Household low-carbon energy optimization method and system considering green electricity consumption and V2H

Abstract

The present invention relates to a household low-carbon energy optimization method and system that considers green electricity consumption and V2H. The method includes: collecting historical household electricity consumption statistics, establishing a carbon emission baseline scenario for household electricity consumption, and constructing a carbon credit emission reduction accounting model based on the emission reduction scenario; establishing a load flexibility optimization model based on the household's non-temperature-controlled flexible load; constructing an air conditioning load model; constructing an electric vehicle load model based on user commuting characteristics, electric vehicle operating parameters, and charging pile operating parameters; and constructing a household energy economic low-carbon optimization model based on the established model. The model is solved with the goal of minimizing electricity purchase costs and comfort losses and maximizing carbon emission reductions, thereby obtaining an optimized household economic low-carbon energy consumption plan. Compared with the existing technology, the present invention achieves quantification of the carbon reduction potential contained in household low-carbon energy consumption through multiple household green electricity consumption methods, scientifically guides household users to economical, low-carbon and comfortable electricity consumption, and facilitates the achievement of energy conservation and carbon reduction goals.

Description

Technical Field

[0001] The present invention relates to the field of household electricity technology, and in particular to a household low-carbon energy optimization method and system considering green electricity consumption and V2H. Background Art

[0002] At present, optimizing the energy consumption behavior of household users has become an effective means to promote energy conservation and emission reduction. As a carbon emission reduction incentive mechanism based on individuals and small organizations, the carbon credit mechanism provides economic incentives for the public to practice a green and low-carbon lifestyle. This mechanism can quantify individual green and low-carbon behaviors, such as energy conservation and emission reduction, green travel, garbage classification, recycling, etc., into greenhouse gas emission reductions, and absorb emission reductions through a variety of incentive methods, thereby increasing the public's participation in low-carbon living, and promoting green and low-carbon awareness while releasing the carbon reduction potential of the majority of residential users. However, the existing carbon credit mechanism mainly focuses on simple estimates of macro-behaviors, and research on the specific impact of the carbon credit mechanism on household low-carbon energy consumption behavior is still insufficient. For example, the invention disclosed in Publication No. CN118709900A discloses a carbon credit carbon reduction accounting method and system for urban residents' electricity saving. This scheme can only estimate the carbon credit carbon reduction amount based on the macro-electricity consumption behavior of urban residents.

[0003] Currently, household electricity loads primarily consist of rigid loads, interruptible loads, shiftable loads, curtailable loads, and temperature-controlled loads. The complex lifestyles of household residents and the operational patterns of their equipment result in highly dynamic and complex load patterns. Given the increasing operational complexity of household loads, efficiently scheduling these diverse loads to achieve energy conservation and emission reduction while maintaining a comfortable lifestyle is a pressing issue.

[0004] With the widespread adoption of renewable energy sources such as distributed photovoltaics and the promotion of electric vehicles, household energy flexibility continues to increase, further enhancing the potential for energy conservation and carbon reduction by optimizing electricity usage. However, there is currently a lack of systematic household load management methods that comprehensively consider household green electricity consumption, carbon credit incentives, electric vehicle-home connectivity, and user lifestyle habits. Existing research has made it difficult to scientifically optimize household low-carbon energy usage behaviors.

[0005] Therefore, there is an urgent need for an economical and low-carbon optimization method for household energy use that takes into account household green electricity consumption, electric vehicle home interconnection, carbon credit and carbon reduction incentives, and multi-dimensional energy comfort, so as to quantify and guide household users to practice an economical and low-carbon family lifestyle in a scientific way. Summary of the Invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art that the existing carbon credit mechanism mainly focuses on simple estimation of macro behavior, and there is insufficient research on the specific impact of the carbon credit mechanism on household low-carbon energy consumption behavior, and to provide a household low-carbon energy consumption optimization method that takes green electricity consumption and V2H into consideration.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A household low-carbon energy optimization method considering green electricity consumption and V2H includes the following steps:

[0009] Collect historical statistical data on household electricity consumption and, combined with electricity carbon emission data for the corresponding region, establish a baseline scenario for household electricity carbon emissions. Considering household green electricity consumption, establish emission reduction scenarios that reflect actual household energy carbon emissions, and construct a carbon credit emission reduction accounting model for low-carbon household energy use.

[0010] Model household non-temperature-controlled flexible loads and establish a load flexibility optimization model; build an air conditioning load model;

[0011] Based on the user's commuting characteristics, the operating parameters of the electric vehicle and the operating parameters of the charging pile, a vehicle-home interconnection operating characteristic model is constructed. The equivalent energy storage characteristics of the electric vehicle are also modeled to construct an electric vehicle load model.

[0012] Based on the carbon credit emission reduction accounting model, load flexibility optimization model, air conditioning load model and electric vehicle load model, a household energy economic low-carbon optimization model is constructed. The household energy economic low-carbon optimization model is solved with the goal of minimizing electricity purchase costs and comfort losses and maximizing carbon emission reductions, and the optimized household economic low-carbon energy plan is obtained.

[0013] Furthermore, the calculation expression for the carbon emission baseline scenario of household electricity consumption is:

[0014]

[0015] Where, is the carbon emission baseline, For the period t The real-time carbon emission factor of the power grid at time The same period in history t The current household power consumption, for i The carbon emission baseline correction coefficient considering the temperature effect of the month is calculated as follows:

[0016]

[0017] Where, The same period in history t Month i The average of the current regional maximum load, is the change in regional load when the temperature rises / falls by 1°C, For the month i The average of the highest and lowest daily temperatures in the current area, The same month in history i The average daily maximum / minimum temperature of the current area.

[0018] Furthermore, the emission reduction scenarios reflecting actual household energy consumption carbon emissions are specifically as follows:

[0019] If the user's home is equipped with distributed photovoltaic equipment, the photovoltaic portion of the electricity used is set to zero for carbon emissions calculation. The corresponding calculation expression for the electricity carbon emission factor is:

[0020]

[0021] Where, For the calculated carbon emissions of emission reduction scenarios, For the period t The real-time carbon emission intensity of electricity purchased from the grid at For the period t The total household load power at For the period t The power generation capacity of household distributed photovoltaic equipment at that time;

[0022] If users purchase green electricity from a power sales company, the carbon emissions of the electricity used are calculated based on the certified electricity purchase details provided by the power sales company. The corresponding calculation expression for the electricity carbon emission factor is:

[0023]

[0024]

[0025] Where, For the period t The carbon intensity of electricity purchased by users from power sales companies at that time, For the k Carbon intensity of electricity, For electricity sales companies during the period t The first k The proportion of electrical energy.

[0026] Furthermore, the carbon credit emission reduction calculation model for household low-carbon energy use is expressed as follows:

[0027]

[0028] Where, The calculated value of carbon credit emission reduction for household energy consumption. is the carbon emission baseline, This is the calculated carbon emissions from the emission reduction scenario.

[0029] Furthermore, the load flexibility optimization model includes a shiftable load model, an interruptible load model, a curtailable load model, and a non-temperature-controlled flexible load comfort model. The shiftable load model, the interruptible load model, and the curtailable load model are used to model shiftable loads, interruptible loads, and curtailable loads, respectively. The non-temperature-controlled flexible load comfort model is used to describe the user's comfort when using non-temperature-controlled flexible loads.

[0030] The expression of the translatable load model is:

[0031]

[0032]

[0033] Where, For translatable loads i In the period t The power, is the zero-one variable of the translational load starting characteristic, Represents a translatable load i In the period t start up, Represents a translatable load i In the period t closure, is the load operation curve vector The vector obtained by expansion and translation, is the feasible operating domain of the translatable load, For the time period;

[0034] The expression of the interruptible load model is:

[0035]

[0036]

[0037] Where, Interruptible load i In the period t Power; Interruptible load i Rated power; is a zero-one variable representing the interruptible load operation state, Represents a translatable load i In the period tIn running state, Represents a translatable load i In the period t is closed, The number of time periods that the load needs to run cumulatively within the preset period;

[0038] The expression for the load reduction is:

[0039]

[0040]

[0041] Where, Can reduce load i In the period t Power; Yes, it indicates that the load can be reduced. i The work of k Zero-one variable in the gear working mode; Can reduce load i The work of k The operating power in the gear working mode is is the feasible operating region where load can be reduced, N The number of adjustable power levels that can reduce the load, m is the load quantity;

[0042] The expression of the non-temperature-controlled flexible load comfort model is:

[0043]

[0044]

[0045] Where, It is the user's comfort level when using non-temperature-controlled flexible loads. It is the time period t The sum of the power of non-temperature-controlled flexible loads in the user's home; When flexible load scheduling is not considered, t The sum of the non-temperature-controlled flexible load power in the user's home, For translatable loads i In the period t The power, Interruptible load j In the period t The power, To reduce load k In the period t power.

[0046] Furthermore, the air conditioning load model includes an indoor temperature change model when a household uses air conditioning, an air conditioning energy efficiency ratio model, and an air conditioning power model. The expression of the indoor temperature change model when a household uses air conditioning is:

[0047]

[0048] Where, 、 Separate moments t Indoor and outdoor temperatures; It is the time period t The operating status of the air conditioner, Indicates the air conditioner is in the time period t In operation; It is the rated power of the air conditioner when it is running; It is the energy efficiency ratio of the air conditioner, which indicates the relationship between the cooling capacity of the air conditioner and the power consumption; 、 are the equivalent thermal resistance and heat capacity of the room, is a discrete time interval, t +1 for the moment t +1;

[0049] The expression of the air conditioning energy efficiency ratio model is:

[0050]

[0051] Where, 、 are the fitting coefficients of the linear approximation function of the air-conditioning energy efficiency ratio;

[0052] The expression of the air conditioning power model is:

[0053]

[0054] Where, It is the time period t Air conditioning load power.

[0055] Furthermore, the air conditioning load model also includes modeling of residents' room temperature comfort. In the process of modeling the room temperature comfort, a predicted average index is selected as an evaluation index for measuring the room temperature comfort of household users, and a predicted dissatisfaction ratio index is introduced to accurately measure the residents' room temperature comfort loss in the optimization objective, thereby obtaining the room temperature comfort modeling result.

[0056] The calculation expression of the predicted average index is:

[0057]

[0058] Where, For the momentt The average forecast index at Is the skin temperature in a comfortable state, For the moment t indoor temperature; It is the metabolic rate of the human body, which is determined by the intensity of the body's metabolic activity; is the thermal resistance of clothing, which is determined by the clothing a person wears;

[0059] The expression of the room temperature comfort modeling result is:

[0060]

[0061] Where, For the room temperature comfort of residents, It is a gathering of time when the family is together. 、 、 are the relevant parameters for calculating the predicted dissatisfaction ratio index, is the number of hours that the household is occupied.

[0062] Furthermore, the electric vehicle home interconnection operation characteristic model includes the following constraints:

[0063]

[0064]

[0065]

[0066]

[0067] Where, Respectively represent the charging and discharging power of the electric vehicle charging pile; 、 Respectively represent the maximum charging and discharging power of electric vehicle charging piles; is the electric vehicle load power; are zero-one variables representing the state of charging or discharging of electric vehicles, It is the feasible domain for electric vehicle charging;

[0068] The expression of the equivalent energy storage characteristic of the electric vehicle is:

[0069]

[0070]

[0071]

[0072] Where, 、 are the charging and discharging efficiencies of electric vehicle charging piles, respectively; is the electric vehicle battery capacity; Is the electric vehicle battery in the period t The state of charge, For electric vehicle batteries in the period t +1 state of charge, is a discrete time interval; 、 They are the upper and lower limits of the electric vehicle SOC; is the cut-off time for electric vehicle charging; This is the battery charge that the user expects for their morning commute.

[0073] Furthermore, the objective function of the household energy economy and low-carbon optimization model is expressed as follows:

[0074]

[0075] Where, It is a normalized evaluation index of energy economy; It is a normalized evaluation index of low-carbon energy use; It is the normalized evaluation index of comfort of non-temperature-controlled load; is the normalized evaluation index of room temperature comfort; 、 、 、 are the weight factors of energy economy, non-temperature control load comfort, room temperature comfort, and low-carbon energy use in the target respectively;

[0076]

[0077]

[0078]

[0079] Where, For the period t The electricity purchase price from the power grid at that time; The time period when energy optimization is not performed t The original household load power at 1000 Hz; For this period t Total household load power; The calculated value of carbon credit emission reduction for household energy consumption; is the incentive price per unit of carbon credit emission reduction, For the period t The household rigid load power is set to a fixed curve; It is the time period t Air conditioning load power; It is the time period t The charging load power of electric vehicles; For other non-temperature controlled flexible loads in the period t Power;

[0080] If a distributed photovoltaic device is installed in the user's home, the user's home only needs to purchase the difference between the electricity used and the photovoltaic power generation from the grid. The normalized evaluation index of energy economy is Corrected to:

[0081]

[0082] Where, It is the time period t The power generation capacity of household distributed photovoltaic equipment at that time;

[0083] If the user family purchases green electricity from the electricity sales company, the energy cost of the user family is calculated based on the electricity price curve published by the electricity sales company. The normalized evaluation index of energy economy is Corrected to:

[0084]

[0085] Where, The time period is set by the electricity sales company t The price of electricity purchased at that time.

[0086] The present invention also provides a household low-carbon energy optimization system that takes into account green electricity consumption and V2H, including a memory and a processor, the memory storing a computer program, and the processor calling the computer program to execute the steps of the above method.

[0087] Compared with the prior art, the present invention has the following advantages:

[0088] (1) The present invention comprehensively considers the characteristics of household green electricity consumption and establishes an accurate accounting model for household low-carbon energy consumption and carbon credit emission reduction; conducts detailed modeling of the user's household load that can be shifted, interrupted, and reduced, and establishes a load flexibility optimization model; constructs an air conditioning load model based on the indoor and outdoor temperature difference, air conditioning operation status, and environmental parameters; constructs an electric vehicle home interconnection V2H model based on the user's commuting characteristics and electric vehicle parameters; and comprehensively constructs a low-carbon optimization model based on carbon credit incentives, with the goal of minimizing electricity purchase costs and comfort losses and maximizing carbon emission reductions; the energy optimization model combines the power purchase price of the power grid, the carbon credit incentive price, the household user's green electricity consumption characteristics, and the user's living habits to achieve the low-carbon optimization goal by adjusting the load operation mode;

[0089] The present invention realizes comprehensive green electricity consumption, electric vehicle home interconnection, carbon reduction incentives and multi-dimensional comfort modeling, quantifies the economic and low-carbon energy consumption behavior of household users in a scientific way, provides technical support for the promotion of carbon credit mechanism and the formulation of low-carbon strategy, and is conducive to the realization of economic and low-carbon energy consumption of household users.

[0090] (2) The load flexibility optimization model provided by the present invention accurately defines the comfort loss and quantifies the impact of the adjustment of the non-temperature-controlled flexible load operation mode on the user's energy comfort; the air-conditioning load model introduces a thermal comfort evaluation index, sets thermal comfort constraints and accurate room temperature comfort modeling, which can ensure that the optimized air-conditioning operation mode takes into account economy, low carbon and comfort; the electric vehicle model takes into account the satisfaction of electric vehicle users and optimizes the electric vehicle charging mode to ensure that both the user's car needs are met and energy efficient utilization is achieved; so that the overall plan properly considers the user's living habits and obtains an economical, low-carbon and comfortable household electricity plan.

[0091] (3) Based on the historical data of household electricity consumption in the region and combined with the regional electricity carbon intensity, the present invention determines the typical power curves and carbon emission baseline values ​​of rigid loads and flexible loads in different time periods, providing a reference standard for carbon emission reduction accounting. In addition, a carbon emission baseline correction coefficient considering the influence of temperature is added each month to achieve further correction of the carbon emission baseline value.

[0092] In calculating the electricity carbon emission factor for the emission reduction scenario, we comprehensively considered two household green electricity consumption methods: distributed photovoltaic utilization and purchasing green electricity contracts through electricity sales companies, achieving accurate calculation of the electricity carbon emission factor;

[0093] The final comprehensive carbon emission baseline value and the carbon emission calculation value of the emission reduction scenario realize the accurate calculation of the carbon emission reduction amount of household low-carbon energy consumption. This calculation method can not only evaluate the carbon emission level of current household electricity consumption behavior, but also provide a clear improvement direction for future low-carbon optimization strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 The figure is a flow chart of a household low-carbon energy optimization method considering green electricity consumption and V2H, provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0095] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0096] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0097] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0098] Example 1

[0099] like Figure 1 As shown, this embodiment provides a household low-carbon energy optimization method considering green electricity consumption and V2H, including the following steps:

[0100] Establishment of a carbon credit emission reduction accounting model S1: Collect and establish a carbon emission baseline scenario for household energy use based on historical household electricity consumption statistics and regional electricity carbon emission data in the corresponding region; consider household green electricity consumption, establish an emission reduction scenario that reflects the actual household energy carbon emissions, and construct a carbon credit emission reduction accounting model for household low-carbon energy use;

[0101] That is, based on the historical data of regional household electricity consumption and combined with the regional electricity carbon intensity, the typical power curves and carbon emission baseline values ​​of rigid loads and flexible loads in different time periods are determined to provide a reference standard for carbon emission reduction accounting. The two household green electricity consumption methods, namely distributed photovoltaic utilization and green electricity contracts purchased through electricity sales companies, are comprehensively considered to rationally design the household actual energy consumption carbon emission accounting method, thereby establishing an accurate accounting model for household low-carbon energy consumption carbon emission reduction.

[0102] Non-temperature-controlled flexible load modeling S2: Model household non-temperature-controlled flexible loads using a vectorization method, construct a feasible load operation domain that takes into account user living habits, quantify the impact of load operation modes on user multi-dimensional comfort, and establish a load flexibility optimization model;

[0103] Specifically, based on the classification of shiftable, interruptible, and curtailable load models, various operating models for non-temperature-controlled flexible loads are constructed to describe the flexibility of their start-stop states and operating power. The model uses a vectorization method to describe the complex operating characteristics of non-temperature-controlled flexible loads, and introduces a feasible operating domain to reasonably model the load operating boundaries determined by user living habits. The comfort changes caused by the intelligent scheduling of non-temperature-controlled flexible loads are modeled using the square of the load curve deviation to ensure that users actually experience the comfort of the modeled loads.

[0104] Air conditioning load modeling S3: Based on the indoor and outdoor temperature difference, air conditioning operating parameters, and room thermal resistance and heat capacity characteristics, an air conditioning load model is constructed, and thermal comfort evaluation indicators are introduced to evaluate room temperature comfort;

[0105] Specifically, the air conditioning load model is constructed using the indoor-outdoor temperature difference and air conditioning operating parameters. Room temperature variations are modeled using room thermal resistance and heat capacity parameters, and the dynamic impact of indoor and outdoor temperatures on the air conditioning energy efficiency ratio is considered. Two thermal comfort evaluation metrics, the predicted average index and the predicted dissatisfaction ratio, are also introduced to model room temperature comfort. A constraint on the predicted average index is set to ensure that optimized air conditioning operation essentially meets user room temperature requirements. The predicted dissatisfaction ratio is used to accurately assess the precise impact of changes in household energy use patterns on room temperature comfort.

[0106] Electric vehicle load modeling S4: Based on user commuting characteristics, electric vehicle operating parameters, and charging pile operating parameters, a vehicle-home interconnection operating characteristic model is constructed, the equivalent energy storage characteristics of electric vehicles are modeled, and constraints on electric vehicle user satisfaction are set;

[0107] Specifically, based on the operating parameters of electric vehicles and charging stations, and taking into account the user's commuting needs, a vehicle-home interconnected operational characteristic model is constructed to ensure that the electric vehicle charging mode can meet the user's commuting needs and fully utilize the equivalent energy storage function to provide flexibility for optimizing household energy use. By defining vehicle satisfaction constraints and setting the electric vehicle power limit during commuting time, the optimized electric vehicle charging mode can promote household energy conservation and emission reduction while meeting the user's vehicle needs.

[0108] Home Energy Management System Low-Carbon Optimization S5: Based on the carbon credit emission reduction accounting model, load flexibility optimization model, air conditioning load model, and electric vehicle load model, a low-carbon optimization model for household energy consumption is constructed. This low-carbon optimization model aims to minimize electricity purchase costs and comfort losses while maximizing carbon emission reductions, and solves the optimized household economic low-carbon energy consumption plan.

[0109] Specifically, the aforementioned models are combined to construct a low-carbon optimization model for the home energy management system, aiming to minimize household energy costs and comfort losses while maximizing carbon emissions reductions. This energy optimization model incorporates grid electricity purchase prices, carbon credit incentives, household green power consumption characteristics, and household lifestyles to achieve the goal of an economical, low-carbon, and comfortable home life by rationally adjusting household load operation patterns.

[0110] Specifically, step S1 includes the following steps:

[0111] S11: Collecting household electricity consumption data is fundamental for optimizing scheduling and modeling carbon emissions baselines. To comprehensively reflect household electricity consumption behavior, it is necessary to collect sufficiently detailed historical electricity consumption data with a long time span. This data should include not only time-of-day electricity consumption but also power curves for various electrical devices. If households are equipped with itemized electricity metering devices, electricity consumption data for specific appliances can be directly obtained, including operating power and time characteristics. If itemized data is not available, appliance operating patterns can be estimated using load curves. The recommended time granularity for this data is hourly or 15-minute intervals, and it should cover all four seasons of the year to reflect seasonal variations in electricity consumption. For example, peak electricity consumption in summer may be concentrated during air conditioning operation, while heating equipment loads are higher in winter, exhibiting significant seasonality. Therefore, the data coverage and time granularity directly determine the accuracy and reliability of subsequent analysis. Based on data collection, household electricity loads need to be further categorized. Based on their operating patterns and controllability, they can be divided into rigid loads and flexible loads. Rigid loads operate at times directly related to immediate user demand, with relatively fixed operating patterns and difficulty in control. Flexible loads, on the other hand, have flexible operating times and offer a certain degree of controllability. Load classification can be accomplished through rule-based methods or clustering algorithms. For example, by analyzing load curves using clustering algorithms, typical electricity consumption patterns of rigid and flexible loads can be extracted, laying the foundation for subsequent carbon emission modeling.

[0112] S12: Baseline scenario carbon emissions are an important benchmark for assessing household low-carbon behavior. First, it is necessary to obtain regional electricity carbon emission intensity data and household electricity consumption statistics from authoritative institutions (such as power grid companies or government departments). The time granularity of the data is recommended to be set to every hour or every 15 minutes to reflect the fluctuating characteristics of grid carbon emissions at different times. Based on the obtained carbon emission intensity data and household electricity consumption data, and according to the energy consumption characteristics of households, a household electricity carbon emission baseline scenario is established:

[0113] (1)

[0114] Where, It is the carbon emissions baseline; It is the time period t Real-time carbon emission factor of the power grid at time ; The same period in history t The electricity consumption of the household at that time is the load, not the net electricity consumption after deducting the photovoltaic power generation; yes i The monthly carbon emission baseline correction coefficient considering the temperature effect is calculated according to formula (2).

[0115] (2)

[0116] Where, The same period in history t Month i The average value of the highest load in the area; It is the change in regional load when the temperature rises or falls by 1°C, and is set based on data published by relevant power grid companies; It is the month i The average daily maximum / minimum temperature in the area (the highest temperature is from July to September, and the lowest temperature is from January to February and December). The same month in history i The average daily maximum / minimum temperature in the area.

[0117] S13: Emission reduction scenarios are used to assess the carbon emissions of households’ actual energy consumption behavior. Their accuracy has a huge impact on the accuracy of emission reduction calculations. They are calculated using real-time electricity carbon emission factors.

[0118] (3)

[0119] Where, is the calculated carbon emissions from the emission reduction scenario; It is the time period t The real-time carbon emission intensity of electricity purchased from the grid at that time; It is the time period t The total household load power at .

[0120] Taking into account the growing demand for green electricity among users and the use of distributed photovoltaics, the emission reduction scenario needs to be adjusted based on the actual situation of the user's family.

[0121] If a user's home is equipped with distributed photovoltaic equipment, the photovoltaic portion of the electricity used should be set to zero in accounting for carbon emissions, that is,

[0122] (4)

[0123] Where, It is the time period t The power generation power of household distributed photovoltaic equipment at that time.

[0124] If users purchase green electricity from a power sales company, the carbon emissions of the electricity they use should be calculated based on the certified power purchase details provided by the power sales company, that is,

[0125] (5)

[0126] Where, It is the time period t The carbon intensity of electricity purchased by users from power sales companies at that time.

[0127] (6)

[0128] Where, It is k The carbon intensity of electricity is 0 for green electricity; The electricity sales company is in the period t The first k The proportion of electrical energy.

[0129] S14. Calculation model for household electricity carbon credit emission reductions:

[0130] (7)

[0131] Where, It is the calculated value of carbon credit emission reduction for household energy consumption.

[0132] The design of this carbon credit emission reduction accounting model provides a theoretical foundation for the assessment and optimization of low-carbon behavior. Its accuracy directly impacts the effectiveness of optimized scheduling and the effectiveness of carbon credit incentives. Therefore, to ensure model reliability, it is recommended that relevant baseline model parameters be regularly revised based on the timely updating of dynamic electricity consumption data and grid carbon emission intensity. This accounting method not only assesses the current carbon emission level of household electricity consumption but also provides clear improvement directions for future low-carbon optimization strategies.

[0133] Step S2 specifically includes the following steps:

[0134] S21. Establish a non-temperature-controlled flexible load operation characteristic model:

[0135] Among typical household electrical equipment, except for temperature-controlled loads such as air conditioners and water heaters, the operating characteristics of most flexible loads can be modeled using shiftable, interruptible, and curtailable load models.

[0136] The characteristic of a portable load is that the user can freely choose the start time, but once started, the electrical equipment must run to the end according to the predetermined workflow and must not be interrupted during use, such as washing machines, dishwashers, etc. i , if its running duration is , the power consumption curve of each working stage of the load can be expressed as Vector Indicates that, considering the user's living habits, the movable load must be operated in the feasible operating domain The translational load can be modeled as follows:

[0137] (8)

[0138] (9)

[0139] Where, It is a translatable load i In the period t Power; is a zero-one variable representing the starting characteristics of the translatable load, Represents a translatable load i In the period t start up; Represents the first t item; is the load operation curve vector The vector obtained by expansion and translation has the following form:

[0140] (10)

[0141] The characteristic of interruptible load is that the load can be interrupted during operation, but the total operation time of the load remains unchanged within a certain period, such as sweeping robots, humidifiers, etc. i If it needs to run cumulatively within a certain period Time period, and taking into account the user's living habits, the interruptible load must be operated in the feasible operation domain The interruptible load can be modeled as follows:

[0142] (11)

[0143] (12)

[0144] Where, Interruptible load i In the period t Power; Interruptible load i Rated power; is a zero-one variable representing the interruptible load operation state, Represents a translatable load i In the period t In running state.

[0145] The characteristic of curtailable load is that the load's operating power can be adjusted within a specific period of time, such as lighting fixtures, televisions, etc. i , suppose there is N The power of the gear can be adjusted, and considering the user's living habits, the load that can be reduced must be operated within the feasible operating range. The curtailable load can be modeled as follows:

[0146] (13)

[0147] (14)

[0148] Where, Can reduce load i In the period t Power; Yes, it indicates that the load can be reduced. i The work of k Zero-one variable in the gear working mode; Can reduce load i The work of k The operating power in the gear working mode.

[0149] S22. Establish a non-temperature-controlled flexible load comfort model:

[0150] When users cannot use non-temperature-controlled flexible loads as they are used to, their comfort will be affected, which can be modeled as:

[0151] (15)

[0152] (16)

[0153] Where, is the user's comfort level when using a non-temperature-controlled flexible load. The comfort loss is characterized by the square of the power curve deviation before and after load optimization. This modeling method conforms to the common sense that making small adjustments to the original load has minimal impact on comfort. It is the time period t The sum of the power of non-temperature-controlled flexible loads in the user's home; When flexible load scheduling is not considered, t The total power of non-temperature-controlled flexible loads in the user's home.

[0154] Step S3 specifically includes the following steps:

[0155] S31. Air conditioning load modeling:

[0156] When a household uses air conditioning equipment, the indoor temperature change can be modeled using the following formula:

[0157] (17)

[0158] Where, 、 Separate moments t Indoor and outdoor temperatures; It is the time period t The operating status of the air conditioner, Indicates the air conditioner is in the time period t In operation; It is the rated power of the air conditioner when it is running; It is the energy efficiency ratio of the air conditioner, which indicates the relationship between the cooling capacity of the air conditioner and the power consumption; 、 are the equivalent thermal resistance and heat capacity of the room, is a discrete time interval, t +1 for the moment t +1, For the moment t and time t +1 for the time interval between them.

[0159] The energy efficiency ratio of the air conditioner can be approximately modeled using the following linear equation,

[0160] (18)

[0161] Where, 、 are the fitting coefficients of the linear approximation function of air conditioning energy efficiency ratio.

[0162] Therefore, the air conditioning load can be modeled as:

[0163] (19)

[0164] Where, It is the time period t Air conditioning load power.

[0165] S32. Modeling of room temperature comfort for household users:

[0166] The predicted mean vote (PMV) is selected as the evaluation index for measuring the room temperature comfort of household users, and the linear engineering simplified formula is used to calculate PMV:

[0167] (20)

[0168] Where, It's time t PMV at the time; is the skin temperature in a comfortable state; It is the metabolic rate of the human body, which is determined by the intensity of the body's metabolic activity; is the thermal resistance of clothing, which is determined by the clothing worn by the person.

[0169] The international standard recommends a PMV value between plus and minus 0.5, so the room temperature comfort constraint is set as:

[0170] (twenty one)

[0171] Where, It is a time for the family to gather together.

[0172] Since different individuals have significant differences in their perception of room temperature comfort, the predicted percentage of dissatisfied (PPD) indicator is further introduced to accurately measure the residents' room temperature comfort loss in the optimization objective. Therefore, the residents' room temperature comfort can be modeled as:

[0173] (twenty two)

[0174] Where, 、 、 are the relevant parameters for calculating PPD, is the number of hours that the household is occupied.

[0175] Step S4 specifically includes the following steps:

[0176] S41. Modeling of electric vehicle home interconnection characteristics:

[0177] Vehicle-to-Home (V2H) connectivity for electric vehicles must meet the following constraints:

[0178] (twenty three)

[0179] (twenty four)

[0180] (25)

[0181] (26)

[0182] in, Respectively represent the charging and discharging power of the electric vehicle charging pile; 、 Respectively represent the maximum charging and discharging power of electric vehicle charging piles; is the electric vehicle load power; are zero-one variables representing the state of charging or discharging of electric vehicles, is the feasible domain of electric vehicle charging, which is set according to the user's daily commuting situation; constraints (23)-(24) are the charging and discharging power constraints of the bidirectional charging pile respectively; constraint (25) represents the relationship between the external load characteristics of the electric vehicle and its charging and discharging power; constraint (26) is used to limit the operating state of the electric vehicle, which cannot be in a state of both charging and discharging.

[0183] S42. Modeling of equivalent energy storage characteristics of electric vehicles:

[0184] (27)

[0185] (28)

[0186] (29)

[0187] in, 、 are the charging and discharging efficiencies of electric vehicle charging piles, respectively; is the electric vehicle battery capacity; Is the electric car battery at the moment t State of Charge (SOC); 、 They are the upper and lower limits of the electric vehicle’s SOC; is the cut-off time for electric vehicle charging, which is determined based on the user's commuting characteristics; is the battery charge that the user expects for their morning commute; is a discrete time interval, t +1 for the moment t +1, For the moment t and time t +1; constraint (27) is the time period coupling characteristic of the electric vehicle SOC; constraint (28) is the upper and lower limit constraints of the electric vehicle battery SOC; constraint (29) is the electric vehicle satisfaction constraint, and the electric vehicle should have been charged to the user's expected power when the user uses the car for commuting in the morning.

[0188] Step S5 specifically includes the following steps:

[0189] S51. Determination of optimization objectives:

[0190] In the optimization objective function, energy economy, energy comfort and low carbon energy use are comprehensively considered.

[0191] The economic factor is related to the electricity purchase price during the time period and aims to minimize the user's electricity purchase cost. The comfort factor reflects the impact of load adjustment on user experience, ensuring that the optimization results will not significantly affect the quality of life while saving energy and reducing emissions. The carbon emission reduction incentive factor maximizes the user's emission reduction benefits through the carbon preferential price of low-carbon electricity:

[0192] (30)

[0193] Where, It is the normalized evaluation index of energy economy, and its calculation method is shown in formula (31); It is the normalized evaluation index of low-carbon energy consumption, and its calculation method is shown in formula (32); 、 、 、 They are the weight factors of energy economy, non-temperature-controlled load comfort, room temperature comfort, and low-carbon energy use in the target.

[0194] (31)

[0195] Where, For the period t The electricity purchase price from the power grid at that time; The time period when energy optimization is not performed t The original household load power at 1000 Hz; For this period t Total household load power; It is the incentive price per unit of carbon credit emission reduction.

[0196] (32)

[0197] The formula for calculating the total household load is,

[0198] (33)

[0199] Where, For the period t The household rigid load power is set to a fixed curve; It is the time period t Air conditioning load power; It is the time period t The charging load power of electric vehicles; For other non-temperature controlled flexible loads in the period t power.

[0200] Taking into account users' demand for green electricity and the utilization of distributed photovoltaics, the energy economy measurement formula needs to be revised based on the actual situation of the user's family.

[0201] If the user's home is equipped with distributed photovoltaic equipment, then the user only needs to purchase the difference between the electricity used and the photovoltaic power generation from the grid. In this case, the energy economy accounting method needs to be revised as follows:

[0202] (34)

[0203] Where, It is the time period t The power generation power of household distributed photovoltaic equipment at that time.

[0204] If users purchase green electricity from a power sales company, their energy costs should be calculated based on the electricity price curve published by the power sales company. In this case, the energy economic accounting method needs to be revised as follows:

[0205] (35)

[0206] Where, The time period is set by the electricity sales company t The price of electricity purchased at that time.

[0207] S52. Constraints and optimization methods:

[0208] Multiple constraints are set for the optimization model, including carbon credit emission reduction accounting constraints, operation constraints of shiftable loads, interruptible loads, and curtailable loads, operation constraints of air conditioners and room temperature PMV comfort constraints, electric vehicle home interconnection operation characteristics, and equivalent energy storage characteristics constraints.

[0209] Using a multi-objective optimization algorithm and iterative calculations, we can reduce electricity purchase costs while increasing users' low-carbon electricity benefits, improving the low-carbon nature of their energy use, and fully ensuring the comfort of their family lives. Ultimately, we generate an economical, low-carbon, and comfortable household energy plan. The final model is as follows:

[0210] (36)

[0211] The present invention also provides a household low-carbon energy optimization system considering green electricity consumption and V2H, including a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the above household low-carbon energy optimization method considering green electricity consumption and V2H.

[0212] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A household low-carbon energy optimization method considering green electricity consumption and V2H, characterized in that: The following steps are involved: Collect historical statistical data on household electricity consumption and combine it with electricity carbon emission data for the corresponding region to establish a baseline scenario for household electricity carbon emissions; Considering household green electricity consumption, establish emission reduction scenarios that reflect household carbon emissions from actual energy use, and construct a carbon credit emission reduction accounting model for household low-carbon energy use; Model household non-temperature-controlled flexible loads and establish a load flexibility optimization model; Build air conditioning load model; Based on the user's commuting characteristics, the operating parameters of the electric vehicle and the operating parameters of the charging pile, a vehicle-home interconnection operating characteristic model is constructed. The equivalent energy storage characteristics of the electric vehicle are also modeled to construct an electric vehicle load model. Based on the carbon credit emission reduction accounting model, load flexibility optimization model, air conditioning load model, and electric vehicle load model, a household energy economic and low-carbon optimization model is constructed. This household energy economic and low-carbon optimization model aims to minimize electricity purchase costs and comfort losses and maximize carbon emission reductions, thereby obtaining an optimized household economic and low-carbon energy plan. The calculation expression for the carbon emission baseline scenario of household electricity consumption is: Where, is the carbon emission baseline, For the period t The real-time carbon emission factor of the power grid at time The same period in history t The current household power consumption, for i The carbon emission baseline correction coefficient considering the temperature effect of the month is calculated as follows: Where, The same period in history t Month i The average of the current regional maximum load, is the change in regional load when the temperature rises / falls by 1°C, For the month i The average of the highest and lowest daily temperatures in the current area, The same month in history i The average daily maximum / minimum temperature of the current area.

2. A household low-carbon energy optimization method considering green electricity consumption and V2H according to claim 1, characterized in that: The emission reduction scenarios reflecting actual household energy carbon emissions are as follows: If the user's home is equipped with distributed photovoltaic equipment, the photovoltaic portion of the electricity used is set to zero for carbon emissions calculation. The corresponding calculation expression for the electricity carbon emission factor is: Where, For the calculated carbon emissions of emission reduction scenarios, For the period t The real-time carbon emission intensity of electricity purchased from the grid at For the period t The total household load power at For the period t The power generation capacity of household distributed photovoltaic equipment at that time; If users purchase green electricity from a power sales company, the carbon emissions of the electricity used are calculated based on the certified electricity purchase details provided by the power sales company. The corresponding calculation expression for the electricity carbon emission factor is: Where, For the period t The carbon intensity of electricity purchased by users from power sales companies at that time, For the k Carbon intensity of electricity, For electricity sales companies during the period t The first k The proportion of electrical energy.

3. The household low-carbon energy optimization method considering green electricity consumption and V2H according to claim 1 is characterized in that: The carbon credit emission reduction calculation model for household low-carbon energy use is expressed as follows: Where, The calculated value of carbon credit emission reduction for household energy consumption. is the carbon emission baseline, This is the calculated carbon emissions from the emission reduction scenario.

4. The household low-carbon energy optimization method considering green electricity consumption and V2H according to claim 1 is characterized in that: The load flexibility optimization model includes a translatable load model, an interruptible load model, a curtailable load model, and a non-temperature-controlled flexible load comfort model. The translatable load model, the interruptible load model, and the curtailable load model are used to model translatable loads, interruptible loads, and curtailable loads, respectively. The non-temperature-controlled flexible load comfort model is used to describe the user's comfort when using non-temperature-controlled flexible loads. The expression of the translatable load model is: Where, For translatable loads i In the period t The power, is the zero-one variable of the translational load starting characteristic, Represents a translatable load i In the period t start up, Represents a translatable load i In the period t closure, is the load operation curve vector The vector obtained by expansion and translation, is the feasible operating domain of the translatable load, For the time period; The expression of the interruptible load model is: Where, Interruptible load i In the period t Power; Interruptible load i Rated power; is a zero-one variable representing the interruptible load operation state, Represents a translatable load i In the period t In running state, Represents a translatable load i In the period t is closed, The number of time periods that the load needs to run cumulatively within the preset period; The expression for the load reduction is: Where, Can reduce load i In the period t Power; Yes, it indicates that the load can be reduced. i The work of k Zero-one variable in the gear working mode; Can reduce load i The work of k The operating power in the gear working mode is is the feasible operating region where load can be reduced, N The number of adjustable power levels that can reduce the load, m is the load quantity; The expression of the non-temperature-controlled flexible load comfort model is: Where, It is the user's comfort level when using non-temperature-controlled flexible loads. It is the time period t The sum of the power of non-temperature-controlled flexible loads in the user's home; When flexible load scheduling is not considered, t The sum of the non-temperature-controlled flexible load power in the user's home, For translatable loads i In the period t The power, Interruptible load j In the period t The power, To reduce load k In the period t power.

5. The household low-carbon energy optimization method considering green electricity consumption and V2H according to claim 1 is characterized in that: The air conditioning load model includes an indoor temperature change model when a household uses air conditioning, an air conditioning energy efficiency ratio model, and an air conditioning power model. The expression of the indoor temperature change model when a household uses air conditioning is: Where, 、 Separate moments t Indoor and outdoor temperatures; It is the time period t The operating status of the air conditioner, Indicates the air conditioner is in the time period t In operation; It is the rated power of the air conditioner when it is running; It is the energy efficiency ratio of the air conditioner, which indicates the relationship between the cooling capacity of the air conditioner and the power consumption; 、 are the equivalent thermal resistance and heat capacity of the room, is a discrete time interval, t +1 for the moment t +1; The expression of the air conditioning energy efficiency ratio model is: Where, 、 are the fitting coefficients of the linear approximation function of the air-conditioning energy efficiency ratio; The expression of the air conditioning power model is: Where, It is the time period t Air conditioning load power.

6. The household low-carbon energy optimization method considering green electricity consumption and V2H according to claim 1 is characterized in that: The air conditioning load model also includes modeling of residents' room temperature comfort. In the process of modeling, a predicted average index is selected as an evaluation index for measuring the room temperature comfort of household users, and a predicted dissatisfaction ratio index is introduced to accurately measure the residents' room temperature comfort loss in the optimization objective, thereby obtaining a room temperature comfort modeling result. The calculation expression of the predicted average index is: Where, For the moment t The average forecast index at Is the skin temperature in a comfortable state, For the moment t indoor temperature; It is the metabolic rate of the human body, which is determined by the intensity of the body's metabolic activity; is the thermal resistance of clothing, which is determined by the clothing a person wears; The expression of the room temperature comfort modeling result is: Where, For the room temperature comfort of residents, It is a gathering of time when the family is together. 、 、 are the relevant parameters for calculating the predicted dissatisfaction ratio index, is the number of hours that the household is occupied.

7. The household low-carbon energy optimization method considering green electricity consumption and V2H according to claim 1 is characterized in that: The electric vehicle home interconnection operation characteristic model includes the following constraints: Where, Respectively represent the charging and discharging power of the electric vehicle charging pile; 、 Respectively represent the maximum charging and discharging power of electric vehicle charging piles; is the electric vehicle load power; are zero-one variables representing the state of charging or discharging of electric vehicles, It is the feasible domain for electric vehicle charging; The expression of the equivalent energy storage characteristic of the electric vehicle is: Where, 、 are the charging and discharging efficiencies of electric vehicle charging piles, respectively; is the electric vehicle battery capacity; Is the electric vehicle battery in the period t The state of charge, For electric vehicle batteries in the period t +1 state of charge, is a discrete time interval; 、 They are the upper and lower limits of the electric vehicle’s SOC; is the cut-off time for electric vehicle charging; This is the battery charge that the user expects for their morning commute.

8. The household low-carbon energy optimization method considering green electricity consumption and V2H according to claim 1 is characterized in that: The objective function of the household energy economy and low-carbon optimization model is expressed as follows: Where, It is a normalized evaluation index of energy economy; It is a normalized evaluation index of low-carbon energy use; It is the normalized evaluation index of comfort of non-temperature-controlled load; is the normalized evaluation index of room temperature comfort; 、 、 、 are the weight factors of energy economy, non-temperature control load comfort, room temperature comfort, and low-carbon energy use in the target respectively; Where, For the period t The electricity purchase price from the power grid at that time; The time period when energy optimization is not performed t The original household load power at 1000 Hz; For this period t Total household load power; The calculated value of carbon credit emission reduction for household energy consumption; is the incentive price per unit of carbon credit emission reduction, For the period t The household rigid load power is set to a fixed curve; It is the time period t Air conditioning load power; It is the time period t The charging load power of electric vehicles; For other non-temperature controlled flexible loads in the period t Power; If a distributed photovoltaic device is installed in the user's home, the user's home only needs to purchase the difference between the electricity used and the photovoltaic power generation from the grid. The normalized evaluation index of energy economy is Corrected to: Where, It is the time period t The power generation capacity of household distributed photovoltaic equipment at that time; If the user family purchases green electricity from the electricity sales company, the energy cost of the user family is calculated based on the electricity price curve published by the electricity sales company. The normalized evaluation index of energy economy is Corrected to: Where, The time period is set by the electricity sales company t The price of electricity purchased at that time.

9. A household low-carbon energy optimization system considering green electricity consumption and V2H, characterized by: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of any one of the methods according to claims 1 to 8.

Images