A community energy management model construction method considering a carbon trading mechanism

Abstract

This invention relates to the field of energy dispatching technology, and more particularly to a method for constructing a community energy management model that takes into account carbon trading mechanisms. The method includes: obtaining basic parameters; constructing a household load classification model; constructing a household electricity cost model; constructing a community energy supply microgrid model; optimizing carbon emission coefficients; constructing a tiered carbon trading model; constructing energy supply microgrid constraints; optimizing the electricity purchase cost, equipment operation and maintenance cost, demand response cost, and community carbon trading cost using the energy supply microgrid objective function; and optimizing the household electricity purchase cost, household carbon trading cost, and subsidies obtained by households participating in demand response using the household electricity consumption objective function. This invention addresses the shortcomings of existing technologies that do not involve carbon trading and calculation mechanisms, relying solely on reducing electricity purchases to reduce carbon emissions, with limited exploration in carbon reduction, and failing to consider reasonable constraints for energy storage and the charging and discharging processes of EVs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy scheduling, in particular to a community energy management model construction method considering a carbon trading mechanism. BACKGROUND

[0002] A community energy management system (CEMS) can meet the convenient and personalized energy use needs of families and realize more scientific and reasonable energy use behaviors through the optimization and direct control of community and family loads. Family demand response behaviors based on the CEMS make household loads controllable resources. The CEMS is divided into an energy supply layer and a family layer. After taking into account the carbon trading mechanism, SOC models are established for the charging and discharging models of energy storage and electric vehicles according to the characteristics of different types of families, and the family loads are connected to form the entire system. Household loads have a high energy consumption proportion and are highly controllable. The use of such resources and the full optimization and scheduling can effectively reduce the peak-valley difference of electrical loads, save household electricity costs and improve the stability of the power grid.

[0003] The prior art document 1 (CN104216358 B) provides an intelligent community low-carbon energy management system based on two-level energy management. The terminal device layer is responsible for collecting the field operation data of each professional subsystem and uploading the data to the monitoring system of the upper layer. The monitoring layer mainly completes the collection of the collected data, operation state monitoring and control, and responds to the control strategy of the upper energy management system to complete the control of the equipment. The building energy management layer completes the analysis and application of the energy use data of the equipment and community public facilities in the building, and realizes the energy optimization function of the building from the perspective of the entire building. The community energy management layer completes the energy use analysis and energy optimization of the equipment in the community. The management system only provides the framework of system construction, does not design the models and scheduling methods in the system, does not classify and model the user electricity load, does not involve the new energy generation system, does not optimize and schedule the energy use, and the technical method does not involve the carbon trading and calculation mechanism. It only reduces carbon emissions by simply reducing electricity purchases, has less exploration in carbon emission reduction, and does not consider reasonable constraint conditions for the charging and discharging processes of energy storage and EVs. SUMMARY

[0004] In view of the deficiencies of the existing algorithm, the technical solution adopted by the present application is: a community energy management model construction method considering a carbon trading mechanism, comprising:

[0005] Step 1: Obtain basic parameters and classify family loads based on family basic load data.

[0006] Step 2: Construct a family load classification model including basic load, reducible load, electric vehicle load and transferable load.

[0007] Further, the formula of the basic load is:

[0008]

[0009] In the formula, Pit(t) represents the power of the ith basic load device of the family at time t; Sti(t) represents the running state of the ith basic load device at time t;

[0010] The cuttable load model is as follows:

[0011]

[0012] In the formula, Pj(t) represents the running power of the jth load at time t; T ac Tt represents the temperature of the device at time t; T p,hl , T p,ht are the upper and lower limits of the required temperature of the device, respectively; Kj represents the refrigeration coefficient of the cuttable load;

[0013] The electric vehicle load model is as follows:

[0014]

[0015] In the formula, Pmax represents the rated power of the transferable load, is a 0-1 variable, which represents the state of EV charging and discharging, respectively;

[0016] The transferable load model is as follows:

[0017]

[0018] In the formula, Pmax represents the rated power of the transferable load; Stk represents the working state of the transferable load k;

[0019] Step three, build a household electricity cost model;

[0020] Further, the formula of the electricity cost model is as follows:

[0021]

[0022] In the formula, Cn(t) represents the electricity cost of the nth family at time t.

[0023] Step four, build a community energy microgrid model including the electricity purchase cost of purchasing electricity from the power grid and the equipment operation and maintenance cost;

[0024] Further, the formula of the electricity purchase cost of purchasing electricity from the power grid is:

[0025]

[0026] wherein, represents the price of purchasing electricity from the grid at time t, represents the amount of electricity purchased from the grid at time t;

[0027] The equipment operation and maintenance cost formula is:

[0028]

[0029] wherein, c pv , c wt , c est , c EV respectively represent the unit output operation and maintenance cost of distributed photovoltaic, distributed wind power, energy storage equipment and EV charging and discharging; respectively represent the wind power and photovoltaic output; respectively represent the discharging power and charging power of the energy storage equipment; respectively represent the discharging power and charging power of the EV;

[0030] Step five, construct the optimized carbon emission coefficient including demand response cost, community carbon trading cost and carbon emission coefficient;

[0031] Further, the formula of the demand response cost is:

[0032]

[0033] wherein, a, b respectively represent the demand response incentive coefficients of the reducible load and the transferable load, C dr represents the demand response incentive cost, respectively represent the amount of reducible and transferable load of the nth household participating in scheduling at time t; c dr represents the incentive unit price of the demand response of the nth household;

[0034] The formula of the community carbon trading cost is:

[0035]

[0036] wherein, c c represents the basic price of carbon emission right transaction, E u represents the total amount of carbon emission of the energy supply microgrid, E s represents the total carbon quota of the community;

[0037] The formula of the carbon emission coefficient is:

[0038]

[0039] EF OM is the marginal emission factor of operation for the power plant cluster, EF BM is the marginal emission factor of construction.

[0040] Step six, build the ladder carbon trading model;

[0041] Further, the formula of the ladder carbon trading model is:

[0042]

[0043] wherein, respectively represent the basic load, the reducible load and the transferable load of the nth household at the t period, EC s is the carbon emission factor, is the carbon trading cost of the household.

[0044] Step seven, build the power supply microgrid constraint conditions including the power balance constraint, the energy storage unit constraint, the electric vehicle SOC, the EV working state and the community power purchase constraint;

[0045] Further, the power balance constraint includes:

[0046]

[0047] wherein, represents the total load of the nth household at the t period, represents the community photovoltaic output at the t period, represents the community wind power output at the t period, represents the community energy storage unit discharge at the t period, represents the community energy storage unit charge at the t period;

[0048] The energy storage unit constraint includes:

[0049]

[0050] wherein, η ST,ch and η ST,dis respectively represent the charge and discharge efficiency of the energy storage unit, and correspond to the charge and discharge power at the t period, represents the energy storage unit storage at the t period, W ST is the total capacity of the energy storage unit, ε represents the self-discharge rate of the energy storage unit, SOC min , SOC max represent the upper and lower limits of the storage state of the storage device, and respectively are the upper limits of the charge and discharge power thereof, and represent the charge and discharge state coefficients of the energy storage unit.

[0051] The EV SOC constraints include:

[0052]

[0053] where η EV,ch and η EV,dis are the EV charging and discharging efficiencies, and are the charging and discharging powers at time t, is the EV storage power at time t, W EV is the total capacity of the EV, and ε is the EV self-discharge rate, SOC min , SOC max are the upper and lower limits of the EV SOC state, and are the upper limits of the charging and discharging powers, and are the EV charging and discharging state coefficients;

[0054] The EV operating states include: EV charging and discharging constraints, EV discharging state constraints, and EV grid-connection state constraints.

[0055] The EV charging and discharging constraints are given by:

[0056]

[0057] where x is a 0-1 variable, indicating the EV charging state, t EV,in , t EV,out are the start and end times of the EV grid-connection;

[0058] The EV discharging state constraints are given by:

[0059]

[0060] where x is a 0-1 variable, indicating the EV discharging state, t EV,in , t EV,out are the start and end times of the EV grid-connection;

[0061] The EV grid-connection state constraints are given by:

[0062]

[0063] where x is a 0-1 variable, indicating the EV grid-connection state, t EV,in , t EV,out are the start and end times of the EV grid-connection;

[0064] The EV working state constraints are as follows:

[0065]

[0066] The community electricity purchase constraints include:

[0067]

[0068] In the formula, p buy,max represents the maximum community electricity purchase amount.

[0069] Step eight, the electricity purchase cost, equipment operation and maintenance cost, demand response cost and community carbon trading cost of purchasing electricity from the power grid are optimized by using the energy supply microgrid target function;

[0070] Further, the formula of the energy supply microgrid target function is as follows:

[0071]

[0072] In the formula, C buy represents the electricity purchase cost of purchasing electricity from the power grid, C eom represents the equipment operation and maintenance cost, C dr represents the demand response cost, represents the community carbon trading cost.

[0073] Step nine, the electricity purchase cost of the family, the family carbon trading cost and the subsidy obtained by the family participating in the demand response are optimized by using the family electricity consumption target function.

[0074] Further, the formula of the family electricity consumption target function is as follows:

[0075]

[0076] In the formula, C use represents the electricity purchase cost of the family, represents the family carbon trading cost, and R dr represents the subsidy obtained by the family participating in the demand response.

[0077] The beneficial effects of the present application are:

[0078] The demand response behavior of the family based on the CEMS makes the household load a controllable resource, and after the carbon trading mechanism is taken into account, the SOC model is established for the charging and discharging model of the energy storage and electric vehicle, and the household load is connected to the whole system; the household load resource is utilized, and is fully optimized and dispatched, so that the community electricity carbon emission is effectively reduced, the household electricity cost is saved, and the community electricity economy is improved.

[0079] At present, the carbon emission factor is calculated uniformly in each province, and is not refined to a single community, and the carbon emission factor obtained by the present application is more accurate in the community carbon emission calculation. Attached Figure Description

[0080] Figure 1 This is a flowchart of the community energy management model construction method that takes into account the carbon trading mechanism of the present invention;

[0081] Figure 2 This invention contributes to the community landscape.

[0082] Figure 3 This is the optimized electric vehicle load of the present invention;

[0083] Figure 4 This invention compares the community carbon emissions.

[0084] Figure 5 This invention compares the amount of electricity purchased through community purchasing.

[0085] Figure 6 This invention provides a comparison of community electricity purchase costs. Detailed Implementation

[0086] The present invention will be further described below with reference to the accompanying drawings and embodiments. The drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0087] like Figure 1 As shown, a method for constructing a community energy management model that takes into account carbon trading mechanisms includes the following steps:

[0088] Step 1: Obtain basic parameters and classify household loads based on basic household load data;

[0089] Basic parameters include: household base load data, wind, solar and energy storage equipment, and EV travel data; household base load data includes: transferable load response and load reduction response; wind, solar and energy storage equipment data includes: rated power, capacity and operation and maintenance costs; EV travel data includes: EV grid connection time, travel mileage, and charging and discharging power.

[0090] Step 2: Construct a household load classification model, which includes: base load, reduceable load, electric vehicle load, and transferable load;

[0091] The basic load model is as follows:

[0092]

[0093] In the formula, This represents the power of the i-th basic load device in the household during time period t; This indicates the operating status of the i-th basic load device during time period t;

[0094] The load reduction model is as follows:

[0095]

[0096] wherein, represents the jth load t period operation power; T ac represents the t period device temperature at this time; T p,hl , T p,ht are the upper and lower limits of the device demand temperature, respectively; represents the refrigeration coefficient of the load that can be cut, and the load with refrigeration demand is 1, and the rest of the cuttable load is 0;

[0097] The electric vehicle load model is as follows:

[0098]

[0099] wherein, represents the charge and discharge rated power of the transferable load, is a 0-1 variable, respectively representing the state of EV charging and discharging;

[0100] The transferable load model is as follows:

[0101]

[0102] wherein, is the rated power of the transferable load; is a 0-1 variable, representing the working state of the transferable load k;

[0103]

[0104] wherein, t start , t end represents the start and end time of the transferable load k allowed to work; α start , β end represents the start and end time of the transferable load k actually working.

[0105] Step three, construct a household electricity cost model;

[0106] The electricity cost model formula is as follows:

[0107]

[0108] wherein, represents the electricity cost of the nth household at t period;

[0109]

[0110] wherein, represents the carbon trading cost of the nth household at t period; This represents the electricity purchase cost for the nth household during time period t;

[0111] The formula for electricity purchase cost is:

[0112]

[0113] In the formula, This represents the electricity purchase price during time period t; This represents the total load of the nth household during time period t; This indicates the amount of electricity purchased by the community during time period t; This indicates the photovoltaic output of the community during time period t; This indicates the wind power output of the community during time period t;

[0114]

[0115] In the formula, These represent the base load, reduceable load, and transferable load of the nth household during time period t, respectively.

[0116] The formula for subsidies received by households participating in demand response is:

[0117]

[0118] In the formula, a and b represent the demand response incentive coefficients for loads that can be reduced and loads that can be transferred, respectively, and c dr Indicates the incentive cost of demand response;

[0119] Step 4: Construct a community energy supply microgrid model; the community energy supply microgrid model includes the cost of purchasing electricity from the grid and the cost of equipment operation and maintenance;

[0120] The formula for the cost of purchasing electricity from the power grid is:

[0121]

[0122] In the formula, This represents the price at which electricity is purchased from the grid during time period t. This represents the amount of electricity purchased from the power grid during time period t;

[0123] The formula for equipment operation and maintenance costs is:

[0124]

[0125] In the formula, c pv c wt c est c EV These represent the unit output operation and maintenance costs of distributed photovoltaic, distributed wind power, energy storage equipment, and EV charging and discharging, respectively. These represent the output of wind power and photovoltaic units, respectively. These represent the discharge power and charging power of the energy storage device, respectively. These represent the discharge power and charging power of the EV, respectively.

[0126]

[0127] In the formula, η pv The value represents the energy conversion efficiency of the photovoltaic power source, S represents the surface area of ​​the photovoltaic panel, and I represents the solar radiation intensity. T represents the atmospheric temperature at time t. s This indicates the outdoor air temperature under standard test conditions.

[0128]

[0129] In the formula, P wt,N This indicates the rated power of the wind turbine generator set, v t v represents the actual wind speed at the location of the wind turbine generator at time t. in Indicates the cut-in wind speed of the fan, v out Indicates the cut-out velocity of the fan, v N This indicates the rated wind speed of the fan.

[0130] Step 5: Optimize carbon emission coefficients, including constructing demand response costs, community carbon trading costs, and carbon emission coefficients;

[0131] The formula for demand response cost is:

[0132]

[0133] In the formula, a and b represent the demand response incentive coefficients for loads that can be reduced and loads that can be transferred, respectively, and C dr Indicates the cost of demand response incentives. c represents the amount of load that can be reduced and transferred for the nth household at time t; dr This represents the incentive unit price for the first household to participate in demand response;

[0134] The formula for the community carbon trading cost is:

[0135]

[0136] In the formula, c c E represents the base price for carbon emissions trading. u E represents the total carbon emissions of the energy microgrid. s Indicates the community's overall carbon allowance;

[0137] E u =E buy ×EC buy (17)

[0138] In the formula, EC buy E represents the carbon emission intensity per unit of active power output of the power grid. buy This indicates the amount of electricity purchased by the community;

[0139]

[0140] In the formula, This represents the amount of electricity purchased from the power grid during time period t;

[0141] The formula for calculating the charging and discharging power of an energy storage device is:

[0142]

[0143] In the formula, These represent the state of charge / discharge coefficients of the energy storage unit, These represent the charging and discharging power of the energy storage unit, respectively.

[0144] The formula for carbon emission coefficient is:

[0145]

[0146] In the formula, EF OM EF is the operating marginal emissions factor generated by the power plant cluster. BM To establish marginal emission coefficients;

[0147] EF OM =E buy ×EF OM,base / (E buy +E gen ) (twenty one)

[0148] In the formula, EF OM Let OM and EF be the simple marginal emission factors of electricity for this community. OM,base For a certain province, the simple marginal emission factor OM and E are calculated based on the power generation companies. buy E purchased electricity for a certain community gen This refers to the amount of new energy power generated in a certain community;

[0149] EF BM =E buy ×EF BM,base / (E buy +E gen ) (twenty two)

[0150] In the formula, EF BM Let EF be the simple marginal emission factor of electricity in a certain community. BM,base E is the simple marginal emission factor of electricity calculated by a certain province based on power generation companies. buy E purchased electricity for a certain community genThis refers to the amount of new energy power generated in a certain community;

[0151] Currently, carbon emission factors are calculated uniformly by each province, without being refined to the calculation of individual communities. Optimizing the calculation of carbon emission factors will make the carbon emission calculation of communities more accurate.

[0152] Step Six: Construct a tiered carbon trading model;

[0153] The formula for the tiered carbon trading model is:

[0154]

[0155]

[0156] In the formula, E p This represents the carbon emission allocation for time period t. This indicates the actual carbon emissions. Let c represent the base load, reduceable load, and transferable load of the nth household during time period t, respectively. c EC represents the base price for carbon emission trading, where μ and λ represent the incentive and penalty coefficients for users purchasing carbon credits in carbon trading, respectively. s Carbon emission coefficient;

[0157] Step 7: Constructing the constraints of the energy microgrid includes: power balance constraints, energy storage unit constraints, electric vehicle SOC, EV operating status, and community electricity purchase constraints;

[0158] Power balance constraints include:

[0159]

[0160] In the formula, This represents the total load of the nth household during time period t. This indicates the community's photovoltaic power output during time period t. This indicates the wind power output of the community during time period t. This represents the discharge amount of the community energy storage unit during time period t. This represents the charging amount of the community energy storage unit during time period t;

[0161] Among them, photovoltaic output constraints include:

[0162]

[0163] In the formula, Indicates the upper limit of photovoltaic power output;

[0164] Wind power output constraints include:

[0165]

[0166] In the formula, This indicates the upper limit of wind power output.

[0167] Energy storage unit constraints include:

[0168]

[0169] In the formula, η ST,ch and η ST,dis These represent the charging and discharging efficiency of the energy storage unit, respectively. and The charging and discharging power corresponding to time t, W represents the amount of electricity stored by the energy storage unit during time period t. ST It is the total capacity of the energy storage unit, ε represents the self-discharge rate of the energy storage unit, and SOC min SOC max Indicates the upper and lower limits of the energy storage state of the energy storage device. and These are their respective upper limits for charging and discharging power. and The variable is 0-1, representing the charge / discharge state coefficient of the energy storage unit;

[0170] Electric vehicle SOC constraints include:

[0171]

[0172] In the formula, η EV,ch and η EV,dis These represent the EV charge / discharge efficiency, and The charging and discharging power corresponding to time t, W represents the amount of electricity stored in an electric vehicle during time period t. EV It is the total capacity of the EV, ε represents the EV self-discharge rate, and SOC min SOC max Indicates the upper and lower limits of the electric vehicle's State of Charge (SOC). and These are their respective upper limits for charging and discharging power. and Represents the state of charge / discharge coefficient of an EV;

[0173] EV operating states include: EV charging and discharging constraints, EV discharging state constraints, and EV grid connection state constraints;

[0174] Formula for EV charge / discharge constraints:

[0175]

[0176] In the formula, The variable is 0-1, representing the EV charging state, t EV,in t EV,outIndicates the start and end times of EV being connected to the power grid;

[0177] Formula for EV discharge state constraints:

[0178]

[0179] In the formula, The variable is 0-1, representing the EV discharge state, t EV,in t EV,out Indicates the start and end times of EV being connected to the power grid;

[0180] Formula for EV grid connection state constraints:

[0181]

[0182] In the formula, The variable is 0-1, representing the state of the EV connected to the grid, t EV,in t EV,out Indicates the start and end times of EV being connected to the power grid;

[0183] The EV operating state constraints are as follows:

[0184]

[0185] Community electricity purchase restrictions include:

[0186]

[0187] In the formula, p buy,max This indicates the community's maximum electricity purchase volume;

[0188] p buy,max ≤1.2×E s / EC buy (35)

[0189] In the formula, E s Indicates the community's total carbon allowance, EC buy This represents the carbon emission coefficient of a unit of active power output in the power grid.

[0190] Step 8: Optimize based on the objective function of the power supply microgrid;

[0191] The formula for the objective function of the power supply microgrid is:

[0192]

[0193] In the formula, C buy C represents the cost of purchasing electricity from the grid. eom C represents the equipment operation and maintenance cost. dr Indicates demand response cost, Indicates the community's carbon trading costs;

[0194] Step 8: Optimize based on the household electricity consumption objective function;

[0195] The formula for the objective function of household electricity consumption is:

[0196]

[0197] In the formula, C use This represents the cost of electricity for a household. R represents the cost of household carbon trading. dr This refers to the subsidies received by households participating in demand response;

[0198] Experimental procedure:

[0199] The scheduling cycle is 24 hours, the simulation step size is 1 hour, and it is divided into 24 time periods from 1 to 24. The basic carbon trading price is 57.80 yuan / ton, and the data comes from the carbon emission allowance (CEA) transaction price in the national carbon market on November 21, 2022. The community data comes from 1,080 households in a certain community, which consists of 30 residential buildings, with 36 households in each building.

[0200] like Figure 2 As shown, the community has installed 108kW photovoltaic panels and 50kW wind power generation equipment on the roof of each building. The community has 648 electric vehicles, each with a 20kWh energy storage unit, totaling 432 units distributed across the rooftops of each building. Each electric vehicle has a 12kWh battery, a maximum charge / discharge power of 15% of its maximum capacity, a charge / discharge efficiency of 90%, a self-discharge rate of 1%, and maximum and minimum states of charge of 0.9 and 0.2, respectively. Table 1 shows information on load reduction, Table 2 on load transfer, Table 3 on time-of-use pricing, Table 4 on the original carbon emission coefficients for electricity consumption in different regions, and Table 5 on typical summer solar radiation and wind power data.

[0201] Load reduction is the primary target for household load regulation, mainly including: air conditioners, water heaters, and electric vehicles, with electric vehicles playing a leading role. The loads that electric vehicles participate in load dispatching include: Figure 3 As shown:

[0202] Table 1. Explanation of Household Reduced Load

[0203]

[0204]

[0205] The main loads that can be transferred include: washing machines, hair dryers, rice cookers, induction cookers, and microwave ovens. The usage of these loads remains the same as before the optimization, and only the usage time is adjusted to reduce the peak-valley difference in household electricity consumption.

[0206] Table 2. Description of Household Transferable Load

[0207]

[0208] Table 3 Time-of-use Electricity Prices

[0209]

[0210] The original carbon emission coefficients were calculated solely from regional power grid generation and electricity exchange. Including community-based renewable energy generation in these coefficients will alter the results. The carbon emission data was optimized using original carbon emission data from South China. As shown in the table, the optimized community carbon emission coefficients are significantly reduced. Figure 4 It is evident that the overall carbon emissions of the community decreased significantly after model optimization; from Figure 5 , Figure 6 It can be seen that the community's daytime electricity consumption has been significantly reduced after the inclusion of the carbon trading mechanism for mechanical optimization. The peak electricity consumption period is concentrated between 00:00 and 06:00 in the early morning, when the economic efficiency of electricity consumption is the highest.

[0211] Table 4 Carbon emission coefficients of electricity consumption in different regions

[0212]

[0213] This invention studies a community energy management model that takes into account the carbon trading mechanism. Under the constraints of user load and electricity consumption habits, it proposes a community electricity total cost optimization model that considers the carbon trading mechanism. By substituting existing community load parameters into the analysis, it is proved that, under the comprehensive consideration of the carbon trading mechanism and the total electricity cost, it can better balance the community's low-carbon development and electricity economy, while taking into account the comfort of household electricity use, reducing community carbon emissions, and reducing the community's overall operating costs.

[0214] Table 5 Typical summer solar radiation and wind data

[0215]

[0216] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for constructing a community energy management model that takes into account carbon trading mechanisms, characterized in that, Includes the following steps: Step 1: Obtain basic parameters and classify household loads based on basic household load data; The formula for the base load is: (1) In the formula, Indicates family in t Time period i The power of each basic load device; express t Time period i Operating status of each basic load device; The load reduction model is as follows: (2) In the formula, Indicates the first j Type of load t Operating power during specific time periods; express t The temperature of the equipment at this time of day; , These are the upper and lower limits of the equipment's required temperature, respectively. The coefficient of performance (COP) indicates the amount of cooling load that can be reduced. The load model for electric vehicles is as follows: (3) In the formula, , This indicates the rated charge and discharge power of the transferable load. , These are 0-1 variables, representing the charging and discharging states of the EV, respectively. The transferable load model is as follows: (4) In the formula, Rated power for transferable loads; For transferable load k The work status; Step 2: Construct a household load classification model that includes base load, reduceable load, electric vehicle load, and transferable load; Step 3: Construct a household electricity cost model; Step 4: Construct a community energy microgrid model that includes the cost of purchasing electricity from the grid and the cost of equipment operation and maintenance; Step 5: Construct an optimized carbon emission coefficient that includes demand response costs, community carbon trading costs, and carbon emission coefficients; The formula for demand response cost is: (15) In the formula, a , b These represent the demand response incentive coefficients for loads that can be reduced and loads that can be transferred, respectively. , They represent the first n The amount of load that can be reduced or transferred by each household during scheduling; This represents the incentive unit price for the first household to participate in demand response; The formula for the community carbon trading cost is: (16) In the formula, This indicates the base price for carbon emissions trading. This indicates the total carbon emissions of the energy microgrid. Indicates the community's overall carbon allowance; The formula for carbon emission coefficient is: (20) In the formula, The operating marginal emission factor generated by the power plant cluster. To establish marginal emission coefficients; Step Six: Construct a tiered carbon trading model; The formula for the tiered carbon trading model is: (23) In the formula, , , They represent the first n Household t Base load, reduceable load, and transferable load for a given period of time. Carbon emission coefficient, Costs of household carbon trading; Step 7: Construct a system including power balance constraints, energy storage unit constraints, and electric vehicle constraints. SOC EV operating status and community power purchase constraints of the microgrid; Step 8: Optimize the electricity purchase cost, equipment operation and maintenance cost, demand response cost, and community carbon trading cost using the energy microgrid objective function; Step 9: Optimize the household electricity purchase cost, household carbon trading cost, and subsidies obtained by the household from participating in demand response using the household electricity consumption objective function.

2. The method for constructing a community energy management model taking into account the carbon trading mechanism according to claim 1, characterized in that, The formula for the electricity cost model is as follows: (6) In the formula, express t Time period n Electricity costs for each household.

3. The method for constructing a community energy management model taking into account the carbon trading mechanism according to claim 1, characterized in that, The formula for the cost of purchasing electricity from the power grid is: (11) In the formula, express t The price of purchasing electricity from the grid during a given period. This represents the amount of electricity purchased from the power grid during time period t; The formula for equipment operation and maintenance costs is: （12） In the formula, , , , These represent the unit output operation and maintenance costs of distributed photovoltaic, distributed wind power, energy storage equipment, and EV charging and discharging, respectively. , These represent the output of wind power and photovoltaic units, respectively. , These represent the discharge power and charging power of the energy storage device, respectively. , They represent EV The discharge power and charging power.

4. The method for constructing a community energy management model taking into account the carbon trading mechanism according to claim 1, characterized in that, Power balance constraints include: (25) In the formula, Indicates the first n Household t Total load during the period express t Community solar power output during certain periods express t Community wind power output during certain periods express t Discharge of community energy storage units during a given time period express t Charging volume of community energy storage units during a given time period; Energy storage unit constraints include: (28) In the formula, and These represent the charging and discharging efficiency of the energy storage unit, respectively. and The charging and discharging power corresponding to time t, This represents the amount of electricity stored in the energy storage unit during time period t. It is the total capacity of the energy storage units. Indicates the self-discharge rate of the energy storage unit. , Indicates the upper and lower limits of the energy storage state of the energy storage device. and These are their respective upper limits for charging and discharging power. and This represents the state of charge / discharge coefficient of the energy storage unit; electric vehicles SOC The constraints include: (29) In the formula, and These represent the EV charge / discharge efficiency, and correspond t The charging and discharging power at different times. This indicates the amount of electricity stored in the electric vehicle during time period t. It is the total capacity of EVs. Indicates the EV self-discharge rate. , Indicates electric vehicles SOC upper and lower limits of the state and These are their respective upper limits for charging and discharging power. and Represents the state of charge / discharge coefficient of an EV; EV operating states include: EV charging and discharging constraints, EV discharging state constraints, and EV grid connection state constraints; Formula for EV charge / discharge constraints: (30) In the formula, The variable is 0-1, representing the EV charging state. , Indicates the start and end times of EV being connected to the power grid; Formula for EV discharge state constraints: (31) In the formula, The variable is 0-1, representing the EV discharge state. , Indicates the start and end times of EV being connected to the power grid; Formula for EV grid connection state constraints: (32) In the formula, The variable is 0-1, representing the state of the EV being connected to the power grid. , Indicates the start and end times of EV being connected to the power grid; The EV operating state constraints are as follows: (33) Community electricity purchase restrictions include: (34) In the formula, This indicates the community's maximum electricity purchase volume. This represents the amount of electricity purchased from the power grid during time period t.

5. The method for constructing a community energy management model taking into account the carbon trading mechanism according to claim 1, characterized in that, The formula for the objective function of the power supply microgrid is: (36) In the formula, This represents the cost of purchasing electricity from the power grid. This indicates the cost of equipment operation and maintenance. Indicates demand response cost, This indicates the cost of community carbon trading.

6. The method for constructing a community energy management model taking into account the carbon trading mechanism according to claim 1, characterized in that, The formula for the objective function of household electricity consumption is: (37) In the formula, This represents the cost of electricity for a household. Indicates the cost of household carbon trading. This refers to the subsidies received by households participating in demand response.

Images