Power package optimization design method based on power market flexibility resources

Abstract

The invention discloses a power package optimization design method based on power market flexibility resources, and relates to the technical field of power markets. The method comprises the following steps: classifying users and designing corresponding electric power packages, namely an electric vehicle exclusive package model, a household energy storage package model, a community sharing package model and an industrial flexible load electric power package model; performing parameter configuration on the power package, and dynamically adjusting the configured parameters according to the real-time load condition of the power grid; and evaluating the flexible resource contribution effect of each power package in the load peak and valley periods, and further optimizing the parameter configuration of the power package according to the evaluation result. By means of the method, the charging time of the electric vehicle can be optimized, home users are encouraged to install energy storage equipment and participate in power grid peak regulation, local consumption and sharing of distributed photovoltaic power are promoted, and the flexibility of industrial users participating in power grid regulation is improved through price signals.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a method for optimizing the design of power packages based on power market flexibility resources. Background Art

[0002] With the rapid growth of new user groups such as electric vehicles, home energy storage devices, and distributed photovoltaic systems, traditional power grids are facing increasing pressure from load fluctuations and power peak load regulation. In response to this change, power companies have introduced incentives such as time-of-use electricity prices to encourage users to use electricity reasonably during peak and off-peak hours. However, the existing electricity packages still have many shortcomings in motivating users to participate in grid regulation and promoting renewable energy consumption, which are specifically manifested in the following aspects: the design of existing electricity packages is mainly based on users' basic electricity demand, and lacks personalized design for different user types and application scenarios; with the large-scale access of distributed photovoltaic systems, users' demand for renewable energy consumption and local consumption continues to increase, but the existing packages are relatively limited in promoting the use of renewable energy; the increase in the proportion of energy has led to an increasing demand for peak load regulation of the power grid, especially during peak load periods, when more flexible resources are needed to maintain the balance of the power grid. The existing packages lack a flexible peak load regulation incentive mechanism, making it difficult to effectively guide users to reduce electricity consumption during peak hours or increase electricity consumption during off-peak hours, resulting in the peak load regulation demand of the power grid being difficult to fully meet; industrial users have high electricity consumption flexibility and adjustability, but traditional packages cannot effectively incentivize industrial users to reduce electricity consumption during peak load periods, or increase electricity consumption when the grid load demand is low, and it is difficult to incorporate the flexibility of industrial load into grid regulation resources, resulting in industrial users failing to play a full role in demand-side management. Summary of the invention

[0003] In view of the above problems, the present invention proposes a method for optimizing the design of electricity packages based on the flexibility resources of the electricity market. By designing an electricity package model of flexibility resources, it improves the user's participation in the operation of the power grid and promotes the healthy development of the electricity market.

[0004] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0005] A method for optimizing power packages based on power market flexibility resources, the method comprising:

[0006] According to the users' electricity consumption characteristics and flexibility resource potential, users are classified into four categories: electric vehicle users, household energy storage users, community photovoltaic users and industrial users, and corresponding electricity packages are designed, namely:

[0007] An electric vehicle-specific package model, including monthly basic electricity charges, available electricity within the package, billing unit price for excess electricity, and contract period;

[0008] A household energy storage package model, including monthly basic electricity charges, daytime electricity prices, nighttime electricity prices, energy storage electricity prices, contract periods, and energy storage equipment subsidies;

[0009] Community sharing package model, including monthly basic electricity charges, available electricity within the package, billing unit price for excess electricity, contract period, and electricity distribution plan based on the community photovoltaic system;

[0010] Industrial flexible load electricity package model, including monthly base electricity charges, electricity prices at different load times, flexibility incentive mechanism and load response options;

[0011] Parameters of the electricity package are configured, including monthly basic electricity charges, available electricity within the package, billing unit price for excess electricity, peak-valley electricity price difference, flexibility reward mechanism and contract period; and the configured parameters are dynamically adjusted according to the real-time load of the power grid;

[0012] Monitor the electricity consumption response data of various users, collect electricity consumption data during peak and non-peak periods, evaluate the flexibility resource contribution of each electricity package during peak and trough loads, and further optimize the parameter configuration of the electricity package based on the evaluation results.

[0013] As a preferred solution of the present invention, the electric vehicle exclusive package model includes: an electric vehicle user's monthly electricity bill model, an electric vehicle user's annual total electricity bill model, an electric vehicle user's monthly excess electricity model, an electric vehicle user's annual excess electricity model, and an electric vehicle user's cost saving model.

[0014] As a preferred solution of the present invention, the monthly electricity fee model of the electric vehicle user is expressed as:

[0015] F total =F base +max(0,E uesd -E included )×P extra ;

[0016] In the formula, F total is the total monthly electricity cost of electric vehicle users; F base The monthly basic electricity fee for electric vehicle users; E uesd is the actual monthly electricity consumption of electric vehicle users; E included P is the available electricity in the package of electric vehicle users; extra The unit price for the excess power consumption of electric vehicle users;

[0017] The annual total electricity cost model of electric vehicle users is expressed as:

[0018]

[0019] In the formula, F total_year E is the total electricity cost of the electric vehicle user during the contract period; uesd,i is the actual electricity consumption of electric vehicle users in the i-th month;

[0020] The monthly excess power model of electric vehicle users is expressed as:

[0021] E extra,i =max(0,E uesd,i -E included );

[0022] In the formula, E extra,i is the excess power of the electric vehicle user in the i-th month;

[0023] The annual excess power model of electric vehicle users is expressed as:

[0024]

[0025] In the formula, E extra_year The total excess electricity consumption during the electric vehicle user's contract period;

[0026] The electric vehicle user cost saving model is expressed as:

[0027] S savings =(P traditional -P extra )×E extra_year ;

[0028] In the formula, S savings Cost savings for electric vehicle users, P traditional This is the charging electricity price for electric vehicle users before they sign up for an electricity package.

[0029] As a preferred solution of the present invention, the household energy storage package model includes a household energy storage user peak-shaving electricity fee model, a household energy storage user energy storage quantity model, an energy storage peak-shaving benefit model, a household energy storage user annual peak-shaving cost model and an energy storage equipment annual average cost model.

[0030] As a preferred solution of the present invention, the peak load electricity fee model of the household energy storage user is expressed as:

[0031]

[0032] In the formula, Total monthly peak-shaving fee for household energy storage users; Basic monthly electricity bill for home energy storage users; Monthly daytime electricity consumption for household energy storage users, is the daytime electricity price; Monthly nighttime electricity consumption for household energy storage users; For energy storage capacity; For nighttime electricity prices; The price of energy storage electricity;

[0033] The household energy storage user energy storage model is expressed as:

[0034]

[0035] In the formula, is the capacity of the energy storage device;

[0036] The energy storage peak load benefit model is expressed as:

[0037]

[0038] In the formula, For the peak load regulation benefits of energy storage;

[0039] The annual peak-shaving cost model for household energy storage users is expressed as:

[0040]

[0041] In the formula, Annual peak-shaving fees for household energy storage users;

[0042] The average annual cost model of the energy storage equipment is expressed as:

[0043]

[0044] In the formula, is the average annual cost of energy storage equipment.

[0045] As a preferred solution of the present invention, the community sharing package model includes a community photovoltaic user power distribution model, a community photovoltaic user electricity fee model, a relationship model between photovoltaic power generation and user power demand, a community photovoltaic user energy-saving benefit model, and a cost-benefit model for user shared power.

[0046] As a preferred solution of the present invention, the community photovoltaic user power distribution model is expressed as:

[0047]

[0048] In the formula, The amount of electricity obtained by community photovoltaic user j from the photovoltaic system; is the actual electricity consumption of community PV user j; The total electricity consumption of all photovoltaic users in the community; is the total electricity generated by the community PV system;

[0049] The electricity fee model of community photovoltaic users is expressed as:

[0050]

[0051] In the formula, The total electricity cost for community photovoltaic users; The monthly basic electricity fee for community photovoltaic users; The amount of electricity actually used by community photovoltaic users; The available power in the package; The unit price for the excess electricity;

[0052] The relationship model between photovoltaic power generation and user power demand is expressed as:

[0053]

[0054] In the formula, The probability of satisfying user needs; is the total electricity generated by the community PV system; The total electricity demand of all users in the community;

[0055] The energy-saving benefit model of community photovoltaic users is expressed as:

[0056]

[0057] In the formula, For energy saving benefits; is the traditional electricity price; is the price of photovoltaic electricity; is the photovoltaic power used;

[0058] The cost-benefit model of user-sharing electricity is expressed as:

[0059]

[0060] In the formula, The cost sharing for users of the billing unit price for electricity exceeding the electricity consumption in each community; is the total cost of sharing electricity in the community; N is the number of users participating in sharing in the community.

[0061] As a preferred solution of the present invention, the industrial flexibility load power package model includes an industrial user monthly electricity bill model, an industrial user flexibility reward model, an industrial user response reward model, and an industrial user annual fee and reward net value model.

[0062] As a preferred solution of the present invention, the monthly electricity fee model of the industrial user is expressed as:

[0063]

[0064] In the formula, Total monthly electricity costs for industrial users; The basic monthly electricity bill for industrial users; Peak electricity consumption for industrial users; The peak electricity price; Off-peak electricity consumption for industrial users; The off-peak electricity price; The normal electricity consumption of industrial users; The normal electricity price;

[0065] The industrial user flexibility reward model is expressed as:

[0066]

[0067] In the formula, load reduction incentives for industrial users; is the benchmark load of industrial users in any period of time, The actual electricity consumption of industrial users; The reward price for each kWh of electricity cut;

[0068] The industrial user response reward model is expressed as:

[0069]

[0070] In the formula, load response incentives for industrial users; Load reduction during response period for industrial users;

[0071] Incentive prices for industrial users to participate in load response;

[0072] The industrial user annual cost and incentive net value model is expressed as:

[0073]

[0074] In the formula, is the net value of annual fees and incentives; Monthly flexibility bonuses for industrial users; Load response incentives for industrial users.

[0075] As a preferred solution of the present invention, the method of evaluating the flexibility resource contribution effect of each power package during load peak and load trough periods and further optimizing the parameter configuration of the power package according to the evaluation results specifically includes:

[0076] Based on the actual electricity consumption data of various users, the flexibility resource contribution C is defined mIt represents the response capability of the mth user during load peak or trough period, and the calculation formula is:

[0077]

[0078] Where, T peak Indicates the time period of peak or low load; L baseline,m is the benchmark load of the mth type of user; L m,t is the actual load of the mth user at time t;

[0079] According to the flexibility resource contribution of different categories of users C m And the importance weight of user category ω m , calculate the comprehensive contribution C of the electricity package total , the formula is:

[0080]

[0081] Where M is the total number of user categories;

[0082] According to the comprehensive contribution C total The parameters of the electricity package configuration are optimized and adjusted. The optimization goal is to maximize the user response incentive effect. The objective function O is expressed as:

[0083]

[0084] Where P m is the price adjustment coefficient of the mth type of electricity package; f(P m ) is the price incentive function;

[0085] Use machine learning models to predict user response behavior under different parameter conditions and adjust parameter configurations. m (t) represents the expected response of the user at time t, and the formula is:

[0086] R m (t) = α·C m +β·P m +γ·H(t);

[0087] Where α, β, and γ are model parameters, and H(t) is a time factor function that reflects the differences in user response behaviors in any period of time.

[0088] The beneficial effects of the present invention are as follows: users are divided into four categories: electric vehicle users, household energy storage users, community photovoltaic users and industrial users, and an exclusive package model is designed for each category of users, with adapted basic electricity charges, electricity prices, reward mechanisms and other elements set to ensure that users of different categories obtain the most appropriate incentive schemes according to their own characteristics, thereby improving the load response effect of users during peak or valley periods; according to the real-time load status of the power grid, the key parameters of each package model (such as the difference in peak and valley electricity prices, the amount of flexibility rewards, etc.) are dynamically adjusted, and the user response incentive is maximized by optimizing the parameter configuration in real time, so that the power grid remains stable when the load fluctuates. Balance, reduce the pressure during peak periods, and improve resource utilization during off-peak periods; by optimizing the charging time of electric vehicle users, encourage them to charge during off-peak periods of the power grid, and promote users to participate in power grid peak regulation through price incentive mechanisms; encourage household users to install energy storage equipment, balance the power grid load by discharging during peak periods of the power grid and storing electricity during off-peak periods, thereby achieving both user economic benefits and power grid stability; promote the local consumption and sharing of distributed photovoltaic power, support users in the community to jointly use photovoltaic resources, and improve energy utilization efficiency; guide industrial users to adjust electricity consumption time and load through price signals, and comprehensively improve the flexibility and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] 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 labor.

[0090] in:

[0091] Figure 1 is a flow chart of the method of the present invention;

[0092] Figure 2 Schematic diagram of the electricity package structure in an embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0094] like Figure 1 , Figure 2As shown in FIG. 1 , an embodiment of the present invention is provided, which provides a method for optimizing the design of power packages based on power market flexibility resources, comprising the following steps:

[0095] S1: According to their electricity consumption characteristics and flexibility resource potential, users are classified into four categories: electric vehicle users, household energy storage users, community photovoltaic users and industrial users, and corresponding electricity packages are designed, namely:

[0096] The electric vehicle exclusive package model includes the monthly basic electricity fee, the available electricity within the package, the billing unit price for the excess electricity, the contract period, etc. For example, the monthly basic electricity fee paid by electric vehicle users is 200 yuan, the package includes 500 kWh of electricity, and the excess electricity is calculated at 0.4 yuan / kWh, and the contract period is 2 years;

[0097] The household energy storage package model includes monthly basic electricity charges, daytime electricity prices, nighttime electricity prices, energy storage electricity prices, contract periods, and energy storage equipment subsidies. For example, if a household energy storage user pays a monthly basic electricity charge of 50 yuan, they can enjoy a daytime electricity price of 0.6 yuan / kWh and a nighttime electricity price of 0.3 yuan / kWh. Users can store electricity at night and use it during high-price hours during the day. The contract period is 2 years, which is suitable for users equipped with household energy storage equipment. The preferential treatment is that first-time contract users can obtain a 10% energy storage equipment subsidy, which is applicable to users who own or plan to install household energy storage equipment. The advantage is that energy storage technology is used to reduce electricity costs during peak hours and smooth out electricity price fluctuations.

[0098] The community sharing package model includes monthly basic electricity charges, available electricity within the package, billing unit price for excess electricity, contract period, and electricity allocation plan based on the community photovoltaic system. For example, if the monthly basic electricity charge is 50 yuan, the package includes 150 kWh of community photovoltaic electricity, and the excess electricity is charged at 0.5 yuan / kWh, with a contract period of 2 years. The total power generation of the community photovoltaic system is allocated according to the actual electricity consumption of each household, which is applicable to users who have installed community photovoltaic power generation equipment. The discount is free installation of electricity meters and related equipment in the first year, which is applicable to users participating in the community energy sharing project. The advantage is to use renewable energy in the community, reduce electricity costs, and promote sustainable development of the community.

[0099] Industrial flexible load power package model, including monthly basic electricity charges, electricity prices at different load periods, flexibility incentive mechanism, load response options, etc.;

[0100] S2: Configure the parameters of the electricity package, including the monthly basic electricity fee, available electricity within the package, the billing unit price for excess electricity, the difference between peak and valley electricity prices, the flexibility reward mechanism and the contract period; and dynamically adjust the configured parameters according to the real-time load of the power grid;

[0101] S3: Monitor the electricity consumption response data of various users, collect electricity consumption data during peak and non-peak periods, evaluate the flexibility resource contribution of each electricity package during peak and trough load periods, and further optimize the parameter configuration of the electricity package based on the evaluation results.

[0102] The electric vehicle exclusive package model encourages the coordinated interaction between electric vehicle users' charging behavior and grid resources, increases the grid's flexibility resources, improves the grid's real-time response and regulation capabilities, and ensures efficient allocation of power resources.

[0103] Specifically, the electric vehicle exclusive package model includes: monthly electricity bill model for electric vehicle users, annual total electricity bill model for electric vehicle users, monthly excess electricity model for electric vehicle users, annual excess electricity model for electric vehicle users and cost saving model for electric vehicle users.

[0104] The monthly electricity bill model for electric vehicle users is expressed as:

[0105] F total =F base +max(0,E uesd -E included )×P extra ;

[0106] In the formula, F total is the total monthly electricity cost of electric vehicle users; F base The monthly basic electricity fee for electric vehicle users, the default value is 200 yuan; E uesd is the actual monthly electricity consumption of electric vehicle users; E included The available electricity in the package of electric vehicle users, the default value is 500 kWh; P extra The unit price for the excess electricity consumption of electric vehicle users, with a default value of 0.4 yuan / kWh;

[0107] The annual total electricity cost model for electric vehicle users is expressed as:

[0108]

[0109] In the formula, F total_year E is the total electricity cost of the electric vehicle user during the contract period; uesd,i is the actual electricity consumption of electric vehicle users in the i-th month;

[0110] The monthly excess electricity consumption model of electric vehicle users is expressed as:

[0111] E extra,i =max(0,E uesd,i -E included );

[0112] In the formula, E extra,iis the excess power of the electric vehicle user in the i-th month;

[0113] The annual excess electricity model of electric vehicle users is expressed as:

[0114]

[0115] In the formula, E extra_year The total excess electricity consumption during the electric vehicle user's contract period;

[0116] The cost saving model of electric vehicle users is expressed as:

[0117] S savings =(P traditional -P extra )×E extra_year ;

[0118] In the formula, S savings Cost savings for electric vehicle users, P traditional This is the charging electricity price for electric vehicle users before they sign up for an electricity package.

[0119] The household energy storage user package model sets electricity prices, contract periods and first-time contract subsidies for different time periods, guiding household energy storage users to store energy during the low-price period at night and use it during the high-price period during the day, thereby reducing peak-shaving pressure.

[0120] Specifically, the household energy storage package model includes the household energy storage user peak-shaving electricity fee model, the household energy storage user energy storage quantity model, the energy storage peak-shaving benefit model, the household energy storage user annual peak-shaving cost model and the energy storage equipment annual average cost model.

[0121] The peak-shaving electricity fee model for household energy storage users is expressed as:

[0122]

[0123] In the formula, Total monthly peak-shaving fee for household energy storage users; It is the monthly basic electricity fee for household energy storage users, with a default value of 50 yuan; Monthly daytime electricity consumption for household energy storage users, The daytime electricity price, the default value is 0.6 yuan / kWh; Monthly nighttime electricity consumption for household energy storage users; For energy storage capacity; The nighttime electricity price is 0.3 yuan / kWh by default; The energy storage electricity price is 0.3 yuan / kWh by default;

[0124] The energy storage model of household energy storage users is expressed as:

[0125]

[0126] In the formula, The capacity of the energy storage device is 200 kWh by default;

[0127] The energy storage peak load regulation benefit model is expressed as:

[0128]

[0129] In the formula, For the peak load regulation benefits of energy storage;

[0130] The annual peak-shaving cost model for household energy storage users is expressed as:

[0131]

[0132] In the formula, Annual peak-shaving fees for household energy storage users;

[0133] The average annual cost model of energy storage equipment is expressed as:

[0134]

[0135] In the formula, is the average annual cost of energy storage equipment.

[0136] The community sharing package model is based on the total power generation of the community photovoltaic system, allocates photovoltaic power according to the proportion of user electricity consumption, sets a monthly basic electricity fee, a billing unit price for excess electricity, and a contract period, and encourages users in the community to give priority to the consumption of photovoltaic power.

[0137] Specifically, the community sharing package model includes a power distribution model for community photovoltaic users, a power fee model for community photovoltaic users, a relationship model between photovoltaic power generation and user power demand, a community photovoltaic user energy-saving benefit model, and a cost-benefit model for user shared power.

[0138] The power distribution model of community photovoltaic users is expressed as:

[0139]

[0140] In the formula, The amount of electricity obtained by community photovoltaic user j from the photovoltaic system; is the actual electricity consumption of community PV user j; The total electricity consumption of all photovoltaic users in the community; is the total electricity generated by the community PV system;

[0141] The electricity fee model for community photovoltaic users is expressed as:

[0142]

[0143] In the formula, The total electricity cost for community photovoltaic users; The monthly basic electricity fee for community photovoltaic users; The amount of electricity actually used by community photovoltaic users; The available power in the package; The unit price for the excess electricity;

[0144] The relationship model between photovoltaic power generation and user power demand is expressed as:

[0145]

[0146] In the formula, The probability of satisfying user needs; is the total electricity generated by the community PV system; The total electricity demand of all users in the community;

[0147] The energy-saving benefit model of community photovoltaic users is expressed as:

[0148]

[0149] In the formula, For energy saving benefits; is the traditional electricity price; is the price of photovoltaic electricity; is the photovoltaic power used;

[0150] The cost-benefit model of users sharing electricity is expressed as:

[0151]

[0152] In the formula, The cost sharing for users of the billing unit price for electricity exceeding the electricity consumption in each community; is the total cost of sharing electricity in the community; N is the number of users participating in sharing in the community.

[0153] The flexible load package model for industrial users encourages industrial users to reduce electricity consumption during peak load periods and provides load response support by setting peak, valley and normal time electricity prices, load response rewards and other mechanisms.

[0154] Specifically, the industrial flexibility load power package model includes an industrial user monthly electricity bill model, an industrial user flexibility reward model, an industrial user response reward model, and an industrial user annual fee and reward net value model.

[0155] The monthly electricity bill model for industrial users is expressed as:

[0156]

[0157] In the formula, Total monthly electricity costs for industrial users; The basic monthly electricity bill for industrial users; Peak electricity consumption for industrial users; The peak electricity price; Off-peak electricity consumption for industrial users; The off-peak electricity price; The normal electricity consumption of industrial users; The normal electricity price;

[0158] The flexibility reward model for industrial users is expressed as:

[0159]

[0160] In the formula, load reduction incentives for industrial users; is the benchmark load of industrial users in any period of time, The actual electricity consumption of industrial users; The reward price for each kWh of electricity cut;

[0161] The industrial user response reward model is expressed as:

[0162]

[0163] In the formula, load response incentives for industrial users; Load reduction during response period for industrial users;

[0164] Incentive prices for industrial users to participate in load response;

[0165] The annual cost and incentive net value model for industrial users is expressed as:

[0166]

[0167] In the formula, is the net value of annual fees and incentives; Monthly flexibility bonus for industrial users; Load response incentives for industrial users.

[0168] In one specific embodiment, the implementation method of step S3 includes:

[0169] Based on the actual electricity consumption data of various users, define the flexibility resource contribution C m It represents the response capability of the mth type of users (such as electric vehicle users, household energy storage users, etc.) during load peak or trough periods. The calculation formula is:

[0170]

[0171] Where, T peak Indicates the time period of peak or low load; L baseline,m is the benchmark load of the mth type of user; L m,t is the actual load of the mth user at time t; this formula calculates the load reduction ratio of the user during peak or off-peak periods, representing the load response contribution of this user category to the power grid;

[0172] According to the flexibility resource contribution of different categories of users C m And the importance weight of user category ω m , calculate the comprehensive contribution C of the electricity package total , the formula is:

[0173]

[0174] Where M is the total number of user categories;

[0175] According to the comprehensive contribution C total The parameters of the electricity package configuration are optimized and adjusted. The optimization goal is to maximize the user response incentive effect. The objective function O is expressed as:

[0176]

[0177] Where P m is the price adjustment coefficient of the mth type of electricity package, which is a specific value or parameter used to directly affect the price incentive of this type of electricity package. It can include various forms of price adjustment measures, such as peak-valley electricity price differences, electricity discounts, flexibility reward amounts, etc. These specific values ​​are used as incentive tools to influence users' electricity consumption behavior; f(P m ) is the price incentive function, which reflects the impact of price adjustment on user response willingness, that is, function f(P m ) The price adjustment factor P m Converted into the actual response effect of the user. Function f(P m ) is modeled based on the user's response behavior to quantify the attractiveness of different incentive levels on the user's electricity consumption behavior. The higher the incentive, the stronger the user's willingness to reduce electricity consumption during peak hours or increase electricity consumption during off-peak hours.

[0178] By maximizing the objective function, the optimal price adjustment coefficient P under different electricity packages is obtained. m , thereby achieving incentives for various types of users and improving the overall response effect;

[0179] Use machine learning models to predict user response behavior under different parameter conditions and adjust parameter configurations. m(t) represents the expected response of the user at time t, and the formula is:

[0180] R m (t) = α·C m +β·P m +γ·H(t);

[0181] In the formula, α, β, and γ are model parameters, and H(t) is a time factor function that reflects the differences in user response behavior in any period of time;

[0182] The model parameters α, β, and γ are dynamically updated through machine learning algorithms (such as support vector machines or deep learning) to predict user response patterns during load peaks or troughs, and the model is trained based on historical response data. The model predicts the user's response effects under different parameter adjustments and makes adjustments based on real-time feedback, making the parameter configuration more precise and the prediction model more accurate and flexible in different scenarios.

[0183] Through hierarchical user contribution calculation and comprehensive evaluation models, the response effect of electricity packages during peak and off-peak load periods can be quantified more accurately; the response contribution is combined with price incentives, and dynamic optimization of parameters is achieved by maximizing the objective function to improve user response enthusiasm; machine learning is introduced to perform machine learning predictions based on users' historical response behaviors, providing a more flexible and intelligent basis for parameter adjustment, with adaptability and predictive capabilities.

[0184] In summary, the present invention divides users into four categories: electric vehicle users, household energy storage users, community photovoltaic users and industrial users, and designs an exclusive package model for each type of user, sets adaptive basic electricity charges, electricity prices, reward mechanisms and other elements, to ensure that different types of users obtain the most suitable incentive scheme according to their own characteristics, thereby improving the load response effect of users during peak or valley periods; according to the real-time load status of the power grid, the key parameters of each package model (such as peak-valley electricity price differences, flexibility reward amounts, etc.) are dynamically adjusted, and the user response incentive is maximized by optimizing the parameter configuration in real time, so that the power grid can maintain balance when the load fluctuates, reducing the pressure during peak periods and improving resource utilization during valley periods. The method of the present invention optimizes the charging time of electric vehicle users, encourages them to charge during the off-peak period of the power grid, and promotes users to participate in the peak load regulation of the power grid through a price incentive mechanism; encourages household users to install energy storage equipment, balances the power grid load by discharging during the peak period of the power grid and storing electricity during the off-peak period, thereby achieving a dual improvement in user economic benefits and power grid stability; promotes the local consumption and sharing of distributed photovoltaic power, supports users in the community to jointly use photovoltaic resources, and improves energy utilization efficiency; guides industrial users to adjust electricity consumption time and load through price signals, and comprehensively improves the flexibility and stability of the power system.

[0185] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for optimizing the design of power packages based on power market flexibility resources, characterized in that: The method includes: classifying users into four categories, namely electric vehicle users, household energy storage users, community photovoltaic users and industrial users, according to their electricity consumption characteristics and flexibility resource potential, and designing corresponding electricity packages, namely: An electric vehicle-specific package model, including monthly basic electricity charges, available electricity within the package, billing unit price for excess electricity, and contract period; A household energy storage package model, including monthly basic electricity charges, daytime electricity prices, nighttime electricity prices, energy storage electricity prices, contract periods, and energy storage equipment subsidies; Community sharing package model, including monthly basic electricity charges, available electricity within the package, billing unit price for excess electricity, contract period, and electricity distribution plan based on the community photovoltaic system; Industrial flexible load electricity package model, including monthly base electricity charges, electricity prices at different load times, flexibility incentive mechanism and load response options; Parameters of the electricity package are configured, including monthly basic electricity charges, available electricity within the package, billing unit price for excess electricity, peak-valley electricity price difference, flexibility reward mechanism and contract period; and the configured parameters are dynamically adjusted according to the real-time load of the power grid; Monitor the electricity consumption response data of various users, collect electricity consumption data during peak and non-peak periods, evaluate the flexibility resource contribution of each electricity package during peak and trough loads, and further optimize the parameter configuration of the electricity package based on the evaluation results.

2. The method for optimizing the design of power packages based on power market flexibility resources according to claim 1, characterized in that: The electric vehicle exclusive package model includes: a monthly electricity bill model for electric vehicle users, an annual total electricity bill model for electric vehicle users, a monthly excess electricity model for electric vehicle users, an annual excess electricity model for electric vehicle users, and a cost saving model for electric vehicle users.

3. The method for optimizing the design of power packages based on power market flexibility resources according to claim 2, characterized in that: The monthly electricity bill model for electric vehicle users is expressed as: F total =F base +max(0,E uesd -E included )×P extra ; In the formula, F total is the total monthly electricity cost of electric vehicle users; F base The monthly basic electricity fee for electric vehicle users; E uesd is the actual monthly electricity consumption of electric vehicle users; E included P is the available electricity in the package of electric vehicle users; extra The unit price for charging electric vehicle users for excess electricity; The annual total electricity cost model of electric vehicle users is expressed as: In the formula, F total_year E is the total electricity cost of the electric vehicle user during the contract period; uesd,i is the actual electricity consumption of electric vehicle users in the i-th month; The monthly excess power model of electric vehicle users is expressed as: AND extra,i =max(0,E uesd,i -AND included ); In the formula, E extra,i is the excess power of the electric vehicle user in the i-th month; The annual excess power model of electric vehicle users is expressed as: In the formula, E extra_year The total excess electricity consumption during the electric vehicle user's contract period; The electric vehicle user cost saving model is expressed as: S savings =(P traditional -P extra )×E extra_year ; In the formula, S savings Cost savings for electric vehicle users, P traditional This is the charging electricity price for electric vehicle users before they sign up for an electricity package.

4. The method for optimizing the design of power packages based on power market flexibility resources according to claim 2, characterized in that: The household energy storage package model includes a household energy storage user peak-shaving electricity fee model, a household energy storage user energy storage quantity model, an energy storage peak-shaving benefit model, a household energy storage user annual peak-shaving cost model and an energy storage equipment annual average cost model.

5. The method for optimizing the design of power packages based on power market flexibility resources according to claim 4, characterized in that: The peak-load electricity fee model for household energy storage users is expressed as: In the formula, Total monthly peak-shaving fee for household energy storage users; Basic monthly electricity bill for home energy storage users; Monthly daytime electricity consumption for household energy storage users, is the daytime electricity price; Monthly nighttime electricity consumption for household energy storage users; For energy storage capacity; For nighttime electricity prices; The price of energy storage electricity; The household energy storage user energy storage model is expressed as: In the formula, is the capacity of the energy storage device; The energy storage peak load benefit model is expressed as: In the formula, For the peak load regulation benefits of energy storage; The annual peak-shaving cost model for household energy storage users is expressed as: In the formula, Annual peak-shaving fees for household energy storage users; The average annual cost model of the energy storage equipment is expressed as: In the formula, is the average annual cost of energy storage equipment.

6. The method for optimizing the design of power packages based on power market flexibility resources according to claim 4, characterized in that: The community sharing package model includes a community photovoltaic user power distribution model, a community photovoltaic user electricity fee model, a relationship model between photovoltaic power generation and user power demand, a community photovoltaic user energy-saving benefit model, and a user sharing power cost-benefit model.

7. A method for optimizing the design of power packages based on power market flexibility resources according to claim 6, characterized in that: The power distribution model of community photovoltaic users is expressed as: In the formula, The amount of electricity obtained by community photovoltaic user j from the photovoltaic system; is the actual electricity consumption of community PV user j; The total electricity consumption of all photovoltaic users in the community; is the total electricity generated by the community PV system; The electricity fee model of community photovoltaic users is expressed as: In the formula, The total electricity cost for community photovoltaic users; The monthly basic electricity fee for community photovoltaic users; The amount of electricity actually used by community photovoltaic users; The available power in the package; The unit price for the excess electricity; The relationship model between photovoltaic power generation and user power demand is expressed as: In the formula, The probability of satisfying user needs; is the total electricity generated by the community PV system; The total electricity demand of all users in the community; The energy-saving benefit model of community photovoltaic users is expressed as: In the formula, For energy saving benefits; is the traditional electricity price; is the price of photovoltaic electricity; is the photovoltaic power used; The cost-benefit model of user-sharing electricity is expressed as: In the formula, The cost sharing for users of the billing unit price for electricity exceeding the electricity consumption in each community; is the total cost of sharing electricity in the community; N is the number of users participating in sharing in the community.

8. The method for optimizing the design of power packages based on power market flexibility resources according to claim 6, characterized in that: The industrial flexibility load power package model includes an industrial user monthly electricity bill model, an industrial user flexibility reward model, an industrial user response reward model, and an industrial user annual fee and reward net value model.

9. The method for optimizing the design of power packages based on power market flexibility resources according to claim 8, characterized in that: The monthly electricity cost model of industrial users is expressed as: In the formula, Total monthly electricity costs for industrial users; The basic monthly electricity bill for industrial users; Peak electricity consumption for industrial users; The peak electricity price; Off-peak electricity consumption for industrial users; The off-peak electricity price; The normal electricity consumption of industrial users; The normal electricity price; The industrial user flexibility reward model is expressed as: In the formula, load reduction incentives for industrial users; is the benchmark load of industrial users in any period of time, The actual electricity consumption of industrial users; The reward price for each kWh of electricity cut; The industrial user response reward model is expressed as: In the formula, load response incentives for industrial users; Load reduction during response period for industrial users; Incentive prices for industrial users to participate in load response; The industrial user annual cost and incentive net value model is expressed as: In the formula, is the net value of annual fees and incentives; Monthly flexibility bonuses for industrial users; Load response incentives for industrial users.

10. The method for optimizing the design of power packages based on power market flexibility resources according to claim 1, characterized in that: The method of evaluating the flexibility resource contribution effect of each power package during peak and trough load periods and further optimizing the parameter configuration of the power package according to the evaluation results specifically includes: Based on the actual electricity consumption data of various users, define the flexibility resource contribution C m It represents the response capability of the mth user during load peak or trough period, and the calculation formula is: Where, T peak Indicates the time period of peak or low load; L baseline,m is the benchmark load of the mth type of user; L m,t is the actual load of the mth user at time t; According to the flexibility resource contribution of different categories of users C m And the importance weight of user category ω m , calculate the comprehensive contribution C of the electricity package total , the formula is: Where M is the total number of user categories; According to the comprehensive contribution C total The parameters of the electricity package configuration are optimized and adjusted. The optimization goal is to maximize the user response incentive effect. The objective function O is expressed as: Where P m is the price adjustment coefficient of the mth type of electricity package; f(P m ) is the price incentive function; Use machine learning models to predict user response behavior under different parameter conditions and adjust parameter configurations. m (t) represents the expected response of the user at time t, and the formula is: R m (t)=α·C m +β·P m +γ·H(t); Where α, β, and γ are model parameters, and H(t) is a time factor function that reflects the differences in user response behaviors in any period of time.

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