Micro-grid optimization scheduling method considering electric vehicle and stepped carbon transaction mechanism

Abstract

The invention discloses a micro-grid optimization scheduling method considering electric vehicles and stepped carbon transactions, and the method comprises the following steps: 1, taking loads, fans and photovoltaic output into consideration, collecting the fan and photovoltaic output and different types of load data in each historical time period, dividing a day into 24 time periods by taking each hour as a unit, and carrying out the calculation of the time periods; establishing an electric vehicle disordered charging model by using a Monte Carlo method; 2, obtaining disordered charging loads of the electric vehicle through a Monte Carlo method, selecting the maximum load to improve the purchase and sale electricity price of a power grid to generate a new time-of-use electricity price of the electric vehicle, and obtaining an ordered charging and discharging model of the electric vehicle; 3, considering the carbon emission of the electric vehicle based on a traditional fuel vehicle under the same driving distance, combining the power grid and renewable energy power supply conditions during charging, converting the emission reduction amount of the electric vehicle into a carbon quota through a datum line method, and metering the carbon emission in the micro-grid through a two-stage method, establishing a stepped carbon transaction model containing the carbon quota of the electric vehicle; and 4, establishing a micro-grid dispatching optimization model containing the electric vehicle, and solving an optimal solution of a target function by using mixed integer linear programming by taking the minimum power operation cost, the maximum carbon benefit and the minimum load mean square error of the micro-grid as the target function.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon economic dispatching of microgrid electric vehicles, and in particular to a microgrid optimization dispatching method taking electric vehicles and tiered carbon trading into account. Background Art

[0002] Driven by the global energy shortage, the development of electric vehicles and the "dual carbon" goal, the coordinated development of new power systems with new energy as the main body and electric vehicles has become inevitable. With the advancement of electric vehicle grid-connected technology, the popularity of electric vehicles in microgrids will continue to increase. However, due to the randomness and uncertainty of electric vehicle access, its large-scale disorderly access will cause a sharp increase in the peak load of the power grid, bringing huge scheduling pressure to microgrids. For this reason, considering renewable energy, electric vehicles and ladder carbon trading mechanisms will be an important development trend of microgrids. Therefore, a microgrid optimization scheduling method taking into account electric vehicles and ladder carbon trading is urgently needed to achieve mutual benefit and win-win results for microgrids and electric vehicles in the electricity-carbon market. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading. The method constructs the premise of involving electric vehicle carbon quotas in the carbon trading mechanism, improves the power grid purchase and sale price in combination with the maximum demand load of electric vehicles to form the time-of-use electricity price of electric vehicles, and proposes a microgrid collaborative optimization low-carbon economic scheduling method. First, a wind, solar and storage microgrid system containing electric vehicles is constructed, and then the time-of-use electricity price component of the power grid purchase and sale price is improved based on the disordered load of electric vehicles; when considering the economic and environmental benefits of electric vehicles under the carbon trading system, a model based on electric vehicle carbon quotas is constructed, which is combined with the tiered carbon trading mechanism; in the microgrid scheduling, the system operation cost and load mean square error are minimized as the goal, so as to achieve the goal of mutual benefit and win-win between microgrids and electric vehicles.

[0004] The technical solution adopted by the present invention is:

[0005] A microgrid optimization dispatching method taking into account electric vehicles and tiered carbon trading comprises the following steps:

[0006] Step 1: Considering the load, wind turbine and photovoltaic output, collect the wind turbine, photovoltaic output and different types of load data in each period of history, divide the day into 24 periods per hour, and then use the Monte Carlo method to establish an electric vehicle disorderly charging model;

[0007] Step 2: Obtain the disordered charging load of electric vehicles through the Monte Carlo method, select the maximum load of disordered charging of electric vehicles to improve the power grid purchase and sale price to generate a new electric vehicle time-of-use electricity price, and obtain an orderly charging and discharging model for electric vehicles;

[0008] Step 3: Considering the carbon emissions of electric vehicles based on traditional fuel vehicles at the same driving distance, and combining the power grid and renewable energy supply during charging, the emission reduction of electric vehicles is converted into carbon quotas through the baseline method, and the two-stage method is used to measure the carbon emissions in the microgrid, and a ladder carbon trading model containing electric vehicle carbon quotas is established;

[0009] Step 4: Establish a microgrid dispatch optimization model containing electric vehicles, take the minimization of the microgrid's electricity operation cost, maximization of carbon benefits and minimization of load mean square error as the objective function, and use mixed integer linear programming to solve the optimal solution of the objective function.

[0010] In step 1, the electric vehicle disorderly charging model is specifically expressed as follows:

[0011] The probability density function of the electric vehicle user's return time is expressed as:

[0012]

[0013] Where f(τ) is the probability density function of the electric vehicle user's return time, τ is the return time, μ τ =17.6,σ τ =3.4;

[0014] The probability density function of the daily mileage of electric vehicles is expressed as:

[0015]

[0016] Where f(d) is the probability density function of the daily mileage of electric vehicles, d is the daily mileage, μ d =3.2,σ d =0.88;

[0017] The charging time required for an electric vehicle is expressed as:

[0018]

[0019] Where E is the power consumption of the electric vehicle when driving, p c is the charging efficiency of electric vehicles, η c Charging efficiency for electric vehicles;

[0020] In step 2, the time-of-use electricity price of the electric vehicle is formulated according to the maximum load of the electric vehicle. The specific expression is as follows:

[0021]

[0022] Where P opTOU (t) is the time-of-use electricity price of the response EV at time t after optimization, P TOU(t) is the original grid time-of-use electricity price at time t, N is the number of EVs dispatched, is the maximum charging power of EV in the entire scheduling cycle, is the peak electricity price during the dispatch time;

[0023] In step 3, a microgrid tiered carbon trading model based on electric vehicle carbon quota is constructed, and the specific expression is as follows:

[0024]

[0025] In the formula, is the carbon emission of fuel vehicles per unit mileage, S fv is the fuel consumption per unit mileage of fuel vehicles, E fuel is the carbon emission coefficient of fuel;

[0026]

[0027] In the formula, P is the carbon emission generated by each unit of electricity consumed by electric vehicles; PG (t) is the output power of the grid side at time t; P DG (t) is the total output power of wind power and photovoltaic power at time t; E pg is the average carbon emission coefficient on the grid side;

[0028]

[0029] In the formula, E EV,N is the total carbon quota of EV, w is the electricity consumption of EV per 100 km;

[0030]

[0031] In the formula, E out is the total CO2 emission in the microgrid, is the carbon emission coefficient of energy equipment in the production and transportation stage, is the carbon emission coefficient of energy equipment in the use phase, P i is the operating power of the energy equipment;

[0032] E=E EV,N +E out -E all

[0033] Where E is the carbon emission trading amount of the microgrid, E EV,N 、E out 、E all They are the total carbon quota of electric vehicles, the total carbon emissions of microgrid systems, and the carbon emission quota;

[0034]

[0035] In the formula, C co2 is the tiered carbon trading cost, C t is the base price of the carbon trading market, l is the length of the carbon emission interval, and λ is the price growth rate;

[0036] In step 4, the objective function is to minimize the power operation cost of the microgrid and minimize the mean square error of the load. The specific expression is as follows:

[0037] f c =C co2 +C BA +C G -G L -G EV -(G DG +G BA )

[0038]

[0039] In the formula, F c is the total operating cost of the microgrid system including EVs; F flu is the mean square error of microgrid load; G EV Charging fees for electric vehicle users; C G is the sum of the revenue and expenditure of microgrid and grid transactions; C BA is the depreciation cost of the battery; G L P is the income of the dispatching center from selling electricity to loads; ev (t) is the load of the electric vehicle during period t; P L (t) is the basic load of the distribution network during period t; P DG (t) is the power of photovoltaic wind power in period t; P BA (t) is the power of the battery during period t; P ave is the average net load value, and the calculation formula is as follows:

[0040]

[0041] Constraints:

[0042] Power boundary constraints:

[0043]

[0044] P DG (t)+P G (t) = P L (t)+P BA (t)+P EV (t)

[0045]

[0046] Where P DG is the total power of wind power and photovoltaic power, is the minimum value of wind power and photovoltaic power, is the maximum power of wind power and photovoltaic power; P G is the interaction power between microgrid and grid, is the minimum transmission power of the tie line, is the maximum transmission power of the interconnection line.

[0047] Energy storage capacity constraints:

[0048] S BAmin ≤S BA ≤S BAmax

[0049] In the formula, S BA is the battery capacity; S BAmin is the lower limit of battery power, S BAmax The upper limit of battery power.

[0050] Constraints on the dispatchable period of electric vehicles:

[0051] T EVi_start ≤T EVi ≤T EVi_end

[0052]

[0053]

[0054] Where, T EVi is the dispatchable time interval of the i-th vehicle, T EVi_start T is the time to start charging. EVi_end is the time to pick up the car; μ2 is the mean value of the time to start charging; is the charging time distribution variance; T EVi_end is the time to pick up the car; μ3 is the mean time to pick up the car; is the variance of the pick-up time distribution.

[0055] Charge and discharge capacity constraints:

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] Where P EVmin is the upper limit of EV discharge power, P EVmax is the upper limit of charging power, P EV is the tram load power; are the maximum and minimum charging power of the i-th EV respectively; are the maximum and minimum discharge power of the i-th EV respectively; are the charging and discharging flags of the i-th EV at time t, which are 0-1 variables, and, represents that the i-th EV is in the charging state at time t, represents that the i-th EV is not in charging state at time t, Similarly; T i,in , T i,out are the grid-connected and grid-off times of the i-th EV, respectively.

[0063] The beneficial effects of the microgrid optimization dispatching method taking into account electric vehicles and the ladder carbon trading mechanism of the present invention are as follows:

[0064] The present invention proposes a microgrid optimization scheduling method that takes into account electric vehicles and a stepped carbon trading mechanism. The method can alleviate the impact of large-scale centralized charging of electric vehicles on the power grid, thereby ensuring power supply stability, improving the economy of the microgrid, promoting the consumption of renewable energy, reducing operating costs, and enhancing market competitiveness. Different from other microgrid scheduling optimization processes, the present invention takes into account a microgrid scheduling method under electric vehicles and a stepped carbon trading mechanism. The method is an orderly charging and discharging method for electric vehicles under the dual incentives of time-of-use electricity prices and a carbon quota mechanism. It can optimize the charging and discharging load of electric vehicles and reduce the cost of users and microgrid systems. Through the stepped carbon trading mechanism, the carbon quota of electric vehicles is integrated into the carbon trading market of the microgrid system, promoting electric vehicle users to respond to the scheduling of microgrids and maximizing low-carbon potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of the operating method proposed by the present invention;

[0066] Figure 2 This is a schematic diagram of the structure of a microgrid dispatching system including electric vehicles proposed in the present invention;

[0067] Figure 3 It is a schematic diagram of the microgrid topology structure of the present invention;

[0068] Figure 4 A flow chart of the mixed integer linear programming algorithm proposed by the present invention;

[0069] Figure 5 This is a comparison diagram of microgrid power balance under disordered state of electric vehicles of the present invention;

[0070] Figure 6 A comparison diagram of microgrid power balance in an orderly state of an electric vehicle according to the present invention;

[0071] Figure 7 The orderly and disordered power comparison and improved time-of-use electricity price diagram of the electric vehicle of the present invention; DETAILED DESCRIPTION

[0072] The present invention provides a microgrid optimization scheduling method taking into account electric vehicles and a tiered carbon trading mechanism. The method constructs a premise of involving electric vehicle carbon quotas in the carbon trading mechanism, and combines the improved grid electricity purchase and sales price and time-of-use electricity price with microgrid collaborative optimization scheduling to achieve orderly charging and discharging of electric vehicles, reduce user costs, and improve the carbon emission reduction benefits of the microgrid. This method first establishes a wind-solar-storage microgrid model containing electric vehicles, and uses the Monte Carlo method to establish an electric vehicle disorderly charging model; then, based on the electric vehicle disorderly charging load obtained by the Monte Carlo method, the maximum load is selected to improve the grid purchase and sale electricity price, and a new electric vehicle time-of-use electricity price is generated; secondly, the carbon emissions of electric vehicles and traditional fuel vehicles at the same driving distance are considered, and combined with the power supply of the grid and renewable energy during charging, the emission reduction is converted into carbon quota through the baseline method, and the two-stage method is used to measure the carbon emissions of the microgrid, and a ladder carbon trading model containing electric vehicle carbon quotas is established; finally, a microgrid scheduling optimization model containing electric vehicles is established under the dual incentives of time-of-use electricity price and carbon quota mechanism, and the minimization of the microgrid's power operation cost and the minimization of the load mean square error are taken as the objective function, and the mixed integer linear programming is used to solve the optimal solution of the objective function.

[0073] Figure 1 is a flow chart of a microgrid optimization dispatching method taking into account electric vehicles and a tiered carbon trading mechanism proposed in the present invention, such as Figure 1 As shown, the method flow is:

[0074] (1) Considering the load, wind turbine and photovoltaic output, the wind turbine, photovoltaic output and different types of load data in each period of history are collected, and a day is divided into 24 periods per hour. Then, the Monte Carlo method is used to establish an electric vehicle disorderly charging model;

[0075] (2) Obtain the disordered charging load of electric vehicles through the Monte Carlo method, and select the maximum load to improve the purchase and sale price of the power grid to generate a new time-of-use electricity price for electric vehicles;

[0076] (3) Considering the carbon emissions of electric vehicles based on traditional fuel vehicles at the same driving distance, and combining the power grid and renewable energy supply during charging, the emission reduction of electric vehicles is converted into carbon quotas through the baseline method, and the two-stage method is used to measure the carbon emissions in microgrids, and a ladder carbon trading model containing electric vehicle carbon quotas is established;

[0077] (4) A microgrid dispatch optimization model containing electric vehicles is established, with the objective function being to minimize the microgrid's electricity operating cost, maximize carbon benefits, and minimize the mean square error of loads. Mixed integer linear programming is used to solve the optimal solution of the objective function.

[0078] Example

[0079] For a certain microgrid system, in order to reflect the effectiveness of the method proposed in the present invention, the following four scenarios are formulated for comparative analysis. Through the orderly charging and discharging method of electric vehicles under the dual incentives of time-of-use electricity prices and carbon quota mechanisms, the low-carbon economic cost of the microgrid system containing electric vehicles can be optimized. Figure 2 This is a schematic diagram of the microgrid dispatching system containing electric vehicles. Figure 3 is a schematic diagram of the microgrid topology. Figure 4 is the flow chart of the mixed integer linear programming algorithm, Figure 5 This is a comparison diagram of microgrid power balance under disordered state of electric vehicles. Figure 6 This is a comparison diagram of microgrid power balance under the orderly state of electric vehicles. Figure 7 It is a comparison of ordered and disordered power of electric vehicles and an improved time-of-use electricity price diagram.

[0080] Scenario 1: Electric vehicles are connected to the microgrid in an unordered manner, and traditional carbon trading mechanisms are considered;

[0081] Scenario 2: Electric vehicles are connected to the microgrid in an unordered manner, and a tiered carbon trading mechanism is considered;

[0082] Scenario 3: Electric vehicles are connected to microgrids in an orderly manner, and traditional carbon trading mechanisms are considered;

[0083] Scenario 4: The optimization method proposed in the present invention.

[0084] A microgrid optimization scheduling method taking into account electric vehicles and a tiered carbon trading mechanism according to an embodiment of the present invention is characterized by comprising the following steps:

[0085] Step 1: Considering the load, wind turbine and photovoltaic output, collect the wind turbine, photovoltaic output and different types of load data in each period of history, divide the day into 24 periods per hour, and then use the Monte Carlo method to establish an electric vehicle disorderly charging model;

[0086] Step 2: Obtain the disordered charging load of electric vehicles through the Monte Carlo method, and select the maximum load to improve the purchase and sale price of the power grid to generate a new time-of-use electricity price for electric vehicles;

[0087] Step 3: Considering the carbon emissions of electric vehicles based on traditional fuel vehicles at the same driving distance, and combining the power grid and renewable energy supply during charging, the emission reduction of electric vehicles is converted into carbon quotas through the baseline method, and the two-stage method is used to measure the carbon emissions in the microgrid, and a ladder carbon trading model containing electric vehicle carbon quotas is established;

[0088] Step 4: Establish a microgrid dispatch optimization model containing electric vehicles, take the minimization of the microgrid's electricity operation cost, maximization of carbon benefits and minimization of load mean square error as the objective function, and use mixed integer linear programming to solve the optimal solution of the objective function.

[0089] In step 1, the electric vehicle disorderly charging model is specifically expressed as follows:

[0090] The probability density function of the electric vehicle user's return time is expressed as:

[0091]

[0092] Where f(τ) is the probability density function of the electric vehicle user's return time, τ is the return time, μ τ =17.6,σ τ =3.4;

[0093] The probability density function of the daily mileage of electric vehicles is expressed as:

[0094]

[0095] Where f(d) is the probability density function of the daily mileage of electric vehicles, d is the daily mileage, μ d =3.2,σ d =0.88;

[0096] The charging time required for an electric vehicle is expressed as:

[0097]

[0098] Where E is the power consumption of the electric vehicle when driving, p c is the charging efficiency of electric vehicles, η c Charging efficiency for electric vehicles;

[0099] In step 2, the time-of-use electricity price of the electric vehicle is formulated according to the maximum load of the electric vehicle. The specific expression is as follows:

[0100]

[0101] Where P opTOU (t) is the time-of-use electricity price of the response EV at time t after optimization, P TOU (t) is the original grid time-of-use electricity price at time t, N is the number of EVs dispatched, is the maximum charging power of EV in the entire scheduling cycle, is the peak electricity price during the dispatch time;

[0102] In step 3, a microgrid tiered carbon trading model based on electric vehicle carbon quota is constructed, and the specific expression is as follows:

[0103]

[0104] In the formula, is the carbon emission of fuel vehicles per unit mileage, S fv is the fuel consumption per unit mileage of fuel vehicles, E fuel is the carbon emission coefficient of fuel;

[0105]

[0106] In the formula, P is the carbon emission generated by each unit of electricity consumed by electric vehicles; PG (t) is the output power of the grid side at time t; P DG (t) is the total output power of wind power and photovoltaic power at time t; E pg is the average carbon emission coefficient on the grid side;

[0107]

[0108] In the formula, E EV,N is the total carbon quota of EV, w is the electricity consumption of EV per 100 km;

[0109]

[0110] In the formula, E out is the total CO2 emission in the microgrid, is the carbon emission coefficient of energy equipment in the production and transportation stage, is the carbon emission coefficient of energy equipment in the use phase, P i is the operating power of the energy equipment;

[0111] E=E EV,N +E out -E all

[0112] Where E is the carbon emission trading amount of the microgrid, E EV,N 、E out 、E allThey are the total carbon quota of electric vehicles, the total carbon emissions of microgrid systems, and the carbon emission quota;

[0113]

[0114] Where: C co2 is the tiered carbon trading cost, C t is the base price of the carbon trading market, l is the length of the carbon emission interval, and λ is the price growth rate;

[0115] In step 4, the objective function is to minimize the power operation cost of the microgrid and minimize the mean square error of the load. The specific expression is as follows:

[0116] f c =C co2 +C BA +C G -G L -G EV -(G DG +G BA )

[0117]

[0118] In the formula, F c is the total operating cost of the microgrid system including EVs; F flu is the mean square error of microgrid load; G EV Charging fees for electric vehicle users; C G is the sum of the revenue and expenditure of microgrid and grid transactions; C BA is the depreciation cost of the battery; G L P is the income of the dispatching center from selling electricity to loads; ev (t) is the load of the electric vehicle during period t; P L (t) is the basic load of the distribution network during period t; P DG (t) is the power of photovoltaic wind power in period t; P BA (t) is the power of the battery during period t; P ave is the average net load value, and the calculation formula is as follows:

[0119]

[0120] Constraints:

[0121] Power boundary constraints:

[0122]

[0123] P DG (t)+P G (t) = P L (t)+P BA (t)+PEV (t)

[0124]

[0125] Where P DG is the total power of wind power and photovoltaic power, is the minimum value of wind power and photovoltaic power, is the maximum power of wind power and photovoltaic power; P G is the interaction power between microgrid and grid, is the minimum transmission power of the tie line, is the maximum transmission power of the interconnection line.

[0126] Energy storage capacity constraints:

[0127] S BAmin ≤S BA ≤S BAmax

[0128] In the formula, S BA is the battery capacity; S BAmin is the lower limit of battery power, S BAmax The upper limit of battery power.

[0129] Constraints on the dispatchable period of electric vehicles:

[0130] T EVi_start ≤T EVi ≤T EVi_end

[0131]

[0132]

[0133] Where, T EVi is the dispatchable time interval of the i-th vehicle, T EVi_start T is the time to start charging. EVi_end is the time to pick up the car; μ2 is the mean value of the time to start charging; is the charging time distribution variance; T EVi_end is the time to pick up the car; μ3 is the mean time to pick up the car; is the variance of the pick-up time distribution.

[0134] Charge and discharge capacity constraints:

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] Where P EVmin is the upper limit of EV discharge power, P EVmax is the upper limit of charging power, P EV is the tram load power; are the maximum and minimum charging power of the i-th EV respectively; are the maximum and minimum discharge power of the i-th EV respectively; are the charging and discharging flags of the i-th EV at time t, which are 0-1 variables, and, represents that the i-th EV is in the charging state at time t, represents that the i-th EV is not in charging state at time t, Similarly; T i,in 、T i,out are the grid-connection and grid-off time of the i-th EV respectively.

[0142] See also Figure 3 In this embodiment, the figure is a schematic diagram of the improved 33-node system topology of the microgrid system, where wind turbines are installed at nodes 4 and 16; photovoltaics are installed at nodes 18 and 30; electric vehicle charging stations are established at nodes 14, 22 and 25; and energy storage devices are connected to nodes 4, 16, 18 and 30.

[0143] See also Figure 5 and Figure 6 In this embodiment, Figure 5 Electric vehicles are concentrated in charging between 15:00 and 24:00. At this time, the wind and solar power output is insufficient, and 2726.30kW needs to be purchased from the main grid to meet the charging demand and basic load, which leads to higher costs for microgrids. Figure 7 In the microgrid, EV charging is concentrated in the periods of 23:00-24:00 and 02:00-07:00 when wind and solar power output is sufficient, and the dispatching center does not need to purchase electricity; electric vehicle discharge is concentrated in the main grid power sales and basic load high periods such as 12:00, 14:00 and 19:00-22:00, so that the input and output of electricity remain low, reducing dependence on the main grid, and selling electricity to the main grid, reducing carbon emissions and costs of microgrids.

[0144] See also Figure 7, in different scenarios, the orderly load of electric vehicles increases the charging load and reduces the discharging load relative to the disordered load. In the 23:00-07:00 period, the electricity price is 0.14 yuan, and electric vehicle charging can reduce user costs. The orderly load increases significantly, and users concentrate on charging during this period. In the 08:00-10:00 and 15:00-18:00 flat electricity price intervals, the electricity price is 0.30 yuan, and users respond to the time-of-use electricity price and increase the charging load. In the 11:00-14:00 and 19:00-22:00 peak electricity price intervals, the electricity price is 0.53 yuan, and the cost of charging electric vehicles is high. Users guide orderly discharge to obtain benefits and promote "peak shaving and valley filling".

[0145] Table 1 Comparison of various cost values ​​in the microgrid system before and after response incentive

[0146]

[0147] Table 2 Comparison of benefits before and after considering the tiered carbon trading mechanism

[0148]

[0149] In terms of economic benefits, Table 5 shows that responding to time-of-use electricity prices and carbon quota incentives can significantly reduce system dispatch costs by 78.03%, grid electricity purchase costs by 42.47%, electric vehicle costs by 56%, and energy storage equipment depreciation costs by 11.97%, while increasing microgrid electricity sales revenue by 8.43%. In terms of environmental benefits, Table 6 shows that in scenarios 3 and 4, responding to time-of-use electricity prices and carbon trading can reduce electric vehicle carbon quotas by 2.3%, carbon emissions by 17.91%-24%, and increase microgrid system carbon revenue by 5.28%-7.32% compared to disorderly charging scenarios 1 and 2. Compared with scenarios 1 and 3 with traditional carbon trading, scenarios 2 and 4 with tiered carbon trading can further reduce the comprehensive energy carbon emission costs by 4%-24% and increase the microgrid system carbon revenue by 1.16%-5.40%. Therefore, scenario 4 performs well in both economic and environmental performance.

[0150] The beneficial effects of the microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading in the embodiment of the present invention are: the microgrid system containing electric vehicles under the dual incentives of time-of-use electricity prices and carbon quota mechanisms can optimize the charging and discharging loads of electric vehicles to reduce the costs of users and microgrid systems, and through the tiered carbon trading mechanism, the carbon quotas of electric vehicles are integrated into the electricity-carbon trading market of the microgrid system, promoting electric vehicle users to respond to the scheduling of microgrids and maximizing low-carbon potential, thereby alleviating the problems of electric vehicle charging and discharging load optimization, carbon emission management and electric vehicle user response scheduling, and is very practical.

[0151] This implementation case is only a preferred specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading, characterized in that: The following steps are involved: Step 1: Considering load, wind turbine and PV output, collect historical data on wind turbine and PV output and different types of loads for each period. Divide the day into 24 periods on an hourly basis and use the Monte Carlo method to establish a model for the disordered charging of electric vehicles. Step 2: Obtain the disordered charging load of electric vehicles through the Monte Carlo method, select the maximum load among them to improve the purchase and sale price of the power grid to generate a new time-of-use electricity price for electric vehicles, and obtain an orderly charging and discharging model for electric vehicles; Step 3: Considering the carbon emissions of electric vehicles based on traditional fuel vehicles over the same driving distance, and combining the grid and renewable energy supply conditions during charging, the electric vehicle emission reductions are converted into carbon quotas through the baseline method. A two-stage method is used to measure carbon emissions in the microgrid, and a tiered carbon trading model that includes electric vehicle carbon quotas is established; Step 4: Establish a microgrid dispatch optimization model containing electric vehicles, with the minimization of the microgrid's electricity operation cost, maximization of carbon benefits, and minimization of the load mean square error as the objective function, and use mixed integer linear programming to solve the optimal solution of the objective function.

2. The microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading according to claim 1 is characterized by: In step 1, the electric vehicle disorderly charging model is specifically expressed as follows: The probability density function of the electric vehicle user's return time is expressed as: Where f(τ) is the probability density function of the electric vehicle user's return time, τ is the return time, μ τ =17.6,σ τ =3.

4. The probability density function of the daily mileage of electric vehicles is expressed as: Where f(d) is the probability density function of the daily mileage of electric vehicles, d is the daily mileage, μ d =3.2,σ d =0.

88. The charging time required for an electric vehicle is expressed as: Where E is the power consumption of electric vehicles when driving, p c is the charging efficiency of electric vehicles, η c Charging efficiency for electric vehicles.

3. The microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading according to claim 1 is characterized by: In step 2, the time-of-use electricity price of the electric vehicle is determined according to its maximum load. The specific expression is as follows: Where, P opTOU (t) is the time-of-use electricity price of the response EV at time t after optimization, P TOU (t) is the original grid time-of-use electricity price at time t, N is the number of EVs dispatched, is the maximum charging power of EV in the entire scheduling cycle, is the peak electricity price during the dispatch time.

4. The microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading according to claim 1 is characterized by: In step 3, a microgrid tiered carbon trading model based on electric vehicle carbon quotas is constructed. The specific expression is as follows: Where, is the carbon emissions of fuel vehicles per unit mileage, S fv is the fuel consumption per unit mileage of fuel vehicles, E fuel is the carbon emission coefficient of fuel. Where, P is the carbon emissions generated by electric vehicles per unit of electricity consumed; PG (t) is the output power of the grid side at time t; P DG (t) is the total output power of wind power and photovoltaic power at time t; E pg is the average carbon emission coefficient on the grid side. Where, E EV,N is the total carbon quota of EV, and w is the electricity consumption of EV per 100 kilometers. Where, E out is the total CO2 emissions in the microgrid, is the carbon emission coefficient of energy equipment in the production and transportation stages, is the carbon emission coefficient of energy equipment during the use phase, P i It is the operating power of energy equipment. E=E EV,N +E out -AND all Where, E is the carbon emission trading amount of the microgrid, E EV,N 、E out 、E all They are the total carbon quota of electric vehicles, the total carbon emissions of the microgrid system, and the carbon emission quota. Where C co2 is the tiered carbon trading cost, C t is the base price of the carbon trading market, l is the length of the carbon emission interval, and λ is the price growth rate.

5. The microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading according to claim 1 is characterized by: In step 4, the objective function is to minimize the power operation cost of the microgrid and minimize the mean square error of the load. The specific expression is as follows: minF c =C co2 +C BA +C G -G L -G EV -(G DG +G BA ) Where, F c is the total operating cost of the microgrid system including EVs; F flu is the mean square error of the microgrid load; G EV Charging fee for electric vehicle users; C G is the sum of revenue and expenditure of microgrid and grid transactions; C BA is the depreciation cost of the battery; G L P is the income of the dispatching center from selling electricity to the load; ev (t) is the load of the electric vehicle during period t; P L (t) is the basic load of the distribution network during period t; P DG (t) is the power of photovoltaic wind power in period t; P BA (t) is the power of the battery during period t; P ave is the average net load value, and the calculation formula is as follows:

6. The microgrid optimization scheduling method taking into account electric vehicles and tiered carbon trading according to claim 1 is characterized by: In step 4, the optimization model constraints are as follows: Power boundary constraints: P DG (t)+P G (t)=P L (t)+P BA (t)+P EV (t) Where, P DG is the total power of wind power and photovoltaic power, is the minimum value of wind power and photovoltaic power, is the maximum power of wind power and photovoltaic power; P G is the interaction power between the microgrid and the grid, is the minimum transmission power of the tie line, is the maximum transmission power of the tie line. Energy storage capacity constraints: S BAmin ≤S BA ≤S BAmax Where S BA is the battery capacity; S BAmin is the lower limit of battery power, S BAmax The upper limit of battery power. Constraints on the dispatchable period of electric vehicles: T EVi_start ≤T EVi ≤T EVi_end Where, T EVi is the dispatchable time interval of the i-th vehicle, T EVi_start T is the time to start charging, EVi_end is the time to pick up the car; μ2 is the mean value of the time to start charging; is the charging time distribution variance; T EVi_end is the time to pick up the car; μ3 is the mean time to pick up the car; is the variance of the pick-up time distribution. Charge and discharge capacity constraints: Where, P EVmin is the upper limit of EV discharge power, P EVmax is the upper limit of charging power, P EV is the tram load power; are the maximum and minimum charging power of the i-th EV respectively; are the maximum and minimum discharge power of the i-th EV respectively; are the charging and discharging flags of the i-th EV at time t, which are 0-1 variables, and, represents that the i-th EV is in the charging state at time t, represents that the i-th EV is not in the charging state at time t, Similarly; T i,in 、T i,out are the grid connection and grid disconnection times of the i-th EV respectively.

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