A method and system for optimizing the dispatching of electric vehicles

Abstract

The present invention relates to a method and system for optimizing the dispatching of a distribution area involving electric vehicles, including: determining the charging and discharging electricity prices of electric vehicles in the distribution area according to the predicted load demand values in the distribution area except for the charging and discharging powers of electric vehicles; determining the optimal operating powers of various power devices in the distribution area according to the charging and discharging electricity prices of electric vehicles in the distribution area; and adjusting the operating powers of various power devices in the distribution area to the optimal operating powers of various power devices in the distribution area. The technical solution provided by the present invention realizes the adjustment of the charging and discharging powers of electric vehicles in the distribution area by dynamically adjusting the charging and discharging electricity prices of electric vehicles, thereby enhancing the ability of the distribution area power grid to absorb intermittent renewable energies such as wind power and photovoltaic power generation; at the same time, the combined use of a distributed energy storage system and electric vehicles can play a role in peak shaving and valley filling of the distribution area power grid, thereby reducing the power grid peak shaving pressure, improving the power grid operation efficiency, delaying and reducing the construction of power sources and power grids, and alleviating the power supply demand of peak loads.

Description

Technical Field

[0001] The present invention relates to the field of energy Internet, and particularly to a method and system for optimizing the dispatching of a distribution area involving electric vehicles. Background Art

[0002] Currently, many electric vehicles are charged through a microgrid. The access of electric vehicles to the microgrid is a trend in the future development of the microgrid.

[0003] The continuous addition of electric vehicle loads to the microgrid will have an unignorable impact on the operation of the microgrid. For example, the growth of uncertain loads in the microgrid and the decline in the power experience of users. At the same time, the development of the technology for electric vehicles to feed power back to the power system makes electric vehicles become mobile energy storage devices, and the impact of electric vehicles on the microgrid is increasing. Therefore, the microgrid must incorporate the charging and discharging of electric vehicles into the entire microgrid dispatching process to improve the dispatching performance of the microgrid.

[0004] Currently, how to formulate an optimal dispatching plan for electric vehicles to participate in the microgrid is a difficult problem to be solved by us. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method for optimizing the dispatching of a distribution area involving electric vehicles. This method adjusts the charging and discharging electricity prices of electric vehicles dynamically to adjust the charging and discharging power of electric vehicles in the distribution area, thereby enhancing the ability of the distribution area power grid to absorb intermittent renewable energy such as wind power and photovoltaic power. At the same time, the combined use of a distributed energy storage system and electric vehicles can play a role in peak shaving and valley filling for the distribution area power grid, thereby reducing the peak shaving pressure of the power grid, improving the operation efficiency of the power grid, delaying and reducing the construction of power sources and power grids, and alleviating the power supply demand during peak loads.

[0006] The purpose of the present invention is achieved by adopting the following technical solutions:

[0007] The present invention provides a method for optimizing the dispatching of a distribution area involving electric vehicles, and the improvement lies in that the method includes:

[0008] Determine the charging and discharging electricity prices of electric vehicles in the distribution area according to the predicted value of the load demand in the distribution area except for the charging and discharging power of electric vehicles;

[0009] Determine the optimal operating power of each power device in the distribution area according to the charging and discharging electricity prices of electric vehicles in the distribution area;

[0010] Adjust the operating power of each power device in the distribution area to the optimal operating power of each power device in the distribution area.

[0011] Preferably, the determining the charging and discharging electricity prices of electric vehicles in the distribution area according to the predicted value of the load demand in the distribution area except for the charging and discharging power of electric vehicles includes:

[0012] Determine the charging price λ of electric vehicles in the distribution area at time t according to the following formula ch (t):

[0013]

[0014] In the formula, α is the weight of the variable part of the charging price of electric vehicles, P ch (t) is the charging power of electric vehicles in the distribution area at time t, P dis (t) is the discharging power of electric vehicles in the distribution area at time t; P Y (t) is the predicted value of the load demand in the distribution area at time t except for the charging and discharging powers of electric vehicles, λ ch,base is the price constant of the fixed part of the charging price of electric vehicles; λ ch is the price constant of the variable part of the charging price of electric vehicles; t ∈ (1~T), and T is the total number of moments in the scheduling period;

[0015] Determine the discharging price λ of electric vehicles in the distribution area at time t according to the following formula dis (t):

[0016]

[0017] In the formula, β is the weight of the variable part of the discharging price of electric vehicles, λ dis is the price constant of the variable part of the discharging price of electric vehicles; λ dis,base is the price constant of the fixed part of the discharging price of electric vehicles.

[0018] Preferably, determining the optimal operating power of each power device in the distribution area according to the charging and discharging prices of electric vehicles in the distribution area includes:

[0019] Substitute the charging and discharging prices of electric vehicles in the distribution area into the pre-constructed distribution area optimal scheduling model, and use the genetic algorithm to solve the pre-constructed distribution area optimal scheduling model to obtain the optimal operating power of each power device in the distribution area;

[0020] Among them, the power devices in the distribution area include: photovoltaic output devices, wind power output devices, combined heat and power devices, energy storage devices and electric vehicle devices in the distribution area.

[0021] Furthermore, determine the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula

[0022]

[0023] In the formula, F is the objective value of the substation area optimal scheduling model, λ1 is the weight of the objective function of distributed energy consumption, f1(t) is the distributed energy consumption in the substation area at time t, λ2 is the weight of the objective function of user charging method satisfaction, f2(t) is the user charging method satisfaction in the substation area at time t, λ3 is the weight of the objective function of carbon emissions, and f3(t) is the carbon emissions in the substation area at time t;

[0024] Among them, the distributed energy consumption f1(t) in the substation area at time t is determined by the following formula:

[0025]

[0026] In the formula, P W (t) is the wind power generation output in the substation area at time t; P PV (t) is the photovoltaic power generation output in the substation area at time t; P ch (t) is the charging power of electric vehicles in the substation area at time t, P dis (t) is the discharging power of electric vehicles in the substation area at time t; P Y (t) is the predicted value of the load demand in the substation area at time t except for the charging and discharging power of electric vehicles, t ∈ (1 to T), and T is the total number of time moments in the scheduling period;

[0027] The user charging method satisfaction f2(t) in the substation area at time t is determined by the following formula:

[0028]

[0029] In the formula, P EV,i,MAX is the charging power of the i-th electric vehicle in the substation area when the user satisfaction is the maximum, P EV,i (t) is the charging power of the i-th electric vehicle in the substation area at time t, Δt is the time interval between two adjacent time moments, i ∈ (1 to N), and N is the total number of electric vehicles in the substation area;

[0030] The carbon emissions f3(t) in the substation area at time t are determined by the following formula:

[0031]

[0032] In the formula, P j (t) is the output of the j-th micro-source device in the substation area at time t; k j is the carbon emission coefficient of the j-th micro-source device in the substation area, j ∈ (1 to M), and M is the total number of micro-source devices in the substation area.

[0033] Furthermore, the constraint conditions of the objective function of the pre-constructed substation area optimal scheduling model are determined by the following formula:

[0034] Determine the user benefit constraint condition of the objective function of the pre-constructed transformer substation area optimal scheduling model according to the following formula:

[0035] c(i)≥c exp

[0036] In the formula, c(i) is the charging and discharging benefit of the i-th electric vehicle within the scheduling period, and c exp is the expected benefit of the user within the scheduling period;

[0037] Among them, determine the charging and discharging benefit c(i) of the i-th electric vehicle within the scheduling period according to the following formula:

[0038]

[0039] In the formula, P ch,i (t) is the charging power of the i-th electric vehicle in the transformer substation area at time t, and P dis,i (t) is the discharging power of the i-th electric vehicle in the transformer substation area at time t, and λ ch (t) is the charging electricity price in the transformer substation area at time t, and λ dis (t) is the discharging electricity price in the transformer substation area at time t, Δt is the time interval between two adjacent times, and t d (i) is the charging and discharging end time of the i-th electric vehicle in the transformer substation area; t a (i) is the charging and discharging start time of the i-th electric vehicle in the transformer substation area;

[0040] Determine the power balance constraint condition of the objective function of the pre-constructed transformer substation area optimal scheduling model according to the following formula:

[0041] P CHPe (t)+P W (t)+P PV (t)+P Grid (t)=P Y (t)+P ch (t)-P dis (t)+δP ESB (t)

[0042] In the formula, P CHPe (t) is the power generation power of the cogeneration unit equipment in the transformer substation area at time t, and P W (t) is the wind power generation output in the transformer substation area at time t, and P PV (t) is the photovoltaic power generation output in the transformer substation area at time t, and P Grid (t) is the electric power purchased from the main power grid in the transformer substation area at time t, and P Y (t) is the predicted value of the load demand in the transformer substation area at time t excluding the charging and discharging power of electric vehicles, and P ch (t) is the charging power of electric vehicles in the transformer substation area at time t, and P dis(t) is the discharge power of electric vehicles in the distribution area at time t; δ is the charge and discharge coefficient of the energy storage device. When δ = 1, the energy storage device charges; when δ = -1, the energy storage device discharges, P ESB (t) is the charge and discharge power of the energy storage device in the distribution area at time t;

[0043] Determine the micro-source output constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0044] P jmin ≤P j (t)≤P jmax

[0045] P j (t) is the output of the j-th micro-source device in the distribution area at time t; P jmin is the minimum output of the j-th micro-source device in the distribution area; P jmax is the maximum output of the j-th micro-source device in the distribution area;

[0046] Determine the electric vehicle discharge constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0047]

[0048] In the formula, P dis,i (t) is the discharge power of the i-th electric vehicle in the distribution area at time t; is the maximum limit of the total electric vehicle discharge in the entire scheduling period;

[0049] Determine the electric vehicle charging constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0050]

[0051] In the formula, P ch,i (t) is the charging power of the i-th electric vehicle in the distribution area at time t; is the maximum limit of the total electric vehicle charging in the entire scheduling period;

[0052] Determine the energy storage power constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0053] S k,min ≤S k (t)≤S k,max

[0054] In the formula, S k (t) is the power of the k-th energy storage system in the distribution area at time t; S k,min is the minimum limit of the power of the k-th energy storage system in the distribution area; S k,maxIt is the maximum power limit of the k-th energy storage system in the substation area.

[0055] Further, the use of the genetic algorithm to solve the pre-constructed substation area optimization scheduling model includes:

[0056] Step 1: Initialize the population and the genetic iteration number ξ = 1;

[0057] Step 2: Determine the fitness of each individual in the population;

[0058] Step 3: Eliminate the individuals in the population whose fitness is lower than the threshold;

[0059] Step 4: Cross the individuals in the population to generate gene recombination;

[0060] Step 5: Modify the actual length of the chromosomes of the individuals in the population according to the equivalent length of the chromosomes of the individuals in the population;

[0061] Step 6: Update the mutation probability and perform mutation operations on each individual in the population;

[0062] Step 7: Determine whether the current iteration number ξ = W holds. If so, output the individual with the highest fitness in the population as the optimal solution of the pre-constructed substation area optimization scheduling model; otherwise, return to Step 2;

[0063] where W is the maximum number of iterations, the fitness function of each individual in the population is f(ε) = 1 / F, F is the target value of the substation area optimization scheduling model, and f(ε) is the fitness value of the ε-th individual in the population.

[0064] Further, the said Step 5 includes:

[0065] If the equivalent length of the chromosomes of the individuals in the population remains unchanged after the individuals in the population cross, and the actual length of the chromosomes of the individuals in the population decreases after the individuals in the population cross, then add 0 at the end of the chromosomes of the individuals in the population until the actual length of the chromosomes of the individuals in the population is the same as that before the individuals in the population cross;

[0066] If the equivalent length of the chromosomes of the individuals in the population remains unchanged after the individuals in the population cross, and the actual length of the chromosomes of the individuals in the population increases after the individuals in the population cross, then randomly delete 0 from the front section of the cross section of the chromosomes of the individuals in the population until the actual length of the chromosomes of the individuals in the population is the same as that before the individuals in the population cross.

[0067] The present invention provides a substation area optimization scheduling system participated by electric vehicles. The improvement lies in that the system includes:

[0068] The first determination module is configured to determine the charging and discharging electricity price of electric vehicles in the distribution area according to the predicted value of the load demand in the distribution area except for the charging and discharging power of electric vehicles.

[0069] The second determination module is configured to determine the optimal operating power of each power device in the distribution area according to the charging and discharging electricity price of electric vehicles in the distribution area.

[0070] The adjustment module is configured to adjust the operating power of each power device in the distribution area to the optimal operating power of each power device in the distribution area.

[0071] Compared with the closest prior art, the beneficial effects of the present invention are as follows:

[0072] The technical solution provided by the present invention determines the charging and discharging electricity price of electric vehicles in the distribution area according to the predicted value of the load demand in the distribution area except for the charging and discharging power of electric vehicles; determines the optimal operating power of each power device in the distribution area according to the charging and discharging electricity price of electric vehicles in the distribution area; adjusts the operating power of each power device in the distribution area to the optimal operating power of each power device in the distribution area; realizes the adjustment of the charging and discharging power of electric vehicles in the distribution area by dynamically adjusting the charging and discharging electricity price of electric vehicles, thereby enhancing the ability of the distribution network to absorb intermittent renewable energy such as wind power and photovoltaic power; the combined use of the distributed energy storage system and electric vehicles can play a role in peak shaving and valley filling for the distribution network, thereby reducing the peak shaving pressure of the power grid, improving the operating efficiency of the power grid, delaying and reducing the construction of power sources and power grids, and alleviating the power supply demand during peak loads.

[0073] The technical solution provided by the present invention uses an improved genetic algorithm to solve the pre-constructed distribution area optimization scheduling model, which speeds up the calculation convergence speed and further improves the efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a flowchart of a distribution area optimization scheduling method involving electric vehicles;

[0075] Figure 2 is a structural diagram of a distribution area optimization scheduling system involving electric vehicles. DETAILED DESCRIPTION OF THE INVENTION

[0076] The following further describes in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0077] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0078] In the best embodiment of the present invention, taking the transformer substation area (the transformer substation area refers to the power supply area of a transformer) as the research object, there is distributed wind and photovoltaic power generation in the transformer substation area, a combined heat and power generation system provides electric energy and heat energy, there are energy storage batteries for stabilizing the power fluctuation in the transformer substation area, and a centralized intelligent charging station for charging and discharging electric vehicles; among them, the charging station is equipped with a monitoring and control system that can detect and record the start and end times of vehicle charging and the current state of charge SOC of the battery.

[0079] The present invention provides a method for optimizing the dispatching of a transformer substation area involving electric vehicles, as Figure 1 shown, the method includes:

[0080] Step 101. Determine the charging and discharging electricity prices of electric vehicles in the transformer substation area according to the predicted value of the load demand in the transformer substation area except for the charging and discharging power of electric vehicles.

[0081] In the best embodiment of the present invention, for the uncertainty of the load in the transformer substation area, a dynamic electricity price adjustment model is specifically set. As can be seen from the above formula, when the load demand in the transformer substation area is high, the electricity price also increases accordingly. And the load demand in the transformer substation area is composed of the load demand except for the charging and discharging power of electric vehicles, the charging power of electric vehicles in the transformer substation area and the discharging power of electric vehicles. Therefore, it can guide users to discharge more and charge less when the load demand in the transformer substation area is high, and discharge less and charge more when the load demand is low, so as to orderly control the charging behavior of electric vehicles, effectively suppress the load fluctuation and improve the user economy. At the same time, it can also play the role of peak shaving and valley filling for the transformer substation area power grid, which is beneficial to the consumption of new energy such as photovoltaic and wind power in the transformer substation area and improves the energy utilization rate.

[0082] Step 102. Determine the optimal operating power of each power device in the transformer substation area according to the charging and discharging electricity prices of electric vehicles in the transformer substation area.

[0083] Step 103. Adjust the operating power of each power device in the transformer substation area to the optimal operating power of each power device in the transformer substation area.

[0084] Specifically, the step 101 includes:

[0085] Determine the charging electricity price λ ch (t) of electric vehicles in the transformer substation area at time t according to the following formula:

[0086]

[0087] Wherein, α is the weight of the variable part of the charging price of the electric vehicle, and P ch (t) is the charging power of the electric vehicle at time t in the distribution area, and P dis (t) is the discharging power of the electric vehicle at time t in the distribution area; P Y (t) is the predicted value of the load demand in the distribution area at time t excluding the charging and discharging powers of the electric vehicles, and λ ch,base is the price constant of the fixed part of the charging price of the electric vehicle; λ ch is the price constant of the variable part of the charging price of the electric vehicle; t ∈ (1 to T), and T is the total number of moments in the scheduling period;

[0088] The discharging price λ dis (t) of the electric vehicle at time t in the distribution area is determined according to the following formula:

[0089]

[0090] Wherein, β is the weight of the variable part of the discharging price of the electric vehicle, and λ dis is the price constant of the variable part of the discharging price of the electric vehicle; λ dis,base is the price constant of the fixed part of the discharging price of the electric vehicle.

[0091] Under the mechanism that electric vehicle users independently respond to the fluctuating electricity price in the distribution area to formulate charging and discharging plans, if static time-of-use electricity price is adopted, the charging and discharging behaviors of high-penetration electric vehicles may lead to new peaks and valleys in the system load, affecting the safe operation of the distribution system.

[0092] Therefore, the present invention proposes a dynamic time-of-use electricity price adjustment model. The time-of-use electricity price consists of two parts, namely, a fixed part and a variable part. The fixed part of the charging price is determined by factors such as the power transmission and distribution cost and the subsidy for the electric vehicle to participate in orderly charging. The fixed part of the discharging price is determined by factors such as the charging cost and the battery loss of the electric vehicle. The variable electricity price part is determined by the load curve. The higher the load in a period, the higher the charging and discharging electricity price, so as to guide the electric vehicle to discharge during the peak load period. The lower the load in a period, the lower the charging and discharging electricity price, so as to guide the electric vehicle to charge during the low load period.

[0093] The distribution system control center modifies the load curve in real time according to the charging and discharging plans of the newly connected electric vehicles submitted by the local electric vehicle dispatching agency, and updates the system time-of-use electricity price at a given time period (such as 15 minutes) based on the latest load curve, so as to guide the electric vehicle users to formulate charging and discharging strategies according to the expectations of the power system dispatching agency.

[0094] Specifically, step 102 includes:

[0095] Substitute the charging and discharging electricity prices of electric vehicles in the substation area into the pre-constructed optimal dispatching model of the substation area, and use the genetic algorithm to solve the pre-constructed optimal dispatching model of the substation area to obtain the optimal operating power of each power device in the substation area;

[0096] Among them, the power devices in the substation area include: photovoltaic power generation equipment, wind power generation equipment, combined heat and power generation equipment, energy storage equipment and electric vehicle equipment in the substation area.

[0097] Furthermore, determine the objective function of the pre-constructed optimal dispatching model of the substation area according to the following formula:

[0098]

[0099] In the formula, F is the target value of the optimal dispatching model of the substation area, λ1 is the weight occupied by the objective function of distributed energy consumption, f1(t) is the distributed energy consumption at time t in the substation area, λ2 is the weight occupied by the objective function of user charging method satisfaction, f2(t) is the user charging method satisfaction at time t in the substation area, λ3 is the weight occupied by the objective function of carbon emissions, and f3(t) is the carbon emissions at time t in the substation area;

[0100] In the best embodiment of the present invention, there are actually 3 objective functions, namely: the objective function of minimizing carbon emissions in the substation area, the objective function of maximizing the charging satisfaction of electric vehicle users in the substation area, and the objective function of maximizing the consumption rate of new energy such as photovoltaic and wind power; according to the actual working conditions, weights are assigned to each objective function, and the weights λ1 + λ2 + λ3 = 1.

[0101] Among them, determine the distributed energy consumption f1(t) at time t in the substation area according to the following formula:

[0102]

[0103] In the formula, P W (t) is the wind power generation output at time t in the substation area; P PV (t) is the photovoltaic power generation output at time t in the substation area; P ch (t) is the charging power of electric vehicles at time t in the substation area, P dis (t) is the discharging power of electric vehicles at time t in the substation area; P Y (t) is the predicted value of the load demand excluding the charging and discharging power of electric vehicles at time t in the substation area, t ∈ (1~T), and T is the total number of moments in the dispatching period;

[0104] Determine the user charging method satisfaction f2(t) at time t in the substation area according to the following formula:

[0105]

[0106] In the formula, PEV,i,MAX is the charging power of the i-th electric vehicle in the area when the user satisfaction is maximized, P EV,i (t) is the charging power of the i-th electric vehicle in the area at time t, Δt is the time interval between two adjacent times, i ∈ (1~N), and N is the total number of electric vehicles in the area;

[0107] In the best embodiment of the present invention, the large-scale electric vehicle participating in the charging optimization scheduling can reduce the costs of the power grid and users, increase the peak shaving capacity of the area, but may affect the satisfaction of charging vehicle users.

[0108] The present invention takes the maximum user satisfaction of electric vehicles as the objective function, considers the satisfaction of the user's charging method, and enables users to participate more actively in the optimization scheduling of the area.

[0109] Since before the electric vehicle users participate in the area optimization scheduling, the users charge immediately after the vehicle returns to end the day's journey, and there is no charging delay at this time, the satisfaction of the user's charging method is maximized in this case.

[0110] For the uncertain fluctuating electricity price in the area, the user needs to change the starting charging time according to the electricity price guidance of the power grid. This means that the vehicle owner may change the original charging habit to participate in the charging scheduling of the power grid in exchange for economic benefits, but this will affect the user's satisfaction.

[0111] The integrated energy system under the area has energy diversity and the complexity of different energy conversion processes. Carbon emissions are generated during the energy conversion process. Therefore, the carbon emission amount f3(t) in the area at time t is determined by the following formula:

[0112]

[0113] In the formula, P j (t) is the output of the j-th micro-source device in the area at time t; k j is the carbon emission coefficient of the j-th micro-source device in the area, j ∈ (1~M), and M is the total number of micro-source devices in the area.

[0114] Furthermore, the constraint conditions of the objective function of the pre-constructed area optimization scheduling model are determined by the following formula:

[0115] The user revenue constraint condition of the objective function of the pre-constructed area optimization scheduling model is determined by the following formula:

[0116] c(i) ≥ c exp

[0117] In the formula, c(i) is the charging and discharging revenue of the i-th electric vehicle during the scheduling period, and c exp is the expected revenue of the user during the scheduling period;

[0118] In the optimal embodiment of the present invention, the purpose of electric vehicle users participating in scheduling is to maximize their own benefits on the premise of meeting the basic charging needs. Under the dynamic electricity price mechanism, electric vehicle users maximize their economic benefits by responding to the dynamic electricity price and charging during low electricity price periods and discharging during high electricity price periods. Here, the economic benefit of the user is regarded as a constraint condition, so that the economic benefit of the user is at the level expected by the user. (This expected level is obtained based on the user benefits in previous years).

[0119] Among them, the charging and discharging benefit c(i) of the i-th electric vehicle within the scheduling period is determined by the following formula:

[0120]

[0121] In the formula, P ch,i (t) is the charging power of the i-th electric vehicle in the distribution area at time t, P dis,i (t) is the discharging power of the i-th electric vehicle in the distribution area at time t, λ ch (t) is the charging electricity price in the distribution area at time t, λ dis (t) is the discharging electricity price in the distribution area at time t, Δt is the time interval between two adjacent moments, t d (i) is the charging and discharging end time of the i-th electric vehicle in the distribution area; t a (i) is the charging and discharging start time of the i-th electric vehicle in the distribution area;

[0122] The power balance constraint condition of the objective function of the pre-constructed distribution area optimal scheduling model is determined by the following formula:

[0123] P CHPe (t)+P W (t)+P PV (t)+P Grid (t)=P Y (t)+P ch (t)-P dis (t)+δP ESB (t)

[0124] In the formula, P CHPe (t) is the power generation power of the combined heat and power unit equipment in the distribution area at time t, P W (t) is the wind power generation output in the distribution area at time t, P PV (t) is the photovoltaic power generation output in the distribution area at time t, P Grid (t) is the electric power purchased from the main power grid in the distribution area at time t, P Y (t) is the load demand forecast value in the distribution area at time t except for the charging and discharging power of electric vehicles, P ch (t) is the charging power of electric vehicles in the distribution area at time tdis (t) is the discharge power of electric vehicles in the distribution area at time t; δ is the charge-discharge coefficient of the energy storage device. When δ = 1, the energy storage device charges; when δ = -1, the energy storage device discharges, and P ESB (t) is the charge-discharge power of the energy storage device in the distribution area at time t;

[0125] Determine the micro-source output constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0126] P jmin ≤P j (t)≤P jmax

[0127] P j (t) is the output of the j-th micro-source device in the distribution area at time t; P jmin is the minimum output of the j-th micro-source device in the distribution area; P jmax is the maximum output of the j-th micro-source device in the distribution area;

[0128] Determine the electric vehicle discharge constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0129]

[0130] In the formula, P dis,i (t) is the discharge power of the i-th electric vehicle in the distribution area at time t; is the maximum limit of the total electric vehicle discharge in the entire scheduling period;

[0131] Determine the electric vehicle charging constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0132]

[0133] In the formula, P ch,i (t) is the charging power of the i-th electric vehicle in the distribution area at time t; is the maximum limit of the total electric vehicle charging in the entire scheduling period;

[0134] Determine the energy storage power constraint condition of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0135] S k,min ≤S k (t)≤S k,max

[0136] In the formula, S k (t) is the power of the k-th energy storage system in the distribution area at time t; S k,min is the minimum limit of the power of the k-th energy storage system in the distribution area;k,max It is the maximum limit of the power of the k-th energy storage system in the substation area.

[0137] In the best embodiment of the present invention, the battery energy storage system can be regarded as a system with a series of continuous state of charge. The battery storage system can be charged or discharged at any time to collect or supply redundant power for the distributed power system. The power S of the k-th energy storage system in the substation area at time t is determined by the following formula k (t):

[0138] S k (t) = (1 - r s,d,k )S k-1 (t) - Δt·P ESB (t)

[0139] In the formula, S k-1 (t) is the power of the k-th energy storage system in the substation area at time t - 1; r s,d,k is the self-discharge rate of the k-th energy storage system in the substation area;

[0140] The combined heat and power generation equipment is a technology that can simultaneously generate heat and electricity by inputting natural gas and efficiently utilize it, with multiple benefits such as improving the quality of heat and power supply and saving energy. The output capacity of the combined heat and power generation equipment depends on the efficiency of gas-to-electricity conversion and gas-to-heat conversion. Therefore, the power generation power P of the combined heat and power generation unit equipment in the substation area at time t is determined by the following formula CHPe (t):

[0141] P CHPe (t) = P CHP-in (t)·η e

[0142] In the formula, η e is the electric conversion efficiency of the combined heat and power generation unit equipment; P CHP-in (t) is the input power of the combined heat and power generation unit equipment in the substation area at time t;

[0143] The output power of the wind turbine system can be greatly affected by wind speed, blade area, and air density. Therefore, the wind power generation output P in the substation area at time t is determined by the following formula W (t):

[0144]

[0145] In the formula, P W,g (t) is the output of the g-th wind power generation equipment in the substation area at time t; g ∈ (1~M), and M is the total number of wind power generation equipment in the substation area;

[0146] The output P of the g-th wind power generation equipment in the substation area at time t is determined by the following formula W,g (t):

[0147]

[0148] Wherein, v g (t) is the actual wind speed of the g-th wind power generation device in the power distribution area at time t; v o,g (t) is the cut-out wind speed of the g-th wind power generation device in the power distribution area at time t; v c,g (t) is the cut-in wind speed of the g-th wind power generation device in the power distribution area at time t; v r,g (t) is the rated wind speed of the g-th wind power generation device in the power distribution area at time t; ρ is the air density; A is the blade area of the g-th wind power generation device in the power distribution area; A g is the power coefficient of the wind power generation device;

[0149] Photovoltaic power generation has the characteristics of no noise and no pollution, and photovoltaic power generation units can be combined with buildings to form building-integrated photovoltaics to save a large amount of space. In order to reduce environmental pollution and reduce the floor area, in the present invention, photovoltaic power generation units are used to provide the electrical load in the power distribution area; since the output of photovoltaic power generation units is affected by factors such as solar radiation intensity and environmental temperature, the power output of photovoltaic power generation units is generally corrected based on the system output under standard test conditions (STC); therefore, the photovoltaic power generation output P in the power distribution area at time t is determined by the following formula PV (t):

[0150]

[0151] Wherein, P PV,f (t) is the output of the f-th photovoltaic power generation device in the power distribution area at time t; f ∈ (1 to S), and S is the total number of photovoltaic power generation devices in the power distribution area;

[0152] The output P of the f-th photovoltaic power generation device in the power distribution area at time t is determined by the following formula PV,f (t):

[0153]

[0154] Wherein, P STC,f is the maximum output power of the f-th photovoltaic power generation device in the power distribution area under standard test environment; k is the power temperature coefficient; is the solar radiation intensity of the f-th photovoltaic power generation device in the power distribution area at time t; G STC solar radiation intensity under standard test environment, is the actual temperature of the battery panel of the f-th photovoltaic power generation device in the power distribution area at time t, and T0 is the reference environmental temperature.

[0155] Specifically, using a genetic algorithm to solve the pre-constructed substation area optimal scheduling model includes:

[0156] Step 1: Initialize the population and the genetic iteration count ξ = 1;

[0157] Step 2: Determine the fitness of each individual in the population;

[0158] Step 3: Eliminate the individuals in the population whose fitness is lower than the threshold;

[0159] Step 4: Cross the individuals in the population to generate gene recombination;

[0160] Step 5: Modify the actual length of the chromosomes of the individuals in the population according to the equivalent length of the chromosomes of the individuals in the population;

[0161] Step 6: Update the mutation probability and perform mutation operations on each individual in the population;

[0162] Step 7: Determine whether the current iteration count ξ = W holds. If so, output the individual with the highest fitness in the population as the optimal solution of the pre-constructed substation area optimal scheduling model; otherwise, return to Step 2;

[0163] Among them, W is the maximum number of iterations, the fitness function of each individual in the population is f(ε) = 1 / F, F is the objective value of the substation area optimal scheduling model, and f(ε) is the fitness value of the ε-th individual in the population.

[0164] In the best embodiment of the present invention, the mutation probability is transformed from static to dynamic related to the square of the iteration count. The mutation crossover probability changes with the iteration count. Experiments show that its optimization result is more stable than the static fixed probability and has a better optimization effect.

[0165] Specifically, Step 5 includes:

[0166] If the equivalent length of the chromosome of an individual in the population remains unchanged after the individuals in the population cross, and the actual length of the chromosome of the individual in the population decreases after the individuals in the population cross, then append 0 at the end of the chromosome of the individual in the population until the actual length of the chromosome of the individual in the population is the same as before after the individuals in the population cross;

[0167] If the equivalent length of the chromosome of an individual in the population remains unchanged after the individuals in the population cross, and the actual length of the chromosome of the individual in the population increases after the individuals in the population cross, then randomly delete 0 in the front section of the crossed part of the chromosome of the individual in the population until the actual length of the chromosome of the individual in the population is the same as before after the individuals in the population cross.

[0168] In the optimal embodiment of the present invention, the processing in step 5 can be considered as the simultaneous implementation of crossover and mutation. According to the knowledge of probability theory, the probability of these two events occurring simultaneously is very small. In a general system, the number of tie-line switches is often much smaller than the number of branch-line switches, which leads to a small number of cases where the equivalent lengths are the same but the actual lengths are different. Therefore, the probability of this situation occurring is also necessarily very small, which is in line with the theory of biological evolution; this step reduces the number of infeasible solutions generated during the calculation process, reduces the number of iterations, and improves the calculation efficiency.

[0169] The present invention provides a substation area optimal scheduling system involving electric vehicles, as Figure 2 shown, the system includes:

[0170] A first determination module, configured to determine the charging and discharging electricity prices of electric vehicles in the substation area according to the predicted load demand value in the substation area except for the charging and discharging power of electric vehicles;

[0171] A second determination module, configured to determine the optimal operating power of each power device in the substation area according to the charging and discharging electricity prices of electric vehicles in the substation area;

[0172] An adjustment module, configured to adjust the operating power of each power device in the substation area to the optimal operating power of each power device in the substation area.

[0173] Specifically, the first determination module is used for:

[0174] Determine the charging electricity price λ ch (t) of electric vehicles in the substation area at time t according to the following formula:

[0175]

[0176] In the formula, α is the weight of the variable part of the charging electricity price of electric vehicles, P ch (t) is the charging power of electric vehicles in the substation area at time t, P dis (t) is the discharging power of electric vehicles in the substation area at time t; P Y (t) is the predicted load demand value in the substation area at time t except for the charging and discharging power of electric vehicles, λ ch,base is the price constant of the fixed part of the charging electricity price of electric vehicles; λ ch is the price constant of the variable part of the charging electricity price of electric vehicles; t ∈ (1 to T), and T is the total number of time instants in the scheduling period;

[0177] Determine the discharging electricity price λ dis (t) of electric vehicles in the substation area at time t according to the following formula:

[0178]

[0179] where β is the weight of the variable part of the electricity price for electric vehicle discharging, and λ dis is the price constant of the variable part of the electricity price for electric vehicle discharging; λ dis,base is the price constant of the fixed part of the electricity price for electric vehicle discharging.

[0180] Specifically, the second determination module is used for:

[0181] Substitute the charging and discharging electricity prices of electric vehicles in the area into the pre-constructed area optimization scheduling model, and use the genetic algorithm to solve the pre-constructed area optimization scheduling model to obtain the optimal operating power of each power device in the area;

[0182] Among them, the power devices in the area include: photovoltaic power generation equipment, wind power generation equipment, combined heat and power equipment, energy storage equipment, and electric vehicle equipment in the area.

[0183] Furthermore, determine the objective function of the pre-constructed area optimization scheduling model according to the following formula:

[0184]

[0185] where F is the objective value of the area optimization scheduling model, λ1 is the weight of the objective function of distributed energy consumption, f1(t) is the distributed energy consumption at time t in the area, λ2 is the weight of the objective function of user charging method satisfaction, f2(t) is the user charging method satisfaction at time t in the area, λ3 is the weight of the objective function of carbon emissions, and f3(t) is the carbon emissions at time t in the area;

[0186] Among them, determine the distributed energy consumption f1(t) at time t in the area according to the following formula:

[0187]

[0188] where P W (t) is the wind power generation output at time t in the area; P PV (t) is the photovoltaic power generation output at time t in the area; P ch (t) is the charging power of electric vehicles at time t in the area, P dis (t) is the discharging power of electric vehicles at time t in the area; P Y (t) is the predicted value of the load demand excluding the charging and discharging power of electric vehicles at time t in the area, t ∈ (1~T), and T is the total number of time points in the scheduling period;

[0189] Determine the user charging method satisfaction f2(t) at time t in the area according to the following formula:

[0190]

[0191] where PEV,i,MAX is the charging power of the i-th electric vehicle in the power distribution area when the user satisfaction is the highest, P EV,i (t) is the charging power of the i-th electric vehicle in the power distribution area at time t, Δt is the time interval between two adjacent times, i ∈ (1~N), and N is the total number of electric vehicles in the power distribution area;

[0192] Determine the carbon emission f3(t) at time t in the power distribution area according to the following formula:

[0193]

[0194] In the formula, P j (t) is the output of the j-th micro-source device in the power distribution area at time t; k j is the carbon emission coefficient of the j-th micro-source device in the power distribution area, j ∈ (1~M), and M is the total number of micro-source devices in the power distribution area.

[0195] Furthermore, determine the constraint conditions of the objective function of the pre-constructed power distribution area optimal scheduling model according to the following formula:

[0196] Determine the user benefit constraint condition of the objective function of the pre-constructed power distribution area optimal scheduling model according to the following formula:

[0197] c(i) ≥ c exp

[0198] In the formula, c(i) is the charge and discharge benefit of the i-th electric vehicle during the scheduling period, c exp is the expected benefit of the user during the scheduling period;

[0199] Among them, determine the charge and discharge benefit c(i) of the i-th electric vehicle during the scheduling period according to the following formula:

[0200]

[0201] In the formula, P ch,i (t) is the charging power of the i-th electric vehicle in the power distribution area at time t, P dis,i (t) is the discharge power of the i-th electric vehicle in the power distribution area at time t, λ ch (t) is the charging electricity price at time t in the power distribution area, λ dis (t) is the discharge electricity price at time t in the power distribution area, Δt is the time interval between two adjacent times, t d (i) is the charge and discharge end time of the i-th electric vehicle in the power distribution area; t a (i) is the charge and discharge start time of the i-th electric vehicle in the power distribution area;

[0202] Determine the power balance constraint condition of the objective function of the pre-constructed power distribution area optimal scheduling model according to the following formula:

[0203] PCHPe (t) + P W (t) + P PV (t) + P Grid (t) = P Y (t) + P ch (t) - P dis (t) + δP ESB (t)

[0204] Wherein, P CHPe (t) is the power generation power of the combined heat and power unit equipment in the distribution area at time t, P W (t) is the wind power output in the distribution area at time t, P PV (t) is the photovoltaic power output in the distribution area at time t, P Grid (t) is the electric power purchased from the main power grid in the distribution area at time t, P Y (t) is the predicted value of the load demand in the distribution area at time t except for the charging and discharging power of electric vehicles, P ch (t) is the charging power of electric vehicles in the distribution area at time t, P dis (t) is the discharging power of electric vehicles in the distribution area at time t; δ is the charge and discharge coefficient of the energy storage device. When δ = 1, the energy storage device is charging. When δ = -1, the energy storage device is discharging, P ESB (t) is the charge and discharge power of the energy storage device in the distribution area at time t;

[0205] Determine the micro-source output constraint conditions of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0206] P jmin ≤ P j (t) ≤ P jmax

[0207] P j (t) is the output of the j-th micro-source device in the distribution area at time t; P jmin is the minimum output of the j-th micro-source device in the distribution area; P jmax is the maximum output of the j-th micro-source device in the distribution area;

[0208] Determine the electric vehicle discharging constraint conditions of the objective function of the pre-constructed distribution area optimization scheduling model according to the following formula:

[0209]

[0210] Wherein, P dis,i (t) is the discharging power of the i-th electric vehicle in the distribution area at time t; is the maximum limit value of the total electric vehicle discharging amount in the entire scheduling period;

[0211] Determine the electric vehicle charging constraint condition of the objective function of the pre-constructed substation area optimal scheduling model according to the following formula:

[0212]

[0213] In the formula, P ch,i (t) is the charging power of the i-th electric vehicle in the substation area at time t; is the maximum limit value of the total electric vehicle charging amount in the entire scheduling period;

[0214] Determine the energy storage power constraint condition of the objective function of the pre-constructed substation area optimal scheduling model according to the following formula:

[0215] S k,min ≤S k (t) ≤ S k,max

[0216] In the formula, S k (t) is the power of the k-th energy storage system in the substation area at time t; S k,min is the minimum limit value of the power of the k-th energy storage system in the substation area; S k,max is the maximum limit value of the power of the k-th energy storage system in the substation area.

[0217] Specifically, solving the pre-constructed substation area optimal scheduling model by using the genetic algorithm includes:

[0218] Step 1: Initialize the population and the genetic iteration number ξ = 1;

[0219] Step 2: Determine the fitness of each individual in the population;

[0220] Step 3: Eliminate the individuals in the population whose fitness is lower than the threshold;

[0221] Step 4: Cross the individuals in the population to generate gene recombination;

[0222] Step 5: Modify the actual length of the chromosomes of the individuals in the population according to the equivalent length of the chromosomes of the individuals in the population;

[0223] Step 6: Update the mutation probability and perform mutation operations on each individual in the population;

[0224] Step 7: Judge whether the current iteration number ξ = W holds. If so, output the individual with the highest fitness in the population as the optimal solution of the pre-constructed substation area optimal scheduling model; otherwise, return to Step 2;

[0225] Among them, W is the maximum number of iterations, and the fitness function of each individual in the population is f(ε) = 1 / F, where F is the objective value of the substation area optimal scheduling model, and f(ε) is the fitness value of the ε-th individual in the population.

[0226] Specifically, step 5 includes:

[0227] If the equivalent length of the chromosome of an individual in the population remains unchanged after crossover of the individuals in the population compared to before, and the actual length of the chromosome of an individual in the population decreases after crossover of the individuals in the population compared to before, then append 0s to the end of the chromosome of the individual in the population until the actual length of the chromosome of the individual in the population after crossover is the same as that before crossover;

[0228] If the equivalent length of the chromosome of an individual in the population remains unchanged after crossover of the individuals in the population compared to before, and the actual length of the chromosome of an individual in the population increases after crossover of the individuals in the population compared to before, then randomly delete 0s from the front section of the crossover part of the chromosome of the individual in the population until the actual length of the chromosome of the individual in the population after crossover is the same as that before crossover.

[0229] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0230] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0231] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0232] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to generate a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process Figure 1 one process or a plurality of processes and / or blocks Figure 1 or steps for realizing the functions specified in a plurality of blocks or a plurality of blocks.

[0233] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the dispatching of a distribution transformer area involving electric vehicles, characterized in that The method includes: Determining the charging and discharging electricity price of electric vehicles in the distribution area according to the predicted value of the load demand in the distribution area except for the charging and discharging power of electric vehicles; Determining the optimal operating power of each power device in the distribution area according to the charging and discharging electricity price of electric vehicles in the distribution area; Adjusting the operating power of each power device in the distribution area to the optimal operating power of each power device in the distribution area; The determining the charging and discharging electricity price of electric vehicles in the distribution area according to the predicted value of the load demand in the distribution area except for the charging and discharging power of electric vehicles includes: Determine the charging electricity price λ of electric vehicles in the distribution area at time t according to the following formula ch (t): Where α is the weight of the variable part of the electric vehicle charging price, and P ch (t) is the charging power of electric vehicles at time t in the distribution area, and P dis (t) is the discharging power of electric vehicles at time t in the distribution area; P Y (t) is the predicted value of the load demand at time t in the distribution area except for the charging and discharging powers of electric vehicles, and λ ch,base is the price constant of the fixed part of the electric vehicle charging price; λ ch is the price constant of the variable part of the electric vehicle charging price; t ∈ (1~T), where T is the total number of time points in the scheduling period; Determine the electricity price λ for electric vehicle discharging at time t in the substation area according to the following formula dis (t): where β is the weight of the variable part of the electricity price for electric vehicle discharging, and λ dis is the price constant of the variable part of the electricity price for electric vehicle discharging; λ dis,base is the price constant of the fixed part of the electricity price for electric vehicle discharging.

2. The method according to claim 1, wherein The determining the optimal operating power of each power device in the distribution area according to the charging and discharging electricity price of electric vehicles in the distribution area includes: Substituting the charging and discharging electricity price of electric vehicles in the distribution area into the pre-constructed distribution area optimal scheduling model, and using the genetic algorithm to solve the pre-constructed distribution area optimal scheduling model to obtain the optimal operating power of each power device in the distribution area; Wherein, the power devices in the distribution area include: photovoltaic output devices, wind power output devices, combined heat and power devices, energy storage devices and electric vehicle devices in the distribution area.

3. The method according to claim 2, characterized in that, Determine the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: In the formula, F is the objective value of the distribution area optimal scheduling model, λ1 is the weight of the distributed energy consumption objective function, f1(t) is the distributed energy consumption at time t in the distribution area, λ2 is the weight of the user charging method satisfaction objective function, f2(t) is the user charging method satisfaction at time t in the distribution area, λ3 is the weight of the carbon emission objective function, and f3(t) is the carbon emission at time t in the distribution area; Wherein, determine the distributed energy consumption f1(t) at time t in the distribution area according to the following formula: Where, P W (t) is the wind power generation output at time t in the substation area; P PV (t) is the photovoltaic power generation output at time t in the substation area; P ch (t) is the charging power of electric vehicles at time t in the substation area, P dis (t) is the discharging power of electric vehicles at time t in the substation area; P Y (t) is the predicted value of the load demand excluding the charging and discharging power of electric vehicles at time t in the substation area, t ∈ (1~T), and T is the total number of moments in the scheduling period; Determine the user charging method satisfaction f2(t) at time t in the distribution area according to the following formula: Where P EV,i,MAX is the charging power of the i-th electric vehicle in the substation area when the user satisfaction is the highest, and P EV,i (t) is the charging power of the i-th electric vehicle in the substation area at time t, Δt is the time interval between two adjacent times, i ∈ (1~N), and N is the total number of electric vehicles in the substation area; Determine the carbon emission f3(t) at time t in the distribution area according to the following formula: Where, P j (t) is the output of the j-th micro-source device in the distribution area at time t; k j is the carbon emission coefficient of the j-th micro-source device in the distribution area, j ∈ (1 to M), and M is the total number of micro-source devices in the distribution area.

4. The method according to claim 2, characterized in that, Determine the constraint conditions of the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: Determine the user benefit constraint conditions of the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: c(i)≥c exp where c(i) is the charging and discharging profit of the i-th electric vehicle during the scheduling period, and c exp is the expected profit of the user during the scheduling period; Wherein, determine the charging and discharging benefit c(i) of the i-th electric vehicle during the scheduling period according to the following formula: Wherein, P ch,i (t) is the charging power of the i-th electric vehicle in the power distribution area at time t, and P dis,i (t) is the discharging power of the i-th electric vehicle in the power distribution area at time t. λ ch (t) is the charging electricity price in the power distribution area at time t, and λ dis (t) is the discharging electricity price in the power distribution area at time t. Δt is the time interval between two adjacent times, and t d (i) is the charging and discharging end time of the i-th electric vehicle in the power distribution area; t a (i) is the charging and discharging start time of the i-th electric vehicle in the power distribution area; Determine the power balance constraint conditions of the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: P CHPe (t) + P W (t) + P PV (t) + P Grid (t) = P Y (t) + P ch (t) - P dis (t) + δP ESB (t) Where, P CHPe (t) is the power generation of the combined heat and power unit equipment in the power distribution area at time t, P W (t) is the wind power generation output in the power distribution area at time t, P PV (t) is the photovoltaic power generation output in the power distribution area at time t, P Grid (t) is the electric power purchased from the main power grid in the power distribution area at time t, P Y (t) is the predicted value of the load demand in the power distribution area at time t except for the charging and discharging power of electric vehicles, P ch (t) is the charging power of electric vehicles in the power distribution area at time t, P dis (t) is the discharging power of electric vehicles in the power distribution area at time t; δ is the charge-discharge coefficient of the energy storage device. When δ = 1, the energy storage device is charging. When δ = -1, the energy storage device is discharging, P ESB (t) is the charge-discharge power of the energy storage device in the power distribution area at time t; Determine the micro-source output constraint conditions of the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: P jmin ≤P j (t)≤P jmax P j (t) is the output of the j-th micro-source device in the substation area at time t; P jmin is the minimum output of the j-th micro-source device in the substation area; P jmax is the maximum output of the j-th micro-source device in the substation area; Determine the electric vehicle discharging constraint conditions of the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: where P dis,i (t) is the discharge power of the i-th electric vehicle in the distribution area at time t; is the maximum limit of the total discharge of electric vehicles in the entire scheduling period; Determine the electric vehicle charging constraint conditions of the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: Where, P ch,i (t) is the charging power of the i-th electric vehicle in the area at time t; is the maximum limit of the total charging amount of electric vehicles in the entire scheduling period; Determine the energy storage power constraint conditions of the objective function of the pre-constructed distribution area optimal scheduling model according to the following formula: S k,min ≤S k (t)≤S k,max Where S k (t) is the power of the k-th energy storage system in the substation area at time t; S k,min is the minimum power limit of the k-th energy storage system in the substation area; S k,max is the maximum power limit of the k-th energy storage system in the substation area.

5. The method according to claim 2, wherein The using the genetic algorithm to solve the pre-constructed distribution area optimal scheduling model includes: Step 1: Initialize the population and the genetic iteration times ξ = 1; Step 2: Determine the fitness of each individual in the population; Step 3: Eliminate the individuals in the population with fitness lower than the threshold; Step 4: Cross the individuals in the population to generate gene recombination; Step 5: Modify the actual length of the chromosomes of the individuals in the population according to the equivalent length of the chromosomes of the individuals in the population; Step 6: Update the mutation probability And perform mutation operations on each individual in the population; Step 7: Determine whether the current iteration number ξ = W holds. If so, output the individual with the highest fitness in the population as the optimal solution of the pre-constructed substation area optimal scheduling model; otherwise, return to Step 2; Wherein, W is the maximum number of iterations, the fitness function of each individual in the population is f(ε) = 1 / F, F is the target value of the substation area optimal scheduling model, and f(ε) is the fitness value of the ε-th individual in the population.

6. The method according to claim 5, characterized in that The said Step 5 includes: If the equivalent length of the chromosome of an individual in the population remains unchanged after crossover of the individuals in the population compared with before, and the actual length of the chromosome of an individual in the population decreases after crossover of the individuals in the population compared with before, then append 0 to the end of the chromosome of the individual in the population until the actual length of the chromosome of the individual in the population after crossover of the individuals in the population is the same as that before; If the equivalent length of the chromosome of an individual in the population remains unchanged after crossover of the individuals in the population compared with before, and the actual length of the chromosome of an individual in the population increases after crossover of the individuals in the population compared with before, then randomly delete 0 from the front section of the crossover part of the chromosome of the individual in the population until the actual length of the chromosome of the individual in the population after crossover of the individuals in the population is the same as that before.

7. An area optimization dispatching system involving electric vehicles, characterized in that: The said system includes: A first determination module, configured to determine the electric vehicle charging and discharging electricity price in the substation area according to the predicted value of the load demand in the substation area except for the electric vehicle charging and discharging power; A second determination module, configured to determine the optimal operating power of each power device in the substation area according to the electric vehicle charging and discharging electricity price in the substation area; An adjustment module, configured to adjust the operating power of each power device in the substation area to the optimal operating power of each power device in the substation area; The said first determination module specifically includes: Determine the charging electricity price λ of electric vehicles in the power distribution area at time t according to the following formula ch (t): where α is the weight of the variable part of the electric vehicle charging price, and P ch (t) is the charging power of electric vehicles at time t in the distribution area, and P dis (t) is the discharging power of electric vehicles at time t in the distribution area; P Y (t) is the predicted value of the load demand excluding the charging and discharging power of electric vehicles at time t in the distribution area, and λ ch,base is the price constant of the fixed part of the electric vehicle charging price; λ ch is the price constant of the variable part of the electric vehicle charging price; t ∈ (1 - T), and T is the total number of moments in the scheduling period; Determine the discharge electricity price λ of electric vehicles in the power distribution area at time t according to the following formula dis (t): where β is the weight of the variable part of the electricity price for the electric vehicle discharging, and λ dis is the price constant of the variable part of the electricity price for the electric vehicle discharging; λ dis,base is the price constant of the fixed part of the electricity price for the electric vehicle discharging.

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