Demand response peak regulation capability assessment method and related equipment

Abstract

The invention relates to the technical field of power demand response, in particular to a demand response peak regulation capability evaluation method and related equipment, and the method specifically comprises the steps: classifying adjustable load resources, and determining load types which comprise a switch type load, a step type load and a temperature control load; carrying out load characteristic analysis based on the load data of the user; generating a load step based on the load data of the user; and finally, based on a load characteristic analysis result and the load step, determining the initial peak load regulation capability of the user, and based on the load type of the user, adjusting the initial peak load regulation capability to determine the peak load regulation capability of the user. Therefore, when the virtual power plant participates in the demand response electricity market, the evaluation of the peak regulation capability of the user can be accurately and efficiently realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power demand response, and in particular to a demand response peak-shaving capability evaluation method and related equipment. Background Art

[0002] Driven by the "dual carbon" strategic goal, the scale of grid-connected new energy such as wind power and photovoltaic power is increasing. Their randomness, volatility and large prediction errors increase the peak-to-valley difference of load, leading to further increase in the peak-shaving pressure of the power grid. At the same time, new energy technologies such as electric vehicles and energy storage are also developing rapidly. How to coordinate various resources of the power system and ensure the safe and stable operation of the power grid has become a hot topic in current power system research.

[0003] The traditional distribution network design method assumes that the voltage at the far end of the feeder is low and the power flow is unidirectional. The introduction of a high proportion of renewable energy increases the reverse power flow in the power grid, making it bidirectional. In addition, the distribution network with a large amount of renewable energy has also become highly dependent on the weather. This will cause many problems and challenges that have not been encountered before, including: 1. Load forecasting. In the past, power generation groups mostly generated electricity according to a fixed plan, but now because renewable energy is highly dependent on weather factors, accurate load forecasting is needed to provide strong support for the dispatch of the power grid. 2. The reliability and safety of the distribution system are crucial to ensure the safe and stable operation of the power grid system. The high proportion of access to renewable energy may cause voltage fluctuations and harmonic distortion problems. The interaction characteristics of the upstream power grid have also changed. Traditionally, local power grids are usually only allowed to provide active power locally, but not directly provide reactive power. The reverse power flow caused by high power generation and low demand in the local power grid is too large, and the active and reactive power flows between the upstream power grid have changed. The active power demand of upstream synchronous generation has decreased, while the reactive power demand has remained unchanged. 3. Equipment over-limit. Equipment over-limit problems include feeder overload, abnormal operation of current and voltage protection devices, etc. These problems need to be properly resolved. 4. Peak load capacity assessment. As new energy is integrated into the power market in various forms, the assessment of user peak load capacity has become crucial. This is not only related to the stable operation and supply and demand balance of the power grid, but also directly affects the absorption efficiency and market competitiveness of new energy. Accurately assessing the user's peak load capacity will help formulate more reasonable power dispatching strategies, optimize resource allocation, reduce wind and solar power abandonment, and promote the coordinated development of new energy and traditional energy, and promote the green transformation and sustainable development of the power market.

[0004] In this context, the new grid should have the following functions: Real-time monitoring of grid operation. Effective management to ensure power quality, voltage stability and overall system operation stability. Equipped with a decision-making system that can make the best decision based on market rules and market dynamics.

[0005] A virtual power plant (VPP) is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the aggregation and coordinated optimization of distributed power sources, energy storage systems, residential electricity, electric vehicles, charging piles, industry, commercial buildings and other resources, so as to participate in the power market and power grid operation as a special electric field. Its application is becoming more and more extensive. However, when virtual power plants participate in the demand response power market, how to evaluate the user's peak load regulation capacity has become an urgent problem to be solved. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide a demand response peak-shaving capacity evaluation method and related equipment to overcome the current problem that the user's peak-shaving capacity cannot be accurately and efficiently evaluated when virtual power plants participate in the demand response power market.

[0007] To achieve the above objectives, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present application provides a method for evaluating demand response peak load regulation capability, comprising:

[0009] Classifying the adjustable load resources and determining the load type, wherein the load type includes switch type load, step type load and temperature control load;

[0010] Carry out load characteristic analysis based on user's load data;

[0011] Generate load steps based on user's load data;

[0012] Based on the load characteristic analysis result and the load step, the user's initial peak-shaving capacity is determined, and based on the user's load type, the initial peak-shaving capacity is adjusted to determine the user's peak-shaving capacity.

[0013] Furthermore, in some embodiments of the present application, the load characteristic analysis based on the user's load data includes:

[0014] For each user, the load data of the user is calibrated based on the maximum load of the user to obtain the calibrated load data; and based on the calibrated load data of the first preset time, an average load curve of the user corresponding to the first preset time is generated, and based on the average load curve, the load difference coefficient, peak-to-valley difference rate and load rate of the user are statistically calculated;

[0015] The calculation formula of the load difference coefficient is:

[0016]

[0017] Among them, K 1iis the load difference coefficient of user i; j is the day mark of the load data, J is the total number of days of the load data; n is the data point mark in the daily load curve, N is the total number of data points contained in the daily load curve; d′ ijn is d ijn The load data after standardization, d ijn is the data value corresponding to the nth data point on the load curve of user i on the jth day; D ij is the load data of user i on day j, m in is the average load of the nth data point of user i;

[0018] The peak-to-valley difference rate is the average value of the ratio of the daily peak-to-valley power per unit value in the first preset time period;

[0019] The load rate is the ratio of the user's daily load average power to the maximum power during the first preset time period.

[0020] Furthermore, in some embodiments of the present application, the load characteristic analysis based on the user's load data further includes:

[0021] For each user, the user's peak and valley load factor is calculated using the following formula:

[0022]

[0023] Among them, K 4i is the peak-valley load factor of user i, k grid is the load factor of the power grid in each period, where k corresponding to the peak period of the power grid grid is 1, and the k corresponding to the normal period of the power grid grid is 0, and the k corresponding to the valley period of the power grid grid is -1.

[0024] Furthermore, in some embodiments of the present application, the load characteristic analysis based on the user's load data further includes:

[0025] For each user, the peak-valley time overlap rate is calculated using the following formula:

[0026]

[0027] Among them, K 5i is the peak-valley period overlap rate of user i, T GF is the peak time duration of the power grid; T GG is the duration of the off-peak period of the power grid; T Fij T is the time duration of the user peak period overlapping with the power grid peak period every day; Gij It is the duration when the user's off-peak period overlaps with the grid's off-peak period.

[0028] Furthermore, in some embodiments of the present application, generating a load step based on the user's load data includes:

[0029] Only the steady-state information in the user's load data is retained, and the transient information in the user's load data is discarded. For the retained steady-state information, its average value, start time and end time are recorded to generate the user's load step.

[0030] Further, in some embodiments of the present application, determining the user's initial peak load regulation capability based on the load characteristic analysis result and the load step includes:

[0031] Screening and determining target users based on the peak-valley load coefficient and the peak-valley time period overlap rate;

[0032] Based on the load step of the target user, a corresponding daily load step curve is generated, and based on the daily load step curve, the primary and secondary reduction loads and the primary flexibility load of the target user are determined;

[0033] Determine the initial peak load regulation capacity of the target user based on the primary and secondary reduction loads and the primary flexibility load;

[0034] Among them, the highest load step in the daily load step curve is defined as the main step, the two load steps beside the main step are defined as secondary steps, the main and secondary reduction loads are the maximum difference between the main step and the secondary step; the main step flexibility load is the standard deviation of the main step load within 15 days; the main step response time is the time required for the main step load to switch to the load steps on both sides.

[0035] Further, in some embodiments of the present application, determining the initial peak load regulation capacity of the target user based on the primary and secondary reduction loads and the primary flexibility load includes:

[0036] The peak shaving capacity of the target user is calculated based on the primary and secondary shedding loads, and the valley filling capacity of the target user is calculated based on the primary and secondary flexibility loads.

[0037] Further, in some embodiments of the present application, adjusting the initial peak load shaving capacity based on the load type of the user to determine the user peak load shaving capacity includes:

[0038] For switch-type loads, the initial peak-shaving capacity is adjusted to the step power value of the nearest load; for step-type loads, the initial peak-shaving capacity is adjusted to the nearest load step.

[0039] In a second aspect, the present application provides a demand response peak load capacity evaluation device, comprising:

[0040] A classification module, used to classify the adjustable load resources and determine the load type, wherein the load type includes switch type load, step type load and temperature control load;

[0041] Load characteristic analysis module, used to perform load characteristic analysis based on user's load data;

[0042] A load step module, for generating load steps based on user's load data;

[0043] The calculation module is used to determine the user's initial peak-shaving capacity based on the load characteristic analysis results and the load steps, and to adjust the initial peak-shaving capacity based on the user's load type to determine the user's peak-shaving capacity.

[0044] In a third aspect, the present application provides a demand response peak load capacity evaluation device, including a processor and a memory, wherein the processor is connected to the memory:

[0045] Wherein, the processor is used to call and execute the program stored in the memory;

[0046] The memory is used to store the program, and the program is at least used to execute the above-mentioned demand response peak-shaving capacity evaluation method.

[0047] The present invention relates to the technical field of power demand response, and specifically to a demand response peak-shaving capacity evaluation method and related equipment, the method specifically comprising: classifying adjustable load resources and determining load types, wherein the load types include switch-type loads, graded loads, and temperature-controlled loads; then performing load characteristic analysis based on the user's load data; then generating load steps based on the user's load data; finally, determining the user's initial peak-shaving capacity based on the load characteristic analysis results and the load steps, and adjusting the initial peak-shaving capacity based on the user's load type to determine the user's peak-shaving capacity. In this way, when a virtual power plant participates in a demand response power market, the user's peak-shaving capacity can be accurately and efficiently evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0049] Figure 1 is a flow chart of a method for evaluating demand response peak load regulation capability provided by an embodiment of the present invention;

[0050] Figure 2is a schematic diagram of load steps in the demand response peak load regulation capability evaluation method provided in an embodiment of the present invention;

[0051] Figure 3 It is a structural schematic diagram of a demand response peak-shaving capability evaluation device provided in an embodiment of the present invention;

[0052] Figure 4 It is a structural schematic diagram of a demand response peak-shaving capability assessment device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0054] Figure 1 is a flow chart of a method for evaluating demand response peak load capacity provided by an embodiment of the present invention. Figure 1 , this embodiment may include the following steps:

[0055] S101. Classify adjustable load resources and determine load types.

[0056] Among them, load types include switch type load, step type load and temperature control load.

[0057] Specifically, in practical applications, virtual power plants have numerous and rich types of adjustable load resources. In order to simplify the model, in this application, the adjustable load resources are generalized and divided into switch-type loads, step-type loads and temperature-controlled loads. Among them, switch-type loads include lighting systems, televisions, computers and other equipment, and their load curves present step-like characteristics due to the switching behavior. Step-type loads include electric fans, induction cookers and other equipment, and their load curves present segmented step-like ladder waveforms. Temperature-controlled loads include air conditioners, electric heating equipment, water heaters, etc., and their load curves present periodic square waves. They start working at a certain temperature and automatically disconnect after reaching the required temperature.

[0058] On this basis, the generalized models of the above three loads can be determined respectively, among which the generalized model of the switch load is as follows:

[0059]

[0060] Where: P t k Indicates the power of the switch load; t s Indicates the load start time; t fIndicates the load shutdown moment; p indicates the step power value of the load.

[0061] The generalized model of graded load is as follows:

[0062]

[0063] Where: P t f is the load power of the graded load; t h is the shifting time of the step-type load; p g-1 and p g They are the front and rear powers of load shifting respectively.

[0064] The thermodynamic equivalent model of the temperature control load is shown as follows:

[0065]

[0066] Among them, P t r Represents the operating thermal power of the temperature control load; T t With T t+1 are the load temperature values ​​at time t and time t+1 respectively; T t 0 represents the ambient temperature at time t; t u represents the working time of the water heater (if there is no water heater, the value is 0), k represents the equivalent coefficient of the relationship between the working time and the temperature change; Δt represents the time interval for status update; R and C represent the equivalent thermal resistance and thermal capacity respectively; s t Indicates the switching state of the temperature control load; λ indicates the thermodynamic equivalent parameter.

[0067] In this way, the load power P can be adjusted t load It can be expressed by the following formula:

[0068]

[0069] S102: Perform load characteristic analysis based on user load data.

[0070] Specifically, the load difference coefficient, peak-valley difference rate, load rate, peak-valley load coefficient, peak-valley period overlap rate and other information can be calculated according to the load characteristics, so as to determine the load adjustable peak period.

[0071] S103: Generate load steps based on the user's load data.

[0072] S104: Determine the user's initial peak-shaving capacity based on the load characteristic analysis results and the load steps, and adjust the initial peak-shaving capacity based on the user's load type to determine the user's peak-shaving capacity.

[0073] Specifically, load steps are generated based on the user's load data, such as stepping the user's historical load curve, so as to clarify the user's load trend during peak and valley periods. Then, based on the determination of the user's initial peak-shaving capacity, the initial peak-shaving capacity is adjusted based on the user's load type determined in the first step to determine the user's peak-shaving capacity.

[0074] The demand response peak-shaving capacity evaluation method provided by the present invention classifies the adjustable load resources to determine the load type, wherein the load type includes switch-type load, graded load and temperature-controlled load; then performs load characteristic analysis based on the user's load data; then generates load steps based on the user's load data; finally, based on the load characteristic analysis results and the load steps, determines the user's initial peak-shaving capacity, and based on the user's load type, adjusts the initial peak-shaving capacity to determine the user's peak-shaving capacity. In this way, when the virtual power plant participates in the demand response power market, the user's peak-shaving capacity can be accurately and efficiently evaluated.

[0075] This application processes the user's historical load data, extracts and establishes an indicator system for evaluating the user's electricity consumption type, and then uses the user's historical load data as a sample to predict the user's potential, providing support for formulating a virtual power plant declaration strategy based on user potential.

[0076] To achieve the above objectives, based on user basic data, historical clearing and load, the present invention adopts the following technical solutions.

[0077] Furthermore, in order to effectively analyze user load, the present application analyzes the user's historical load from multiple angles, including load characteristic analysis based on the user's load data, which may specifically include: for each user, standardizing the load data based on the user's maximum load to obtain the standardised load data; and based on the standardised load data for a first preset time period (such as one month), generating an average load curve of the user corresponding to the first preset time period, and using the average load curve as a reference, statistically calculating the user's load difference coefficient, peak-to-valley difference rate and load rate; and for each user, calculating the user's peak-to-valley load coefficient and peak-to-valley period overlap rate.

[0078] Specifically, considering that the load data of users may vary greatly, in order to eliminate the difference between user volumes, the load data of each user is first per-unit based on the maximum load of each user, and the expression is:

[0079]

[0080] Where: i = 1, 2, ..., I, is the user mark, I is the total number of users; j = 1, 2, ..., J, is the day mark, J is the total number of days included in the load data; n = 1, 2, ..., N, is the data point mark in the daily load curve, N is the number of data points included in the daily load curve; d′ ijn is d ijn The load data after standardization, d ijn is the data value corresponding to the nth data point on the load curve of user i on the jth day.

[0081] On this basis, load characteristics analysis is performed through the per-unit load data, including:

[0082] In order to analyze the daily changes in user load over a period of time and calculate the user's load difference coefficient, the following is based on the average load curve of user i in one month, and defines the calculation load difference coefficient K 1i for:

[0083]

[0084] Among them, D ij is the load data of user i on day j, m in is the average load of the nth data point of user i. It should be noted that the load difference coefficient reflects the difference between the shapes of the daily load curves of users, which can be used to judge the regularity of the user's electricity consumption behavior within a month. The smaller the value, the higher the similarity of the user's load curve and the better the regularity. On the contrary, the greater the change of the user's load curve and the worse the regularity.

[0085] Furthermore, in order to characterize the volatility of the user's daily load, for each user, this application takes the per-unit load curve data (obtained from the load data) as the basis and defines the average value of the ratio of the per-unit peak-to-valley power of the user in J days of a month as the peak-to-valley difference rate K 2i The calculation formula is as follows:

[0086]

[0087] The above peak-to-valley difference rate reflects the fluctuation of the maximum and minimum load values. Further, in order to reflect the overall fluctuation of the user's daily load, this application uses the load rate to represent it, which is defined as the ratio of the user's daily load average power to the maximum power. The specific calculation formula is:

[0088]

[0089] Furthermore, in order to reflect the power consumption of users during the peak and valley periods of the power grid, the peak and valley load factor of users is defined and calculated. The specific calculation formula is:

[0090]

[0091] Among them, k grid is the load factor of the power grid in each period, and its value is as follows:

[0092]

[0093] Understandably, K 4i If the value is positive and high, it means that the peak and valley periods of the user are highly consistent with the peak and valley periods of the power grid, and the user is a peak-facing user; if it is negative, it means that the peak and valley periods are exactly opposite to the peak and valley periods of the power grid, and the user is a peak-avoiding user. For continuous users, their load basically remains near the average value, and K 4i The value can be positive or negative, but its value is generally very small.

[0094] Furthermore, the peak-valley overlap rate of users is calculated (in practical applications, this can be done only for peak-facing and peak-avoiding users), with the peak time length T of the power grid as GF Valley period duration T GG As a benchmark, the time duration of the user's peak period overlapping with the power grid's peak period is T Fij The length of time that the user's valley period overlaps with the grid's valley period is T Gij , define the peak-valley time overlap rate of the user as K 5i , which is used as one of the indicators for evaluating the peak load-shaving capability of the user. The specific calculation formula is:

[0095]

[0096] It can be understood that the various indicators in the above load characteristic analysis can be used to determine the load peak-shaving period, to evaluate the user's ability to participate in the demand response market in different time periods, and through the above classification results, peak-valley load coefficient and peak-valley period overlap rate, combined with the subsequently generated load steps, to further evaluate the user's peak-shaving ability.

[0097] Furthermore, the demand response peak-shaving capacity assessment method provided in the present application also includes generating load steps based on the user's load data (the load data in this part is also historical load data, but recent load data is preferentially selected), including only retaining the steady-state information in the user's load data and discarding the transient information in the user's load data, and for the retained steady-state information, recording its average value, start time and end time to generate the user's load step.

[0098] Specifically, the load step can accurately describe the user's load state, and the load difference between the steps reflects the user's adjustable load. The load step in this application is described in detail below. The load step refers to the state change of a period of time in the user's daily load curve, and the local change rate α is:

[0099]

[0100] Where: p is the local load (such as Figure 2 Δp is the differential value of the overall load, that is, the load difference between two adjacent points. The subscripts min, max, mean, and std represent the minimum, maximum, mean, and variance, respectively.

[0101] In this application, the identification and calculation of load steps refers to retaining only the steady-state information of the load data when processing the load data, and discarding the transient information between the load data. For the retained steady-state information, the average value, start time and end time of this load data are recorded. For the discarded transient information, it can be approximated by the height difference and transition time between the steps. The specific load step diagram is shown in 2 (in Figure 2 The blue curve is the curve of user load data, and the line of another color is the generated load step curve, in which each step is the load step calculated by the above identification).

[0102] In practical applications, we can take the daily load data of the user in the previous 15 days obtained with a sampling frequency of 96 times / day as an example, and find out all the steps by traversing the daily load data. The specific steps are as follows.

[0103] First, define the initial data and judgment conditions. Define a candidate step array a to store the initial load value, and use the local change rate α < 3 as the load step judgment condition:

[0104] a=W[1,2,3,4,5,6,7]

[0105] Then traverse the user's load data W (historical load data) to identify the load steps. The traversal method is:

[0106]

[0107] According to the above formula, a is updated. The initial value of t is 8, which represents the latest position of the traversal. The update method of t is: 1 If W(t) belongs to a step, then t = t + 1; 2 If W(t) is not on the step but W(t-1) is on the step, then t = t + 3; 3 If neither W(t) nor W(t-1) is on the step, then t = t + 1.

[0108] And record the information of the identified load step, for example, record the information of the i-th load step:

[0109] P j (i) = [P j,1 (i),P j，2(i),P j，3 (i)],j∈[1,n i ]

[0110] Where: P j，1 (i), P j，2 (i), P j，3 (i) are the average value, start time and end time of the original historical load data of the load step; n i is the number of load steps identified in the entire daily load curve.

[0111] It can be understood that by stepping the historical load curve, the load trend of users during peak and valley periods can be clearly identified.

[0112] On this basis, the user peak-shaving capacity is evaluated based on the load type, load characteristic analysis results and load steps to determine the user peak-shaving capacity, including: screening and determining the target users based on the user's peak-valley load coefficient and the peak-valley period overlap rate; generating the corresponding daily load step curve based on the target user's load step, and determining the target user's primary and secondary reduction loads and the main-order flexibility load based on the daily load step curve; determining the target user's initial peak-shaving capacity based on the primary and secondary reduction loads and the main-order flexibility load; including calculating the target user's peak-shaving capacity based on the primary and secondary reduction loads, and calculating the target user's valley-filling capacity based on the main-order flexibility load; finally, for switch-type loads, the initial peak-shaving capacity is adjusted to the step power value of the nearest load; for graded loads, the initial peak-shaving capacity is adjusted to the nearest load gear.

[0113] Specifically, firstly, the target users are screened and determined according to the overlap between the user's peak and valley periods and the power grid's peak and valley periods, and the period overlap evaluation index R is specifically defined. The users whose period overlap evaluation index R is greater than a preset value are determined as the target users for actual peak load regulation. The specific formula is as follows:

[0114] R=pK 4i +qK 5i

[0115] In this formula, p and q are both evaluation coefficients and can be set to 0.5.

[0116] Then, the peak load regulation capability of the target user is analyzed according to the load step. Specifically, the peak load regulation capability of the user can be analyzed from the following perspectives:

[0117] 1. Maximum peak load regulation capability

[0118] The highest load step in the daily load step curve (a curve generated based on the user's load steps in a day) is called the main step, and the two load steps next to the main step are called secondary steps. The maximum difference between the main step and the secondary step is used as the primary and secondary reduction loads. The specific calculation formula is as follows:

[0119]

[0120] In the formula, p peak，1 (i) is the maximum value of the load step height; p peak-1，1 (i), p peak+1，1 (i) is the height of the load step next to the highest load step. It can be understood that the larger the value of primary and secondary load reduction, the greater the potential for users to participate in peak load reduction. In the above project, the average value of primary and secondary load reduction within 15 days is considered.

[0121] 2. Main stage flexibility load

[0122] Peak flexibility refers to the standard deviation of the main load within 15 days. The larger the value, the stronger the flexibility of the load and the greater the potential for users to participate in peak load shaving. The calculation formula is as follows:

[0123]

[0124] In the formula, p peak，1 15 days peak，1 The average value of (i).

[0125] 3. Main-order response time

[0126] The main-order response time refers to the time required for main-order load reduction, that is, the time required to switch to the load steps on both sides. The smaller the main-order response time, the greater the potential for users to participate in peak shaving.

[0127] On this basis, the peak-shaving capacity of the target user can be calculated based on the primary and secondary load reduction, and the valley-filling capacity of the target user can be calculated based on the primary and secondary flexible load. It can be understood that when the aggregator, that is, the user, participates in the demand response peak-shaving market, the peak-shaving capacity is the difference between the current step load and the adjacent minimum step load in a certain period of time. Conversely, if the demand response valley-filling market is participated in, the valley-filling capacity is the difference between the maximum step load and the current step in a certain period of time. In this way, the initial peak-shaving capacity of the user is determined.

[0128] Finally, the initial peak-shaving capacity determined above is adjusted to determine the user's peak-shaving capacity, including: for switch-type loads, the initial peak-shaving capacity is adjusted to the step power value of the nearest load; for graded loads, the initial peak-shaving capacity is adjusted to the nearest load grade. In addition, for temperature-controlled loads, the initial peak-shaving capacity of the user can be directly used as the final peak-shaving capacity.

[0129] In summary, this application completes the load peak-shaving capacity assessment of demand-side response through load classification, load characteristic analysis, load steps and peak-shaving capacity calculation. Given that the current demand-response power market is still in its infancy, power reform faces multiple challenges, including ensuring the safe and stable operation of the power system, promoting the efficient consumption of new energy, strengthening energy-saving and emission reduction measures, and enhancing user participation enthusiasm. The peak-shaving capacity assessment on the user side is particularly important. It can not only provide users with a scientific and reasonable declaration basis when participating in demand response, ensuring that users reasonably arrange electricity plans according to their actual conditions, but also provide strong data support for the production planning and scheduling of the power system. By accurately assessing the peak-shaving potential on the user side, the power system can more flexibly formulate scheduling strategies and optimize resource allocation, thereby promoting the full consumption of new energy while ensuring the safe and stable operation of the power grid, and promoting the realization of energy-saving and emission reduction goals, and ultimately building a more efficient, green and interactive power market system.

[0130] The demand response peak-shaving capacity evaluation method provided in this application fully considers the overlap rate between user load and market peak and valley periods, and effectively taps the user's peak-shaving potential at critical moments; a weighted method is used to reasonably weight different load characteristic indicators, making the evaluation results more scientific, while effectively distinguishing the importance of peak and valley periods; the application of the load step method significantly reduces the error caused by random load values, further improving the accuracy of the evaluation; by dividing the load data into different steps, the evaluation process becomes more stable, providing strong support for the formulation of reasonable peak-shaving strategies; in addition, based on a detailed analysis of the load type, the evaluation results are fine-tuned to more accurately grasp the user's peak-shaving potential. In summary, the evaluation method provided in this application is both practical and accurate, and can provide strong support for the evaluation of the peak-shaving capacity of users in the power market.

[0131] Based on the same inventive concept, the present application also provides a demand response peak load capacity evaluation device, Figure 2 is a schematic diagram of load steps in the demand response peak load regulation capability evaluation method provided by an embodiment of the present invention, such as Figure 3 As shown, the demand response peak-shaving capability evaluation device provided in the present application includes: a classification module 11, a load characteristic analysis module 12, a load step module 13 and a calculation module 14.

[0132] The classification module 11 is used to classify the adjustable load resources and determine the load type, wherein the load type includes switch type load, graded type load and temperature control load. The load characteristic analysis module 12 is used to perform load characteristic analysis based on the user's load data; the load step module 13 is used to generate load steps based on the user's load data; the calculation module 14 is used to determine the user's initial peak-shaving capacity based on the load characteristic analysis results and the load steps, and adjust the initial peak-shaving capacity based on the user's load type to determine the user's peak-shaving capacity.

[0133] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0134] Based on the same inventive concept, the present invention also provides a demand response peak load capacity evaluation device for implementing the above method embodiment. Figure 4 is a schematic diagram of the structure of a demand response peak load capacity evaluation device provided by an embodiment of the present invention, such as Figure 4 As shown, the demand response peak-shaving capability evaluation device of this embodiment includes a processor 21 and a memory 22, and the processor 21 is connected to the memory 22. The processor 21 is used to call and execute the program stored in the memory 22; the memory 22 is used to store the program, and the program is at least used to execute the demand response peak-shaving capability evaluation method in the above embodiment.

[0135] The specific implementation scheme of the demand response peak-shaving capability evaluation device provided in the embodiment of the present application can refer to the implementation scheme of the demand response peak-shaving capability evaluation method in any of the above embodiments, which will not be repeated here.

[0136] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0137] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0138] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0139] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0140] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0141] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0142] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0143] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0144] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating demand response peak load capacity, characterized in that: include: Classifying the adjustable load resources and determining the load type, wherein the load type includes switch type load, step type load and temperature control load; Carry out load characteristic analysis based on user's load data; Generate load steps based on user's load data; Based on the load characteristic analysis result and the load step, the user's initial peak-shaving capacity is determined, and based on the user's load type, the initial peak-shaving capacity is adjusted to determine the user's peak-shaving capacity.

2. The demand response peak load capacity evaluation method according to claim 1 is characterized in that: The load characteristic analysis based on the user's load data includes: For each user, the load data of the user is calibrated based on the maximum load of the user to obtain the calibrated load data; and based on the calibrated load data of the first preset time, an average load curve of the user corresponding to the first preset time is generated, and based on the average load curve, the load difference coefficient, peak-to-valley difference rate and load rate of the user are statistically calculated; The calculation formula of the load difference coefficient is: Among them, K 1i is the load difference coefficient of user i; j is the day mark of the load data, J is the total number of days of the load data; n is the data point mark in the daily load curve, N is the total number of data points contained in the daily load curve; d′ ijn is d ijn The load data after standardization, d ijn is the data value corresponding to the nth data point on the load curve of user i on the jth day; D ij is the load data of user i on day j, m in is the average load of the nth data point of user i; The peak-to-valley difference rate is the average value of the ratio of the daily peak-to-valley power per unit value in the first preset time period; The load rate is the ratio of the user's daily load average power to the maximum power during the first preset time period.

3. The demand response peak load capacity evaluation method according to claim 2 is characterized in that: The load characteristic analysis based on the user's load data also includes: For each user, the user's peak and valley load factor is calculated using the following formula: Among them, K 4i is the peak-valley load factor of user i, k grid is the load factor of the power grid in each period, where k corresponding to the peak period of the power grid grid is 1, and the k corresponding to the normal period of the power grid grid is 0, and the k corresponding to the valley period of the power grid grid is -1.

4. The demand response peak load capacity evaluation method according to claim 3 is characterized in that: The load characteristic analysis based on the user's load data also includes: For each user, the peak-valley time overlap rate is calculated using the following formula: Among them, K 5i is the peak-valley period overlap rate of user i, T GF is the peak time duration of the power grid; T GG is the duration of the off-peak period of the power grid; T Fij T is the time duration of the user peak period overlapping with the power grid peak period every day; Gij It is the duration when the user's off-peak period overlaps with the grid's off-peak period.

5. The demand response peak load capacity evaluation method according to claim 4 is characterized in that: The generating of load steps based on the load data of the user comprises: Only the steady-state information in the user's load data is retained, and the transient information in the user's load data is discarded. For the retained steady-state information, its average value, start time and end time are recorded to generate the user's load step.

6. The demand response peak load capacity evaluation method according to claim 5 is characterized in that: The determining of the user's initial peak load regulation capacity based on the load characteristic analysis result and the load step includes: Screening and determining target users based on the peak-valley load coefficient and the peak-valley time period overlap rate; Based on the load step of the target user, a corresponding daily load step curve is generated, and based on the daily load step curve, the primary and secondary reduction loads and the primary flexibility load of the target user are determined; Determine the initial peak load regulation capacity of the target user based on the primary and secondary reduction loads and the primary flexibility load; Among them, the highest load step in the daily load step curve is defined as the main step, the two load steps beside the main step are defined as secondary steps, the main and secondary reduction loads are the maximum difference between the main step and the secondary step; the main step flexibility load is the standard deviation of the main step load within 15 days; the main step response time is the time required for the main step load to switch to the load steps on both sides.

7. The demand response peak load capacity evaluation method according to claim 6 is characterized in that: The determining the initial peak load regulation capacity of the target user based on the primary and secondary reduction loads and the primary flexibility load comprises: The peak shaving capacity of the target user is calculated based on the primary and secondary shedding loads, and the valley filling capacity of the target user is calculated based on the primary and secondary flexibility loads.

8. The demand response peak load capacity evaluation method according to claim 6, characterized in that: The adjusting the initial peak-shaving capacity based on the load type of the user to determine the peak-shaving capacity of the user includes: For switch-type loads, the initial peak-shaving capacity is adjusted to the step power value of the nearest load; for step-type loads, the initial peak-shaving capacity is adjusted to the nearest load step.

9. A demand response peak load capacity evaluation device, characterized in that: include: A classification module, used to classify the adjustable load resources and determine the load type, wherein the load type includes switch type load, step type load and temperature control load; Load characteristic analysis module, used to perform load characteristic analysis based on user's load data; A load step module, for generating load steps based on user's load data; The calculation module is used to determine the user's initial peak-shaving capacity based on the load characteristic analysis results and the load steps, and to adjust the initial peak-shaving capacity based on the user's load type to determine the user's peak-shaving capacity.

10. A demand response peak load capacity assessment device, characterized in that: The invention comprises a processor and a memory, wherein the processor is connected to the memory: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the demand response peak-shaving capacity evaluation method described in any one of claims 1-7.

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