An auxiliary design system for electricity sales strategies of aggregators based on multi-dimensional load characteristics

Abstract

The present invention discloses an auxiliary design system for an aggregator's electricity sales strategy based on multi-dimensional load characteristics, which relates to the technical field of power market. The system comprises a load characteristic analysis module: used by the aggregator to analyze historical data, consider the uncertainty of the load, and classify user loads based on the load characteristics of a two-dimensional cloud model; a green electricity consumption integral calculation method design module: used to consider the adaptability of the slope of the green electricity output curve and the user load curve, and the aggregator uses an integral method to evaluate the user's behavior in consuming green electricity; an aggregator electricity sales strategy optimization module: used to establish an aggregator-user electricity sales strategy two-layer model based on short-term predicted electricity volume, medium- and long-term market electricity purchase strategies, and green electricity consumption integral calculation methods, and to optimize the aggregator electricity sales strategy based on multi-dimensional load characteristics, make full use of user-side adjustable resources, improve the aggregator's own profits, optimize user electricity consumption behavior, and further promote green electricity consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to an auxiliary design system for aggregator power sales strategy based on multi-dimensional characteristics of loads. Background Art

[0002] Aggregators aggregate user-side resources to participate in the electricity market and design diverse electricity sales strategies to enhance their competitiveness. Furthermore, with the large-scale development of clean energy and the implementation of the renewable energy quota system on the electricity sales side, aggregators must assume a consumption responsibility weighted according to their annual electricity sales.

[0003] The load resources on the electricity consumption side are diverse. Aggregators use the accumulated large-scale historical load data to fully analyze the multi-dimensional characteristics of the load and guide it to actively participate in the power system dispatch, which can further meet the demand for continuous large-scale grid connection and absorption of new energy.

[0004] Current domestic research primarily focuses on aggregators' trading strategies for integrating diverse markets, but there is little in-depth research on how aggregators can design user-oriented electricity sales strategies based on load characteristics, promote green power consumption, and design user-oriented electricity sales strategies. Therefore, analyzing the load characteristics of user-side resources, fully utilizing these resources, and optimizing user-oriented electricity sales strategies by aggregators, guiding them to actively participate in power system scheduling, and softening user-side load demands, are of great practical significance for addressing the large-scale consumption of clean energy generation. Summary of the Invention

[0005] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide an auxiliary design system for aggregator electricity sales strategy based on multi-dimensional load characteristics.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An auxiliary design system for aggregator electricity sales strategy based on multi-dimensional load characteristics, including:

[0008] Load characteristic analysis module: used by aggregators to analyze historical user load data, consider load uncertainty, and classify user loads based on the load characteristics of the two-dimensional cloud model to obtain user classification results;

[0009] Green power consumption credit calculation method design module: This module is used to consider the compatibility of the green power output curve and the slope of the user load curve, and allows aggregators to design an evaluation method for user green power consumption behavior;

[0010] The user classification results obtained from the load characteristics analysis module and the evaluation method of user green power consumption behavior from the green power consumption points calculation method design module are input into the aggregator power sales strategy optimization module to optimize the power sales strategy for different user categories.

[0011] Aggregator power sales strategy optimization module: used to establish an aggregator-user power sales strategy two-layer model based on short-term power forecast, medium- and long-term market power purchase strategy, and green power consumption credit calculation method, and to optimize the aggregator power sales strategy based on the multi-dimensional characteristics of the load.

[0012] Preferably, the aggregator analyzes historical data based on the peak-valley two-dimensional cloud model load characteristics, and finally obtains four typical load categories: basic load, curtailable load, transferable load, and shiftable load by clustering and calculating the similarity of the two-dimensional load characteristics.

[0013] Preferably, the process of obtaining four types of typical loads is as follows:

[0014] The reverse cloud model calculation is performed on the typical historical electricity consumption data of each user's load peak and valley sections to obtain the peak-valley two-dimensional cloud digital load characteristics of user i m: peak section Low point by These six numerical features are used as input, where They represent the expected, entropy, and super entropy of the cloud digital load characteristics of user i during the peak load period; They represent the expected, entropy, and super entropy of the cloud digital load characteristics during the low load period of user i and m, respectively.

[0015] Four typical load types are finally obtained through clustering:

[0016]

[0017] by Characterize the uncertainty of load during peak period and valley period respectively;

[0018] The following two formulas are used to characterize the similarity of peak-valley two-dimensional load characteristics:

[0019]

[0020]

[0021] The first category is basic load:

[0022] The second category is the load that can be reduced:

[0023] The third category is transferable loads:

[0024] sim p (u,v),sim v At least one of (x,y) is less than 1;

[0025] The fourth category is the translatable load:

[0026] sim p (u,v),sim v (x,y) both tend to 1.

[0027] Preferably, the green electricity consumption points calculation method design module considers the adaptability of the green electricity output curve and the slope of the user load curve, designs a green electricity consumption points calculation method, rewards users whose electricity consumption behavior is more consistent with the green electricity output, and further promotes green electricity consumption.

[0028] Preferably, the green electricity consumption points calculation method is as follows:

[0029] The benefits of green electricity consumption by user i based on points are as follows:

[0030]

[0031] Among them, ω is the unit income of the user's green electricity consumption points, is the amount of green electricity consumed by user i at time t, k is the unit income of the user consuming green electricity, n i G is the points of green electricity consumption by user i, t+1 , G t are the green power output at time (t+1) and time t, d i,(t+1) d i,t are the power demand of user i at time (t+1) and time t respectively.

[0032] Preferably, the n i The adaptability of the slope of the green power output curve and the user load curve is taken into account, and used as the integral for the load aggregator to consider the user's consumption of green power.

[0033] Preferably, the establishment of the aggregator-user electricity sales strategy two-layer model is achieved by establishing an upper layer with the goal of maximizing the aggregator's profit and a lower layer with the goal of minimizing the user's electricity cost.

[0034] Preferably, the process of establishing the upper layer with the goal of maximizing aggregator profits is as follows:

[0035]

[0036] in,

[0037] r sell Indicates the revenue of load aggregators from selling electricity to users:

[0038]

[0039] Where, The time period is 24 hours. For user collection, is the price per unit of type r load at time t by the load aggregator, is the power demand of type r load of user i at time t;

[0040] r as The revenue generated by the load aggregator aggregating user loads and participating in the ancillary service market is:

[0041]

[0042] Where S t,as is the unit price of the adjustable load participating in the ancillary service market at time t, Δd i,t The amount of power that user i can adjust at time t;

[0043] c spot The cost of electricity purchased by load aggregators in the spot market is:

[0044]

[0045] Where S t,spot is the unit price of electricity purchased from the spot market at time t, is the amount of electricity purchased from the spot market at time t;

[0046] c in,green Indicates that the load aggregator based on points encourages users to consume green electricity costs:

[0047]

[0048] Where, is the annual green electricity consumption decomposed to time t;

[0049] C med_long Indicates the medium- and long-term electricity purchase costs of load aggregators, including the annual thermal power and green power purchase costs broken down into that month, and the monthly market power purchase costs:

[0050] C med_long =P year,th Q th +P year,green Q green +P mon Q mon

[0051] Where, P year,th is the annual thermal power contract price, Q th To decompose the annual thermal power generation to the month, P year,green is the annual green power consumption contract price, Q green To decompose the annual green power consumption of the month, P mon is the monthly market electricity purchase price, Q mon It is the monthly market electricity purchase amount.

[0052] Preferably, the constraints are:

[0053] Load aggregators design price constraints for class R loads:

[0054]

[0055] Where, They are the upper and lower limits of the sales price for class R load respectively;

[0056] Power balance constraints:

[0057]

[0058] Where, is the annual thermal power generation at time t, is the monthly electricity purchased from the market up to time t;

[0059] d i,t =D i,t +Δd i,t

[0060] Where D i,t Predict the power consumption of user i at time t;

[0061]

[0062] Where, The predicted power consumption of user i and type r load at time t is: is the adjustable power of type r load of user i at time t;

[0063]

[0064] Preferably, the lower layer aims to minimize the user's electricity cost

[0065]

[0066] The constraints are:

[0067]

[0068] Where, bl represents the basic load, cl represents the curtailable load, fl represents the transferable load, and ll represents the translational load. The predicted power consumption of user m based on the i-th load at time t is: The amount of electricity that user i n can reduce at time t is is the load demand of user i j at time t, is the load demand of user i k at time t;

[0069]

[0070] Where, The predicted power consumption that user i n can reduce at time t is: The amount of load regulation power that user i n can reduce at time t;

[0071]

[0072] Where, is the power consumption of user i j's transferable load at time t, is the transferable load and adjustable power of user i j at time t;

[0073]

[0074]

[0075]

[0076] Where, is the power consumption forecast of user k i at time t, i,k,t,t' is a variable of 0 or 1, when α i,k,t,t' = 1, it means that user i k can transfer the load from time t' to time t, α i,k,t”,t is a variable of 0 or 1, when α i,k,t”,t =1, it means that user i k can transfer the load from time t to time t″.

[0077] α i,k,t”,t' =0,|t'-t"|≥h i,k,max

[0078] Where h i,k,max is the maximum shift period of the shiftable load of user i k.

[0079] Beneficial effects of the present invention:

[0080] The present invention makes full use of user-side adjustable resources, improves the profits of aggregators themselves, optimizes user electricity consumption behavior, and further promotes green electricity consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0082] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0083] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0084] like Figure 1 As shown, a system for assisting the design of electricity sales strategies for aggregators based on multi-dimensional load characteristics includes:

[0085] Load characteristic analysis module: used by aggregators to analyze historical user load data, consider load uncertainty, and classify user loads based on the load characteristics of the two-dimensional cloud model to obtain user classification results;

[0086] Green power consumption credit calculation method design module: This module is used to consider the compatibility of the green power output curve and the slope of the user load curve, and allows aggregators to design an evaluation method for user green power consumption behavior;

[0087] The user classification results obtained from the load characteristics analysis module and the evaluation method of user green power consumption behavior from the green power consumption points calculation method design module are input into the aggregator power sales strategy optimization module to optimize the power sales strategy for different user categories.

[0088] Aggregator power sales strategy optimization module: used to establish an aggregator-user power sales strategy two-layer model based on short-term power forecast, medium- and long-term market power purchase strategy, and green power consumption credit calculation method, and to optimize the aggregator power sales strategy based on the multi-dimensional characteristics of the load.

[0089] The aggregator analyzes historical data based on the peak-valley two-dimensional cloud model load characteristics, and finally obtains four typical load categories: basic load, curtailable load, transferable load, and shiftable load by clustering and calculating the similarity of the two-dimensional load characteristics.

[0090] The process of obtaining four types of typical loads is as follows:

[0091] The reverse cloud model calculation is performed on the typical historical electricity consumption data of each user's load peak and valley sections to obtain the peak-valley two-dimensional cloud digital load characteristics of user i m: peak section Low point by These six numerical features are used as input, where They represent the expected, entropy, and super entropy of the cloud digital load characteristics of user i during the peak load period; They represent the expected, entropy, and super entropy of the cloud digital load characteristics during the low load period of user i and m, respectively.

[0092] Four typical load types are finally obtained through clustering:

[0093]

[0094] by Characterize the uncertainty of load during peak period and valley period respectively;

[0095] The following two formulas are used to characterize the similarity of peak-valley two-dimensional load characteristics:

[0096]

[0097]

[0098] The first category is basic load:

[0099] The second category is the load that can be reduced:

[0100] The third category is transferable loads:

[0101] sim p (u,v),sim v At least one of (x,y) is less than 1;

[0102] The fourth category is the translatable load:

[0103] sim p (u,v),sim v (x,y) both tend to 1.

[0104] It needs to be further explained that, during the specific implementation process, the green electricity consumption points calculation method design module takes into account the adaptability of the green electricity output curve and the slope of the user load curve, designs the green electricity consumption points calculation method, rewards users whose electricity consumption behavior is more in line with the green electricity output, and further promotes green electricity consumption.

[0105] It should be further explained that, in the specific implementation process, the green electricity consumption points calculation method is as follows:

[0106] The benefits of green electricity consumption by user i based on points are as follows:

[0107]

[0108] Among them, ω is the unit income of the user's green electricity consumption points, is the amount of green electricity consumed by user i at time t, k is the unit income of the user consuming green electricity, n i G is the points of green electricity consumption by user i, t+1 , Gt are the green power output at time (t+1) and time t, d i,(t+1) d i,t are the power demand of user i at time (t+1) and time t respectively.

[0109] It should be further explained that, in the specific implementation process, the n i The adaptability of the slope of the green power output curve and the user load curve is taken into account, and used as the integral for the load aggregator to consider the user's consumption of green power.

[0110] It needs to be further explained that, in the specific implementation process, the establishment of the aggregator-user electricity sales strategy two-tier model is achieved by establishing an upper-tier electricity sales strategy two-tier model with the goal of maximizing the aggregator's profits and a lower-tier model with the goal of minimizing the user's electricity costs.

[0111] It should be further explained that, in the specific implementation process, the process of establishing the upper layer with the goal of maximizing the profit of the aggregator is as follows:

[0112]

[0113] in,

[0114] r sell Indicates the revenue of load aggregators from selling electricity to users:

[0115]

[0116] Where, The time period is 24 hours. For user collection, is the price per unit of type r load at time t by the load aggregator, is the power demand of type r load of user i at time t;

[0117] r as The revenue generated by the load aggregator aggregating user loads and participating in the ancillary service market is:

[0118]

[0119] Where S t,as is the unit price of the adjustable load participating in the ancillary service market at time t, Δd i,t The amount of power that user i can adjust at time t;

[0120] c spot The cost of electricity purchased by load aggregators in the spot market is:

[0121]

[0122] Where S t,spotis the unit price of electricity purchased from the spot market at time t, is the amount of electricity purchased from the spot market at time t;

[0123] c in,green Indicates that the load aggregator based on points encourages users to consume green electricity costs:

[0124]

[0125] Where, is the annual green electricity consumption decomposed to time t;

[0126] C med_long Indicates the medium- and long-term electricity purchase costs of load aggregators, including the annual thermal power and green power purchase costs broken down into that month, and the monthly market power purchase costs:

[0127] C med_long =P year,th Q th +P year,green Q green +P mon Q mon

[0128] Where, P year,th is the annual thermal power contract price, Q th To decompose the annual thermal power generation to the month, P year,green is the annual green power consumption contract price, Q green To decompose the annual green power consumption of the month, P mon is the monthly market electricity purchase price, Q mon It is the monthly market electricity purchase amount.

[0129] Preferably, the constraints are:

[0130] Load aggregators design price constraints for class R loads:

[0131]

[0132] Where, They are the upper and lower limits of the sales price for class R load respectively;

[0133] Power balance constraints:

[0134]

[0135] Where, is the annual thermal power generation at time t, is the monthly electricity purchased from the market up to time t;

[0136] d i,t =D i,t +Δd i,t

[0137] Where D i,t Predict the power consumption of user i at time t;

[0138]

[0139] Where, The predicted power consumption of user i and type r load at time t is: is the adjustable power of type r load of user i at time t;

[0140]

[0141] It should be further explained that, in the specific implementation process, the lower layer aims to minimize the user's electricity cost.

[0142]

[0143] The constraints are:

[0144]

[0145] Where, bl represents the basic load, cl represents the curtailable load, fl represents the transferable load, and ll represents the translational load. The predicted power consumption of user m based on the i-th load at time t is: The amount of electricity that user i n can reduce at time t is is the load demand of user i j at time t, is the load demand of user i k at time t;

[0146]

[0147] Where, The predicted power consumption that user i n can reduce at time t is: The amount of load regulation power that user i n can reduce at time t;

[0148]

[0149] Where, is the power consumption of user i j's transferable load at time t, is the transferable load and adjustable power of user i j at time t;

[0150]

[0151]

[0152]

[0153] Where, is the power consumption forecast of user k i at time t, i,k,t,t' is a variable of 0 or 1, when α i,k,t,t' = 1, it means that user i k can transfer the load from time t' to time t, α i,k,t”,t is a variable of 0 or 1, when α i,k,t”,t =1, it means that user i k can transfer the load from time t to time t″.

[0154] α i,k,t”,t' =0,|t'-t"|≥h i,k,max

[0155] Where h i,k,max is the maximum shift period of the shiftable load of user i k.

[0156] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0157] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

Claims

1. A system for assisting the design of electricity sales strategies for aggregators based on multi-dimensional load characteristics, characterized by: include: Load characteristic analysis module: used by aggregators to analyze historical user load data, consider load uncertainty, and classify user loads based on the load characteristics of the two-dimensional cloud model to obtain user classification results; The aggregator analyzes the user load history data based on the peak-valley two-dimensional cloud model load characteristics, clusters them, and calculates the similarity of the two-dimensional load characteristics to ultimately obtain four typical load categories: base load, curtailable load, transferable load, and shiftable load; The process of obtaining four types of typical loads is as follows: The reverse cloud model calculation is performed on the typical historical electricity consumption data of each user's load peak and valley sections to obtain the peak-valley two-dimensional cloud digital load characteristics of user i m: peak section , trough period ;by These six numerical features are used as input, where 、 、 They represent the expected, entropy, and super entropy of the cloud digital load characteristics of user i during the peak load period; 、 、 They represent the expected, entropy, and super entropy of the cloud digital load characteristics of user i during the low load period; Four typical loads are finally obtained through clustering: 、 by 、 Characterize the uncertainty of load during peak period and valley period respectively; The following two formulas are used to characterize the similarity of peak-valley two-dimensional load characteristics: The first category is basic load: The second category is the load that can be reduced: The third category is transferable loads: , 、 At least one is less than 1; The fourth category is the translatable load: , 、 All tend to 1; Green power consumption credit calculation method design module: This module is used to consider the compatibility of the green power output curve and the slope of the user load curve, and allows aggregators to design an evaluation method for user green power consumption behavior; The user classification results obtained from the load characteristics analysis module and the evaluation method of user green power consumption behavior from the green power consumption points calculation method design module are input into the aggregator power sales strategy optimization module to optimize the power sales strategy for different user categories. Aggregator electricity sales strategy optimization module: used to establish an aggregator-user electricity sales strategy two-layer model based on short-term forecast electricity volume, medium- and long-term market electricity purchase strategy, and green electricity consumption credit calculation method, and to optimize the aggregator electricity sales strategy based on the multi-dimensional characteristics of the load.

2. The system for designing electricity sales strategies for aggregators based on multi-dimensional load characteristics according to claim 1 is characterized in that: The green electricity consumption points calculation method design module takes into account the adaptability of the green electricity output curve and the slope of the user load curve, designs a green electricity consumption points calculation method, rewards users whose electricity consumption behavior is more consistent with the green electricity output, and further promotes green electricity consumption.

3. The system for designing electricity sales strategies for aggregators based on multi-dimensional load characteristics according to claim 2 is characterized in that: The calculation method of green electricity consumption points is as follows: The benefits of green electricity consumption by user i based on points are as follows: in, Unit income for users to consume green electricity, The amount of green electricity consumed by user i at time t, The unit income of green electricity consumption for users, Points for i users to consume green electricity, 、 They are the green power output at time (t+1) and time t, 、 are the power demand of user i at time (t+1) and time t respectively.

4. The system for designing electricity sales strategies for aggregators based on multi-dimensional load characteristics according to claim 3 is characterized in that: The adaptability of the slope of the green power output curve and the user load curve is taken into account, and used as the integral for the load aggregator to consider the user's consumption of green power.

5. The system for designing electricity sales strategies for aggregators based on multi-dimensional load characteristics according to claim 1 is characterized in that: The aggregator-user electricity sales strategy two-layer model is established by establishing an upper layer with the goal of maximizing the profit of the aggregator and a lower layer with the goal of minimizing the electricity cost of the user.

6. The system for designing electricity sales strategies for aggregators based on multi-dimensional load characteristics according to claim 5 is characterized in that: The process of building the upper layer with the goal of maximizing aggregator profits is as follows: in, Indicates the revenue of load aggregators from selling electricity to users: Where, The time period is 24 hours. For user collection, is the price per unit of type r load at time t by the load aggregator, is the power demand of type r load of user i at time t; The revenue generated by the load aggregator aggregating user loads and participating in the ancillary service market is: Where, is the unit price of the adjustable load participating in the ancillary service market at time t, The amount of power that user i can adjust at time t; The cost of electricity purchased by load aggregators in the spot market is: Where, is the unit price of electricity purchased from the spot market at time t, is the amount of electricity purchased from the spot market at time t; Indicates that the load aggregator based on points encourages users to consume green electricity costs: Where, is the annual green electricity consumption decomposed to time t; Indicates the medium- and long-term electricity purchase costs of load aggregators, including the annual thermal power and green power purchase costs broken down into that month, and the monthly market power purchase costs: Where, is the annual thermal power contract price, To decompose the annual thermal power generation to that month, is the annual green power consumption contract price, To decompose the annual green electricity consumption for that month, is the monthly market electricity purchase price, It is the monthly market electricity purchase amount.

7. The system for designing electricity sales strategies for aggregators based on multi-dimensional load characteristics according to claim 6 is characterized in that: The constraints are: Load aggregators design price constraints for class R loads: Where, 、 They are the upper and lower limits of the sales price for class R load respectively; Power balance constraints: Where, is the annual thermal power generation at time t, is the monthly electricity purchased from the market up to time t; Where, Predict the power consumption of user i at time t; Where, The predicted power consumption of user r class load at time t is: is the adjustable power of type r load of user i at time t; 。 8. The system for designing electricity sales strategies for aggregators based on multi-dimensional load characteristics according to claim 7 is characterized in that: The lower layer aims to minimize the user's electricity cost The constraints are: Where, bl represents the basic load, cl represents the curtailable load, fl represents the transferable load, and ll represents the translational load. is the predicted power consumption of user m based on the load at time t, The amount of electricity that user i n can reduce at time t is is the load demand of user i j at time t, is the load demand of user i k at time t; Where, The predicted power consumption that user i n can reduce at time t is: The amount of load regulation power that user i n can reduce at time t; Where, is the power consumption of user i j's transferable load at time t, is the transferable load and adjustable power of user i j at time t; Where, The power consumption of user k i at time t can be predicted by the load shifting function, is a variable of 0 or 1, when When , it means that user i k can transfer the load from time t' to time t, is a variable of 0 or 1, when When , it means that user i k can transfer the load from time t to time t''; Where, is the maximum shift period of the shiftable load of user i k.

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