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

Abstract

The invention relates to a household low-carbon energy consumption optimization method and system considering green power consumption and V2H, and the method comprises the steps: collecting household power consumption historical statistical data, building a carbon emission reference line scene of household power consumption, and constructing a carbon popularity emission reduction amount accounting model in combination with an emission reduction scene; establishing a load flexibility optimization model according to the household non-temperature-control flexible load; constructing an air conditioner load model; according to the commuting characteristics of the user, the operation parameters of the electric vehicle and the operation parameters of the charging pile, constructing an electric vehicle load model; and on the basis of the established model, constructing a household energy consumption economic low-carbon optimization model, and solving by taking minimization of electricity purchase cost and comfort loss and maximization of carbon emission reduction as targets to obtain an optimized household economic low-carbon energy consumption scheme. Compared with the prior art, the method has the advantages that carbon reduction potential contained in household low-carbon energy consumption is quantified in a multi-element household green power consumption mode, household users are scientifically guided to use power economically and comfortably in a low-carbon mode, and the purposes of energy conservation and carbon reduction are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of household electricity, and in particular to a method and system for optimizing low-carbon household energy use 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 inclusive mechanism provides economic incentives for the public to practice a green and low-carbon lifestyle. This mechanism can quantify personal green and low-carbon behaviors, such as energy conservation and emission reduction, green travel, garbage classification, recycling, etc., into greenhouse gas emission reduction amounts, and absorb the emission reduction amounts through various reward methods, improving the public's participation in low-carbon life, releasing the carbon reduction potential of the vast number of residential users while promoting the awareness of green and low-carbon. However, the existing carbon inclusive mechanism mainly focuses on simple estimation of macro behaviors, and the research on the specific impact of the carbon inclusive mechanism on household low-carbon energy consumption behaviors is insufficient. For example, a method and system for calculating carbon inclusive carbon reduction for urban residents' electricity conservation disclosed in the invention with the publication number CN118709900A can only estimate the carbon inclusive carbon reduction amount from the macro electricity consumption behaviors of urban residents.

[0003] Currently, household electricity loads mainly consist of rigid loads, interruptible loads, shiftable loads, reducible loads, and temperature control loads. The complex living habits of household residents and the operation rules of equipment result in a highly dynamic and complex load operation mode. Especially in the context of the increasing complexity of household load operation, how to efficiently dispatch diverse household loads to achieve energy conservation and emission reduction while ensuring living comfort is an urgent problem to be solved.

[0004] With the popularization of renewable energy such as distributed photovoltaics and the promotion of electric vehicles, the flexibility of household energy use has been continuously improved, and the energy conservation and carbon reduction potential of users' optimized electricity consumption behaviors has been further enhanced. However, there is currently a lack of a systematic household load management method that can comprehensively consider household green electricity consumption, carbon inclusive incentives, vehicle-to-home interconnection of electric vehicles, and users' living habits, and it is difficult for existing research to scientifically optimize household low-carbon energy consumption behaviors.

[0005] Therefore, there is an urgent need for a method for optimizing household energy use in an economical and low-carbon manner that considers household green electricity consumption, vehicle-to-home interconnection of electric vehicles, carbon inclusive carbon reduction incentives, and multi-dimensional energy use comfort, so as to quantitatively guide household users to practice an economical and low-carbon household lifestyle in a scientific way. Summary of the Invention

[0006] The object of the present invention is to overcome the deficiencies of the existing carbon inclusive mechanism mainly targeting the simple estimation of macro behaviors and the lack of research on the specific impact of the carbon inclusive mechanism on household low-carbon energy use behaviors, and to provide a household low-carbon energy use optimization method considering green power consumption and V2H.

[0007] The object of the present invention can be achieved through the following technical solutions: A household low-carbon energy use optimization method considering green power consumption and V2H, comprising the following steps: Collect historical statistical data of household electricity consumption, and combine with the power carbon emission data of the corresponding region to establish a carbon emission baseline scenario for household electricity consumption; considering household green power consumption, establish a emission reduction scenario reflecting the actual energy use carbon emissions of the household, and construct a carbon inclusive emission reduction amount accounting model for household low-carbon energy use; Model the non-thermostatic flexible load of the household to establish a load flexibility optimization model; construct an air-conditioning load model; According to the commuting characteristics of users, the operating parameters of electric vehicles and the operating parameters of charging piles, construct an electric vehicle vehicle-to-home interconnection operating characteristics model, and model the equivalent energy storage characteristics of electric vehicles to construct an electric vehicle load model; Based on the carbon inclusive emission reduction amount accounting model, the load flexibility optimization model, the air-conditioning load model and the electric vehicle load model, construct a household energy use economic low-carbon optimization model. The household energy use economic low-carbon optimization model is solved with the goal of minimizing the electricity purchase cost and comfort loss and maximizing the carbon emission reduction amount to obtain an optimized household economic low-carbon energy use plan.

[0008] Furthermore, the calculation expression of the carbon emission baseline scenario of the household electricity consumption is: In the formula, is the carbon emission baseline, is the real-time carbon emission factor of the power grid at time t is the load electricity consumption of the current household at the historical same period time is the historical same period time t is the carbon emission baseline correction factor considering the temperature influence in month is i The calculation expression of the carbon emission baseline correction factor is: In the formula, is the average value of the highest load in the current region in the month t to which the historical same period time i belongs, is the change amount of the regional load when the temperature rises / decreases by 1℃, is the month iThe average of the daily maximum / minimum temperatures in the current area, is the average of the daily maximum / minimum temperatures in the current area for the same month in history. i The average of the daily maximum / minimum temperatures in the current area.

[0009] Furthermore, the emission reduction scenario reflecting the actual carbon emissions of household energy use is specifically as follows: If a distributed photovoltaic device is installed in the user's household, the photovoltaic part of the electricity used is zeroed for carbon emission accounting, and the calculation expression for the corresponding electricity carbon emission factor is: In the formula, is the carbon emission of the emission reduction scenario obtained through accounting, is the time period t The real-time carbon emission intensity of purchasing electricity from the power grid at this time, is the time period t The total household load power at this time, is the time period t The power generation power of the household distributed photovoltaic device at this time; If the user purchases green electricity through a power sales company, the carbon emissions of the electricity used are accounted for according to the certified electricity purchase details provided by the power sales company, and the calculation expression for the corresponding electricity carbon emission factor is: In the formula, is the time period t The carbon intensity of the electricity purchased by the user from the power sales company at this time, is the k The carbon intensity of the nth type of electricity, is the power sales company at the time period t For the electricity purchased by the user at this time, the proportion of the nth type of electricity. k

[0010] Furthermore, the expression of the carbon inclusive emission reduction amount accounting model for low-carbon household energy use is: In the formula, is the calculated value of the carbon inclusive emission reduction amount for the user's household energy use, is the carbon emission baseline, is the carbon emission of the emission reduction scenario obtained through accounting.

[0011] Furthermore, the load flexibility optimization model includes a shiftable load model, an interruptible load model, a curtailable load model, and a comfort model for non-thermostatic flexible loads. The shiftable load model, the interruptible load model, and the curtailable load model are respectively used to model shiftable loads, interruptible loads, and curtailable loads. The comfort model for non-thermostatic flexible loads is used to describe the comfort level of users when using non-thermostatic flexible loads; The expression of the shiftable load model is: In the formula, is the shiftable load i at time period t power, is a binary variable of the start-up characteristic of the shiftable load, indicating that the shiftable load i at time period t starts up, indicating that the shiftable load i at time period t shuts down, is the vector obtained by expanding and shifting the load operation curve vector , is the feasible operation range of the shiftable load, is the time period; The expression of the interruptible load model is: In the formula, is the interruptible load i at time period t power; is the rated power of the interruptible load i ; is a binary variable representing the operation state of the interruptible load, indicating that the shiftable load i at time period t is in the operating state, indicating that the shiftable load i at time period t is in the off state, is the number of time periods that the load needs to accumulate operation within the preset period; The expression of the curtailable load is: In the formula, is the curtailable load iDuring the time period t power; is a binary variable indicating the work of the load that can be curtailed i at the k gear operating mode; is the operating power of the load that can be curtailed i at the k gear operating mode, is the feasible operating region of the load that can be curtailed, N is the number of adjustable power gears of the load that can be curtailed, m is the number of loads; The expression of the non-thermostatic flexible load comfort model is: In the formula, is the comfort level of the user using the non-thermostatic flexible load, is the time period t the total power of the non-thermostatic flexible loads in the user's home; is the total power of the non-thermostatic flexible loads in the user's home without considering flexible load scheduling during the time period t , is the power of the shiftable load i during the time period t , is the power of the interruptible load j during the time period t , is the power of the load that can be curtailed k during the time period t .

[0012] Furthermore, the air-conditioning load model includes an indoor temperature change model, an air-conditioning energy efficiency ratio model, and an air-conditioning power model when the home uses air-conditioning. The expression of the indoor temperature change model when the home uses air-conditioning is: In the formula, , are the indoor and outdoor temperatures at time t respectively; is the operating state of the air-conditioning during the time period t , indicates that the air-conditioning is in the operating state during the time period t ; is the rated power of the air-conditioning when it is operating; is the energy efficiency ratio of the air-conditioning, indicating the relationship between the cooling capacity and the power consumption of the air-conditioning; , are the equivalent thermal resistance and heat capacity of the room respectively, is a discrete time interval, t +1 is the moment t +1; The expression of the air conditioner energy efficiency ratio model is: In the formula, , are the fitting coefficients of the linear approximation function of the air conditioner energy efficiency ratio respectively; The expression of the air conditioner power model is: In the formula, is the air conditioner load power at time period t .

[0013] Furthermore, the air conditioner load model also includes the modeling of the room temperature comfort of residents. In the process of modeling the room temperature comfort, the predicted mean vote is selected as the evaluation index for measuring the room temperature comfort of household users, and the predicted percentage dissatisfied index is introduced to accurately measure the comfort loss of residents to the room temperature in the optimization objective, and the modeling result of the room temperature comfort is obtained; The calculation expression of the predicted mean vote is: In the formula, is the predicted mean vote at time t , is the skin temperature in the comfort state, is the indoor temperature at time t ; is the human metabolic rate, which is determined by the intensity of human metabolic activities; is the clothing thermal resistance, which is determined by the clothing worn by people; The expression of the modeling result of the room temperature comfort is: In the formula, is the room temperature comfort of residents, is the set of times when there are people in the family, , , are the relevant parameters for calculating the predicted percentage dissatisfied index respectively, is the number of time periods when there are people in the family.

[0014] Furthermore, the electric vehicle-home interconnection operation characteristic model includes the following constraints: In the formula, respectively represent the charging and discharging powers of the electric vehicle charging pile; , respectively represent the maximum charging and discharging powers of the electric vehicle charging pile; is the electric vehicle load power; are binary variables representing the charging or discharging state of the electric vehicle respectively, is the electric vehicle charging feasible region; The expression of the equivalent energy storage characteristics of the electric vehicle is: In the formula, , are the charging and discharging efficiencies of the electric vehicle charging pile respectively; is the electric vehicle battery capacity; is the state of charge of the electric vehicle battery at time period t , is the state of charge of the electric vehicle battery at time period t +1, is the discrete time interval; , are the upper and lower limits of the electric vehicle SOC respectively; is the cut-off time for electric vehicle charging; is the battery power expected by the user during morning commuting.

[0015] Furthermore, the expression of the objective function of the household energy economy and low-carbon optimization model is: In the formula, is the normalized evaluation index of energy economy; is the normalized evaluation index of energy low-carbon; is the normalized evaluation index of non-thermostatic load comfort; is the normalized evaluation index of room temperature comfort; , , , are the weight factors of energy economy, non-thermostatic load comfort, room temperature comfort, and energy low-carbon in the objective respectively; In the formula, is at time periodt Grid purchase price at time Is the time period without energy optimization t Original household load power at time Is the time period t Total household load power Is the carbon inclusive emission reduction accounting value of the user's household energy consumption; Is the incentive price per unit of carbon inclusive emission reduction, Is the time period t Household rigid load power, set as a fixed curve; Is the time period t Air conditioner load power; Is the time period t Electric vehicle charging load power; Is other non-thermostatic flexible load at time period t Power; If a distributed photovoltaic device is installed in the user's household, the user's household only needs to purchase the difference between the electricity consumption and the photovoltaic power generation from the power grid. The normalized evaluation index of energy economy Is modified to: In the formula, Is the time period t Photovoltaic power generation power of the household distributed photovoltaic device at time; If the user's household purchases green electricity through a power sales company, the energy consumption cost of the user's household is calculated according to the electricity price curve announced by the power sales company. The normalized evaluation index of energy economy Is modified to: In the formula, Is the time period set by the power sales company t Price of purchased electric energy at time.

[0016] The present invention also provides a household low-carbon energy consumption optimization system considering green electricity consumption and V2H, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method described above.

[0017] Compared with the prior art, the present invention has the following advantages: (1) The present invention comprehensively considers the characteristics of household green electricity consumption, establishes an accurate accounting model for the carbon inclusive emission reduction of low-carbon household energy use; finely models the shiftable, interruptible, and reducible loads of the user's household respectively, and establishes a load flexibility optimization model; constructs an air-conditioning load model according to the indoor-outdoor temperature difference, the operating state of the air conditioner, and environmental parameters; constructs a vehicle-to-home (V2H) model for the interconnection between electric vehicles and households according to the user's commuting characteristics and electric vehicle parameters; and comprehensively constructs a low-carbon optimization model in combination with carbon inclusive incentives, with the goal of minimizing the electricity purchase cost and comfort loss and maximizing the carbon emission reduction; the energy use optimization model combines the grid electricity purchase price, the carbon inclusive incentive price, the household green electricity consumption characteristics of household users, and the user's living habits, and realizes the low-carbon optimization goal by adjusting the load operation mode. The present invention realizes comprehensive green electricity consumption, vehicle-to-home interconnection of electric vehicles, carbon emission reduction incentives, and multi-dimensional comfort modeling, scientifically quantifies the economic and low-carbon energy use behaviors of household users, provides technical support for the popularization of the carbon inclusive mechanism and the formulation of low-carbon strategies, and is conducive to realizing the economic and low-carbon energy use of household users.

[0018] (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 operation mode of non-thermostatic flexible loads on the energy use comfort of users; the air-conditioning load model introduces a thermal comfort evaluation index, sets thermal comfort constraints and accurate room temperature comfort modeling, and can ensure that the optimized air-conditioning operation mode takes into account economy, low carbon, and comfort; the electric vehicle model considers the satisfaction of electric vehicle use, optimizes the electric vehicle charging mode, and ensures that both the user's vehicle use needs are met and efficient energy utilization is achieved; the overall solution properly considers the user's living habits and obtains an economic, low-carbon, and comfortable household electricity plan.

[0019] (3) Based on the historical household electricity consumption data of the region and combined with the regional power 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, provides a comparison standard for carbon emission reduction accounting, and adds a carbon emission baseline correction coefficient considering temperature influence for each month to further correct the carbon emission baseline value; For the calculation of the power carbon emission factor in the emission reduction scenario, the present invention comprehensively considers two household green electricity consumption methods, namely distributed photovoltaic utilization and the purchase of green electricity contracts through electricity sales companies, and realizes the accurate calculation of the power carbon emission factor; Finally, by integrating the carbon emission baseline value and the carbon emission calculation value in the emission reduction scenario, the present invention realizes the accurate accounting of the carbon inclusive emission reduction of low-carbon household energy use. This accounting method can not only evaluate the carbon emission level of current household electricity use behaviors, but also provide a clear improvement direction for future low-carbon optimization strategies. Description of the Drawings

[0020] Figure 1Schematic flowchart of a household low-carbon energy use optimization method considering green electricity consumption and V2H provided in an embodiment of the present invention. Detailed implementation manners

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0022] 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 claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0023] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0024] Embodiment 1 As Figure 1 shown, this embodiment provides a household low-carbon energy use optimization method considering green electricity consumption and V2H, including the following steps: Establishment of carbon inclusive emission reduction amount accounting model S1: Collect and establish a carbon emission baseline scenario for household energy use based on the historical statistical data of household electricity consumption and regional power carbon emissions in the corresponding region; considering household green electricity consumption, establish an emission reduction scenario reflecting the actual carbon emissions of household energy use, and construct a carbon inclusive emission reduction amount accounting model for household low-carbon energy use; That is, based on the historical data of regional household electricity consumption, combined with the regional power carbon intensity, determine the typical power curves and carbon emission baseline values of rigid loads and flexible loads in different time periods, providing a comparison standard for carbon emission reduction accounting; comprehensively considering two household green electricity consumption methods, namely distributed photovoltaic utilization and purchasing green electricity contracts through electricity sales companies, reasonably design a household actual energy use carbon emission accounting method, so as to establish an accurate accounting model for the carbon inclusive emission reduction amount of household low-carbon energy use; Modeling of non-temperature-controlled flexible loads S2: Model the household non-temperature-controlled flexible loads through a vectorization method, construct a load feasible operation domain considering the user's living habits, quantify the impact of the load operation mode on the user's multi-dimensional comfort, and establish a load flexibility optimization model; That is, based on the classification of translatable, interruptible, and load-sheddable models, multiple operation models of non-thermostatic flexible loads are constructed to describe the flexibility of their start-stop states and operating powers; the models describe the complex operating characteristics of non-thermostatic flexible loads through vectorization methods and introduce 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-thermostatic flexible loads are modeled by the square of the one-norm of the load curve deviation to ensure that the comfort models the actual feelings of load users. Air-conditioning load modeling S3: Based on the indoor-outdoor temperature difference, air-conditioning operating parameters, and room thermal resistance and heat capacity characteristics, an air-conditioning load model is constructed, and a thermal comfort evaluation index is introduced to evaluate the room temperature comfort. That is, an air-conditioning load model is constructed through the indoor-outdoor temperature difference and air-conditioning operating parameters, the room temperature change characteristics are modeled using room thermal resistance and heat capacity parameters, and the dynamic influence of indoor and outdoor temperatures on the air-conditioning energy efficiency ratio is considered. At the same time, two thermal comfort evaluation indexes, the predicted mean vote and the predicted percentage dissatisfied, are introduced to model the room temperature comfort, and the predicted mean vote constraint is set to ensure that the optimized air-conditioning operation basically meets the user's room temperature requirements, and the predicted percentage dissatisfied is used to accurately evaluate the precise impact of the change in household energy use patterns on the room temperature comfort.

[0025] Electric vehicle load modeling S4: According to the user's commuting characteristics, electric vehicle operating parameters, and charging pile operating parameters, an electric vehicle home-to-vehicle interconnected operating characteristics model is constructed to model the equivalent energy storage characteristics of electric vehicles, and an electric vehicle user satisfaction constraint is set. That is, based on the electric vehicle operating parameters and charging pile operating parameters, considering the user's commuting vehicle use requirements, an electric vehicle home-to-vehicle interconnected operating characteristics model is constructed to ensure that the electric vehicle charging mode can meet the user's commuting vehicle use requirements and can fully play the role of equivalent energy storage to provide flexibility for household energy optimization. By defining the user satisfaction constraint, the electric vehicle battery power boundary at the commuting vehicle use time is set to ensure that the optimized electric vehicle charging mode not only promotes household energy conservation and emissions reduction but also meets the user's vehicle use requirements.

[0026] Low-carbon optimization of the home energy management system S5: Based on the carbon offset 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 low-carbon optimization model aims to minimize the electricity purchase cost and comfort loss and maximize the carbon emission reduction, and the optimized household economic low-carbon energy use plan is obtained by solving. That is, a low-carbon optimization model of the home energy management system is constructed by integrating the above models, with the goal of minimizing the household energy use cost and comfort loss while maximizing the carbon emission reduction. The energy use optimization model combines the grid electricity purchase price, carbon offset incentive price, household user green electricity consumption characteristics, and user living habits to achieve the goal of an economical, low-carbon, and comfortable household life by reasonably adjusting the household load operation mode.

[0027] Specifically, step S1 includes the following steps: S11: The collection of household electricity consumption data is the basic work for optimizing scheduling and carbon emission baseline modeling. To comprehensively reflect household electricity consumption behavior, it is necessary to collect historical electricity consumption data that is fine enough and has a long time span. These data not only include time-of-use electricity consumption, but also the power curves of various electrical appliances. If the household is equipped with sub-metering equipment for electricity consumption, the electricity consumption data of specific appliances can be directly obtained, including operating power and time characteristics; if there is no sub-item data, the operating mode of the appliances can be estimated through the load curve. The time granularity of the data is recommended to be set to every hour or every 15 minutes, and it is necessary to cover all four seasons of the year to reflect the seasonal changes in electricity consumption. For example, the electricity consumption peak in summer may be concentrated in the air conditioner operating period, while the heating equipment load is high in winter, and the seasonal characteristics are significant. Therefore, the coverage range and time granularity of the data directly determine the accuracy and reliability of subsequent analysis. On the basis of data collection, it is necessary to further classify the household electricity load. According to the operating rules and controllable characteristics of the load, it is divided into rigid load and flexible load. The operating time of the rigid load is directly related to the immediate needs of users, and its operating rules are relatively fixed and difficult to control; while the flexible load has a flexible operating time and has a certain potential for control. Load classification can be completed through rule-based methods or clustering algorithms. For example, by using clustering algorithms to analyze the load curve, the typical electricity consumption patterns of rigid load and flexible load can be extracted, laying a foundation for subsequent carbon emission modeling.

[0028] S12: The carbon emission of the baseline scenario is an important benchmark for evaluating the low-carbon behavior of households. First, it is necessary to obtain the carbon emission intensity data of regional electricity and the electricity consumption statistics data of residential households 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 residential household electricity consumption data, according to the energy consumption characteristics of residential households, a carbon emission baseline scenario for household electricity consumption is established: (1) wherein, is the carbon emission baseline; is the time period t when the real-time carbon emission factor of the power grid; is the historical same period time period t when the household load electricity consumption, rather than the net electricity consumption obtained after deducting the photovoltaic power generation; is i the carbon emission baseline correction coefficient considering temperature influence in month, calculated according to formula (2).

[0029] (2) wherein, is the average value of the highest load in this region during the same historical period t the month to which it belongs i of the highest load in this region; is the change in regional load when the temperature rises / falls by 1°C, set according to the data published by the relevant power grid company; is the month i average value of the daily highest / lowest temperature in this region (the highest temperature from July to September, and the lowest temperature from January to February and December), is the month in the same historical period i average value of the daily highest / lowest temperature in this region.

[0030] S13: The emission reduction scenario is used to evaluate the carbon emissions of the actual energy consumption behavior of households. Its accuracy has a great impact on the accuracy of emission reduction accounting, and real-time electricity carbon emission factors are used for calculation.

[0031] (3) In the formula, is the carbon emission of the emission reduction scenario obtained through accounting; is the time period t real-time carbon emission intensity of purchasing electric energy from the power grid; is the time period t total household load power at this time.

[0032] Considering the increasing demand for green electricity by users and the utilization of distributed photovoltaics, the emission reduction scenario needs to be corrected according to the actual situation of the user's household.

[0033] If there is a distributed photovoltaic device installed in the user's household, the photovoltaic part of the electricity consumed should be set to zero for carbon emission accounting, that is (4) In the formula, is the time period t power generation power of the household distributed photovoltaic device at this time.

[0034] If the user purchases green electricity through an electricity sales company, the carbon emissions of the electricity consumed should be accounted for according to the certified electricity purchase details provided by the electricity sales company, that is (5) In the formula, is the time period t carbon intensity of the electricity purchased by the user from the electricity sales company at this time.

[0035] (6) In the formula, is the k carbon intensity of the nth type of electricity, and its value is 0 for green electricity; It is the proportion of the t -th type of electric energy purchased for users by the electricity selling company during the k time period.

[0036] S14. Accounting model for carbon inclusive emission reduction of household electricity consumption: (7) In the formula, is the calculated value of the carbon inclusive emission reduction of the user's household energy consumption.

[0037] The design of this carbon inclusive emission reduction accounting model provides a theoretical basis for the evaluation and optimization of low-carbon behaviors, and its accuracy directly affects the effect of optimal dispatching and the effectiveness of carbon inclusive emission reduction incentives. Therefore, to ensure the reliability of the model, it is recommended to regularly correct the relevant parameters of the baseline model on the basis of timely updating of dynamic electricity consumption data and grid carbon emission intensity. This accounting method can not only evaluate the carbon emission level of current household electricity consumption behaviors, but also provide a clear improvement direction for future low-carbon optimization strategies.

[0038] Step S2 specifically includes the following steps: S21. Establish a non-thermostatic flexible load operation characteristic model: Among typical household electrical appliances, except for thermostatic loads such as air conditioners and water heaters, the operation characteristics of most flexible loads can be modeled using a load model with translatable, interruptible, and reducible loads.

[0039] The characteristic of translatable load is that users can freely choose the starting time, but once started, the electrical appliance needs to run to the end according to the predetermined work process and cannot be interrupted during use, such as washing machines and dishwashers. For translatable load i , if its operation duration is , the electricity consumption curve of each working stage of the load can be represented by a vector with a dimension of . Considering the user's living habits, the translatable load must operate within the feasible operation domain , so the translatable load can be modeled in the following form: (8) (9) In the formula, is the power of the translatable load i during the t time period; is a binary variable representing the starting characteristic of the translatable load, indicating that the translatable load i starts during the t time period; represents the tItem; is the load operation curve vector The vector obtained by expansion and translation has the following form: (10) Interruptible loads are characterized by the ability to be interrupted during the load operation process, but the total duration of the load operation remains unchanged within a certain period, such as a floor cleaning robot, a humidifier, etc. For interruptible loads i , if it needs to run cumulatively for time periods within a certain period, and considering the user's living habits, the interruptible load must operate within the feasible operation domain , so the interruptible load can be modeled in the following form: (11) (12) In the formula, is the interruptible load i at time period t power; is the rated power of the interruptible load i ; is a binary variable representing the operating state of the interruptible load, indicating that the shiftable load i is in the operating state at time period t .

[0040] The characteristics of the load that can be curtailed are that the operating power of the load can be adjusted during specific time periods, such as lighting fixtures, televisions, etc. For the load that can be curtailed i , suppose it has N gear powers that can be adjusted, and considering the user's living habits, the load that can be curtailed must operate within the feasible operation domain , so the load that can be curtailed can be modeled in the following form: (13) (14) In the formula, is the load that can be curtailed i at time period t power; is a binary variable indicating that the operation of the load that can be curtailed i is in the k th gear working mode; is the operating power of the load that can be curtailed i when it is in the k th gear working mode.

[0041] S22. Establish a comfort model for non-thermostatic flexible loads: When users cannot use non - temperature - controlled flexible loads according to their habits, their comfort will be affected, which can be modeled as: (15) (16) Wherein, is the comfort level of users using non - temperature - controlled flexible loads. The loss of comfort level is characterized by the square of the first norm of the power curve deviation before and after load optimization. This modeling method conforms to the common sense that making a small adjustment to the original load has a minimal impact on comfort; is the time period t the total power of non - temperature - controlled flexible loads in the user's home at time; is the total power of non - temperature - controlled flexible loads in the user's home at time t when flexible load scheduling is not considered.

[0042] Step S3 specifically includes the following steps: S31. Air - conditioner load modeling: When the household uses air - conditioner equipment, the change of indoor temperature can be modeled by the following formula: (17) Wherein, , are the indoor and outdoor temperatures at time t respectively; is the operating state of the air - conditioner in the time period t , indicating that the air - conditioner is in the operating state in the time period t ; is the rated power of the air - conditioner when it is running; is the energy efficiency ratio of the air - conditioner, indicating the relationship between the cooling capacity and the power consumption of the air - conditioner; , are the equivalent thermal resistance and heat capacity of the room respectively, is the discrete time interval, t + 1 is the time t + 1, is the time t and the time t + 1.

[0043] The energy efficiency ratio of the air - conditioner can be approximately modeled using the following linear equation, (18) Wherein, , are the fitting coefficients of the linear approximation function of the air - conditioner energy efficiency ratio respectively.

[0044] Therefore, the air - conditioner load can be modeled as: (19) In the formula, is the time period t of the air conditioning load power.

[0045] S32. Modeling the room temperature comfort of household users: Select the Predicted Mean Vote (PMV) as the evaluation index for measuring the room temperature comfort of household users, and use a linear engineering simplified formula to calculate PMV: (20) In the formula, is the time t when PMV; is the skin temperature in the comfortable state; is the human metabolic rate, which is determined by the intensity of human metabolic activities; is the clothing thermal resistance, which is determined by the clothing worn by people.

[0046] The recommended value of PMV by the international standard is between plus and minus 0.5. Therefore, the room temperature comfort constraint is set as: (21) In the formula, is the set of times when there are people in the family.

[0047] Since there are obvious differences in the perception of room temperature comfort among different individuals, the Predicted Percentage of Dissatisfied (PPD) index is further introduced to accurately measure the comfort loss of residents to room temperature in the optimization objective. Thus, the room temperature comfort of residents can be modeled as: (22) In the formula, , , are the relevant parameters for calculating PPD respectively, is the number of time periods when there are people in the family.

[0048] Step S4 specifically includes the following steps: S41. Modeling the vehicle-to-home interconnection characteristics of electric vehicles: The vehicle-to-home (V2H) of electric vehicles needs to meet the following constraints: (23) (24) (25) (26) Among them, respectively represent the charging and discharging power of the electric vehicle charging pile; and respectively represent the maximum charging and discharging power of the electric vehicle charging pile; is the electric vehicle load power; are respectively binary variables representing the charging or discharging state of the electric vehicle. is the charging feasible region of the electric vehicle, which is set according to the user's daily commuting vehicle usage situation; Constraints (23)-(24) are respectively the charging and discharging power constraints of the bidirectional charging pile; Constraint (25) represents the relationship between the load external characteristics of the electric vehicle and its charging and discharging power; Constraint (26) is used to limit the operating state of the electric vehicle, and it cannot be in a state of both charging and discharging.

[0049] S42. Modeling of the equivalent energy storage characteristics of electric vehicles: (27) (28) (29) Among them, and are respectively the charging and discharging efficiencies of the electric vehicle charging pile; is the electric vehicle battery capacity; is the state of charge (SOC) of the electric vehicle battery at time t ; and are respectively the upper and lower limits of the electric vehicle SOC; is the cut-off time for electric vehicle charging, which is determined according to the user's commuting characteristics; is the battery power expected by the user when commuting in the morning; is the discrete time interval, t +1 is time t +1, is time t and time t +1; Constraint (27) is the inter-period coupling characteristic of the electric vehicle SOC; Constraint (28) is the upper and lower limit constraint of the electric vehicle battery SOC; Constraint (29) is the vehicle usage satisfaction constraint of the electric vehicle. When the user commutes in the morning, the electric vehicle should have been charged to the expected power by the user.

[0050] Step S5 specifically includes the following steps: S51. Determination of the optimization objective: In the optimization objective function, the energy economy, energy comfort, and energy low-carbon characteristics are comprehensively considered.

[0051] The economic factor is related to the time-of-use electricity purchase price and aims to minimize the user's electricity purchase cost; the comfort factor reflects the impact of load adjustment on the user experience and ensures that the optimization result will not significantly affect the quality of life while achieving energy conservation and emission reduction; the carbon emission reduction incentive factor maximizes the user's emission reduction benefits through the carbon inclusive price of low-carbon electricity consumption: (30) In the formula, is the normalized evaluation index of energy economy, and its calculation method is shown in formula (31); is the normalized evaluation index of energy low-carbon, and its calculation method is shown in formula (32); and and and are the weight factors of energy economy, non-thermostatic load comfort, room temperature comfort, and energy low-carbon in the target, respectively.

[0052] (31) In the formula, is the grid electricity purchase price at time t ; is the original household load power at time t without energy optimization; is the total household load power at time t ; is the incentive price per unit of carbon inclusive emission reduction.

[0053] (32) The calculation formula for the total household load is (33) In the formula, is the household rigid load power at time t , which is set as a fixed curve; is the air-conditioning load power at time t ; is the electric vehicle charging load power at time t ; is the power of other non-thermostatic flexible loads at time t .

[0054] Considering the user's demand for green electricity and the utilization of distributed photovoltaics, the measurement formula for energy economy needs to be corrected according to the actual situation of the user's household.

[0055] If there is a distributed photovoltaic device installed in the user's household, then it only needs to purchase the difference between the electricity consumption and the photovoltaic power generation from the grid, and the energy economy accounting method needs to be corrected to: (34) In the formula, is the time period t and is the power generation power of the household distributed photovoltaic equipment at that time.

[0056] If the user purchases green electricity through an electricity sales company, the energy consumption cost should be calculated based on the electricity price curve announced by the electricity sales company. Then, the energy economy accounting method needs to be corrected to: (35) In the formula, is the time period set by the electricity sales company t and is the price of the purchased electric energy at that time.

[0057] S52. Constraints and optimization methods: Multiple constraints are set for the optimization model, including carbon inclusive emission reduction accounting constraints, operating constraints of shiftable load, interruptible load, and curtailable load, operating constraints of air conditioners and room temperature PMV comfort constraints, operating characteristics of electric vehicle-to-home interconnection, equivalent energy storage characteristics constraints, etc.

[0058] A multi-objective optimization algorithm is adopted. Through iterative calculation, while reducing the electricity purchase cost, the low-carbon electricity consumption income of users is improved, the low-carbon degree of user energy consumption is increased, and the comfort of users' family life is fully guaranteed. Finally, an economic, low-carbon and comfortable household energy use plan is generated. The final model is as follows: (36) The present invention also provides a household low-carbon energy use optimization system considering green electricity consumption and V2H, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the above-mentioned household low-carbon energy use optimization method considering green electricity consumption and V2H.

[0059] The above has described in detail the preferred specific embodiments of the present invention. It should be understood that those of ordinary skill in the art can make many modifications and variations according to the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field of the present invention based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the protection scope determined by the claims.

Claims

1. A household low-carbon energy consumption optimization method considering green power consumption and V2H, characterized in that It includes the following steps: Collect historical statistical data of household electricity consumption, and combine with the electricity carbon emission data of the corresponding region to establish a carbon emission baseline scenario for household electricity consumption; Considering the consumption of green electricity in households, establish a reduction scenario reflecting the carbon emissions of actual household energy use, and construct a carbon inclusive emission reduction volume accounting model for low-carbon household energy use; Model the non-thermostatic flexible load of the household, and establish a load flexibility optimization model; Construct an air-conditioning load model; According to the commuting characteristics of users, the operating parameters of electric vehicles and the operating parameters of charging piles, construct an operating characteristic model of electric vehicle-home interconnection, model the equivalent energy storage characteristics of electric vehicles, and construct an electric vehicle load model; Based on the carbon inclusive emission reduction volume accounting model, the load flexibility optimization model, the air-conditioning load model and the electric vehicle load model, construct a household energy use economic low-carbon optimization model. The household energy use economic low-carbon optimization model is solved with the goal of minimizing the electricity purchase cost and comfort loss and maximizing the carbon emission reduction amount, and an optimized household economic low-carbon energy use plan is obtained.

2. The family low-carbon energy consumption optimization method considering green electricity consumption and V2H according to claim 1, wherein The calculation expression of the carbon emission baseline scenario of the household electricity consumption is: In the formula, is the carbon emission baseline, is the time period t when the real-time carbon emission factor of the power grid, is the historical same period t when the electricity consumption of the current household load, is i the carbon emission baseline correction factor considering the temperature influence in month, and the calculation expression of the carbon emission baseline correction factor is: In the formula, is the average value of the highest load in the current area during the same period in history t for the month i and is the average value of the highest load in the current area. is the change in the regional load when the temperature rises or drops by 1°C. is the month i and is the average value of the highest or lowest daily temperature in the current area. is the month in the same period in history i and is the average value of the highest or lowest daily temperature in the current area.

3. The family low-carbon energy consumption optimization method considering green power consumption and V2H according to claim 1 is characterized in that, The reduction scenario reflecting the carbon emissions of actual household energy use is specifically: If the user's household installs distributed photovoltaic equipment, the photovoltaic part of the electricity used is set to zero for carbon emission accounting, and the calculation expression of the corresponding electricity carbon emission factor is: Wherein, is the carbon emission of the emission reduction scenario obtained through accounting, is the time period, t is the real-time carbon emission intensity of the electric energy purchased from the power grid during the time period is the time period, t is the total household load power during the time period is the time period, t is the power generation power of the household distributed photovoltaic equipment during the time period; If the user purchases green electricity through an electricity sales company, the carbon emissions of the electricity used are accounted according to the certified electricity purchase details provided by the electricity sales company, and the calculation expression of the corresponding electricity carbon emission factor is: Wherein, is the carbon intensity of the electric energy purchased by the user from the electricity sales company during the time period t , is the carbon intensity of the k th type of electric energy, is the proportion of the t th type of electric energy in the electric energy purchased by the electricity sales company for the user during the time period k .

4. A household low-carbon energy consumption optimization method considering green power consumption and V2H according to claim 1, characterized in that The expression of the carbon inclusive emission reduction volume accounting model for low-carbon household energy use is: In the formula, is the calculated value of the carbon inclusive emission reduction of the user's household energy consumption, is the carbon emission baseline, is the carbon emission of the emission reduction scenario obtained through calculation.

5. A method for optimizing low-carbon energy use in households considering green electricity consumption and V2H, characterized in that, The load flexibility optimization model includes a shiftable load model, an interruptible load model, a reducible load model and a non-thermostatic flexible load comfort model. The shiftable load model, the interruptible load model and the reducible load model are respectively used to model the shiftable load, the interruptible load and the reducible load, and the non-thermostatic flexible load comfort model is used to describe the comfort of users using non-thermostatic flexible loads; The expression of the shiftable load model is: Wherein, is the shiftable load i at time period t power, is a binary variable of the starting characteristic of the shiftable load, indicating that the shiftable load i at time period t starts, indicating that the shiftable load i at time period t is shut down, is the load operation curve vector vector obtained by extension and translation, is the feasible operation region of the shiftable load, is the time period; The expression of the interruptible load model is: Wherein, is the interruptible load i at time period t power; is the interruptible load i rated power; is a binary variable indicating the operating state of the interruptible load, indicating the shiftable load i at time period t is in the operating state, indicating the shiftable load i at time period t is in the off state, is the number of time periods that the load needs to accumulate operation within a preset period; The expression of the reducible load is: In the formula, is the load that can be curtailed i at time period t with the power; is a binary variable indicating that the operation of the load that can be curtailed i is in the k th gear operating mode; is the operating power of the load that can be curtailed i when its operation is in the k th gear operating mode; is the feasible operating range of the load that can be curtailed; N is the number of adjustable power gears of the load that can be curtailed; m is the number of loads; The expression of the non-thermostatic flexible load comfort model is: Wherein, is the comfort level of the user using the non-thermostatic flexible load, is the time period, t and is the total power of the non-thermostatic flexible loads in the user's home at time period is the total power of the non-thermostatic flexible loads in the user's home at time period t without considering the flexible load scheduling, is the power of the shiftable load i at time period t , is the power of the interruptible load j at time period t , is the power of the curtailable load k at time period t .

6. The family low-carbon energy consumption optimization method considering green power consumption and V2H according to claim 1 is characterized in that, The air-conditioning load model includes an indoor temperature change model, an air-conditioning energy efficiency ratio model and an air-conditioning power model when the household uses the air conditioner. The expression of the indoor temperature change model when the household uses the air conditioner is: In the formula, and are the indoor and outdoor temperatures at time t respectively; is the operating state of the air conditioner during the time period t , indicating that the air conditioner is in the operating state during the time period t ; is the rated power of the air conditioner when it is operating; is the energy efficiency ratio of the air conditioner, indicating the relationship between the cooling capacity and the power consumption of the air conditioner; and are the equivalent thermal resistance and heat capacity of the room respectively, is the discrete time interval, t +1 is the time t +1; The expression of the air-conditioning energy efficiency ratio model is: In the formula, and are the fitting coefficients of the linear approximation function of the air conditioner energy efficiency ratio, respectively; The expression of the air-conditioning power model is: In the formula, is the time period t of the air-conditioning load power.

7. A method for optimizing household low-carbon energy consumption considering green electricity consumption and V2H according to claim 1, characterized in that The air-conditioning load model also includes the modeling of the room temperature comfort of residents. In the process of room temperature comfort modeling, the predicted mean vote is selected as the evaluation index for measuring the room temperature comfort of household users, and the predicted percentage dissatisfied index is introduced to accurately measure the comfort loss of residents to the room temperature in the optimization goal, and the room temperature comfort modeling result is obtained; The calculation expression of the predicted mean vote is: wherein, is the predicted mean vote at time t ; is the skin temperature in a comfortable state, is the indoor temperature at time t ; is the human metabolic rate, which is determined by the intensity of human metabolic activities; is the clothing thermal resistance, which is determined by the clothing worn by a person; The expression of the room temperature comfort modeling result is: In the formula, is the room temperature comfort level of residents, is the set of times when there are people in the family, , , are the relevant parameters for calculating the predicted percentage of dissatisfied people index respectively, is the number of time periods when there are people in the family.

8. The home low-carbon energy consumption optimization method considering green electricity consumption and V2H according to claim 1, characterized in that, The operating characteristic model of electric vehicle-home interconnection includes the following constraints: In the formula, respectively represent the charging and discharging powers of the electric vehicle charging pile; , respectively represent the maximum charging and discharging powers of the electric vehicle charging pile; is the electric vehicle load power; are respectively binary variables indicating the charging or discharging state of the electric vehicle, is the electric vehicle charging feasible region; The expression of the equivalent energy storage characteristics of the electric vehicle is: In the formula, and are the charging and discharging efficiencies of the electric vehicle charging pile respectively; is the battery capacity of the electric vehicle; is the state of charge of the electric vehicle battery at time t ; is the state of charge of the electric vehicle battery at time t +1; is the discrete time interval; and are the upper and lower limits of the electric vehicle SOC respectively; is the cut-off time for electric vehicle charging; is the battery power expected by the user when commuting by car in the morning.

9. The household low-carbon energy use optimization method considering green electricity consumption and V2H according to claim 1, wherein The expression of the objective function of the household energy economy and low-carbon optimization model is as follows: In the formula, is the normalized evaluation index of energy utilization economy; is the normalized evaluation index of low-carbon energy utilization; is the normalized evaluation index of the comfort of non-thermostatic loads; is the normalized evaluation index of room temperature comfort; , , , are the weight factors of energy utilization economy, non-thermostatic load comfort, room temperature comfort, and low-carbon energy utilization in the target, respectively; In the formula, is the grid power purchase price during time period t ; is the original household load power during time period t without energy optimization; is the total household load power during time period t ; is the accounting value of the carbon inclusive emission reduction for the user's household energy consumption; is the incentive price per unit of carbon inclusive emission reduction, is the rigid household load power during time period t , which is set as a fixed curve; is the air-conditioning load power during time period t ; is the electric vehicle charging load power during time period t ; is the power of other non-temperature-controlled flexible loads during time period t . If a distributed photovoltaic device is installed in a user's home, the user's home only needs to purchase the difference in electricity consumption and photovoltaic power generation from the power grid, and the normalized evaluation index of energy use economy is corrected to: Wherein, is the time period t when the power generation of the household distributed photovoltaic device; If a user's family purchases green electricity through an electricity sales company, the energy consumption cost of the user's family is calculated based on the electricity price curve announced by the electricity sales company, and the normalized evaluation index of the energy consumption economy is corrected to: Wherein, is the time period set by the electricity selling company t and is the price for purchasing electric energy at that time.

10. A home low-carbon energy consumption optimization system considering green power consumption and V2H, characterized in that It includes a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method according to any one of claims 1 to 9.

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