Method for designing charging electricity price of electric vehicle for exciting new energy consumption

Abstract

The invention relates to a method for designing the charging electricity price of an electric vehicle for exciting new energy consumption, and the method comprises the following steps: evaluating the charging behavior of the electric vehicle, and calculating the charging load of the electric vehicle; dividing peak and valley periods of new energy power supply through a membership function; calculating an elastic response relational expression of the electric vehicle load demand to the electricity price, and calculating the charging cost; taking the minimum new energy power supply total amount and the minimum charging cost as targets, constructing a consumption model, and setting a target function and a constraint condition of the consumption model; and resolving the consumption model through a genetic algorithm to obtain an electric vehicle charging electricity price adjustment design scheme. Compared with the prior art, the method has the advantages that the influence of the electricity price on the load change of the power grid can be predicted in combination with the fluctuation change of the new energy power generation capacity, and the consumption of the new energy is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of charging optimization scheduling, and in particular to a method for designing electric vehicle charging electricity prices to encourage consumption of new energy. Background Art

[0002] With the promotion and use of electric vehicles, the demand for electric vehicle charging has increased significantly. At the same time, the development of new energy technologies, such as photovoltaics and wind power, has provided a more environmentally friendly source of electricity for the power grid. Compared with the continuous stability of traditional thermal power generation, the power generation capacity of new energy technologies has significant peaks and valleys.

[0003] In order to achieve the effect of peak shaving and valley filling, existing technologies have shown an emphasis on demand response, trying to optimize charging behavior by understanding and predicting users' sensitivity to changes in electricity prices. The implementation of peak and valley time-of-use electricity price policies effectively encourages electric vehicle users to charge during low-load periods of the power grid.

[0004] However, in terms of dynamic adjustment of demand response, existing technologies fail to take into account the volatility of renewable energy power generation capacity itself, making it difficult to predict the impact of electricity prices on grid load changes, resulting in incomplete consideration of the relationship between renewable energy consumption and electric vehicle charging demand. Therefore, it is necessary to propose a method to accurately describe the relationship between the two and to design charging electricity prices in order to stimulate renewable energy consumption. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for designing electric vehicle charging electricity prices to encourage the consumption of new energy.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for designing electric vehicle charging electricity prices to encourage new energy consumption includes the following steps:

[0008] S1: Evaluate the charging behavior of electric vehicles and calculate the charging load of electric vehicles;

[0009] S2: Divide the peak and valley periods of renewable energy power supply through membership function;

[0010] S3: Calculate the elastic response relationship between electric vehicle load demand and electricity price, and calculate the charging cost;

[0011] S4: With the goal of minimizing the total amount of new energy power supply and the charging cost, build a consumption model and set the objective function and constraints of the consumption model;

[0012] S5: The consumption model is solved by genetic algorithm to obtain the design scheme for adjusting the charging price of electric vehicles.

[0013] Furthermore, the charging behavior of electric vehicles is represented by a probability density function.

[0014] Furthermore, the calculation expression of the charging load of electric vehicles is:

[0015]

[0016] Where P ev (t) is the charging load of electric vehicles in period t; N is the number of electric vehicles; P ev,i (t) is the charging power of electric vehicle i in period t.

[0017] Furthermore, the response relationship between electric vehicle load demand and electricity price is:

[0018]

[0019] In the formula, is the load demand of electric vehicles in period i after the impact, ε ii is the elastic coefficient, ε ij is the mutual elastic coefficient, is the initial load demand of electric vehicles in period i, q i is the electric vehicle load demand in period i, is the initial electricity price in period i, ρ i is the electricity price in period i, is the initial electricity price in period j, ρ j is the electricity price in period j.

[0020] Furthermore, the expression of the charging start time of the electric vehicle is:

[0021]

[0022] In the formula, μ S is the average value of the charging start time, σ s is the standard deviation of the charging start time.

[0023] Furthermore, the objective function of the consumption model includes the objective function of minimizing the total amount of power generation and the objective function of minimizing the total charging cost of electric vehicle users. The calculation expression of the objective function of minimizing the total amount of power generation is:

[0024]

[0025] Where P ev,i (t) represents the charging power of the i-th electric vehicle in the t-th period; P w,i (t), P pv,i (t) represents the power generation of the i-th power generation unit in the t-th time period;

[0026] The calculation expression for solving the objective function of minimizing the total charging cost of electric vehicle users is:

[0027]

[0028] Where n represents the number of electric vehicles connected to the grid; P ev,i (t) represents the charging power of the i-th electric vehicle; c(t) represents the charging cost of the electric vehicle during period t; Δt ev,i Represents the charging time of the i-th electric vehicle in the t-th period.

[0029] Furthermore, the constraints of the consumption model include electric vehicle charging constraints, power generation unit active power constraints and electricity price constraints.

[0030] Furthermore, the calculation expression of the electric vehicle charging constraint is:

[0031]

[0032]

[0033] In the formula, are the upper and lower limits of the electric vehicle charging power respectively; They are the upper and lower limits of SOC when charging an electric vehicle.

[0034] Furthermore, the calculation expression of the active power constraint of the power generation unit is:

[0035] P Gimin ≤P Gi ≤P Gimax

[0036] Where P Gimax , P Gimin They represent the upper and lower limits of the distributed generation output respectively.

[0037] Furthermore, the calculation expression of the electricity price constraint is:

[0038] π min ≤π t ≤π max

[0039] In the formula, π max , π min They are the upper and lower limits of time-of-use electricity prices respectively.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1) The present invention divides the peak and valley periods of electricity consumption by membership function, establishes the elastic response relationship of electric vehicle load demand to electricity price and the consumption model, uses the objective function and constraint conditions of the consumption model to smooth the load fluctuation of the distribution network, solves the consumption model by genetic algorithm, obtains the electricity price design scheme that matches the charging load with the new energy power generation capacity, guides electric vehicles to charge in an orderly manner, and fully consumes new energy.

[0042] 2) The present invention regards the peak and valley periods as fuzzy sets and determines the membership degree of each moment through membership functions. This method can more flexibly and accurately reflect the real-time changes of peaks and valleys.

[0043] 3) The present invention considers the demand response on the load side to establish an electricity price model, through which the impact of electricity price changes on user consumption can be effectively reflected.

[0044] 4) The consumption model established by the present invention combines the charging behavior of electric vehicles with the intermittent fluctuations of renewable energy power generation to balance the fluctuations of renewable energy access to the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the process of the present invention.

[0046] Figure 2 Flowchart for solving the genetic algorithm.

[0047] Figure 3 This is a schematic diagram of the daily power generation of renewable energy.

[0048] Figure 4 This is the distribution diagram of electric vehicle charging load before the implementation of time-of-use electricity prices.

[0049] Figure 5 This is the distribution diagram of electric vehicle charging load after the implementation of time-of-use electricity prices.

[0050] Figure 6 Optimizing curve for electric vehicles to absorb new energy. DETAILED DESCRIPTION

[0051] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0052] Example 1

[0053] like Figure 1 As shown, the present invention is a method for designing electric vehicle charging electricity prices to encourage the consumption of new energy, comprising the following steps:

[0054] S1: Evaluate the charging behavior of electric vehicles and calculate the charging load of electric vehicles;

[0055] S2: Divide the peak and valley periods of renewable energy power supply through membership function;

[0056] S3: Calculate the elastic response relationship between electric vehicle load demand and electricity price, and calculate the charging cost;

[0057] S4: With the goal of minimizing the total amount of new energy power supply and the charging cost, build a consumption model and set the objective function and constraints of the consumption model;

[0058] S5: The consumption model is solved by genetic algorithm to obtain the design scheme for adjusting the charging price of electric vehicles.

[0059] The specific steps are:

[0060] S1: Evaluate the charging behavior of electric vehicles and calculate the charging load of electric vehicles;

[0061] The data is normalized, the probability distribution of the start charging time and the daily driving distance is fitted, the charging load of the electric vehicle is calculated, and the charging load of each electric vehicle in the area is accumulated to obtain the electric vehicle cluster load.

[0062] In this step, according to the statistical results, users charge their vehicles between 10:00 and 24:00, and most people use electric vehicles for short and medium distances. Now assume that users charge their vehicles between 10:00 and 24:00, and most people use electric vehicles for short and medium distances, and the start charging time satisfies the normal distribution, and the probability density function is:

[0063]

[0064] In the formula, μ S is the average value of the starting charging time, σ s is the standard deviation of the charging start time. In this embodiment, μ s =17.6;σ s =3.4;

[0065] To simplify the calculation, this embodiment regards the charging process of the electric vehicle as a constant power P c Charge;

[0066] According to the charging characteristics of electric vehicle power batteries, the state of charge of electric vehicles is:

[0067] SOC=(1-d / d m )SOC′

[0068] In the formula, SOC is the state of charge of the electric vehicle, d is the daily driving distance of the electric vehicle, and d mis the maximum driving distance of the electric vehicle, and SOC′ is the state of charge before the first trip.

[0069] The charging time of a single electric vehicle is:

[0070]

[0071] Where, t c is the charging time; C is the battery capacity of the electric vehicle.

[0072] The electric vehicle cluster load is obtained by accumulating the charging load of each electric vehicle in the area:

[0073]

[0074] Where, P ev (t) is the charging load of electric vehicles in period t; N is the number of electric vehicles; P ev,i (t) is the charging power of electric vehicle i in period t.

[0075] S2: Divide the peak and valley periods of renewable energy power supply through membership function;

[0076] Using the principles of fuzzy mathematics, the peak period and valley period of renewable energy power generation are regarded as two fuzzy sets, and the membership degree of any time within the period is established through the fuzzy membership function. The size of the membership degree can be directly planned into different time periods by analyzing it.

[0077] In this step, based on the measured data of renewable energy power generation in the known target area, assuming that a is the lowest output point of renewable energy and b is the highest output point of renewable energy, each moment constitutes a set T = {t1, t2…t 24}, the corresponding new energy output value constitutes a set P = {p1, p2…p 24}, P p , P v They represent the peak time and the valley time respectively. According to the theory of fuzzy mathematics, point a is at P p The membership degree is 0, in P v The membership degree is 1, and point b is in P p The membership degree is 1, in P v The membership degree is 0, and the semi-trapezoidal membership function expression is:

[0078]

[0079] In the formula, u pt is the membership degree of the peak period, u vt is the membership degree of the valley period.

[0080] In this scheme, fuzzy sets and membership functions are used to describe the degree to which each moment belongs to a peak period or a valley period. This method no longer absolutely divides a period into peak or valley, but assigns a "peak" or "valley" membership degree to each period based on the actual situation of renewable energy power generation. This fuzzy division method makes the definition of peak and valley periods more flexible and accurate, and can better reflect the actual changes in renewable energy power generation.

[0081] S3: Calculate the elastic response relationship between electric vehicle load demand and electricity price, and calculate the charging cost;

[0082] The sensitivity of load demand to electricity prices is called demand price elasticity, that is, within a certain time frame, changes in electricity prices cause changes in load demand, and this relationship is characterized by the rate of change of load demand and the rate of change of electricity prices.

[0083] In this step, when the electricity price increases, the corresponding demand also decreases. This relationship is characterized by the elasticity coefficient. The larger the elasticity coefficient, the greater the impact of the electricity price on the demand, that is, the greater the sensitivity of the demand to the electricity price. Since the load demand is not only affected by the electricity price at that moment, but also by the adjacent moments, the calculation expression of the elasticity coefficient is:

[0084]

[0085] In the formula, in the formula, is the load demand of electric vehicles in period i after the impact, is the initial load demand of electric vehicles in period i, q i is the electric vehicle load demand in period i, is the initial electricity price in period i, ρ i is the electricity price in period i, is the initial electricity price in period j, ρ j is the electricity price in period j, ε ij is the mutual elasticity coefficient, which indicates the impact of the electricity price at time i on the charging demand at time j, ε ii is the self-elasticity coefficient, which indicates the impact of the current electricity price on the demand at that time. The relationship between charging demand and electricity price in each period can be expressed as follows:

[0086]

[0087] In the formula, M represents the price elasticity matrix. Using the price elasticity matrix and the rate of change of electricity prices, the corresponding change in electric vehicle charging load can be obtained. The elasticity coefficient matrix for typical peak, flat and valley periods is a third-order matrix:

[0088]

[0089] Therefore, when only the self-elasticity coefficient is considered, the charging load of electric vehicles in period i is Transformed into The expression is:

[0090]

[0091] When considering the mutual elasticity coefficient, the electric vehicle charging load in period i is given by Transformed into The expression is:

[0092]

[0093] Among them, i and j are different time periods. Taking into account the self-elasticity coefficient and the mutual elasticity coefficient, when the time-of-use electricity price policy is implemented, the charging load demand response of electric vehicles in time period i is:

[0094]

[0095] From the functional relationship, we can know that when the peak and valley time-of-use electricity prices, original load data and demand elasticity coefficient are known, the electric vehicle load demand in each period can be obtained. According to the price elasticity matrix and the rate of change of electricity prices, it can be used to predict the change in the charging load of electric vehicles.

[0096] In this solution, the concept of price elasticity is introduced to measure the impact of electricity price changes on the demand for electric vehicle charging, thus providing a scientific basis for electricity price design and adjustment. By deeply analyzing the sensitivity of electric vehicle users to electricity price changes, this solution can more accurately predict and adjust the charging behavior of electric vehicles and guide the grid load to a more balanced and efficient direction.

[0097] S4: With the goal of minimizing the total amount of new energy power supply and the charging cost, build a consumption model and set the objective function and constraints of the consumption model;

[0098] Taking into account the orderly charging of electric vehicles, a new energy consumption model for electric vehicles is constructed with the goal of minimizing the total amount of unconsumed new energy power generation and minimizing the charging costs of car owners.

[0099] The objective functions of the consumption model include:

[0100] In order to reduce the waste of renewable energy power generation, by controlling the local consumption of electric vehicles and batteries, the objective function for minimizing the total amount of renewable energy power generation that has not been consumed is:

[0101]

[0102] Where P ev,i(t) represents the charging power of the i-th electric vehicle in the t-th period, in kW; P w,i (t), P pv,i (t) represents the power generation of the i-th wind power unit and photovoltaic power generation unit in the t-th time period, in kW;

[0103] In order to take into account the interests of both users and the power grid, the objective function to minimize the total charging cost of electric vehicle users is:

[0104]

[0105] Where n represents the number of electric vehicles connected to the grid, in units of vehicles; P ev,i (t) represents the charging power of the i-th electric vehicle, in kW; c(t) represents the charging cost of the electric vehicle in period t, in yuan / (kW·h); Δt ev,i Represents the charging time of the i-th electric vehicle in the t-th period, in h.

[0106] The constraints of the consumption model include:

[0107] The calculation expression of electric vehicle charging constraint is:

[0108]

[0109]

[0110] In the formula, are the upper and lower limits of the electric vehicle charging power respectively; They are the upper and lower limits of SOC when charging an electric vehicle.

[0111] The calculation expression of the active power constraint of the power generation unit is:

[0112] P Gimin ≤P Gi ≤P Gimax

[0113] Where P Gimax , P Gimin They represent the upper and lower limits of the distributed generation output respectively.

[0114] The calculation expression of electricity price constraint is:

[0115] π min ≤π t ≤π max

[0116] In the formula, π max , π min They are the upper and lower limits of time-of-use electricity prices respectively.

[0117] The optimization model constructed in this solution not only considers the impact of electric vehicle charging on the power grid, but also incorporates the amount of renewable energy power generation as an important parameter. By optimizing this model, the charging period and electricity price strategy can be adjusted in a timely manner while meeting the charging needs of users to maximize the consumption of renewable energy power generation. This comprehensive optimization method can significantly improve the utilization efficiency of renewable energy, reduce the amount of renewable energy that is forced to be abandoned due to excess power generation, and also help reduce the charging costs of electric vehicle users, achieving a dual improvement in economic and environmental benefits.

[0118] S5: The consumption model is solved by genetic algorithm to obtain the design scheme for adjusting the charging price of electric vehicles.

[0119] The weighted sum method is used to integrate multiple objective functions into a single objective function. Since the dimensions of each objective function are different, the objective functions are first normalized and then the weighted sum is taken. The specific process is as follows: Figure 2 shown.

[0120] The objective function expression is:

[0121]

[0122] w1+w2=1

[0123] In the formula, f 1max is the electricity generation of new energy; f 2max is the charging cost under traditional car usage habits; w1 and w2 are weight coefficients of the objective function. In this embodiment, w1=0.5, w2=0.5.

[0124] This embodiment selects the measured data of a charging station in a certain area to simulate the charging of electric vehicles. The charging station is equipped with a wind turbine with a capacity of 700kW, a total rated capacity of 38kW PV panels, and a total of 200 EVs. The daily power generation of new energy is as follows: Figure 3 As shown in the figure, the initial charging time and daily mileage of EVs follow the normal distribution N(17.6,3.4 2 ) and log-normal distribution log-N(3.2,0.88 2 ). The charging power is 7kW, the battery capacity is 60kW·h, the vehicle SOC is no more than 0.9 when charging, and no less than 0.4 when traveling. The example sets the electricity price update time interval Δt to 1h and the number of time periods T to 24. Disorderly charging uses a fixed electricity price, and a horizontal electricity price is used for all 24 time periods, which is equal to the normal time period electricity price under the time-of-use electricity price.

[0125] First, the output power membership degree corresponding to each moment in the daily power generation curve of renewable energy is calculated, and the results are shown in Table 1.

[0126] Table 1 Peak-valley membership at each time

[0127]

[0128] Let function f(t i ) is the time period division function:

[0129]

[0130] Among them, δ is the set threshold. When the peak / valley membership at a certain moment is greater than δ, it indicates that it has obvious peak / valley period characteristics, and the moment is classified into the corresponding peak / valley period; if the membership is less than δ, the moment is classified into the normal period. This paper combines other academic achievements and sets it to 0.7. According to the calculation results in the above table, it can be concluded that new energy power generation is in the valley period at 9, 10, 19, and 21, in the normal period at 3, 4, 8, 11, 12, 13, 17, 18, 20, and 22, and in the peak period at 1, 2, 5, 6, 7, 14, 15, 16, 23, and 24.

[0131] The initial electricity price is set at 1 yuan / (kW·h), the minimum charging price shall not be lower than the marginal cost of electricity generation, 0.25 yuan / (kW·h), and the maximum price shall not be higher than 2 yuan / (kW·h). The price elasticity matrix Q of electric vehicles during peak, valley and normal periods can be obtained:

[0132]

[0133] The multi-objective function and multi-constraint electric vehicle charging load model are solved by genetic algorithm to obtain the peak, valley and normal period electricity prices. The time-of-use electricity price results are shown in Table 2.

[0134] Table 2 Peak-valley and flat electricity prices

[0135]

[0136] In the two cases of disorderly charging at a fixed electricity price and orderly charging at a time-of-use electricity price, the charging load diagram of electric vehicles in each period is as follows: Figure 4 , Figure 5 As shown. It can be seen that in the case of disorderly charging at a fixed electricity price, users will not respond to the electricity price, and the charging load distribution is mainly affected by the behavior habits of car owners. The use of time-of-use electricity prices to orderly charge and reasonably adjust the EVs charging load can transfer the random charging load from the peak electricity consumption period in the evening to the peak period of new energy power generation, thereby effectively smoothing the grid load curve.

[0137] In addition, the peak period of EV charging does not completely coincide with the peak period of renewable energy generation. The superposition of the two will result in a large amount of renewable energy generation that cannot be absorbed, and at certain times there will be a peak in reverse absorption, such as Figure 6As shown in Figure 3. Between 4 and 8 and 13 and 16 o'clock, a total of 1337.31kW of new energy generation could not be absorbed, and between 18 and 22 o'clock in the evening, a reverse absorption peak of 524.12kW was generated due to the access of EVs. After the adoption of the electricity price incentive policy, the orderly charging of 200 electric vehicles absorbed the new energy generation between 4 and 8 and 13 and 16 o'clock to 842.58kW. Table 3 shows that the total charging cost of EVs also dropped from 1935.63 yuan to 1255.34 yuan. Compared with disorderly charging, the total amount of new energy generation that was not absorbed by the orderly charging strategy was also reduced by 22.21%, which shows that the orderly charging strategy promotes the absorption of new energy by EVs.

[0138] The simulation results of the example show that the electric vehicle charging electricity price design method for encouraging the consumption of new energy proposed by the technology of the present invention can shift the charging load in time while meeting the needs of users, thereby achieving the effect of maximizing the consumption of new energy and reducing the charging costs of electric vehicles.

[0139] Table 3 Optimization results and comparison

[0140]

[0141] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for designing electric vehicle charging electricity prices to encourage the consumption of new energy, characterized in that: The following steps are involved: S1: Evaluate the charging behavior of electric vehicles and calculate the charging load of electric vehicles; S2: Divide the peak and valley periods of renewable energy power supply through membership function; S3: Calculate the elastic response relationship between electric vehicle load demand and electricity price, and calculate the charging cost; S4: With the goal of minimizing the total amount of new energy power supply and the charging cost, build a consumption model and set the objective function and constraints of the consumption model; S5: The consumption model is solved by genetic algorithm to obtain the design scheme for adjusting the charging price of electric vehicles.

2. A method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 1, characterized in that: The charging behavior of the electric vehicle is represented by a probability density function.

3. A method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 1, characterized in that: The calculation expression of the charging load of the electric vehicle is: Where P ev (t) is the charging load of electric vehicles in period t; N is the number of electric vehicles; P ev,i (t) is the charging power of electric vehicle i in period t.

4. The method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 1, characterized in that: The elastic response relationship between the electric vehicle load demand and the electricity price is: In the formula, is the load demand of electric vehicles in period i after the impact, ε ii is the elastic coefficient, ε ij is the mutual elastic coefficient, is the initial load demand of electric vehicles in period i, q i is the electric vehicle load demand in period i, is the initial electricity price in period i, ρ i is the electricity price in period i, is the initial electricity price in period j, ρ j is the electricity price in period j.

5. The method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 1, characterized in that: The expression of the charging start time of the electric vehicle is: In the formula, μ S is the average value of the charging start time, σ s is the standard deviation of the charging start time.

6. A method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 1, characterized in that: The objective function of the consumption model includes an objective function for minimizing the total amount of power generation and an objective function for minimizing the total charging cost of electric vehicle users. The calculation expression of the objective function for minimizing the total amount of power generation is: Where P ev,i (t) represents the charging power of the i-th electric vehicle in the t-th period; P w,i (t), P pv,i (t) represents the power generation of the i-th power generation unit in the t-th time period; The calculation expression of the objective function for minimizing the total charging cost of electric vehicle users is: Where n represents the number of electric vehicles connected to the grid; P ev,i (t) represents the charging power of the i-th electric vehicle; c(t) represents the charging cost of the electric vehicle during period t; Δt ev,i Represents the charging time of the i-th electric vehicle in the t-th period.

7. A method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 1, characterized in that: The constraints of the consumption model include electric vehicle charging constraints, power generation unit active power constraints and electricity price constraints.

8. A method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 7, characterized in that: The calculation expression of the electric vehicle charging constraint is: In the formula, are the upper and lower limits of the electric vehicle charging power respectively; They are the upper and lower limits of SOC when charging an electric vehicle.

9. A method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 7, characterized in that: The calculation expression of the active power constraint of the power generation unit is: P Gimin ≤P Gi ≤P Gimax Where P Gimax , P Gimin They represent the upper and lower limits of the distributed generation output respectively.

10. A method for designing electric vehicle charging electricity prices to encourage new energy consumption according to claim 7, characterized in that: The calculation expression of the electricity price constraint is: π min ≤π t ≤π max In the formula, π max , π min They are the upper and lower limits of time-of-use electricity prices respectively.

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