexible load response potential analysis method and system, and the method comprises the steps: determining low-voltage transformer area flexible load response potential impact factors, and building evaluation indexes corresponding to the impact factors; analyzing the influence degree of each evaluation index on the flexible load response potential, and establishing a quantitative standard and a corresponding weight of the corresponding influence degree; determining the connection degree between the quantized value of each evaluation index and the corresponding highest quantized value, and calculating the total connection degree of the user in combination with the weight of each evaluation index; and calculating the membership degree of the user by using the variable fuzzy set according to the total connection degree of the user, and determining the response potential level of the user according to the membership degree. According to the invention, the method improves the accuracy of an evaluation result, improves the adjustment capability of the flexible load in the transformer area, and improves the local absorption capability of the transformer area taking the new energy as a main body through researching the energy consumption scene of resource diversification in the low-voltage transformer area in the new energy high-permeability environment and comprehensively considering various factors influencing the response potential of the flexible load.

Description

Technical Field

[0001] The present invention relates to the field of new energy access technology for electric power, and in particular to a method and system for analyzing the flexible load response potential of a low-voltage station area. Background Art

[0002] Photovoltaic power generation, as a typical representative of renewable energy, has the advantages of being clean, safe, reliable, and not restricted by geographical location. With the rapid growth of flexible resources such as distributed photovoltaics, electric vehicles, energy storage, and flexible loads in low-voltage substations, the large-scale access of flexible resources has led to a huge change in the energy structure in the substation, and the volatility, intermittency, and randomness of the source and load in the substation have become more significant, which has brought many adverse effects on the balance of supply and demand, power supply reliability, etc. in the low-voltage substation. In addition, with the continuous growth of the penetration rate of new energy in low-voltage substations, the power grid in some areas has reached its bearing limit, which has brought huge challenges to the management model, multi-source control technology, and rapid response capabilities of the low-voltage substation. At the same time, affected by factors such as the region, substation load type, and load level, the electricity consumption characteristics of micro-enterprises, commercial and residential users in the substation are diverse and complex, making it difficult to achieve unified optimization and management of energy consumption in the substation. In order to accurately evaluate the new energy access capacity of the substation, realize the real-time balance of the active distribution network power, ensure the power supply reliability, and improve the clean and low-carbon energy consumption level of high penetration and new energy low-voltage substations, it is urgent to solve the problem of overly single influencing factors considered in the existing load response potential assessment technology.

[0003] The prior art document (CN116914760A) discloses a method for quantitatively evaluating the flexible load response potential of a power system. It includes taking the electricity satisfaction and electricity consumption as the basis for the interaction between the user and the power grid under the premise of considering the user behavior, using the fuzzy subset method to establish a basic model of the user group for the variable, and obtaining a flexible load model of the power system that considers the user's needs based on the basic model; using the user participation and the user's corresponding cooperation as the evaluation criteria for the response potential factor, after quantitative processing, a flexible load response potential factor based on the user load is obtained; based on the flexible load model and the flexible load response potential factor, a potential quantitative evaluation algorithm is designed, and actual potential verification is carried out.

[0004] However, its shortcomings are that the existing technology is too dependent on user behavior and simple economic incentive mechanisms, ignoring the impact of multi-dimensional factors, resulting in certain deficiencies in the accuracy and real-time nature of the evaluation results. At the same time, the technology is weak in considering the multi-user coordination mechanism during the load response process, making it difficult to achieve dynamic adjustment of the demand-side response of different users. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a low-voltage area flexible load response potential analysis method, which mainly solves the problem that the existing load response potential evaluation technology focuses on considering single influencing factors such as user behavior and economic incentives.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention provides a method for analyzing the potential of flexible load response in a low-voltage area, comprising the following contents:

[0008] Determine the influencing factors of the flexible load response potential of the low-voltage substation, and establish evaluation indicators corresponding to each influencing factor; the evaluation indicators include one or more of user participation level, user response coordination, load classification and grading, equipment response capacity, user economic level, user response reward, timeliness and accuracy;

[0009] Analyze the impact of each evaluation index on the flexible load response potential, and establish the quantitative standard and corresponding weight of the corresponding impact degree;

[0010] Determine the degree of connection between the quantitative value of each evaluation indicator and the corresponding highest quantitative value, and calculate the total degree of connection of the user in combination with the weight of each evaluation indicator;

[0011] According to the total connection degree of users, the membership degree of users is calculated by using variable fuzzy sets. The response potential level of users is determined according to the membership degree, and the flexible load response potential analysis results of low-voltage substations are obtained.

[0012] Optionally, the user participation level is the willingness of power users to participate in grid load allocation, which is characterized by the number of historical participation in grid load allocation; the user response cooperation is the user's cooperation when the grid issues a demand response task, which is quantified by the user's task completion status; the load classification and grading is divided into primary load, secondary load, tertiary load and quaternary load according to the importance of the load; the equipment response capacity is the operating power of the equipment participating in the response on the demand side at the operating time; the user's economic level is the user's electricity expenditure; the user's response reward is the reward provided by the grid when the user participates in the demand side response; timeliness is the evaluation timeliness of the specified service, which can be used to determine whether the information transmission from the grid side to the user side of the demand side response instruction is real-time update; accuracy is whether the demand side response instruction is issued correctly.

[0013] Optionally, the response reward that the user can obtain is calculated according to the following formula:

[0014]

[0015] in,

[0016] C i is the reward received by user i,

[0017] α i To compensate for the peak shifting,

[0018] β i The unit price for peak shaving compensation is:

[0019] γ i To compensate for the peak load reduction and valley load filling,

[0020] Indicates peak power consumption.

[0021] To reduce the peak power consumption,

[0022] To reduce the peak power consumption,

[0023] |Δt i |Indicates the length of the off-peak time.

[0024] Optionally, the timeliness of the specified service wj at time t is calculated according to the following formula:

[0025] s j (t,k)=e -μ·t

[0026] in,

[0027] s j (t,k)∈(0,1],

[0028] j is the service identifier in the user evaluation request,

[0029] μ is the forgetting rate of service wj at the current moment t, which is used to characterize the degree of forgetting of user's service evaluation over time.

[0030] Optionally, analyzing the influence of each evaluation indicator on the flexible load response potential, establishing the quantitative standard and corresponding weight of the corresponding influence degree includes: dividing the evaluation indicators into three different levels, the first level is the target level, the second level is the evaluation indicator level, and the third level is the quantitative standard level; analyzing the influence of each evaluation indicator on the flexible load response potential, comparing all evaluation indicators, and constructing a judgment matrix; each element in the judgment matrix represents the importance of one evaluation indicator relative to another evaluation indicator; solving the maximum eigenvalue of the judgment matrix, and determining the eigenvector corresponding to the maximum eigenvalue as the weight of each evaluation indicator.

[0031] Optionally, the degree of connection between the quantized value of each evaluation indicator and the corresponding maximum quantized value is determined according to the following formula:

[0032]

[0033] in,

[0034] s i is the quantitative value of the evaluation index i;

[0035] u i is the maximum quantitative value s of the evaluation index i and the evaluation index i i,max The degree of connection between them;

[0036] w i is the weight of evaluation index i.

[0037] Optionally, the membership degree is calculated using a variable fuzzy set according to the following formula:

[0038]

[0039] in,

[0040] U is the total connection degree of the user,

[0041] c is the membership center point,

[0042] σ is the membership width.

[0043] Optionally, determining the user response potential level according to the membership degree includes:

[0044] When the membership degree is greater than 0.8, the user response potential level is the first level; and / or, when 0.4 is less than the membership degree ≤ 0.8, the user response potential level is the second level; and / or, when the membership degree is less than 0.4, the user response potential level is the third level.

[0045] The second aspect of the present invention provides a low-voltage area flexible load response potential analysis system, comprising a first module, a second module, a third module and a fourth module.

[0046] The first module is used to determine the influencing factors of the flexible load response potential of the low-voltage substation area and establish evaluation indicators corresponding to each influencing factor; the evaluation indicators include one or more of the user participation level, user response coordination, load classification and grading, equipment response capacity, user economic level, user response reward, timeliness and accuracy;

[0047] The second module is used to analyze the impact of each evaluation index on the flexible load response potential and establish the quantitative standard and corresponding weight of the corresponding impact degree;

[0048] The third module is used to determine the connection degree between the quantitative value of each evaluation indicator and the corresponding highest quantitative value, and calculate the total connection degree of the user in combination with the weight of each evaluation indicator;

[0049] The fourth module is used to calculate the user's membership degree based on the user's total connection degree using variable fuzzy sets, determine the user's response potential level based on the membership degree, and obtain the flexible load response potential analysis results of the low-voltage substation area.

[0050] The third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the above-mentioned low-voltage area flexible load response potential analysis method when loaded into the processor.

[0051] The fourth invention of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned low-voltage area flexible load response potential analysis method.

[0052] Compared with the prior art, the beneficial effects of the present invention include at least:

[0053] The present invention comprehensively considers multi-dimensional factors such as user behavior, load characteristics, equipment capabilities, etc., and adopts a three-scale hierarchical analysis method to accurately determine the weight of each indicator, and introduces set pair analysis theory to calculate the connection between each indicator, thereby improving the accuracy and flexibility of the evaluation results.

[0054] This invention studies the energy use scenarios of diversified resources in low-voltage areas under high penetration of new energy, comprehensively considers various factors that affect the response potential of flexible loads, fully taps the potential of flexible load regulation in the area, and improves the flexible load regulation capacity in the area, especially under the condition of high proportion of new energy access, which can better achieve load balance. At the same time, the real-time performance of the evaluation results has been improved, which helps the power grid to make rapid adjustments during the demand-side response process, and further enhances the power reliability of the area and the absorption capacity of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0056] Figure 1 This is an energy usage scenario of diversified resources in a low-voltage area under a high-penetration environment of new energy provided by an embodiment of the present invention;

[0057] Figure 2 It is a schematic diagram of a low-voltage area flexible load response potential analysis algorithm flow provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0059] The implementation environment of the embodiment of the present invention is as follows Figure 1 As shown, it includes the total meter of the substation, photovoltaic energy storage device, energy controller, energy circuit breaker, branch monitoring terminal, smart IoT energy meter, and user layer. The user layer can include electric vehicle charging piles, residential loads, commercial loads, photovoltaics and energy storage. The user layer is connected to the smart IoT energy meter and is monitored in real time by the branch monitoring terminal. In this implementation environment, the substation collects users' electricity consumption information in real time through various intelligent devices and monitoring systems, calculates users' flexible load response potential, and then optimizes the new energy consumption and load regulation capabilities, improving the overall energy efficiency of the substation and the stability of the power grid.

[0060] like Figure 2 As shown, Example 1 of the present invention proposes a method for analyzing the flexible load response potential of a low-voltage area, which mainly solves the problem that the existing load response potential assessment technology focuses on single influencing factors such as user behavior and economic incentives. The embodiment of the present invention considers multiple factors that affect the flexible load response potential, including eight types of influencing factors such as user participation level, user response coordination, load classification and grading. Specifically, the following steps are included:

[0061] S1: Determine the influencing factors of the flexible load response potential of the low-voltage substation area and establish evaluation indicators corresponding to each influencing factor.

[0062] Optionally, the evaluation indicators include one or more of user participation level, user response coordination, load classification and grading, equipment response capacity, user economic level, user response reward, timeliness and accuracy.

[0063] The user participation level refers to the willingness of power users to participate in grid load dispatching, which is characterized by the number of historical participations in grid load dispatching.

[0064] User response cooperation refers to the user's cooperation when the power grid issues a demand response task, which is quantified by the user's task completion status.

[0065] Load classification and grading: The classification of flexible load forecasting is divided into four categories: municipal load, industrial load, residential load and commercial load. The classification of flexible load forecasting is generally divided according to the importance of the load. Level 1 load refers to the load that will definitely cause death, serious personal injury, serious economic loss, or bad social impact after power outage; Level 2 load refers to the load that may cause casualties, serious economic loss, or minor social security incidents after power outage; Level 3 load refers to the load that will cause inconvenience in life or interruption of continuity of important production lines after power outage; Level 4 load refers to the load that has basically no impact after power outage.

[0066] The equipment response capacity is the operating power of the equipment on the demand side that can participate in the response at the time of operation.

[0067] User economic level: At present, the grid policy that encourages users to participate in demand response is basically reflected in electricity price incentives. Generally speaking, the higher the economic level of users, the less sensitive they are to electricity price incentives, and the more difficult it is to encourage them to participate in demand response through electricity price regulation, and the lower their demand response level. Generally, the economic level of the same type of users is judged by the electricity charges over a period of time. The higher the electricity charges, the better their economic level, and vice versa.

[0068] The response reward that users can receive is the reward provided by the power grid when users participate in demand-side response. Assuming that the reward mechanism is certain, the response categories are divided into three categories: peak shifting, peak shaving, and peak shaving and valley filling.

[0069] Optionally, the reward C received by user i participating in the demand-side response i It can be calculated by the following formula:

[0070]

[0071] in,

[0072] α i To compensate for the peak shifting,

[0073] β i The unit price for peak shaving compensation is:

[0074] γ i To compensate for the peak load reduction and valley load filling,

[0075] Indicates peak power consumption.

[0076] To reduce the peak power consumption,

[0077] To reduce the peak power consumption,

[0078] |Δt i |Indicates the length of the off-peak time.

[0079] Timeliness is the evaluation timeliness of the specified service, which can be used to determine whether the information transmitted from the grid side to the user side in response to the instruction issued by the demand side is updated in real time.

[0080] Optionally, the specified service w is calculated according to the following formula j Evaluation validity at time t:

[0081] s j (t,k)=e -μ·t

[0082] in,

[0083] s j (t,k)∈(0,1],

[0084] j is the service identifier in the user evaluation request,

[0085] μ is the service w at the current time t j The forgetting rate,

[0086] The forgetting rate is used to characterize the degree to which users forget previous service evaluations over time. According to the service identifier j in the user evaluation request, the service w at the current time t can be obtained. j The forgetting rate μ. The value range of μ is usually between [0, 1]. The closer the forgetting rate is to 0, the deeper the user forgets the service and the weaker the memory of the service.

[0087] Specifically, the forgetting rate is calculated according to the following formula:

[0088] μ=e -λt·K(j)

[0089] It should be noted that in an actual system, the forgetting rate can also be determined based on actual business needs, historical data or expert experience.

[0090] Accuracy refers to whether the instructions issued by the demand side response are correct.

[0091] S2: Analyze the impact of each evaluation index on the flexible load response potential, and establish quantitative standards and corresponding weights for the corresponding impact levels.

[0092] Optionally, the specific quantitative standards of each evaluation indicator are given in the following table, and the quantitative value s of each evaluation indicator can be obtained: i (i=1,2,…,8):

[0093] Table 1

[0094]

[0095]

[0096] In S2, the weight w of each evaluation indicator i (i=1,2,…,8) is determined by the three-scale analytic hierarchy process, including:

[0097] (1) Constructing a hierarchical model: The evaluation indicators are decomposed into different levels. The top level is the goal (i.e., evaluating the flexible load response potential), the middle level is the various evaluation indicators (such as user participation, equipment response capacity, etc.), and the bottom level is the scoring criteria for each indicator.

[0098] (2) Construct judgment matrix A:

[0099]

[0100] In which, each element a in the matrix A ij (1≤i,j≤8) indicates the importance of evaluation indicator i relative to evaluation indicator j. The importance is scaled from 1 to 9, where 1 means both are equally important, 9 means i is extremely important compared to j, and there is a ji =1 / a ij Each scale value is determined based on actual business needs, historical data or expert experience.

[0101] (3) Solving the eigenvector: Calculate the maximum eigenvalue of matrix A and its corresponding eigenvector The weight w of each evaluation index can be obtained i (i=1,2,…,8). The weight reflects the influence of each evaluation indicator on the target layer.

[0102] In this way, the weight of each evaluation indicator can be quantified, which increases the flexibility and applicability of subsequent decision-making methods.

[0103] S3: Determine the connection degree between the quantitative value of each evaluation indicator and the highest quantitative value, and calculate the total connection degree of the user in combination with the weight of each evaluation indicator.

[0104] The highest quantitative value represents the ideal state corresponding to the evaluation indicator.

[0105] Optionally, using the set pair analysis evaluation theory, establish the quantitative value s of each evaluation indicator for each user i The connection degree u with the ideal state (i.e. the state corresponding to the quantitative value of 5 points) i .u i is a value between 0 and 1, which is used to indicate the degree of closeness between two sets. The closer the connection degree is to 1, the closer the evaluation index of the user is to the ideal state; conversely, the closer the connection degree is to 0, the farther the evaluation index of the user is from the ideal state. The connection degree u between the quantitative value of each user's indicator and the ideal state i Calculate according to the following formula:

[0106]

[0107] Among them, s i,max is the maximum quantitative value corresponding to the evaluation index i.

[0108] After calculating the connection degree of each evaluation indicator, we can use the weight w of each evaluation indicator to i , calculate the user's total connection degree U:

[0109]

[0110] The total connection degree comprehensively reflects the degree to which the user is close to the ideal state in all evaluation indicators. The higher the connection degree, the closer the user's response potential is to the ideal state.

[0111] S4: According to the total connection degree of the user, the user's membership is calculated using variable fuzzy sets, and the user's response potential level is determined according to the membership, and the flexible load response potential analysis results of the low-voltage substation are obtained.

[0112] Optionally, the variable fuzzy set theory is used to calculate the user's membership η for different evaluation levels according to the user's total connection degree U. The Gaussian function is used as the fuzzy set membership function to calculate the membership η, that is:

[0113]

[0114] Among them, c is the membership center point and σ is the membership width.

[0115] Optionally, determining the user response potential level according to the membership degree includes: when the membership degree is greater than 0.8, the user response potential level is the first level; and / or, when 0.4<membership degree≤0.8, the user response potential level is the second level; and / or, when the membership degree is less than 0.4, the user response potential level is the third level.

[0116] Specifically, according to the calculated membership degree and Table 2, the user's response potential level can be obtained:

[0117] Table 2 User response potential level judgment criteria

[0118] Membership User Response Potential Level η>0.8 high 0.4<η≤0.8 middle η<0.4 Low

[0119] The setting of response potential level and the division of membership range are the processes of further processing and classifying the flexible load response potential assessment results based on variable fuzzy set theory, aiming to improve the readability and practicality of the assessment results.

[0120] Embodiment 2 of the present invention provides a low-voltage area flexible load response potential analysis system, which runs the low-voltage area flexible load response potential analysis method described in embodiment 1, and the system includes: a first module, a second module, a third module and a fourth module.

[0121] The first module is used to determine the influencing factors of the flexible load response potential of the low-voltage substation and establish evaluation indicators corresponding to each influencing factor; the evaluation indicators include one or more of user participation level, user response coordination, load classification and grading, equipment response capacity, user economic level, user response reward, timeliness and accuracy.

[0122] The second module is used to analyze the impact of each evaluation index on the flexible load response potential and to establish the quantitative standards and corresponding weights of the corresponding impact levels.

[0123] The third module is used to determine the connection degree between the quantitative value of each evaluation indicator and the corresponding highest quantitative value, and calculate the total connection degree of the user in combination with the weight of each evaluation indicator.

[0124] The fourth module is used to calculate the user's membership degree based on the user's total connection degree using variable fuzzy sets, determine the user's response potential level based on the membership degree, and obtain the flexible load response potential analysis results of the low-voltage substation area.

[0125] Embodiment 3 of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the method for analyzing the potential of flexible load response of a low-voltage substation area described in Embodiment 1 is implemented.

[0126] Embodiment 4 of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a method for analyzing the potential of flexible load response in a low-voltage substation area according to Embodiment 1 is implemented.

[0127] Compared with the prior art, the beneficial effects of the present invention include at least:

[0128] The present invention comprehensively considers multi-dimensional factors such as user behavior, load characteristics, equipment capabilities, etc., and adopts a three-scale hierarchical analysis method to accurately determine the weight of each indicator, and introduces set pair analysis theory to calculate the connection between each indicator, thereby improving the accuracy and flexibility of the evaluation results.

[0129] This invention studies the energy use scenarios of diversified resources in low-voltage areas under high penetration of new energy, comprehensively considers various factors that affect the response potential of flexible loads, fully taps the potential of flexible load regulation in the area, and improves the flexible load regulation capacity in the area, especially under the condition of high proportion of new energy access, which can better achieve load balance. At the same time, the real-time performance of the evaluation results has been improved, which helps the power grid to make rapid adjustments during the demand-side response process, and further enhances the power reliability of the area and the absorption capacity of new energy.

[0130] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0131] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0132] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0133] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing the potential of flexible load response in a low voltage area, which is characterized by comprising the following contents: Determine the influencing factors of the flexible load response potential of the low-voltage substation, and establish evaluation indicators corresponding to each influencing factor; the evaluation indicators include one or more of user participation level, user response coordination, load classification and grading, equipment response capacity, user economic level, user response reward, timeliness and accuracy; Analyze the impact of each evaluation index on the flexible load response potential, and establish the quantitative standard and corresponding weight of the corresponding impact degree; Determine the degree of connection between the quantitative value of each evaluation indicator and the corresponding highest quantitative value, and calculate the total degree of connection of the user in combination with the weight of each evaluation indicator; According to the total connection degree of users, the membership degree of users is calculated by using variable fuzzy sets. The response potential level of users is determined according to the membership degree, and the flexible load response potential analysis results of low-voltage substations are obtained.

2. The method for analyzing the potential of low-voltage area flexible load response according to claim 1 is characterized in that: The user participation level is the willingness of power users to participate in grid load dispatching, which is characterized by the number of historical participations in grid load dispatching; The user response cooperation degree is the user's cooperation degree when the power grid issues a demand response task, which is quantitatively represented by the user's task completion status; Load classification and grading are divided into primary load, secondary load, tertiary load and quaternary load according to load importance; The equipment response capacity is the operating power of the equipment participating in the response on the demand side at the time of operation; The economic level of the user is the user's electricity expenditure; The response reward that users can receive is the reward provided by the power grid when users participate in demand-side response; Timeliness is the evaluation timeliness of the specified service, which can be used to determine whether the information transmission from the grid side to the user side in response to the instruction issued by the demand side is updated in real time; Accuracy refers to whether the demand-side response instructions are issued correctly.

3. The method for analyzing the potential of low-voltage area flexible load response according to claim 2 is characterized in that: The response reward that users can receive is calculated according to the following formula: in, C i is the reward received by user i, α i To compensate for the peak shifting, β i The unit price for peak shaving compensation is: γ i To compensate for the peak load reduction and valley load filling, Indicates peak power consumption. To reduce peak power consumption, To reduce the peak power consumption, |Δt i |Indicates the length of the off-peak time.

4. The method for analyzing the potential of low-voltage area flexible load response according to claim 2 is characterized in that: Calculate the specified service w according to the following formula j Timeliness at time t: s j (t,k)=e -μ·t in, s j (t,k)∈(0,1], j is the service identifier in the user evaluation request, μ is the service w at the current time t j The forgetting rate is used to characterize the degree to which users forget their service evaluations over time.

5. The method for analyzing the potential of low-voltage area flexible load response according to any one of claims 1 to 4, characterized in that: Analyze the influence of each evaluation index on the flexible load response potential, and establish the corresponding quantitative standards and corresponding weights including: The evaluation indicators are divided into three different levels: the first level is the target level, the second level is the evaluation indicator level, and the third level is the quantitative standard level; Analyze the influence of each evaluation index on the flexible load response potential, compare all evaluation indexes, and construct a judgment matrix; each element in the judgment matrix represents the importance of the evaluation index; The maximum eigenvalue of the judgment matrix is ​​solved, and the eigenvector corresponding to the maximum eigenvalue is determined as the weight of each evaluation index.

6. The method for analyzing the potential of low-voltage area flexible load response according to claim 5 is characterized in that: The degree of connection between the quantitative value of each evaluation index and the corresponding maximum quantitative value is determined according to the following formula: in, s i is the quantitative value of the evaluation index i; u i is the maximum quantitative value s of the evaluation index i and the evaluation index i i,max The degree of connection between them; w i is the weight of evaluation index i.

7. The method for analyzing the potential of low-voltage area flexible load response according to claim 6 is characterized by: The membership degree is calculated using the variable fuzzy set according to the following formula: in, U is the total connection degree of the user, c is the membership center point, σ is the membership width.

8. The low voltage area flexible load response potential analysis algorithm according to claim 7 is characterized by: Determining the user response potential level based on membership includes: When the membership degree is greater than 0.8, the user response potential level is the first level; and / or, When 0.4<membership degree≤0.8, the user response potential level is the second level; and / or, When the membership degree is less than 0.4, the user response potential level is the third level.

9. A low-voltage area flexible load response potential analysis system using the low-voltage area flexible load response potential analysis method according to any one of claims 1 to 8, characterized in that: include: The first module is used to determine the influencing factors of the flexible load response potential of the low-voltage substation area and establish evaluation indicators corresponding to each influencing factor; the evaluation indicators include one or more of the user participation level, user response coordination, load classification and grading, equipment response capacity, user economic level, user response reward, timeliness and accuracy; The second module is used to analyze the impact of each evaluation index on the flexible load response potential and establish the quantitative standard and corresponding weight of the corresponding impact degree; The third module is used to determine the connection degree between the quantitative value of each evaluation indicator and the corresponding highest quantitative value, and calculate the total connection degree of the user in combination with the weight of each evaluation indicator; The fourth module is used to calculate the user's membership degree based on the user's total connection degree using variable fuzzy sets, determine the user's response potential level based on the membership degree, and obtain the flexible load response potential analysis results of the low-voltage substation area.

10. An electronic device, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the low-voltage area flexible load response potential analysis method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the low-voltage area flexible load response potential analysis method described in any one of claims 1 to 8 are implemented.

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