Load aggregator optimization scheduling method and system based on user peak regulation value

Abstract

The invention relates to the technical field of energy equipment optimization scheduling, in particular to a load aggregator optimization scheduling method and system based on user peak regulation value. The adjustable resources are classified, clustering indexes are provided according to classification results, and a typical adjustable load curve is formed through clustering analysis; aiming at a typical adjustable load curve formed based on an adjustable load group, proposing a compensation pricing method based on a user peak regulation value; and according to a pricing method, constructing a load aggregator optimization scheduling model based on the time-phased peak regulation value. According to the method, the peak regulation value of the user is fully considered, the user compensation pricing is given accordingly, and the method has great significance in the aspects of load aggregator optimization scheduling, design of a user compensation pricing mechanism and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of energy equipment optimization scheduling, and in particular to a load aggregator optimization scheduling method based on user peak load regulation value. Background Art

[0002] Power optimization dispatch refers to the rational arrangement of power generation and load resources to achieve the lowest cost and highest efficiency operation mode under the premise of meeting power demand and system safety. In the context of power marketization, optimization dispatch not only involves traditional power generation dispatch, but also needs to comprehensively consider the access of renewable energy, load response and dynamic changes in grid operation. Through accurate load forecasting and power generation planning, power optimization dispatch can effectively balance power supply and demand, reduce system operating costs, and maximize the utilization of renewable energy. Using advanced algorithms and models, dispatch decision makers can analyze price signals and load conditions in the power market in real time, thereby optimizing the operating sequence of power plants, reducing peak loads, and improving power generation utilization during off-peak hours.

[0003] Load aggregators play a vital role in the context of power demand response. As a commercial organization in the power market, load aggregators effectively improve the responsiveness of the power system by centrally managing decentralized adjustable load resources. In the short term when power prices fluctuate and the system operating status changes, load aggregators can quickly motivate users to adjust their electricity consumption behavior, thereby achieving peak shifting and valley filling, reducing power demand during peak hours, and smoothing the load curve. In addition, load aggregators are also of great significance in optimizing the operation mode of power plants, which can enhance the grid's ability to absorb intermittent distributed energy and improve the level of interaction between the grid and power users. By effectively integrating users, not only the stability and efficiency of grid operation are improved, but also positive contributions are made to the flexibility and sustainable development of the power market. However, there are still significant defects in the design of user compensation pricing mechanism for load aggregators. For example, the current user compensation mechanism still adopts a fixed pricing method. However, this mechanism cannot fully measure the peak-shaving value of users and cannot adapt to the uncertainty of current source-load power generation. In current academic research, when faced with the balance of interests of multiple subjects, the master-slave game model is mostly used. However, in practical applications, the complexity of the master-slave game model has become the main obstacle to promoting the above pricing mechanism. This is not only because the structure of the model itself may not satisfy the Karush-Kuhn-Tucker conditions, but also because the computing resources and time required to solve the model are extremely large, further limiting the possibility of its widespread application. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the present invention provides a load aggregator optimization scheduling method based on user peak-shaving value, which can solve the problems of insufficient dynamic adjustment capability, low mode conversion efficiency, difficulty in optimizing the charging and discharging process, and insufficient health status monitoring existing in traditional methods.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a load aggregator optimization scheduling method based on user peak load value, comprising: classifying adjustable resources, proposing clustering indicators for the classification results, and forming a typical adjustable load curve through cluster analysis;

[0007] According to the typical adjustable load curve formed by adjustable load groups, a compensation pricing method based on user peak load value is proposed;

[0008] According to the pricing method, a load aggregator optimization scheduling model based on the peak-shaving value of different time periods is constructed.

[0009] As a preferred solution of the load aggregator optimization scheduling method based on user peak-shaving value described in the present invention, wherein: the adjustable resources are classified into distributed energy storage loads, curtailable loads, and shiftable loads.

[0010] As a preferred solution of the load aggregator optimization scheduling method based on user peak-shaving value described in the present invention, wherein: the clustering indicators proposed for the classification results include clustering indicators proposed for the reducible load and the shiftable load regulation characteristics.

[0011] As a preferred solution of the load aggregator optimization scheduling method based on user peak load value described in the present invention, wherein: the load that can be reduced includes:

[0012] The primary and secondary load reduction is expressed as,

[0013]

[0014] Where: is the i-th curtailable load peak in a typical day; is the load value in the time period adjacent to the peak value;

[0015] The peak dynamic load is expressed as,

[0016]

[0017] Where: is the average value of the i-th load that can be reduced in a typical day;

[0018] The translatable load includes:

[0019] The intraday peak-to-valley difference rate is expressed as,

[0020]

[0021] Where: are the load peak and valley values ​​of the i-th translatable load respectively;

[0022] The load fluctuation rate is expressed as,

[0023] γ 4 =σ III (i) / μ III (i)

[0024] Where: III (i),μ III (i) are the load variance and mean of the typical day's shiftable load i.

[0025] As a preferred solution of the load aggregator optimization scheduling method based on user peak-shaving value described in the present invention, wherein: the compensation pricing method based on user peak-shaving value is expressed as:

[0026]

[0027] In the formula, Represents the daily power baselines of three types of adjustable loads, They represent the daily power curves of three types of adjustable load peak regulation. Respectively represent the active power difference in each period before and after the peak regulation of Class I, Class II and Class III adjustable loads;

[0028]

[0029]

[0030] In the formula, They are the state variables of three types of adjustable loads participating in the peak load auxiliary market, among which Moving in and out of state variables by translatable loads It consists of two parts. is the LA forward dispatching energy storage load, M and λ represent the maximum and minimum values ​​respectively;

[0031] In view of the fact that there are multiple peaks in one day in the current power peak-shaving market, the importance and urgency of peak-shaving in different periods vary. The peak-shaving value weight coefficient of each period is introduced, and the calculation formula is as follows:

[0032]

[0033] Where: They are the peak and valley values ​​of the daily load baseline of the mth load among the three types of adjustable loads; They represent the peak load regulation value of each user in each type of adjustable load at each time period of the day;

[0034] In order to fully measure the peak-shaving priorities of different adjustable loads at different times and help LA allocate the peak-shaving budget more reasonably, the peak-shaving priority variation coefficient of each period is introduced. The calculation formula is as follows:

[0035]

[0036] The compensation pricing for typical adjustable users in each period is calculated as follows:

[0037]

[0038] In the formula, are the compensation prices for each typical user, C all is the peak-shaving budget, α I ,α II ,α III The adjustment coefficients for the three types of adjustable load compensation pricing are: It is a time-of-use electricity price.

[0039] As a preferred solution of the method for optimizing scheduling based on user peak-shaving value and load aggregator described in the present invention, the load aggregator optimization scheduling model based on time-sharing peak-shaving value is constructed to optimize the load aggregator scheduling cost to a minimum, and the constraints include: power balance constraints, electric vehicle loads, reducible loads, shiftable load models and output constraints, and user satisfaction constraints.

[0040] As a preferred solution of the load aggregator optimization scheduling method based on user peak load value described in the present invention, the power balance constraint is:

[0041]

[0042] Electric vehicle load constraints include on-grid, off-grid constraints, power constraints, and charging power constraints:

[0043]

[0044] In the formula, and The time when electric vehicles are connected to the grid or off the grid. is the electric vehicle power during period t, η is the charging efficiency of the electric vehicle, are the minimum and maximum power of electric vehicles, is the charging power of the electric vehicle load at a certain moment, y t is the state variable of electric vehicle on-grid and off-grid. t When it is 0, it means the electric vehicle is off-grid, and when it is 1, it is connected to the grid;

[0045] The load reduction constraints include the maximum number of reductions and the maximum reduction duration:

[0046]

[0047] Where N max The maximum number of times the load can be reduced is The maximum continuous reduction time of the load that can be reduced;

[0048] The load constraints that can be shifted include the total amount of electricity in a single dispatch cycle and the minimum continuous operation constraint:

[0049]

[0050]

[0051] In the formula, The minimum continuous operation time of the load that can be translated;

[0052] Adjustable load user participation willingness constraints:

[0053] The user's participation in load aggregation peak load regulation is determined by two factors: the degree to which electricity demand is met and whether the benefits obtained from participating in power peak load regulation meet expectations;

[0054]

[0055] In the formula, are the electricity charges of load-side users before they participate in the peak-shaving market, There are three types of load user satisfaction.

[0056] As a preferred solution of the load aggregator optimization dispatching system based on user peak load shaving value described in the present invention, it includes: the clustering module is used to roughly classify the adjustable load group according to distributed energy storage, reducible load, and shiftable load, propose clustering indicators for the classification results of the load group, and form a typical adjustable load curve through clustering analysis;

[0057] The pricing module is used to quantify the compensation pricing method of the user's peak-shaving value for the typical adjustable load curve and the situation of multiple peaks in one day in the current power peak-shaving market. The method comprehensively considers factors such as the user's power consumption before and after peak-shaving, time-of-use electricity price, load aggregator's peak-shaving budget, and the difference in peak-shaving value of users in different time periods;

[0058] A model building module is used to build a load aggregator optimization scheduling model based on the peak-shaving value of time periods: the load aggregator optimization scheduling model based on the peak-shaving value of time periods takes the minimum scheduling cost of load aggregators as the optimization goal, and the constraints include constraints on electric vehicles on and off the grid, constraints on the maximum and minimum power of electric vehicle groups, constraints on the maximum number of times that load can be cut per day, constraints on the maximum reduction time of load that can be cut, constraints on the total amount of load that can be shifted per day, constraints on the minimum continuous operation of shiftable loads, constraints on the willingness to participate in adjustable loads, and power balance constraints.

[0059] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of any one of the methods described in the method based on user peak-shaving value and load aggregator optimization scheduling method are implemented.

[0060] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the load aggregator optimization scheduling methods based on user peak-shaving value are implemented.

[0061] Beneficial effects of the present invention: The present invention proposes a load aggregator optimization scheduling method based on the user peak-shaving value. The method takes the minimum LA peak-shaving cost as the objective function, comprehensively considers the interactive relationship between the power dispatching center, LA, and load-side users, forms a typical user electricity consumption curve based on the cluster analysis of adjustable load regulation characteristics, and proposes a differentiated calculation method for multi-period peak-shaving value. While ensuring the comfort of each user, the peak-shaving value of each period of a typical user is evaluated, and then a load aggregator optimization scheduling and compensation pricing model based on limited peak-shaving fund allocation and time-divided peak-shaving value is established, and the practicality of the proposed method is verified through actual examples. This method fully considers the user's peak-shaving value and gives the user compensation pricing based on this. It is of great significance in the optimization scheduling of load aggregators and the design of user compensation pricing mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 A flow chart of a method for optimizing the scheduling of load aggregators and compensation pricing based on user peak load shaving value provided by an embodiment of the present invention.

[0064] Figure 2A schematic diagram of a typical daily electricity consumption curve of an electric vehicle user according to a compensation pricing based on user peak shaving value and a load aggregator optimization scheduling method provided by an embodiment of the present invention.

[0065] Figure 3 A schematic diagram of a typical user daily electricity consumption curve that can be reduced according to a compensation pricing based on user peak-shaving value and a load aggregator optimization scheduling method provided by an embodiment of the present invention.

[0066] Figure 4 A schematic diagram of a typical shiftable user daily electricity consumption curve for a compensation pricing method based on user peak load shaving value and a load aggregator optimization scheduling method provided by an embodiment of the present invention.

[0067] Figure 5 A peak-shaving demand curve based on user peak-shaving value compensation pricing and load aggregator optimization scheduling method provided by an embodiment of the present invention.

[0068] Figure 6 Scenario 1 of the compensation pricing based on user peak-shaving value and load aggregator optimization scheduling method provided by an embodiment of the present invention is the market participation and pricing situation of typical electric vehicle users.

[0069] Figure 7 Scenario 1 of the compensation pricing based on user peak-shaving value and load aggregator optimization scheduling method provided as an embodiment of the present invention can typically reduce user participation in the market and pricing situations.

[0070] Figure 8 Scenario 2 of the compensation pricing based on user peak-shaving value and load aggregator optimization scheduling method provided by an embodiment of the present invention shows the market participation and pricing situation of typical electric vehicle users.

[0071] Fig. 9 Scenario 2 of the compensation pricing based on user peak-shaving value and load aggregator optimization scheduling method provided as an embodiment of the present invention can typically reduce user participation in the market and pricing situations.

[0072] Fig.10 Scenario 2 of the load aggregator optimization scheduling method based on user peak-shaving value provided by an embodiment of the present invention is a typical example of user participation in the market and pricing. DETAILED DESCRIPTION

[0073] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0074] Example 1

[0075] This is the first embodiment of the present invention, which provides a load aggregator optimization scheduling method based on user peak load shaving value, including:

[0076] S1: Classify the adjustable resources, propose clustering indicators based on the classification results, and form a typical adjustable load curve through cluster analysis;

[0077] The present invention roughly divides adjustable loads into three categories according to their response characteristics:

[0078] Class I load (distributed energy storage): Electric vehicles currently account for more than 10% of the load in the distribution area and can be used as distributed energy storage equipment in industrial parks;

[0079] Class II load (reducible load): refers to equipment that responds to demand by reducing power consumption, mainly represented by high-power loads such as industrial equipment and commercial air conditioners;

[0080] Class III load (shiftable load): refers to equipment whose total load remains unchanged within a scheduling cycle, but allows flexible adjustment of the load amount in different time periods. It is mainly represented by some industrial production equipment, washing machines, water heaters, etc.

[0081] The clustering index that can reduce users is:

[0082] 1) Reducible load: The peak load regulation method of this type of load is to directly limit the current power consumption of the equipment. Therefore, the clustering characteristic indicators of this type of load are:

[0083] a) Primary and secondary load reductionγ 1 : Refers to the load peak value in the daily load curve and the load difference between two adjacent time periods; the larger its value, the greater the load reduction potential of user i. The calculation method is as follows:

[0084]

[0085] Where: is the i-th curtailable load peak in a typical day; It is the load value in the time period adjacent to the peak value.

[0086] b) Peak flexibility load γ 2 : Refers to the standard deviation of the load on a typical day. The larger the value, the more flexible the load is and the greater the potential for the load to participate in real-time peak shaving. The calculation method is as follows:

[0087]

[0088] Where: is the mean value of the i-th load that can be reduced in a typical day.

[0089] The clustering index of the users that can be translated is:

[0090] 2) Shiftable load: This type of load may be shifted in and out within a day, so the clustering characteristic index of this type of load is:

[0091] a) Intraday peak-to-valley difference γ 3 : Refers to the ratio of the peak-to-valley difference of typical daily load to the peak value. The larger the value, the greater the potential for translation. The calculation method is as follows:

[0092]

[0093] Where: are the load peak and valley values ​​of the i-th translatable load respectively.

[0094] b) Load fluctuation rate γ 4 : Refers to the variance of the load curve of user i in a typical day divided by the mean. The greater the load volatility, the greater the potential for load i to shift. The calculation method is as follows:

[0095] γ 4 =σ III (i) / μ III (i)

[0096] Where: III (i),μ III (i) are the load variance and mean of the typical day's shiftable load i.

[0097] Based on the above clustering characteristics, the Euclidean distance is used to calculate the distance between each point in the data set and the centroid, and the above two adjustable loads are clustered. The specific steps are as follows:

[0098] 1) Determine the number of clusters of two adjustable loads and randomly select the initial centroid;

[0099] 2) Calculate the distance between each point in the data set and the centroid and assign it to the cluster where the centroid has the smallest distance;

[0100] 3) All adjustable load data sets are classified and the centroids of each cluster are recalculated;

[0101] 4) If the distance between the newly calculated centroid and the original centroid is less than the threshold, clustering is successful; otherwise, repeat steps (2)-(4).

[0102] After the above clustering steps, typical users of type m and type l will be clustered to participate in peak load regulation, so as to reduce the difficulty of LA regulation and meet the energy demand of users to the greatest extent.

[0103] S2: For the typical adjustable load curve formed by the adjustable load group, a compensation pricing method based on the user's peak load value is proposed;

[0104] A compensation pricing method based on user peak load shaving value includes:

[0105]

[0106] In the formula, Represents the daily power baselines of three types of adjustable loads, They represent the daily power curves of three types of adjustable load peak regulation. They respectively represent the active power difference in each period before and after peak-shaving of Class I, Class II and Class III adjustable loads, that is, the response capacity of participating in the peak-shaving market.

[0107]

[0108]

[0109] In the formula, They are the state variables of three types of adjustable loads participating in the peak load auxiliary market, among which Moving in and out of state variables by translatable loads It consists of two parts. The LA forward dispatches the energy storage load (i.e., the power before peak regulation is greater than the power after peak regulation), and M and λ represent the maximum and minimum values, respectively.

[0110] The peak load value weight coefficient of each period is calculated as follows:

[0111]

[0112] Where: They are the peak and valley values ​​of the daily load baseline of the mth load among the three types of adjustable loads; Represents each type of adjustable load.

[0113] Peak load priority change coefficient The larger the coefficient, the higher the peak load value during this period. The calculation formula is as follows:

[0114]

[0115] The compensation pricing for each typical user in each period is calculated as follows:

[0116]

[0117] In the formula, are the compensation prices for each typical user, C all is the peak-shaving budget, αI ,α II ,α III The adjustment coefficients for the three types of adjustable load compensation pricing are: It is a time-of-use electricity price.

[0118] S3: According to the pricing method, a load aggregator optimization scheduling model based on the peak load value of different time periods is constructed.

[0119] Based on the above method, a load aggregator optimization scheduling model based on the peak-shaving value of time periods is constructed. The model optimizes the target of minimizing the dispatching cost of load aggregators. The constraints include: power balance constraint, electric vehicle load, reducible load, shiftable load model and output constraint, and user satisfaction constraint.

[0120] This model takes minimizing the peak-shaving cost of LA as the optimization goal:

[0121]

[0122] Where F is the LA peak load cost, ε is the penalty coefficient for non-response peak load, The amount of user unresponsiveness.

[0123] Since the compensation cost of the movable load has nonlinear characteristics, an intermediate variable is introduced Convert the nonlinear part into linear form.

[0124]

[0125] At this point, the objective function looks like this:

[0126]

[0127] As a preferred solution of the load aggregator optimization scheduling method described in the present invention, the load aggregator optimization scheduling model constraint conditions based on the user peak load value include:

[0128] 1) Class I load (distributed energy storage)

[0129] Currently, the charging behavior of electric vehicles on and off the grid is highly random. and Indicates the time period during which electric vehicles are connected to the grid or off the grid. The formula is as follows:

[0130]

[0131] Electric vehicle power state constraints:

[0132]

[0133] In the formula, is the electric vehicle power during period t, η is the charging efficiency of the electric vehicle, are the minimum and maximum power of electric vehicles, is the charging power of the electric vehicle load at a certain moment. t is the state variable of electric vehicle on-grid and off-grid. t When it is 0, it means the electric vehicle is off the grid, and when it is 1, it is connected to the grid.

[0134] The off-grid power consumption limit for electric vehicles is that the total power consumption shall not be less than 75%. The specific formula is as follows:

[0135]

[0136] 2) Class II load (load that can be reduced)

[0137] The maximum number of load reductions and the maximum load reduction duration constraints are:

[0138]

[0139] Where N max is the maximum number of times the load can be reduced; The maximum continuous reduction time for which the load can be reduced.

[0140] 3) Class III load (translatable load)

[0141] The total amount of shiftable load in a single dispatch cycle is zero and the minimum continuous running time is constrained:

[0142]

[0143] In the formula, The minimum continuous operating time for a translatable load.

[0144] 4) Constraints on the willingness of users to participate in adjustable load:

[0145]

[0146] In the formula, are the electricity charges of load-side users before they participate in the peak-shaving market, There are three types of load user satisfaction.

[0147] 5) Power balance constraints

[0148]

[0149] In the formula, The daily peak-shaving demand released by the power grid center.

[0150] Example 2

[0151] The second embodiment of the present invention provides a load aggregator optimization scheduling system based on user peak load value, which is characterized by: including:

[0152] The clustering module is used to roughly classify the adjustable load group according to distributed energy storage (electric vehicles), load that can be cut, and load that can be shifted, propose clustering indicators based on the classification results of the load group, and form a typical adjustable load curve through cluster analysis;

[0153] The pricing module is used to quantify the compensation pricing method of the user's peak-shaving value for the typical adjustable load curve and the situation of multiple peaks in one day in the current power peak-shaving market. The method comprehensively considers factors such as the user's power consumption before and after peak-shaving, time-of-use electricity price, load aggregator's peak-shaving budget, and the difference in peak-shaving value of users in different time periods;

[0154] A model building module is used to build a load aggregator optimization scheduling model based on the peak-shaving value of time periods: the load aggregator optimization scheduling model based on the peak-shaving value of time periods takes the minimum scheduling cost of load aggregators as the optimization goal, and the constraints include constraints on electric vehicles on and off the grid, constraints on the maximum and minimum power of electric vehicle groups, constraints on the maximum number of times that load can be cut per day, constraints on the maximum reduction time of load that can be cut, constraints on the total amount of load that can be shifted per day, constraints on the minimum continuous operation of shiftable loads, constraints on the willingness to participate in adjustable loads, and power balance constraints.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0156] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0157] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0158] 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.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0160] Example 3

[0161] refer to Figure 2-Figure 10, which is the third embodiment of the present invention, provides a load aggregator optimization scheduling method based on user peak-shaving value. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0162] Using the method of the patent of this invention, a case analysis is carried out taking a park in Jiangsu Province as an example.

[0163] 1) Basic data

[0164] Taking the power consumption of adjustable loads in a typical working day in a certain park as an example, the system uses 24 hours as a scheduling cycle and the unit scheduling time is 1 hour. The time-of-use electricity prices are shown in Table 1. The LA peak-shaving budget is set to 6,000 yuan. The penalty coefficient for unresponsiveness is set to 1.5 yuan / kWh. Assuming that there are 100 electric vehicles in the park, each of which has a battery capacity of 50kWh, the initial power of the electric vehicle group is 2500kWh, the charging power is 5kW, the charging efficiency is 0.9, and the electric vehicle is connected to the grid and off the grid at 9:00 and 19:00 respectively. The typical daily power consumption curve of electric vehicle users in the park is as follows: Figure 2 The load that can be reduced in the park consists of 4,000 air conditioners. After clustering, the typical user curve that can be reduced is as follows: Figure 3 As shown in Figure 1, the maximum number of load reductions per day is 8, and the maximum continuous reduction time per day is 5 hours. The load that can be shifted in the park consists of 6,000 fans and related indoor power equipment. After clustering, the user curve that can be shifted is as follows: Figure 4 As shown in the figure, the minimum continuous operation time of the shiftable load is 2 hours. The satisfaction of electric vehicle users, curtailable users, and shiftable load users must reach 0.75, 0.6, and 0.6 respectively to meet the conditions for participating in market transactions. The peak demand curve is shown in Figure 5 shown.

[0165] Table 1 Time-of-use electricity price

[0166]

[0167] This paper sets up the following two scenarios to compare their load dispatch optimization results, capital usage, etc. to verify the superiority of the above model. The scenarios are as follows:

[0168] Scenario 1: Fixed compensation pricing, where the compensation price for adjustable resources refers to the peak shaving compensation price in Zhejiang Province from July to September 2023. The compensation prices for electric vehicles, curtailable resources, and shiftable resources are 0.85 yuan / kWh, 1 yuan / kWh, and 0.95 yuan / kWh respectively;

[0169] Scenario 2: Set the compensation price for each typical user based on the peak load value of each period, with a peak load budget of 6,000 yuan;

[0170] This embodiment uses an Intel Core i5-12400F processor, 8*2GB DDR4 memory, 1024GB SSD storage and NVIDIA GeForce GTX 4060ti graphics card, and the operating system is Windows 11 Professional 64-bit version. The simulation and optimization work is carried out in GAMS version 45.

[0171] The specific optimization results are as follows: Figure 6-Figure 10 As shown, Figure 6-Figure 7 The market participation and pricing of typical electric vehicles and reduced users in scenario 1. Figure 8-Figure 10 The figure shows the market participation and pricing of typical electric vehicles, reducible and transferable users in scenario 2. In scenario 1, transferable users do not participate in the market and are not shown separately.

[0172] The comparative analysis of pricing methods in scenarios 1 and 2 is as follows:

[0173] Table 2 Total scheduling cost, scheduling cost of each typical user and non-response amount in scenarios 1 and 2

[0174]

[0175]

[0176] Table 3 Comparison of average user satisfaction

[0177]

[0178] As shown in Table 2, the dispatching cost under different budgets in Scenario 2 is 20% lower than the fixed pricing in Scenario 1, which shows that the pricing method based on peak-shaving value orientation is more economically advantageous. In addition, the fixed pricing method in Scenario 1 did not respond to users at 8:00, while in Scenario 2, users actively responded to the market peak-shaving demand, which further shows that the pricing method in Scenario 2 can better stimulate user potential and effectively balance the interests between the upper power grid, LA and power users. In terms of user satisfaction, compared with Scenario 1, the satisfaction of electric vehicle users in Scenario 2 increased by an average of 27%, the satisfaction of users who can be cut decreased by an average of 5.8%, and the satisfaction of users who can be shifted did not change. This shows that the pricing method in Scenario 2 has greatly improved the satisfaction of electric vehicle users at the expense of the satisfaction of users who can be cut, making the overall user satisfaction more reasonable.

[0179] According to the theoretically derived formula and simulation results, the following conclusions can be drawn:

[0180] This paper introduces adjustable load regulation characteristic indicators such as primary and secondary reducible loads, performs cluster analysis on adjustable resources, effectively reduces the technical difficulty of LA in the subsequent scheduling process, and takes the minimum peak-shaving scheduling cost as the optimization goal. The load aggregator optimizes the scheduling model based on the peak-shaving value of time periods to schedule users. Compared with traditional fixed compensation pricing, the scheduling cost is reduced by an average of 20%, the satisfaction of electric vehicle users is increased by an average of 27%, the satisfaction of users who can be transferred remains unchanged, and the satisfaction of users who can be cut is reduced by an average of 5.8%. While greatly reducing the scheduling cost, the overall user satisfaction is improved.

[0181] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A load aggregator optimization scheduling method based on user peak load shaving value, characterized by: include, Classify the adjustable resources, propose clustering indicators based on the classification results, and form a typical adjustable load curve through cluster analysis; According to the typical adjustable load curve formed by adjustable load groups, a compensation pricing method based on user peak load value is proposed; According to the pricing method, a load aggregator optimization scheduling model based on the peak-shaving value of different time periods is constructed.

2. The load aggregator optimization scheduling method based on user peak load shaving value according to claim 1, characterized in that: The classification of adjustable resources includes distributed energy storage loads, curtailable loads, and shiftable loads.

3. The load aggregator optimization scheduling method based on user peak load shaving value according to claim 2, characterized in that: The clustering index proposed for the classification result includes proposing clustering index for the load that can be reduced and the load that can be shifted regulation characteristics.

4. The load aggregator optimization scheduling method based on user peak load shaving value according to claim 3 is characterized by: The load that can be reduced includes: The primary and secondary load reduction is expressed as, Where: is the i-th curtailable load peak in a typical day; is the load value in the time period adjacent to the peak value; The peak dynamic load is expressed as, Where: is the mean value of the i-th load that can be reduced in a typical day; The translatable load includes: The intraday peak-to-valley difference rate is expressed as, Where: are the load peak and valley values ​​of the i-th translatable load respectively; The load fluctuation rate is expressed as, c4=s III (i) / m III (i) Where: III (i),μ III (i) are the load variance and mean of the typical day's shiftable load i.

5. The load aggregator optimization scheduling method based on user peak load shaving value according to claim 4, characterized in that: The compensation pricing method based on user peak load value is expressed as: -P t I.max ≤ΔP t I ≤P t I.max In the formula, Represents the daily power baselines of three types of adjustable loads, They represent the daily power curves of three types of adjustable load peak regulation. Respectively represent the active power difference in each period before and after the peak load regulation of Class I, Class II and Class III adjustable loads; In the formula, They are the state variables of three types of adjustable loads participating in the peak load auxiliary market, among which The state variables are moved in and out by the translatable load Two parts, ΔP t I,a is the LA forward dispatching energy storage load, M and λ represent the maximum and minimum values ​​respectively; In view of the fact that there are multiple peaks in one day in the current power peak-shaving market, the importance and urgency of peak-shaving in different periods vary. The peak-shaving value weight coefficient of each period is introduced, and the calculation formula is as follows: Where: They are the peak and valley values ​​of the daily load baseline of the mth load among the three types of adjustable loads; They represent the peak load regulation value of each user in each type of adjustable load at each time period of the day; In order to fully measure the peak-shaving priorities of different adjustable loads at different times and reasonably allocate the peak-shaving budget, the peak-shaving priority variation coefficient of each period is introduced. The calculation formula is as follows: The compensation pricing for typical adjustable users in each period is calculated as follows: In the formula, are the compensation prices for each typical user, C all is the peak-shaving budget, α I ,α II ,α III The adjustment coefficients for the three types of adjustable load compensation pricing are: It is a time-of-use electricity price.

6. The load aggregator optimization scheduling method based on user peak load shaving value according to claim 5, characterized in that: The load aggregator optimization dispatching model based on the peak load regulation value of different time periods is constructed to minimize the dispatching cost of the load aggregator; The constraints include: power balance constraint, electric vehicle load, curtailable load, shiftable load model and output constraint, and user satisfaction constraint.

7. The load aggregator optimization scheduling method based on user peak load shaving value according to claim 6, characterized in that: The power balance constraint is: Electric vehicle load constraints include on-grid, off-grid constraints, power constraints, and charging power constraints: Where, T1 I and The time when electric vehicles are connected to the grid or off the grid. is the electric vehicle power during period t, η is the charging efficiency of the electric vehicle, are the minimum and maximum power of electric vehicles, is the charging power of the electric vehicle load at a certain moment, y t is the state variable of electric vehicle on-grid and off-grid. t When it is 0, it means the electric vehicle is off-grid, and when it is 1, it is connected to the grid; The load reduction constraints include the maximum number of reductions and the maximum reduction duration: Where N max The maximum number of times the load can be reduced is The maximum continuous reduction time of the load that can be reduced; The load constraints that can be shifted include the total amount of electricity in a single dispatch cycle and the minimum continuous operation constraint: In the formula, The minimum continuous operation time of the load that can be translated; Adjustable load user participation willingness constraints: The user's participation in load aggregation peak load regulation is determined by two factors: the degree to which electricity demand is met and whether the benefits obtained from participating in power peak load regulation meet expectations; 8. A system based on the load aggregator optimization scheduling method based on user peak load shaving value according to any one of claims 1 to 7, characterized in that: include: Clustering module, pricing module, model building module.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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