Household low-carbon power consumption optimization method and system considering carbon profit incentive

Abstract

The invention relates to a household low-carbon power consumption optimization method and system considering carbon profit incentive, and the method comprises the steps: building a household power consumption carbon emission reference line based on household power consumption historical statistical data and regional power carbon emission intensity; modeling the family transferable load, quantifying the influence of the load operation time on the user comfort, and establishing a load flexibility optimization model; based on the indoor and outdoor temperature difference, the air conditioner operation state and the equipment thermal resistance, an air conditioner load model is built, and comfort constraint is set; constructing a water heater load curve model according to the hot water use condition of the user, the running state of the water heater and the environment condition; and integrating the models to construct a low-carbon optimization model with the purposes of minimizing the electricity purchase cost and comfort loss and maximizing the carbon emission reduction, and solving by combining with the carbon popularity incentive price. Compared with the prior art, the method has the advantages that the low-carbon power consumption behaviors of the household users are quantified in a scientific mode, and the goal of low-carbon emission reduction is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity optimization, and in particular to a method and system for optimizing household low-carbon electricity consumption in consideration of carbon universal incentives. Background Art

[0002] At present, the optimization of household electricity consumption behavior 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 participate in low-carbon life. This mechanism can quantify individual green behaviors, such as energy-saving electricity, green travel, and garbage classification, into greenhouse gas emission reductions, and increase public participation through rewards. However, the existing carbon credit mechanism mainly focuses on simple estimates of macro behaviors, and there are still deficiencies in household electricity load modeling and low-carbon assessment in complex scenarios.

[0003] At present, household electricity loads are mainly composed of rigid loads (such as air conditioners and water heaters) and transferable loads (such as washing machines and rice cookers). The living habits of household users and the operation rules of equipment make the loads highly dynamic and complex. Especially when the flexibility of electricity loads is insufficient, how to ensure user comfort while saving energy is an urgent problem to be solved.

[0004] In addition, with the popularization of renewable energy such as distributed photovoltaics, household electricity consumption interacts more frequently with the power grid, and the potential for users to optimize their electricity consumption behavior is further enhanced. However, there is currently a lack of systematic modeling methods that can combine user load characteristics, carbon credit incentives, and user living habits. Existing research has difficulty in achieving accurate evaluation and scientific optimization of household low-carbon electricity consumption behavior.

[0005] Therefore, there is an urgent need for a low-carbon assessment and optimization method for household electricity consumption that takes into account user load characteristics, low-carbon incentives, and comfort, so as to quantify the low-carbon behavior of household users in a scientific way. Summary of the invention

[0006] The purpose of the present invention is to overcome the defect of the above-mentioned prior art that there is a lack of a systematic modeling method that can combine user load characteristics, carbon preferential incentives and user living habits, and to provide a household low-carbon electricity optimization method and system that takes carbon preferential incentives into consideration.

[0007] The purpose of the present invention can be achieved by the following technical solutions: A method for optimizing household low-carbon electricity consumption considering carbon credit incentives comprises the following steps: Collect and establish a carbon emission baseline for household electricity based on historical statistical data of household electricity consumption in the corresponding area and the carbon emission intensity of electricity in the corresponding area; The household transferable load is modeled by vectorization method, a feasible operation domain considering the user's living habits is constructed, the impact of load operation time on user comfort is quantified, and a load flexibility optimization model is established; Based on the indoor and outdoor temperature difference, air conditioning operation status, and equipment thermal resistance and thermal capacity characteristics, an air conditioning load model is constructed and comfort constraints are set; According to the user's hot water usage, the operating status of the water heater and the environmental conditions, a water heater temperature change model is constructed to obtain the water heater load curve model, and the hot water demand comfort constraint is set; Based on the load flexibility optimization model, air conditioning load model and water heater load curve model, a 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. In combination with the carbon inclusive incentive price, the optimized household electricity consumption plan is solved.

[0008] Furthermore, the household electricity consumption history data includes time-sharing electricity consumption and power curves of various electrical equipment. According to the operating law and adjustable characteristics of the load, the collected household electricity consumption history data is classified into rigid load and transferable load; The calculation process of the carbon emission baseline is as follows: Based on the actual situation of household electricity consumption, two scenarios are used to calculate the carbon emission baseline. Scenario 1 takes the average carbon emission of household electricity consumption in the whole society as the baseline, and uses the advanced coefficient to ensure the additionality of emission reduction, which is suitable for households with small monthly electricity consumption; Scenario 2 takes the household's historical carbon emission of electricity consumption in the same period as the baseline, and ensures the additionality of emission reduction by comparing with itself, which is suitable for households with large monthly electricity consumption.

[0009] The expression of carbon emission baseline scenario 1 is: In the formula, It is the carbon emission baseline for scenario 1; It is the total electricity consumption of all households in the society; is the total number of households in the society; is the coefficient of advancement; It is the benchmark carbon intensity of household electricity consumption in the low-carbon electricity carbon credit methodology.

[0010] The expression of carbon emission baseline scenario 2 is: In the formula, is the carbon emission baseline under scenario 2; It is the time period t The real-time carbon emission factor of the power grid at the time of The same period in history t The household's electricity consumption at that time.

[0011] Furthermore, the expression of the load flexibility optimization model is: In the formula, For translatable load i In the period t The power, is the zero-one variable of the shiftable load starting characteristic, Represents a translatable load i In the period t start up, is the load operation curve vector The vector obtained by expansion and translation is Represents the first t item, It is the feasible operation domain of the electrical equipment; In the formula, For translatable load i The running duration, is the power of the translatable load i at the first period step_start at the beginning of operation, is the power of the translatable load i at the last period step_end of the operation.

[0012] Furthermore, the method quantifies the impact of load operation time on user comfort through a comfort loss index, and the expression of the comfort loss index is: In the formula, For users in the time period t Start transferable load i Comfort, Use load for users i The preferred operating domain of , when the user starts the load within the preferred operating domain, the comfort level is 1; For translatable load i The feasible operation domain of , when the user starts the load outside the feasible operating domain, the comfort level is 0.

[0013] Furthermore, the expression of the air conditioning load model is: In the formula, , Separate moments t Indoor and outdoor temperatures, For the period t The operating status of the air conditioner, Indicates the air conditioner is in the time period t In operation; is the rated power of the air conditioner when it is running; , They are the equivalent thermal resistance and equivalent heat capacity of the air conditioner. It is the time period t The air conditioning load power, is the time interval of each period.

[0014] Furthermore, the expression of the comfort constraint is: In the formula, It's time t PMV at the time, PMV is the selected prediction average index, is the skin temperature in a comfortable state; is the metabolic rate of the human body, is the thermal resistance of the clothing, It is a time for the family to gather together.

[0015] Furthermore, the expression of the water heater water temperature change model is: In the formula, It is the water temperature in the water heater at the next moment when the user does not use hot water during period t. It's time t The indoor temperature is the water temperature in the water heater during period t; It is the time period t The operating status of the water heater, Indicates the water heater is in the time period t In operation; is the rated power of the water heater when in operation; , They are the equivalent thermal resistance and equivalent heat capacity of the water heater; It is the time period t The amount of hot water used by the household at that time; is the volume of the water heater tank; It is the temperature of the cold water added to the water heater after the user uses water. is the time interval of each period; The expression of the water heater load curve model is: In the formula, It is the time period t The household water heater load power.

[0016] Furthermore, the expression of the hot water demand comfort constraint is: In the formula, , They are the upper and lower limits of the water heater temperature set by the user. It is a time for the family to gather together.

[0017] Furthermore, the expression of the low-carbon optimization model is: In the formula, is the cost weight factor, For the period t The power purchase price from the grid at that time, The time period when energy optimization is not performed t The original household load power at For the period t The total household load power, is the incentive price per unit of carbon credit emission reduction, For carbon credit emission reduction, is the comfort weight factor, is the amount of transferable load in the household, is the zero-one variable of the shiftable load starting characteristic, For users in the time period t Start transferable load i Comfort level; The low-carbon optimization model uses the load flexibility optimization model, the air conditioning load model and the water heater load curve model as constraints; The calculation expression of carbon credit emission reduction is: In the formula, It is the carbon emission baseline for household electricity consumption, and the corresponding scenario is selected and calculated based on the actual situation of the household; It is the time period t The carbon intensity of the electricity purchased from the grid by the household at that time, The household rigid load power in period t, It is the time period t The household water heater load power, It is the time periodt The air conditioning load power, For translatable load i In the period t of power.

[0018] The present invention also provides a household low-carbon electricity optimization system taking into account carbon universal incentives, 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.

[0019] Compared with the prior art, the present invention has the following advantages: (1) The present invention models household transferable loads separately 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 equipment thermal resistance and thermal capacity characteristics; constructs a water heater load curve model based on the user's hot water usage, the water heater operation status, etc.; thereby comprehensively constructing a low-carbon optimization model with the goal of minimizing electricity purchase costs and comfort losses and maximizing carbon emission reduction; the low-carbon optimization model combines the power purchase price of the power grid, the carbon inclusive incentive price, and the load transfer strategy to achieve the low-carbon goal by adjusting the load operation time; After integrating user load characteristics, low-carbon incentives and comfort, the present invention quantifies the low-carbon behavior of household users in a scientific way, provides technical support for the promotion of carbon credit mechanisms and the formulation of low-carbon policies, and is conducive to achieving energy conservation and emission reduction in electricity consumption by household users.

[0020] (2) The load flexibility optimization model provided by the present invention quantifies the impact of load operation time on user comfort by defining comfort loss; the air conditioning load model introduces the PMV index and sets comfort constraints to ensure that the air conditioning operation after optimization is balanced between energy saving and comfort; the water heater load curve model combines the hot water demand comfort and optimizes the water heater operation mode to ensure that the user's hot water demand is met and energy is used efficiently; the overall plan takes into account the user's living habits and comfort to obtain the optimal household electricity consumption plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention provides a flowchart of a household low-carbon electricity optimization method taking into account carbon credit incentives provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 here can be arranged and designed in various different configurations.

[0023] 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 claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] 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, further definition and explanation thereof is not required in subsequent drawings.

[0025] Example 1 like Figure 1 As shown, this embodiment provides a method for optimizing household low-carbon electricity consumption considering carbon credit incentives, including the following steps: Setting of carbon credit accounting benchmark S1: Collect and establish a carbon emission baseline for household electricity based on historical statistical data of household electricity consumption in the corresponding area and the carbon emission intensity of electricity in the corresponding area; That is, based on the historical data of household electricity consumption and combined with the regional electricity carbon intensity, the typical power curves and carbon emission benchmark values ​​of non-transferable loads (such as air conditioners and water heaters) and transferable loads (such as washing machines and rice cookers) in different time periods are determined to provide a reference standard for carbon emission reduction accounting; Shiftable load modeling S2: Model household shiftable loads through vectorization methods, construct feasible operation domains that take into account user living habits, quantify the impact of load operation time on user comfort, and establish a load flexibility optimization model; That is, an operation model of transferable loads is constructed to describe the flexibility of their start-up time and working duration. The model describes the workflow of the load through a vectorization method, and introduces a feasible operation domain and a user preferred operation domain to quantify the impact of load transfer on user comfort. The comfort loss caused by user living habits is modeled in a linear attenuation manner to ensure that the model fits the actual electricity consumption behavior. Air conditioning load modeling S3: Based on the indoor and outdoor temperature difference, air conditioning operation status, and equipment thermal resistance and thermal capacity characteristics, build an air conditioning load model and set comfort constraints; That is, the air conditioning load model is constructed through the indoor and outdoor temperature difference and the air conditioning operation status, and the room temperature changes are simulated using thermal resistance and thermal capacity parameters. At the same time, the Predicted Mean Vote (PMV) indicator is introduced to evaluate the room temperature comfort, and the PMV constraint is set within the range of -0.5 to 0.5 to ensure that the optimized air conditioning operation meets the user's comfort needs.

[0026] Water heater load modeling S4: Based on the user's hot water usage, the operating status of the water heater and the environmental conditions, a water heater water temperature change model is constructed to obtain a water heater load curve model, and a hot water demand comfort constraint is set; That is, the water temperature change is simulated by using the heat capacity and thermal resistance model of the water heater tank, taking into account the user's hot water demand and water use behavior, to ensure that the water heater can provide hot water of sufficient temperature within a specific time. By defining the hot water comfort constraint, the water temperature range is set to ensure that the optimized water heater operation is both energy-saving and meets user needs.

[0027] Low-carbon optimization of home energy management system S5: Based on the load flexibility optimization model, air conditioning load model and water heater load curve model, a low-carbon optimization model is constructed. The low-carbon optimization model aims to minimize the electricity purchase cost and comfort loss and maximize carbon emission reduction. Combined with the carbon inclusive incentive price, the optimized household electricity consumption plan is solved; That is, the above models are combined to build a low-carbon optimization model for the home energy management system, with the goal of minimizing the household electricity purchase cost and comfort loss while maximizing carbon emission reduction. The optimization model combines the grid electricity purchase price, carbon inclusive incentive price and load transfer strategy to achieve the low-carbon goal by adjusting the load operation time.

[0028] Step S1 specifically includes the following steps: S11: The collection of household electricity consumption data is the basis for optimizing scheduling and modeling carbon emission baselines. In order to fully reflect household electricity consumption behavior, it is necessary to collect sufficiently detailed historical electricity consumption data with a long span. These data include not only time-of-day electricity consumption, but also power curves of various electrical equipment. If the household is equipped with a sub-item electricity metering device, the electricity consumption data of specific electrical appliances can be directly obtained, including operating power and time characteristics; if there is no sub-item data, the operating mode of the appliance 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 must cover all four seasons of the year to reflect the seasonal changes in electricity consumption. For example, the peak electricity consumption in summer may be concentrated in the air-conditioning operation period, while the water heater or heating equipment load in winter is high, with significant seasonal characteristics. Therefore, the coverage and time granularity of the data directly determine the accuracy and reliability of subsequent analysis. Based on data collection, household electricity loads need to be further classified. According to the operating rules and adjustable characteristics of the load, it is divided into rigid load and transferable load. The operating time of rigid loads is directly related to the immediate needs of users, such as air conditioners, water heaters and other equipment, whose operating rules are relatively fixed and difficult to regulate; while transferable loads have flexible operating times and have certain regulation potential, such as washing machines, rice cookers and other equipment. Load classification can be completed through rule-based methods or clustering algorithms. For example, by analyzing the load curve using a clustering algorithm, the typical electricity consumption patterns of rigid loads and transferable loads can be extracted, laying the foundation for subsequent carbon emission modeling.

[0029] S12: Modeling of carbon emission baselines is an important tool for evaluating 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). These data need to distinguish between peak and trough periods to reflect the fluctuating characteristics of grid carbon emissions at different times. At the same time, for areas with a high penetration rate of distributed power sources, the carbon emission intensity needs to be corrected in combination with the output characteristics of distributed energy. For example, in areas where photovoltaic power generation accounts for a high proportion, the carbon emission intensity during photovoltaic high output periods will be significantly reduced. Based on the obtained carbon emission intensity data and household electricity consumption data, and according to the energy consumption characteristics of households, a reasonable carbon inclusive baseline scenario is selected to form a carbon emission baseline for household electricity consumption, which mainly includes two scenarios: Scenario 1 takes the average carbon emissions of household electricity consumption in the whole society as the baseline, and uses the advancement coefficient to ensure the additionality of emission reductions. It is suitable for households with small monthly electricity consumption. Scenario 2 takes the household's historical carbon emissions of electricity consumption in the same period as the baseline, and ensures the additionality of emission reductions by comparing with itself. It is suitable for households with large monthly electricity consumption.

[0030] The expression of carbon emission baseline scenario 1 is: ; (1) In the formula, It is the carbon emission baseline for scenario 1; It is the total electricity consumption of all households in the society; is the total number of households in the society; is the coefficient of advancement; It is the benchmark carbon intensity of household electricity consumption in the low-carbon electricity carbon credit methodology.

[0031] The expression of carbon emission baseline scenario 2 is: ; (2) In the formula, is the carbon emission baseline under scenario 2; It is the time period t The real-time carbon emission factor of the power grid at the time of The same period in history t The household's electricity consumption at that time.

[0032] This baseline provides a reference standard for the evaluation and optimization of low-carbon behavior, and its accuracy directly affects the effect of optimized scheduling and the scientific nature of the evaluation. Therefore, to ensure the reliability of the model, it is recommended to regularly revise the baseline model based on the dynamic electricity consumption data and the update of the carbon emission intensity of the power grid. The baseline can not only evaluate the carbon emission level of current household electricity consumption behavior, but also provide a clear direction for improvement for future low-carbon optimization strategies.

[0033] Step S2 specifically includes the following steps: S21. Establish a model of the load operation domain that can be translated: Among household electrical appliances, except for heat loads such as air conditioners and water heaters, most household appliances have the characteristics of shiftable loads. Users can freely choose the start time, but after starting, the electrical appliances need to run to the end according to the predetermined workflow. i , if its running duration is The power consumption curve of each working stage of the load can be expressed as Vector It is referred to as load operation curve vector Considering the user's living habits, electrical equipment must be operated within the feasible operating range. So it can be modeled as follows: ; (3) ; (4) In the formula, 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: ; (5) In the formula, is the power of the translatable load i at the first period step_start at the beginning of operation, is the power of the translatable load i at the last period step_end of the operation.

[0034] S21. Establishing a translational load comfort model: When users are unable to use transferable loads as they are used to, there is a loss of comfort, which can be defined as: ; (6) In the formula, Is the user in the time period t Start transferable load i Comfort level; is the user usage load i The preferred operating domain of , when the user starts the load within the preferred operating domain, the comfort level is 1; It is a translatable load i The feasible operation domain of When the user starts the load outside the feasible operating domain, the comfort level is 0; when the user starts the load within the feasible operating domain and outside the preferred operating domain, the comfort level decays linearly.

[0035] Step S3 specifically includes the following steps: S31. Air conditioning load curve modeling: When a household uses air conditioning equipment, the indoor temperature change can be modeled using the following formula: ; (7) In the formula, , 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; is the rated power of the air conditioner when it is running; , They are the equivalent thermal resistance and equivalent heat capacity of the air conditioner respectively.

[0036] Therefore, the air conditioning load curve can be modeled as: ; (8) In the formula, It is the time period t Air conditioning load power.

[0037] S32. Measuring the room temperature comfort of household users: The predicted mean vote (PMV) is selected to measure the room temperature comfort of household users, and the linear engineering simplified formula is used to calculate PMV: ; (9) In the formula, 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; It is the thermal resistance of clothing, which is determined by the way people wear clothing.

[0038] The recommended value of PMV in international standards is between plus and minus 0.5, so the room temperature comfort constraint is set as follows: ; (10) In the formula, It is a time for the family to gather together.

[0039] Step S4 specifically includes the following steps: S41. Load curve modeling of water heater: The following formula is used to model the temperature change process of the water stored in the inner tank of the electric water heater when the household uses it, and the dynamic change of the water temperature is simulated by the thermal physics model. Specifically, according to the user's hot water usage, the operating status of the water heater and the environmental conditions, the following mathematical model is established: ; (11) ; (12) In the formula, It is the water temperature in the water heater at the next moment when the user does not use hot water during period t. is the water temperature in the water heater during period t; It is the time period t The operating status of the water heater, Indicates the water heater is in the time period t In operation; is the rated power of the water heater when in operation; , They are the equivalent thermal resistance and equivalent heat capacity of the water heater; It is the time period t The amount of hot water used by the household at that time; is the volume of the water heater tank; It is the temperature of the cold water added to the water heater after the user uses water, and is set to the indoor temperature.

[0040] Thus, the load curve of the water heater can be modeled as: ; (13) In the formula, It is the time period t The household water heater load power.

[0041] S42. Setting constraints on hot water comfort: The water heater should ensure that there is enough hot water to meet a certain temperature when the household needs hot water. Therefore, this section defines the hot water comfort constraint as the water temperature of the water heater can be maintained within a certain range while considering the hot water demand: ; (14) In the formula, , They are the upper and lower limits of the water temperature of the water heater set by the user.

[0042] Step S5 specifically includes the following steps: S51. Determination of optimization objectives: In the optimization objective function, economic factors, user comfort factors and carbon emission reduction incentive factors are comprehensively considered.

[0043] The economic factor is related to the electricity purchase price during the period, aiming to minimize the user's electricity purchase cost; the comfort factor reflects the impact of load adjustment on user experience, ensuring that the optimization result 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: ; (15) S52. Constraints and optimization methods: Multiple constraints are set for the optimization model, including the operation requirements of household rigid loads and transferable loads, household comfort constraints (such as the PMV index of air conditioning), and other requirements.

[0044] By adopting a multi-objective optimization algorithm and iterative calculation, we can reduce the cost of electricity purchase while improving the user's low-carbon electricity benefits and ensure that the comfort requirements are strictly met. Finally, we generate an optimized scheduling plan for household electricity consumption. The final model is as follows: ; (16) S53. Calculation of carbon credit emission reductions: ; (17) In the formula, is the carbon intensity of electricity purchased from the grid during period t.

[0045] ; (18) In the formula, is the household rigid load power in period t, set as a fixed curve, It is the carbon emission baseline for household electricity consumption, and the corresponding scenario is selected and calculated based on the actual situation of the household; It is the time period t The carbon intensity of the electricity purchased from the grid by the household at that time, It is the time period t The household water heater load power, It is the time period t The air conditioning load power, For translatable load i In the period t of power.

[0046] The present invention also provides a household low-carbon electricity consumption optimization system considering carbon preferential incentives, 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 household low-carbon electricity consumption optimization method considering carbon preferential incentives as described above.

[0047] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for optimizing household low-carbon electricity consumption considering carbon credit incentives, characterized in that: The following steps are involved: Collect and establish a carbon emission baseline for household electricity based on historical statistical data of household electricity consumption in the corresponding area and the carbon emission intensity of electricity in the corresponding area; The household transferable load is modeled by vectorization method, a feasible operation domain considering the user's living habits is constructed, the impact of load operation time on user comfort is quantified, and a load flexibility optimization model is established; Based on the indoor and outdoor temperature difference, air conditioning operation status, and equipment thermal resistance and thermal capacity characteristics, an air conditioning load model is constructed and comfort constraints are set; According to the user's hot water usage, the operating status of the water heater and the environmental conditions, a water heater temperature change model is constructed to obtain the water heater load curve model, and the hot water demand comfort constraint is set; Based on the load flexibility optimization model, air conditioning load model and water heater load curve model, a 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. The carbon credit emission reduction is calculated according to the carbon emission baseline, and the optimized household electricity plan is solved in combination with the carbon credit incentive price.

2. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 1, characterized in that: The household electricity consumption history data includes time-sharing electricity consumption and power curves of various electrical equipment. According to the operating rules and adjustable characteristics of the load, the collected household electricity consumption history data is classified into rigid load and transferable load; The calculation process of the carbon emission baseline is as follows: Based on the actual situation of household electricity consumption, the carbon emission baseline is calculated according to Scenario 1 and Scenario 2. Scenario 1 takes the average carbon emission of household electricity consumption in the whole society as the baseline, and uses the advanced coefficient to ensure the additionality of emission reduction, which is suitable for households with small monthly electricity consumption; Scenario 2 takes the household's historical carbon emission of electricity consumption in the same period as the baseline, and ensures the additionality of emission reduction by comparing with itself, which is suitable for households with large monthly electricity consumption; In scenario 1, the carbon emission baseline is expressed as: In the formula, It is the carbon emission baseline for scenario 1; It is the total electricity consumption of all households in the society; is the total number of households in the society; is the coefficient of advancement; It is the benchmark carbon intensity of household electricity consumption in the low-carbon electricity carbon credit methodology; Under scenario 2, the expression of the carbon emission baseline is: In the formula, is the carbon emission baseline under Scenario 2; It is the time period t The real-time carbon emission factor of the power grid at that time; The same period in history t The household's electricity consumption at that time.

3. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 1, characterized in that: The expression of the load flexibility optimization model is: In the formula, For translatable load i In the period t The power, is the zero-one variable of the shiftable load starting characteristic, Represents a translatable load i In the period t start up, is the vector obtained by expanding and translating the load operation curve vector. Represents the first t item, It is the feasible operation domain of the electrical equipment; In the formula, For translatable load i The running duration, is the power of the translatable load i at the first period step_start at the beginning of operation, is the power of the translatable load i at the last period step_end of the operation.

4. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 1, characterized in that: The method quantifies the impact of load operation time on user comfort through a comfort loss index, and the expression of the comfort loss index is: In the formula, For users in the time period t Start transferable load i Comfort, Use load for users i The preferred operating domain of , when the user starts the load within the preferred operating domain, the comfort level is 1; For translatable load i The feasible operation domain of , when the user starts the load outside the feasible operating domain, the comfort level is 0.

5. The method for optimizing household low-carbon electricity consumption considering carbon credit incentives according to claim 1, characterized in that: The expression of the air conditioning load model is: In the formula, , Separate moments t Indoor and outdoor temperatures, For the period t The operating status of the air conditioner, Indicates the air conditioner is in the time period t In operation; is the rated power of the air conditioner when it is running; , They are the equivalent thermal resistance and equivalent heat capacity of the air conditioner. It is the time period t The air conditioning load power, is the time interval of each period.

6. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 5, characterized in that: The expression of the comfort constraint is: In the formula, It's time t PMV at the time, PMV is the selected prediction average index, is the skin temperature in a comfortable state; is the metabolic rate of the human body, is the thermal resistance of the clothing, It is a time for the family to gather together.

7. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 1, characterized in that: The expression of the water temperature variation model of the water heater is: In the formula, It is the water temperature in the water heater at the next moment when the user does not use hot water in time period t; It's time t The indoor temperature is the water temperature in the water heater during period t; It is the time period t The operating status of the water heater, Indicates the water heater is in the time period t In operation; is the rated power of the water heater when in operation; , They are the equivalent thermal resistance and equivalent heat capacity of the water heater respectively; It is the time period t The amount of hot water used by the household at that time; is the volume of the water heater tank; It is the temperature of the cold water added to the water heater after the user uses water. is the time interval of each period; The expression of the water heater load curve model is: In the formula, It is the time period t The household water heater load power.

8. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 7, characterized in that: The expression of the hot water demand comfort constraint is: In the formula, , They are the upper and lower limits of the water heater temperature set by the user. It is a time for the family to gather together.

9. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 1, characterized in that: The expression of the low-carbon optimization model is: In the formula, is the cost weight factor, For the period t The power purchase price from the grid at that time, The time period when energy optimization is not performed t The original household load power at For the period t The total household load power, is the incentive price per unit of carbon credit emission reduction, For carbon credit emission reduction, is the comfort weight factor, is the amount of transferable load in the household, is the zero-one variable of the shiftable load starting characteristic, For users in the time period t Start transferable load i Comfort level; The low-carbon optimization model uses the load flexibility optimization model, the air conditioning load model and the water heater load curve model as constraints; The calculation expression of carbon credit emission reduction is: In the formula, It is the carbon emission baseline for household electricity consumption, and the corresponding scenario is selected and calculated based on the actual situation of the household; It is the time period t The carbon intensity of the electricity purchased from the grid by the household at that time, The household rigid load power in period t, It is the time period t The household water heater load power, It is the time period t The air conditioning load power, For translatable load i In the period t of power.

10. A household low-carbon electricity optimization system considering carbon universal incentives, characterized in that: 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 described in claims 1 to 9.

Images