A Method and System for Optimizing Low-Carbon Electricity Consumption in Households Considering Carbon Inclusive Incentives

Abstract

The present invention relates to a method and system for optimizing low-carbon electricity consumption in households considering carbon inclusive incentives, including establishing a carbon emission baseline for household electricity consumption based on historical household electricity consumption statistical data and regional electricity carbon emission intensity; modeling the transferable loads in the household, quantifying the impact of load operation time on user comfort, and establishing a load flexibility optimization model; constructing an air-conditioning load model based on the indoor-outdoor temperature difference, the operating state of the air conditioner, and the thermal resistance of the equipment, and setting comfort constraints; constructing a water heater load curve model according to the user's hot water usage, the operating state of the water heater, and environmental conditions; comprehensively combining the above models to construct a low-carbon optimization model aiming to minimize the electricity purchase cost and comfort loss and maximize the carbon emission reduction, and solving it in combination with the carbon inclusive incentive price. Compared with the prior art, the present invention realizes the scientific quantification of the low-carbon electricity consumption behavior of household users, which is beneficial to achieving the low-carbon emission reduction goal.

Description

Technical Field

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

[0002] At present, optimizing the electricity consumption behavior of household users has become an effective means to promote energy conservation and emissions 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 participate in low-carbon life. This mechanism can quantify personal green behaviors, such as energy-saving electricity consumption, green travel, and garbage classification, into greenhouse gas emission reduction amounts, and improve public participation through rewards. However, the existing carbon inclusive mechanism mainly focuses on simple estimation of macro behaviors, and there are still deficiencies in modeling household electricity loads and low-carbon assessment in complex scenarios.

[0003] Currently, household electricity loads mainly consist of rigid loads (such as air conditioners and water heaters) and shiftable loads (such as washing machines and rice cookers). The living habits of household users and the operating rules of equipment result in highly dynamic and complex loads. 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, the interaction between household electricity and the power grid is more frequent, and the potential for users to optimize their electricity consumption behavior is further enhanced. However, there is currently a lack of a systematic modeling method that can combine user load characteristics, carbon inclusive incentives, and user living habits, and it is difficult for existing research to achieve accurate assessment and scientific optimization of household low-carbon electricity consumption behavior.

[0005] Therefore, there is an urgent need for a method for low-carbon assessment and optimization of household electricity consumption that considers user load characteristics, low-carbon incentives, and comfort, so as to scientifically quantify the low-carbon behaviors of household users. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for optimizing low-carbon electricity consumption in households considering carbon inclusive incentives, in order to overcome the defect of the existing technology that lacks a systematic modeling method that can combine user load characteristics, carbon inclusive incentives, and user living habits.

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

[0008] A method for optimizing low-carbon electricity consumption in households considering carbon inclusive incentives includes the following steps:

[0009] Collect and, based on the historical statistical data of household electricity consumption in the corresponding area, combine with the electricity carbon emission intensity in the corresponding area to establish a carbon emission baseline for household electricity consumption;

[0010] Model the household transferable load through a vectorization method, construct a feasible operating region considering the user's living habits, quantify the impact of load operation time on user comfort, and establish a load flexibility optimization model;

[0011] Based on the indoor-outdoor temperature difference, the operating state of the air conditioner, and the thermal resistance and heat capacity characteristics of the equipment, construct an air conditioner load model and set comfort constraints;

[0012] According to the user's hot water usage, the operating state of the water heater, and the environmental conditions, construct a water temperature change model of the water heater, obtain a load curve model of the water heater, and set comfort constraints for hot water demand;

[0013] Based on the load flexibility optimization model, the air conditioner load model, and the water heater load curve model, construct a low-carbon optimization model. The low-carbon optimization model aims to minimize the electricity purchase cost and comfort loss and maximize the carbon emission reduction, and combines the carbon inclusive incentive price to solve for the optimized household electricity consumption plan.

[0014] Furthermore, the household electricity historical data includes the time-of-use electricity consumption and the power curves of various electrical equipment. According to the operating rules and adjustable characteristics of the load, classify the collected household electricity historical data into rigid loads and transferable loads;

[0015] The specific calculation process of the carbon emission baseline is as follows:

[0016] Based on the actual situation of residential household electricity consumption, calculate the carbon emission baseline using two scenarios. In Scenario 1, the average carbon emission of household electricity consumption in the whole society is used as the baseline, and the advanced coefficient is used to ensure the additionality of the emission reduction, which is suitable for households with relatively small monthly electricity consumption; in Scenario 2, the carbon emission of the household's electricity consumption in the same historical period is used as the baseline, and the additionality of the emission reduction is ensured by comparing with itself, which is suitable for households with relatively large monthly electricity consumption.

[0017] The expression of the carbon emission baseline in Scenario 1 is:

[0018]

[0019] In the formula, is the carbon emission baseline in Scenario 1; is the total electricity consumption of all residential households in the whole society; is the total number of residential households in the whole society; is the advanced coefficient; is the baseline carbon intensity of household electricity consumption in the carbon inclusive methodology for low-carbon electricity.

[0020] The expression of the carbon emission baseline in Scenario 2 is:

[0021]

[0022] Wherein, is the carbon emission baseline under Scenario 2; 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 electricity consumption of this household.

[0023] Furthermore, the expression of the load flexibility optimization model is:

[0024]

[0025]

[0026] Wherein, is the shiftable load i at the time period t power, is the binary variable of the start-up characteristic of the shiftable load, indicates the shiftable load i at the time period t start, is the load operation curve vector vector obtained by expansion and translation, represents the first t item in the vector, is the feasible operation region of the electrical equipment;

[0027]

[0028] Wherein, is the shiftable load i operating duration, is the power of the shiftable load i at the first time period step_start when starting to operate, is the power of the shiftable load i at the last time period step_end when operating.

[0029] Furthermore, the method quantifies the impact of the load operation time on the user comfort through the comfort loss index, and the expression of the comfort loss index is:

[0030]

[0031] Wherein, is the comfort of the user when starting the shiftable load t at the time period i , is the preferred operation region for the user to use the load i , which can be expanded and written in interval form , when the user starts the load within the preferred operation region, the comfort is 1; For the translatable load i of the feasible operating region, which can be expanded and written in interval form , when the user starts the load outside the feasible operating region, the comfort level is 0.

[0032] Furthermore, the expression of the air-conditioning load model is as follows:

[0033]

[0034]

[0035] In the formula, , are the indoor and outdoor temperatures at time t respectively, is the operating state of the air conditioner during time period t , indicating that the air conditioner is in the operating state during time period t ; is the rated power of the air conditioner during operation; , are the equivalent thermal resistance and equivalent heat capacity of the air conditioner respectively, is the air-conditioning load power during time period t , is the time interval for each time period.

[0036] Furthermore, the expression of the comfort constraint is as follows:

[0037]

[0038]

[0039] In the formula, is the PMV at time t , PMV is the selected predicted mean vote, is the skin temperature in the comfortable state; is the human metabolic rate, is the clothing thermal resistance, is the set of times when there are people in the family.

[0040] Furthermore, the expression of the water temperature change model of the water heater is as follows:

[0041]

[0042]

[0043] In the formula, is the water temperature in the water heater at the next moment when it is considered that the user does not use hot water during time period t, is the timet is the indoor temperature, while is the water temperature in the water heater during period t; is the period t is the operating state of the water heater, indicating that the water heater is in the operating state during period t ; is the rated power when the water heater is operating; 、 are the equivalent thermal resistance and equivalent heat capacity of the water heater respectively; is the period t when the household's hot water consumption is; is the volume of the water heater's inner tank; is the cold water temperature replenished into the water heater after the user uses water, is the time interval for each period;

[0044] The expression of the water heater load curve model is:

[0045]

[0046] In the formula, is the period t of the household water heater load power.

[0047] Furthermore, the expression of the hot water demand comfort constraint is:

[0048]

[0049] In the formula, 、 are the upper and lower limits of the water heater water temperature set by the user respectively, is the set of times when there are people in the household.

[0050] Furthermore, the expression of the low-carbon optimization model is:

[0051]

[0052] In the formula, is the cost weight factor, is the grid power purchase price during period t ; is the original household load power during period t before energy optimization, is the total household load power during period t ; is the incentive price per unit of carbon inclusive reduction, is the carbon inclusive reduction, is the comfort weight factor, is the number of transferable loads in the household, is a binary variable for the start-up characteristic of shiftable load, is the user's comfort level when starting shiftable load during period t starting shiftable load i ;

[0053] The low-carbon optimization model is constrained by a load flexibility optimization model, an air-conditioning load model, and a water heater load curve model;

[0054] The calculation expression of the carbon inclusive emission reduction is as follows:

[0055]

[0056]

[0057] wherein, is the carbon emission baseline of household electricity consumption, which is calculated by selecting the corresponding scenario according to the actual situation of the household; is the carbon intensity of the electric energy purchased by the household from the power grid during period t ; is the rigid load power of the household during period t, is the water heater load power of the household during period t ; is the air-conditioning load power of the household during period t ; is the shiftable load i during period t power.

[0058] The present invention also provides a household low-carbon electricity consumption optimization system considering carbon inclusive incentive, 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 method described above.

[0059] Compared with the prior art, the present invention has the following advantages:

[0060] (1) The present invention models the household shiftable load respectively to establish a load flexibility optimization model; constructs an air-conditioning load model according to the indoor and outdoor temperature difference, the operating state of the air conditioner, and the thermal resistance and heat capacity characteristics of the equipment; constructs a water heater load curve model according to the user's hot water usage and the operating state of the water heater, etc.; thus comprehensively constructs a low-carbon optimization model with the goal of minimizing the electricity purchase cost and comfort loss and maximizing the carbon emission reduction; the low-carbon optimization model combines the power grid electricity purchase price, the carbon inclusive incentive price, and the load transfer strategy to achieve the low-carbon goal by adjusting the load operation time;

[0061] After the present invention realizes the comprehensive user load characteristics, low-carbon incentives, and comfort, it quantifies the low-carbon behaviors of household users in a scientific manner, provides technical support for the promotion of the carbon inclusive mechanism and the formulation of low-carbon policies, and is conducive to achieving energy conservation and emission reduction in household electricity consumption.

[0062] (2) The load flexibility optimization model provided by the present invention realizes the quantification of 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, which can ensure that the optimized air-conditioning operates at the balance between energy conservation and comfort; the water heater load curve model combines the comfort of hot water demand and optimizes the operation mode of the water heater to ensure that both the user's hot water demand is met and energy is efficiently utilized; the overall solution takes into account the user's living habits and comfort to obtain the optimal household electricity consumption plan. Description of the Drawings

[0063] Figure 1 It is a schematic flow chart of a household low-carbon electricity consumption optimization method considering carbon inclusive incentives provided in an embodiment of the present invention. Detailed Embodiments

[0064] 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, 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.

[0065] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present invention claimed, 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.

[0066] 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.

[0067] Embodiment 1

[0068] As Figure 1 shown, this embodiment provides a household low-carbon electricity consumption optimization method considering carbon inclusive incentives, including the following steps:

[0069] Setting of the carbon inclusive accounting benchmark S1: Collect and establish the carbon emission baseline of household electricity consumption based on the historical statistical data of household electricity consumption in the corresponding region and in combination with the electricity carbon emission intensity of the corresponding region;

[0070] 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-shiftable loads (such as air conditioners and water heaters) and shiftable loads (such as washing machines and rice cookers) in different time periods are determined to provide a comparison standard for carbon emission reduction accounting;

[0071] Modeling of shiftable loads S2: Model the household shiftable loads through a vectorization method, construct a feasible operation domain considering the user's living habits, quantify the impact of load operation time on user comfort, and establish a load flexibility optimization model;

[0072] That is, construct an operation model for shiftable loads to describe the flexibility of their start-up time and working duration; the model describes the working process 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 the user's living habits is modeled in a linear decay manner to ensure that the model fits the actual electricity consumption behavior;

[0073] Modeling of air conditioner loads S3: Based on the indoor-outdoor temperature difference, the operation state of the air conditioner, and the thermal resistance and heat capacity characteristics of the equipment, construct an air conditioner load model and set comfort constraints;

[0074] That is, construct an air conditioner load model through the indoor-outdoor temperature difference and the operation state of the air conditioner, and use thermal resistance and heat capacity parameters to simulate the change of room temperature. At the same time, introduce the Predicted Mean Vote (PMV) index to evaluate the room temperature comfort, and set the constraint that PMV is within the range of -0.5 to 0.5 to ensure that the optimized operation of the air conditioner meets the comfort requirements of users.

[0075] Modeling of water heater loads S4: According to the user's hot water usage, the operation state of the water heater, and the environmental conditions, construct a water temperature change model of the water heater, obtain a water heater load curve model, and set hot water demand comfort constraints;

[0076] That is, use the heat capacity and thermal resistance model of the water heater inner tank to simulate the water temperature change, consider the user's hot water demand and water usage behavior, and ensure that the water heater can provide hot water at a sufficient temperature within a specific time. By defining hot water comfort constraints, set the water temperature range to ensure that the optimized operation of the water heater is both energy-saving and meets the user's needs.

[0077] Low-carbon optimization of the household energy management system S5: Based on the load flexibility optimization model, the air conditioner load model, and the water heater load curve model, construct a low-carbon optimization model. This low-carbon optimization model aims to minimize the electricity purchase cost and comfort loss and maximize the carbon emission reduction, and combined with the carbon inclusive incentive price, solve to obtain an optimized household electricity consumption plan;

[0078] That is, a low-carbon optimization model of the household energy management system is constructed by integrating the above models, with the goal of minimizing the household electricity purchase cost and comfort loss while maximizing the carbon emission reduction. The optimization model combines the grid electricity purchase price, the carbon inclusive incentive price, and the load transfer strategy to achieve the low-carbon goal by adjusting the load operation time.

[0079] Step S1 specifically includes the following steps:

[0080] S11: The collection of household electricity data is the basic work for optimal scheduling and carbon emission baseline modeling. To comprehensively reflect household electricity consumption behavior, it is necessary to collect historical electricity consumption data that is sufficiently detailed and has a long time span. These data not only include the 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, including the operating power and time characteristics, can be directly obtained; 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 should 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 operation period, while the load of water heaters or heating equipment is higher in winter, with significant seasonal characteristics. 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 loads and transferable loads. The operating time of rigid loads is directly related to the immediate needs of users, such as equipment like air conditioners and water heaters, and their operating rules are relatively fixed and difficult to control; while transferable loads have flexible operating times and certain control potential, such as equipment like washing machines and rice cookers. 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 loads and transferable loads can be extracted, laying a foundation for subsequent carbon emission modeling.

[0081] S12: The modeling of the carbon emission baseline is an important tool for evaluating household low-carbon behavior. 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). These data need to distinguish between peak and off-peak periods to reflect the fluctuating characteristics of grid carbon emissions at different times. At the same time, for regions with a high penetration rate of distributed power sources, the carbon emission intensity also needs to be corrected in combination with the output characteristics of distributed energy. For example, in regions with a high proportion of photovoltaic power generation, the carbon emission intensity during high photovoltaic output periods will be significantly reduced. Based on the obtained carbon emission intensity data and residential household electricity consumption data, and according to the energy consumption characteristics of residential households, a reasonable carbon inclusive baseline scenario is selected to form the carbon emission baseline for household electricity consumption, mainly including two scenarios:

[0082] Scenario 1 uses the average carbon emissions 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 relatively small monthly electricity consumption; Scenario 2 uses the carbon emissions of household electricity consumption in the same period of history as the baseline, and ensures the additionality of emission reduction by comparing with itself, which is suitable for households with relatively large monthly electricity consumption.

[0083] The expression of the carbon emission baseline in Scenario 1 is:

[0084] ; (1)

[0085] In the formula, is the carbon emission baseline in Scenario 1; is the total electricity consumption of all resident households in the whole society; is the total number of resident households in the whole society; is the advanced coefficient; is the baseline carbon intensity of household electricity consumption in the low-carbon electricity consumption carbon inclusion methodology.

[0086] The expression of the carbon emission baseline in Scenario 2 is:

[0087] ; (2)

[0088] In the formula, is the carbon emission baseline in Scenario 2; 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 this household.

[0089] This baseline provides a control standard for the evaluation and optimization of low-carbon behaviors, and its accuracy directly affects the effect of optimal dispatching and the scientific nature of evaluation. Therefore, to ensure the reliability of the model, it is recommended to regularly correct the baseline model based on the update of dynamic electricity consumption data and the carbon emission intensity of the power grid. The baseline can not only evaluate the carbon emission level of the current household electricity consumption behavior, but also provide a clear improvement direction for future low-carbon optimization strategies.

[0090] Step S2 specifically includes the following steps:

[0091] S21. Establish a model of the operable load shifting operation domain:

[0092] Among household electrical appliances, except for thermal loads such as air conditioners and water heaters, most household electrical appliances have the characteristics of operable load shifting. Users can freely choose the starting time, but after starting, the electrical appliances need to run to the end according to the predetermined work process. For the operable load i , if its operation duration is , the electricity consumption curve of each working stage of the load can be represented by a dimension of vector denoted as the load operation curve vector for short Considering the user's living habits, the electrical equipment must operate within the feasible operation domain and can thus be modeled in the following form:

[0093] ; (3)

[0094] ; (4)

[0095] In the formula, is the shiftable load i at time period t ; is a binary variable representing the starting characteristic of the shiftable load, indicating that the shiftable load i starts at time period t ; represents the t th item in the vector; is the vector obtained by expanding and shifting the load operation curve vector and has the following form:

[0096] ; (5)

[0097] In the formula, is the power of the shiftable load i at the first time period step_start when it starts to operate, is the power of the shiftable load i at the last time period step_end when it operates.

[0098] S21. Establish the comfort model of the shiftable load:

[0099] When the user cannot use the shiftable load according to their habits, there will be a comfort loss, which can be defined as:

[0100] ; (6)

[0101] In the formula, is the comfort of the user when starting the shiftable load t at time period i ; is the preferred operation domain of the user when using the load i and can be expanded and written in interval form . When the user starts the load within the preferred operation domain, their comfort is 1; is the feasible operation domain of the shiftable load i and can be expanded and written in interval form When the user starts the load outside the feasible operation range, the comfort level is 0; when the user starts the load within the feasible operation range but outside the preferred operation range, the comfort level decays linearly.

[0102] Step S3 specifically includes the following steps:

[0103] S31. Air-conditioning load curve modeling:

[0104] When a household uses air-conditioning equipment, the indoor temperature change can be modeled using the following formula:

[0105] ; (7)

[0106] In the formula, 、 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; 、 are the equivalent thermal resistance and equivalent heat capacity of the air conditioner respectively.

[0107] Thus, the air-conditioning load curve can be modeled as:

[0108] ; (8)

[0109] In the formula, is the air-conditioning load power during the time period t .

[0110] S32. Measuring the room temperature comfort level of household users:

[0111] Select the Predicted Mean Vote (PMV) to measure the room temperature comfort level of household users, and use a linear engineering simplified formula to calculate PMV:

[0112] ; (9)

[0113] In the formula, is the PMV at time t ; is the skin temperature in the comfort 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.

[0114] The recommended value of PMV by international standards is between plus and minus 0.5. Therefore, the room temperature comfort level constraint is set in this section as:

[0115] ; (10)

[0116] Wherein, is the set of times when there are people in the family.

[0117] Step S4 specifically includes the following steps:

[0118] S41. Modeling the load curve of the water heater:

[0119] The following formula is used to model the change process of the water temperature in the inner tank of the electric water heater when the family uses it, and the dynamic change of the water temperature is simulated through a thermophysical model. Specifically, according to the user's hot water usage, the operating state of the water heater, and the environmental conditions, the following mathematical model is established:

[0120] ; (11)

[0121] ; (12)

[0122] Wherein, is the water temperature in the water heater at the next moment when it is considered that the user does not use hot water in time period t, is the water temperature in the water heater in time period t; is the time period t the operating state of the water heater, indicates that the water heater is in the operating state in time period t ; is the rated power of the water heater when it is running; , are the equivalent thermal resistance and equivalent heat capacity of the water heater respectively; is the hot water consumption of the family at time period t ; is the volume of the inner tank of the water heater; is the cold water temperature supplemented into the water heater after the user uses water, which is set as the indoor temperature.

[0123] Thus, the load curve of the water heater can be modeled as:

[0124] ; (13)

[0125] Wherein, is the load power of the household water heater at time period t .

[0126] S42. Set constraints on hot water comfort:

[0127] The water heater should ensure that there is enough hot water at a certain temperature for household users when there is a hot water demand. Therefore, in this section, the hot water comfort constraint is defined as that the water temperature of the water heater can be maintained within a certain range considering the hot water demand:

[0128] ; (14)

[0129] In the formula, and are the upper and lower limits of the water temperature of the water heater set by the user respectively.

[0130] Step S5 specifically includes the following steps:

[0131] S51, Determination of the optimization objective:

[0132] In the optimization objective function, the economic factor, user comfort factor, and carbon emission reduction incentive factor are comprehensively considered.

[0133] The economic factor is related to the time-of-use electricity purchase price, aiming to minimize the user's electricity purchase cost; the comfort factor reflects the impact of load adjustment on the user experience, ensuring 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:

[0134] ; (15)

[0135] S52, Constraint conditions and optimization methods:

[0136] Multiple constraints are set for the optimization model, including the operating requirements of household rigid loads and shiftable loads, household comfort constraints (such as the PMV index of air conditioners), etc.

[0137] A multi-objective optimization algorithm is adopted. Through iterative calculation, while reducing the electricity purchase cost, the user's low-carbon electricity consumption benefits are improved, and the comfort requirements are strictly met. Finally, an optimized scheduling plan for household electricity consumption is generated. The final model is as follows:

[0138] ; (16)

[0139] S53, Calculation of carbon inclusive emission reduction amount:

[0140] ; (17)

[0141] In the formula, is the carbon intensity of the electric energy purchased from the power grid at time t.

[0142] ; (18)

[0143] In the formula, is the household rigid load power at time period t, set as a fixed curve, is the carbon emission baseline for household electricity consumption, calculated by selecting the corresponding scenario according to the actual situation of the household; is the time period t when the carbon intensity of the electric energy purchased by the household from the power grid, is the time period t of the household water heater load power, is the time period t of the air conditioner load power, is the shiftable load i at the time period t power.

[0144] The present invention also provides a household low-carbon electricity consumption optimization system considering carbon inclusive 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 above-mentioned household low-carbon electricity consumption optimization method considering carbon inclusive incentives.

[0145] 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 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 consumption plan is solved in combination with the carbon credit incentive price. 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.

2. 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.

3. A household low-carbon electricity optimization method considering carbon credit 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.

4. A household low-carbon electricity optimization method considering carbon universal 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.

5. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 4, 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.

6. 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.

7. A household low-carbon electricity optimization method considering carbon universal incentives according to claim 6, 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.

8. The method for optimizing household low-carbon electricity consumption considering carbon credit 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.

9. 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 according to claims 1 to 8.

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