A method for optimizing the bidding volume and price of user participation in the power peak shaving ancillary services market

Abstract

This invention discloses a method for optimizing the bidding quantity and price of users participating in the power peak shaving ancillary services market. First, the adjustable power of users participating in the power peak shaving ancillary services market is calculated. Second, an optimization model for the user's load adjustment bidding quantity and unit price is constructed. Based on the user's adjustable load capacity, combined with the user's adjustable power and dissatisfaction, and considering the bidding success rate among users to improve the success rate of winning bids, a reasonable decision is made on both quantity and price. The overall power adjustment quantity of the user is optimized. Finally, the adjustment quantity of electrical equipment is decomposed based on the overall power adjustment quantity to maximize the user's own interests. This results in the overall optimal decision on the bidding quantity and price for users participating in the power peak shaving ancillary services market. This method can more effectively mobilize user load to participate in power system peak shaving, which has important practical significance for ensuring the economic and stable operation of the power system.

Description

Technical Field

[0001] This invention relates to the technical field of power peak shaving ancillary services market, specifically to a method for optimizing the quantity and price of user participation in the power peak shaving ancillary services market application. Background Technology

[0002] my country's power peak-shaving ancillary service market is gradually opening up to the user side. Users can participate directly or through load aggregators. Users participating in the market need to report their peak-shaving capacity and price. Currently, the aggregation of flexible loads such as electric vehicles, energy storage, air conditioning, electric heating, and lighting to participate in grid peak shaving is becoming a research hotspot. There are already optimization decision models that combine electricity prices and incentives to achieve single-objective economic optimality for electric vehicle charging and air conditioning operation, which can significantly reduce users' electricity costs while achieving peak shaving and valley filling effects in the power system. However, existing user participation in power peak-shaving ancillary service quantity and price optimization decision models have the disadvantages of a single optimization objective and insufficient comprehensive consideration.

[0003] When making decisions regarding the quantity and price of electricity peak shaving ancillary services, users need to comprehensively consider their load adjustment benefits, reduced comfort levels, and bidding success rates. Adjusting flexible loads can alter users' lifestyles or production plans, sacrificing their electricity comfort. In addition to considering the subsidies from participating in the electricity peak shaving ancillary services market, users must also consider the cost of the reduced comfort resulting from flexible load adjustments. Market competition among users affects their bidding probability, thus impacting their peak shaving benefits. All these factors simultaneously influence users' quantity and price bids for electricity peak shaving ancillary services. Summary of the Invention

[0004] This invention addresses the problems existing in the prior art by proposing a method for optimizing the bidding quantity and price of user participation in the power peak shaving ancillary services market. Based on the user's adjustable power load, it considers the dissatisfaction and gains caused by changes in user electricity consumption behavior patterns, and takes into account the bidding success rate of users in the game. This method can support users' bidding decisions in the power peak shaving ancillary services market, fully mobilize user load to participate in power system peak shaving, and combine user adjustable power and dissatisfaction to determine reasonable quantity and price, thereby maximizing their own interests, improving the success rate of bidding, and maximizing the overall expected benefits.

[0005] The technical solution for achieving this invention is: a method for optimizing the bidding volume and price of user participation in the power peak-shaving ancillary services market, characterized by comprising the following steps:

[0006] 1) Calculation of adjustable power for user participation in the power peak shaving ancillary service market:

[0007] ① Perform adjustable power calculation modeling for all electrical devices:

[0008]

[0009] In the formula: P agg (t) represents the aggregated power of user load at time t, in kW; N represents the total number of air conditioners in the air conditioning cluster, in units; n represents the number of air conditioners in the air conditioning cluster, in units; s n (t) represents the operating state of the nth air conditioner, with 1 for starting and 0 for stopping; P n TCL (t) represents the rated power of the nth air conditioner, in kW; P agg TCL (t) represents the aggregated power of the air conditioning cluster at time t, in kW;

[0010] M represents the total number of electric vehicles in the electric vehicle cluster, in units of vehicles; m represents the number of electric vehicles in the electric vehicle cluster, in units of vehicles; α m (t) represents the operating state of the m-th electric vehicle, with 1 for charging and 0 for stopping; P m EV (t) represents the charging power of the m-th electric vehicle, in kW; P agg EV (t) represents the aggregated power of the electric vehicle cluster at time t, in kW; L represents the total number of energy storage devices in the energy storage cluster, in units; l represents the number of energy storage devices in the energy storage cluster, in units; k l (t) represents the operating state of the l-th energy storage device, where charging is 1, stopping is 0, and discharging is -1; P l ES (t) represents the charging and discharging power of the l-th energy storage device, in kW; P agg ES (t) represents the aggregated power of the energy storage cluster at time t, in kW.

[0011] ② Set the following constraints for calculating the maximum adjustable power of user load:

[0012] A. Considering user comfort requirements:

[0013] Air conditioning load set temperature range,

[0014] 22℃≤T set (t)≤28℃ (2)

[0015] B. Energy storage load constraints:

[0016] SOC ESmin ≤SOC ES (i)≤SOC ESmax (3)

[0017]

[0018] Where: SOC ES (i) represents the state of charge (SOC) value of the i-th energy storage at that moment; ESmin This is the minimum value for the state of charge (SOC) of the energy storage system. ES ma x represents the upper limit of the state of charge of the energy storage battery; N cid ES The number of charge-discharge cycles of the energy storage system in one day; N max ES This represents the maximum number of charge / discharge cycles per day.

[0019] C. Electric vehicle load constraints:

[0020] SOC EVmin ≤SOC EV (i)≤SOC EVmax (5)

[0021] Where: SOC EV (i) represents the state of charge (SOC) of the i-th electric vehicle at that moment; EVmin State of Charge (SOC) is the minimum value for the state of charge of an electric vehicle. EVmax This represents the upper limit of the state of charge (SOC) of an electric vehicle battery.

[0022] ③ Calculate the maximum adjustable power of the user load:

[0023] The maximum adjustable power of user load is the sum of the maximum adjustable power of each air conditioner, electric vehicle and energy storage, which is the aggregate power of user adjustable electrical load before adjustment minus the aggregate power after maximum adjustment.

[0024] 2) Construct a user load reporting peak-shaving capacity and price optimization model:

[0025] ① Optimization Objective 1: On a daily basis, maximize the difference between the incentive income a user receives from participating in power peak shaving ancillary services and the increase in electricity costs due to adjusting electricity consumption, or the sum of the difference and the decrease in electricity costs due to adjusting electricity consumption. In other words, maximize the user's income from participating in power peak shaving ancillary services within a single day.

[0026] maxf1(C DR ,ΔP)=W TOU +W DR (6)

[0027] In the formula: f1(C DR ΔP) represents the revenue earned by users from participating in power peak shaving ancillary services, in yuan; C DR Quotation for day-ahead peak shaving ancillary services, unit: RMB / kWh; ΔP is the user's load regulation power, unit: kW; W TOUThis represents the decrease in electricity costs due to changes in the user's electricity usage. A decrease in electricity costs is positive, and an increase is negative. Unit: Yuan; W DR Incentive revenue earned by users for participating in day-ahead peak shaving ancillary services, in yuan;

[0028] f1(C DR The decision variable for ΔP is the price C quoted by the user for participating in day-ahead ancillary services. DR The load adjustment amount ΔP; the revenue consists of two parts, one part is the incentive revenue W for users participating in ancillary services. DR The other part is the reduction in electricity expenditure by users themselves. TOU ;

[0029] In equation (6), W represents the reduction in electricity costs due to the user's change in electricity consumption behavior. TOU The calculation formula is:

[0030]

[0031] In the formula C TOU To implement the daily real-time electricity price, the unit is: yuan / kWh; ΔP is the user load regulation power, the unit is: kW;

[0032] In equation (6), the incentive W obtained by the user for participating in power peak shaving ancillary services TOU The calculation formula is:

[0033]

[0034] In the formula: [T0, T1] represents the time interval for users to participate in the power peak shaving ancillary service market, [T0, T1] ≤ [0, 24]; C DR Price quotes for users participating in power peak shaving ancillary services, unit: yuan / kWh;

[0035] The success rate of bidding is expressed by the following formula:

[0036]

[0037] In the formula: P(C DR ) is C DR At this price, the probability of a user successfully bidding; This is the average of the highest successful bid and the lowest unsuccessful bid for a user.

[0038] ② Optimization Objective Two: Maintain optimal user satisfaction with electricity usage;

[0039] The relationship between user satisfaction and changes in electricity consumption is fitted, and the optimal objective function for maintaining the optimal user electricity satisfaction is:

[0040] minf2(ΔP)=e kΔP (10)

[0041] In the formula: f2(ΔP) represents the user's dissatisfaction with electricity consumption; ΔP represents the user's load regulation power, in kW; k represents the user's sensitivity to a reduction in electricity consumption;

[0042] ③ Establish a dual optimization objective of comprehensively improving user satisfaction and bidding success rate:

[0043] To optimize both user satisfaction and user benefits while considering the success rate of bidding, a dual-objective optimization function is constructed:

[0044]

[0045] In the formula: P(C DR )f1(C DR ΔP) represents the expected user revenue;

[0046] 3) Optimization of overall peak-shaving capacity and pricing for users:

[0047] Multi-objective collaborative optimization that maximizes user benefit expectation and minimizes dissatisfaction involves finding the function f1(C) through parameter variables. DR The Pareto optimal solutions for f1(ΔP) and f2(ΔP) can be efficiently computed using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The main computational process of NSGA-II is as follows:

[0048] ① Randomly generate an initial population of size N, and after non-dominated sorting, obtain the first generation offspring population through the three basic operations of selection, crossover, and mutation of the genetic algorithm.

[0049] ② Starting from the second generation, the parent population and the offspring population are merged and a rapid non-dominated sort is performed. At the same time, the crowding degree of individuals in each non-dominated layer is calculated, and suitable individuals are selected to form a new parent population based on the non-dominated relationship and the crowding degree of the individuals.

[0050] ③ Generate a new offspring population through basic operations of the genetic algorithm: and so on, until the program termination condition is met; 4) Decomposition of electrical equipment regulation:

[0051] After the user load adjustment calculation is completed, the adjustment amount is decomposed to each load device to complete the generation of the control strategy, so as to achieve the best user electricity satisfaction and construct the hierarchical relationship of user electricity load; lower-level loads are cut off first, and higher-level loads are cut off last; if the user specifies the load priority, the user's intention shall prevail; if no priority is specified, the load is adjusted in the default order of energy storage, electric vehicles, and air conditioning.

[0052] Furthermore, in step 1), C. Electric vehicle load constraint condition, the electric vehicle has a certain power demand, and at a specific time point, the SOC of the electric vehicle is guaranteed to be higher than the set value;

[0053]

[0054] In the formula: t fix For a specific point in time; SOC fix EV The lower limit of SOC at a specific point in time;

[0055]

[0056] Where: N cid EV The number of times an electric vehicle is charged and discharged in a day; N max EV This is the maximum number of times an electric vehicle can be charged and discharged in a single day.

[0057] The beneficial effects of the method for optimizing the quantity and price of user participation in the power peak shaving ancillary services market as described in this invention are as follows:

[0058] 1. A method for optimizing the bidding quantity and price of users participating in the power peak shaving ancillary services market. This method combines the advantages of multi-objective optimization methods and overcomes the shortcomings of single-objective optimization methods. Based on the adjustable capacity of user electricity load, it can better support users' bidding decisions in the power peak shaving ancillary services market, fully mobilize user load to participate in power system peak shaving, and optimize users' electricity consumption patterns and improve their electricity efficiency by allowing load resources to participate in the electricity market. This can also improve the system's intermittent renewable energy access capacity and enhance system safety, stability, and flexibility, thus having significant social and practical application value.

[0059] 2. A method for optimizing the bidding quantity and price of users participating in the power peak shaving ancillary services market. Based on the user's adjustable power load capacity, this method considers the dissatisfaction and benefits caused by users changing their power consumption behavior patterns, as well as the success rate of bidding among users. This method makes the bidding quantity and price decision for users participating in the power peak shaving ancillary services market, which can more effectively mobilize user load to participate in power system peak shaving and has important practical significance for ensuring the economic and stable operation of the power system. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a method for optimizing the quantity and price of user participation in the power peak-shaving ancillary services market.

[0061] Figure 2 This is a schematic diagram of a user power system configuration, representing a method for optimizing the quantity and price of user participation in the power peak-shaving ancillary services market.

[0062] Figure 3 This is a flowchart illustrating the optimization process for user equipment load power regulation, which is a method for optimizing the quantity and price of user participation in the power peak shaving ancillary services market. Detailed Implementation

[0063] The following is in conjunction with the appendix Figure 1-3 The present invention will be further described in detail below with reference to specific embodiments. The specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0064] As attached Figure 1 The diagram shows a flowchart of a method for optimizing the quantity and price of user participation in the power peak shaving ancillary services market.

[0065] As attached Figure 2 As shown, the electrical loads that users can call include: energy storage clusters, electric vehicle clusters, and air conditioning clusters.

[0066] A method for optimizing the bidding volume and price of user participation in the power peak-shaving ancillary services market, characterized by comprising the following steps:

[0067] 1) Calculation of adjustable power for user participation in the power peak shaving ancillary service market:

[0068] ① Perform adjustable power calculation modeling for all electrical devices:

[0069]

[0070] In the formula: P agg (t) represents the aggregated power of user load at time t, in kW; N represents the total number of air conditioners in the air conditioning cluster, in units; n represents the number of air conditioners in the air conditioning cluster, in units; s n (t) represents the operating state of the nth air conditioner, with 1 for starting and 0 for stopping; P n TCL (t) represents the rated power of the nth air conditioner, in kW; P agg TCL (t) represents the aggregated power of the air conditioning cluster at time t, in kW; M represents the total number of electric vehicles in the electric vehicle cluster, in vehicles; m represents the number of electric vehicles in the electric vehicle cluster, in vehicles; α m (t) represents the operating state of the m-th electric vehicle, with 1 for charging and 0 for stopping; P m EV (t) represents the charging power of the m-th electric vehicle, in kW; P agg EV (t) represents the aggregated power of the electric vehicle cluster at time t, in kW; L represents the total number of energy storage devices in the energy storage cluster, in units; l represents the number of energy storage devices in the energy storage cluster, in units; k l(t) represents the operating state of the l-th energy storage device, where charging is 1, stopping is 0, and discharging is -1; P l ES (t) represents the charging and discharging power of the l-th energy storage device, in kW; P agg ES (t) represents the aggregated power of the energy storage cluster at time t, in kW.

[0071] ② Set the following constraints for calculating the maximum adjustable power of user load:

[0072] A. Considering user comfort requirements:

[0073] Air conditioning load set temperature range,

[0074] 22℃≤T set (t)≤28℃ (15)

[0075] B. Energy storage load constraints:

[0076] SOC ESmin ≤SOC ES (i)≤SOC ESmax (16)

[0077]

[0078] Where: SOC ES (i) represents the state of charge (%) of the i-th energy storage at that moment; SOC ESmin State of Charge (SOC) is the minimum value (%) for energy storage. ESmax The upper limit of the state of charge (%) of the energy storage battery; N cid ES The number of times the energy storage system is charged and discharged in one day; N max ES The maximum number of times a day that a device can charge and discharge;

[0079] C. Electric vehicle load constraints:

[0080] SOC EVmin ≤SOC EV (i)≤SOC EVmax (18)

[0081] Where: SOC EV (i) represents the state of charge (%) of the i-th electric vehicle at this moment; SOC EVmin State of Charge (SOC) is the minimum value (%) for electric vehicles. EVmax This represents the upper limit (%) of the state of charge of an electric vehicle battery.

[0082] Electric vehicles have certain electricity requirements, and at specific times, it is necessary to ensure that the SOC of electric vehicles is higher than the set value.

[0083]

[0084] In the formula: t fix For a specific point in time; SOC fix EV The lower limit of SOC at a specific point in time;

[0085]

[0086] Where: N cid EV The number of times an electric vehicle is charged and discharged in a day; N max EV The maximum number of times an electric vehicle can be charged and discharged in a single day;

[0087] ③ Calculate the maximum adjustable power of the user load:

[0088] The maximum adjustable power of the user load is the sum of the maximum adjustable power of each air conditioner, electric vehicle and energy storage, which is the aggregate power of the user's adjustable electrical load before adjustment minus the aggregate power after the maximum adjustment.

[0089] 2) Construct an optimization model for user load adjustment declaration volume and declaration unit price:

[0090] ① Optimization Objective 1: On a daily basis, maximize the difference between the incentive income a user receives from participating in power peak shaving ancillary services and the increase in electricity costs due to adjusting electricity consumption, or the sum of the difference and the decrease in electricity costs due to adjusting electricity consumption. In other words, maximize the user's income from participating in power peak shaving ancillary services within a single day.

[0091] maxf1(C DR ,ΔP)=W TOU +W DR (twenty one)

[0092] In the formula: f1(C DR ΔP) represents the revenue (in yuan) earned by users from participating in power peak shaving ancillary services; C DR Quotation for day-ahead peak shaving ancillary services (RMB / kWh); ΔP is the user's load regulation power (kW); W TOU This represents the decrease in electricity costs due to changes in the user's electricity usage (a decrease in electricity costs is positive, and an increase in electricity costs is negative, expressed in yuan); W DR Incentive revenue (RMB) earned by users for participating in day-ahead peak shaving ancillary services;

[0093] f1(C DR The decision variable for ΔP is the price C quoted by the user for participating in day-ahead ancillary services.DR The load adjustment amount ΔP; the revenue consists of two parts, one part is the incentive revenue W for users participating in ancillary services. DR The other part is the reduction in electricity expenditure by users themselves. TOU ;

[0094] In equation (21), W represents the reduction in electricity costs due to the user's change in electricity consumption behavior. TOU The calculation formula is:

[0095]

[0096] In the formula C TOU —The daily real-time electricity price (RMB / kWh); ΔP —User load regulation power (kW).

[0097] In equation (21), the incentive W obtained by the user for participating in power peak shaving ancillary services TOU The calculation formula is:

[0098]

[0099] Where: [T0, T1]——the time interval for users to participate in the power peak shaving ancillary service market, [T0, T1]≤[0,24]; C DR — Pricing from users participating in power peak shaving ancillary services;

[0100] The success rate of bidding is expressed by the following formula:

[0101]

[0102] In the formula: P(C DR ) is C DR At a given price, the probability of a user successfully bidding. This is the average of the highest successful bid and the lowest unsuccessful bid for a user.

[0103] ② Optimization Objective Two: Maintain optimal user satisfaction with electricity usage;

[0104] The relationship between user satisfaction and changes in electricity consumption is fitted, and the optimal objective function for maintaining the optimal user electricity satisfaction is:

[0105] minf2(ΔP)=e kΔP (25)

[0106] In the formula: f2(ΔP) represents the user's dissatisfaction with electricity consumption; ΔP represents the user's load regulation power (kW); k represents the user's sensitivity to a reduction in electricity consumption;

[0107] ③ Establish a dual optimization objective of comprehensively improving user satisfaction and bidding success rate:

[0108] To optimize both user satisfaction and user benefits while considering the success rate of bidding, a dual-objective optimization function is constructed:

[0109]

[0110] In the formula: P(C DR )f1(C DR ΔP) represents the expected user revenue;

[0111] 3) Overall user optimization:

[0112] Multi-objective collaborative optimization that maximizes user benefit expectation and minimizes dissatisfaction involves finding the function f1(C) through parameter variables. DR The Pareto optimal solutions for f1(ΔP) and f2(ΔP) can be efficiently computed using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The main computational process of NSGA-II is as follows:

[0113] ① Randomly generate an initial population of size N, and after non-dominated sorting, obtain the first generation offspring population through the three basic operations of selection, crossover, and mutation of the genetic algorithm.

[0114] ② Starting from the second generation, the parent population and the offspring population are merged and a rapid non-dominated sort is performed. At the same time, the crowding degree of individuals in each non-dominated layer is calculated, and suitable individuals are selected to form a new parent population based on the non-dominated relationship and the crowding degree of the individuals.

[0115] ③ Generate a new offspring population through the basic operations of the genetic algorithm: and so on, until the conditions for program termination are met;

[0116] 4) Breakdown of electrical equipment adjustment parameters:

[0117] As attached Figure 3 As shown, after the user load adjustment calculation is completed, the adjustment amount is decomposed to each load device to complete the generation of the control strategy, so as to achieve the best user electricity satisfaction and construct the hierarchical relationship of user electricity load; lower-level loads are cut off first, and higher-level loads are cut off last; if the user specifies the load priority, the user's intention shall prevail; if no priority is specified, the load is adjusted in the default order of energy storage, electric vehicles, and air conditioning.

[0118] The above description is merely a preferred embodiment of the present invention and is not restrictive. It should be noted that those skilled in the art can make various improvements and modifications, or even equivalents, without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the bidding volume and price of user participation in the power peak-shaving ancillary services market, characterized in that: It includes the following steps: 1) Calculation of adjustable power for user participation in the power peak shaving ancillary service market: ① Perform adjustable power calculation modeling for all electrical devices: In the formula: P agg (t) represents the aggregated power of user load at time t, in kW; N represents the total number of air conditioners in the air conditioning cluster, in units; n represents the number of air conditioners in the air conditioning cluster, in units. s n (t) represents the operating state of the nth air conditioner, with 1 for starting and 0 for stopping; P n TCL (t) represents the rated power of the nth air conditioner, in kW; P agg TCL (t) represents the aggregated power of the air conditioning cluster at time t, in kW; M represents the total number of electric vehicles in the electric vehicle cluster, in units of vehicles; m represents the number of electric vehicles in the electric vehicle cluster, in units of vehicles. α m (t) represents the operating state of the m-th electric vehicle, with 1 for charging and 0 for stopping; P m EV (t) represents the charging power of the m-th electric vehicle, in kW; P agg EV (t) represents the aggregate power of the electric vehicle cluster at time t, in kW; L represents the total number of energy storage devices in the energy storage cluster, in units of: devices; l represents the number of energy storage devices in the energy storage cluster, in units of: devices; k l (t) represents the operating state of the l-th energy storage device, where charging is 1, stopping is 0, and discharging is -1; P l ES (t) represents the charging and discharging power of the l-th energy storage device, in kW; P agg ES (t) represents the aggregated power of the energy storage cluster at time t, in kW; ② Set the following constraints for calculating the maximum adjustable power of user load: A. Considering user comfort requirements: Air conditioning load set temperature range, 22℃≤T set (t)≤28℃ (2) B. Energy storage load constraints: SOC ESmin ≤SOC ES (i)≤SOC ESmax (3) Where: SOC ES (i) represents the state of charge (SOC) value of the i-th energy storage at that moment; ESmin This is the minimum value for the state of charge (SOC) of the energy storage system. ESmax N represents the upper limit of the state of charge of the energy storage battery. cid ES The number of charge-discharge cycles of the energy storage system in one day; N max ES This represents the maximum number of charge / discharge cycles per day. C. Electric vehicle load constraints: SOC EVmin ≤SOC EV (i)≤SOC EVmax (5) Where: SOC EV (i) represents the state of charge (SOC) of the i-th electric vehicle at that moment; EVmin State of Charge (SOC) is the minimum value for the state of charge of an electric vehicle. EVmax This represents the upper limit of the state of charge (SOC) of an electric vehicle battery. ③ Calculate the maximum adjustable power of the user load: The maximum adjustable power of the user load is the sum of the maximum adjustable power of each air conditioner, electric vehicle and energy storage, which is the aggregate power of the user's adjustable electrical load before adjustment minus the aggregate power after the maximum adjustment. 2) Construct a user load reporting peak-shaving capacity and price optimization model: ① Optimization Objective 1: On a daily basis, maximize the difference between the incentive income a user receives from participating in power peak shaving ancillary services and the increase in electricity costs due to adjusting electricity consumption, or the sum of the difference and the decrease in electricity costs due to adjusting electricity consumption. In other words, maximize the user's income from participating in power peak shaving ancillary services within a single day. maxf1(C DR ,ΔP)=W TOU +W DR (6) In the formula: f1(C DR ΔP) represents the revenue earned by users from participating in power peak shaving ancillary services, in yuan; C DR Quotation for day-ahead peak shaving ancillary services, unit: RMB / kWh; ΔP is the user's load regulation power, unit: kW; W TOU This represents the reduction in electricity costs due to changes in the user's electricity usage. A decrease in electricity costs is positive, and an increase in electricity costs is negative. The unit is yuan. W DR Incentive revenue earned by users for participating in day-ahead peak shaving ancillary services, in yuan; f1(C DR The decision variable for ΔP is the price C quoted by the user for participating in day-ahead ancillary services. DR The load adjustment amount ΔP; the revenue consists of two parts, one part is the incentive revenue W for users participating in ancillary services. DR The other part is the reduction in electricity expenditure by users themselves. TOU ; In equation (6), W represents the reduction in electricity costs due to the user's change in electricity consumption behavior. TOU The calculation formula is: In the formula C TOU To implement the daily real-time electricity price, the unit is: yuan / kWh; ΔP is the user load regulation power, the unit is: kW; In equation (6), the incentive W obtained by the user for participating in power peak shaving ancillary services TOU The calculation formula is: In the formula: [T0, T1] represents the time interval for users to participate in the power peak shaving ancillary service market, [T0, T1] ≤ [0, 24]; C DR Price quotes for users participating in power peak shaving ancillary services, unit: yuan / kWh; The success rate of bidding is expressed by the following formula: In the formula: P(C DR ) is C DR At a given price, the probability of a user successfully bidding. This is the average of the highest successful bid and the lowest unsuccessful bid for a user. ② Optimization Objective Two: Maintain optimal user satisfaction with electricity usage; The relationship between user satisfaction and changes in electricity consumption is fitted, and the optimal objective function for maintaining the optimal user electricity satisfaction is: minf2(ΔP)=e kΔP (10) In the formula: f2(ΔP) represents the user's dissatisfaction with electricity consumption; ΔP represents the user's load regulation power, in kW; k represents the user's sensitivity to a reduction in electricity consumption; ③ Establish a dual optimization objective of comprehensively improving user satisfaction and bidding success rate: To optimize both user satisfaction and user benefits while considering the success rate of bidding, a dual-objective optimization function is constructed: In the formula: P(C DR )f1(C DR ΔP) represents the expected user revenue; 3) Optimization of overall peak-shaving capacity and pricing for users: Multi-objective collaborative optimization that maximizes user benefit expectation and minimizes dissatisfaction involves finding the function f1(C) through parameter variables. DR The Pareto optimal solutions for f1(ΔP) and f2(ΔP) can be efficiently computed using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The main computational process of NSGA-II is as follows: ① Randomly generate an initial population of size N, and after non-dominated sorting, obtain the first generation offspring population through the three basic operations of selection, crossover, and mutation of the genetic algorithm. ② Starting from the second generation, the parent population and the offspring population are merged and a rapid non-dominated sort is performed. At the same time, the crowding degree of individuals in each non-dominated layer is calculated, and suitable individuals are selected to form a new parent population based on the non-dominated relationship and the crowding degree of the individuals. ③ Generate a new offspring population through the basic operations of the genetic algorithm: and so on, until the conditions for program termination are met; 4) Breakdown of electrical equipment adjustment parameters: After the user load regulation calculation is completed, the regulation is decomposed to each load device to complete the generation of the control strategy, so as to achieve the best user electricity satisfaction and construct the hierarchical relationship of user electricity load. Lower-priority loads are cut off first, while higher-priority loads are cut off last. If the user specifies the load priority, the user's intention will prevail. If no priority is specified, the load will be adjusted in the default order of energy storage, electric vehicles, and air conditioning.

2. The method for optimizing the quantity and price of user participation in the power peak-shaving ancillary service market according to claim 1, characterized in that, In step 1), C. Electric vehicle load constraint condition, the electric vehicle has a certain power demand, and at a specific time point, the SOC of the electric vehicle is guaranteed to be higher than the set value. In the formula: t fix For a specific point in time; SOC fix EV The lower limit of SOC at a specific point in time; Where: N cid EV The number of times an electric vehicle is charged and discharged in a day; N max EV This is the maximum number of times an electric vehicle can be charged and discharged in a single day.

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