CN111310966A - Location selection and optimal configuration method of microgrid including electric vehicle charging station - Google Patents

Abstract

The invention discloses a method for site selection and optimal configuration of a microgrid including an electric vehicle charging station. First, based on the Voronoi diagram, the concept of charging service radius is proposed by considering the radiation area of the microgrid system and the distance from the service boundary to the system center. The location coordinates of the system are determined by taking the maximum sum of the charging service radius as the objective function, so that the microgrid system with charging stations has the widest service range. Aiming at the limitations of independent research on distributed energy and electric vehicle charging station planning, the present invention proposes to establish an electric vehicle charging system with the goal of reducing the overall economic cost of both the microgrid and electric vehicle users and reducing the total load fluctuation of the microgrid A dual-objective programming model for the microgrid system of the station. It effectively overcomes the deficiencies of the existing technology and has good economic and social value.

Description

Location selection and optimal configuration method of microgrid including electric vehicle charging station

technical field

The invention relates to the technical field of grid configuration, in particular to a method for site selection and optimal configuration of a micro grid including an electric vehicle charging station.

Background technique

With the rapid development of my country's economy, energy shortages and environmental pollution problems also arise. Therefore, distributed renewable clean energy such as wind power and photovoltaic power generation and electric vehicles have received extensive attention from all walks of life. Because the uncertainty and intermittency of wind and solar output will have a serious impact on the power quality of the power grid, the microgrid emerges as the times require. Microgrid is a regional power system with distributed power generation, which can effectively alleviate the impact of wind and solar uncertainty on the power grid by absorbing wind and solar output through regional internal loads. For electric vehicles, according to my country's current coal-based energy structure, electric vehicles directly obtain electricity from the traditional power grid, and the pollutants emitted by power generation will not fundamentally reduce the pressure of environmental pollution. On the other hand, the disordered charging behavior of a large number of electric vehicles further increases the peak load of the microgrid, resulting in an increase in the peak-to-valley difference of the microgrid load and an increase in the installed capacity of the distributed power supply. The construction cost of the microgrid increases, which affects the planning and operation of the microgrid. safety and economy. Therefore, by controlling the charging behavior of electric vehicles, it is of high economic benefit and great social significance to conduct research on the optimal configuration of the microgrid with electric vehicles.

The purpose of the optimal configuration of the microgrid is to balance the supply and demand relationship between the load and the distributed power supply (the grid-connected microgrid also needs to consider the energy exchange with the large power grid), and there are intermittent renewable energy in the distributed power supply. Fully analyze and forecast load demand and natural resource conditions. Different from the traditional microgrid planning, the optimal configuration result in the microgrid including the electric vehicle charging station has a high coupling relationship with the charging behavior of the electric vehicle. Fully consider the influence of electric vehicle charging strategy on the configuration results, and then carry out comprehensive planning for specific optimization objectives and constraints.

In the existing research, domestic and foreign scholars and institutions have achieved some results on the planning of electric vehicle charging stations in the power system. The basic method is to consider the voltage stability margin and system loss to select the location of the electric vehicle fast charging station, and to evaluate the maximum acceptable charging station capacity based on the reliability of the grid. In recent years, some studies have proposed a charging station planning method that considers the traffic flow, the coupling between the traffic network and the power grid structure, the user's driving consumption and the user's charging waiting time, etc., to calculate the optimal planning capacity of the charging station.

Existing research mainly focuses on the optimal configuration of distributed power generation and electric vehicle charging stations independently, and there are few studies on the optimal configuration of microgrids including electric vehicle charging stations. And most of these studies are mainly based on the economics of realizing the optimal configuration of microgrids, but in the optimal configuration of microgrids containing electric vehicle charging stations, due to the randomness and volatility of distributed power and electric vehicle load demand, it may lead to The microgrid load fluctuates frequently, and the peak-to-valley load difference is large, which will affect the economy, safety and stability of the microgrid. In addition, the existing research also has incomplete consideration for the location of charging stations.

SUMMARY OF THE INVENTION

The technical problems to be solved by the present invention are: aiming at the limitations of independent planning and research of distributed power sources and electric vehicle charging stations in the microgrid, and the vacancy in the research on the location selection of the microgrid system including the charging network for electric vehicles, the present invention provides A method for location selection and optimal configuration of a microgrid including an electric vehicle charging station to solve the above problems is proposed.

The present invention is achieved through the following technical solutions:

For the location method of microgrid including electric vehicle charging station, the charging service radius R _s is defined, as shown in formula (1):

In the formula, Ni is the number of edges in the service area of the microgrid system _i including the electric vehicle charging station; ω _n is the weight coefficient of the nth edge of the Voronoi polygon, and _dhn is the Euclidean distance from the head point of the nth edge to i ; d _tn is the Euclidean distance from the end point of the nth edge to the system i; l _n and l _k are the lengths of the nth and kth edges in the Voronoi polygon, respectively.

In order to more reasonably plan the location of the microgrid system including electric vehicle charging stations, so that the system covers the widest range of services, this application gives the indicator of charging service radius. The size of the Voronoi polygon corresponding to the microgrid system of the charging station, on the other hand, also needs to consider the distance from the service boundary of the microgrid system including the electric vehicle charging station to itself.

Further, in the case of constructing a set number of microgrid systems including electric vehicle charging stations in any load concentration area planned by the municipality, the charging station location planning with the maximum sum of all system service radii as the optimization goal is as follows: As stated in (3):

In the formula, M is the number of planned and constructed microgrid systems including electric vehicle charging stations.

Based on the above location selection method, the optimal configuration method of the microgrid including electric vehicle charging station is carried out, and the dual-objective optimization with the minimum economic cost and the minimum load fluctuation as the optimization goal is as follows:

Economic cost objective f ₁ function:

C=C _i +C _om +C _cs +C _ex +C _charge +C _loss (4);

In the formula, C is the total planned cost, C _i is the construction cost of four types of distributed power sources such as wind power, photovoltaic, diesel engine and energy storage system, C _om is the operation cost, C _cs is the construction cost of the charging station, and C _ex is the microgrid and grid Energy exchange cost, C _charge is the charging cost of electric vehicle users, C _loss is the loss of load cost, the unit is yuan;

Load fluctuation target f2 function _:

为t时刻微电网的基础负荷量；

为M辆电动汽车在无序充电场景下t时刻的充电负荷大小。In the formula, P _{load-fluctuation} is the load fluctuation amount,

is the basic load of the microgrid at time t;

is the charging load of M electric vehicles at time t in the disordered charging scenario.

By reducing the load fluctuation, on the one hand, the loss in the process of power transmission and distribution can be reduced, and on the other hand, the safe and stable operation of the power grid can be ensured. The total load mean square error can be used to characterize the fluctuation of the microgrid load. The smaller the mean square error, the more stable the load change.

Further, described C _i is the construction cost of four types of distributed power sources of wind power, photovoltaic, diesel engine and energy storage system as shown in formula (6), the present invention can adopt the equivalent annual value cost method to calculate:

In formula (6), B is the type of distributed power generation; C _b is the installation cost of the b-class distributed power generation; r is the discount rate, usually 8%; l _b is the b-class distributed power generation life cycle;

The operating cost C _om is shown in formula (7), and is calculated by using the operating management coefficient:

为t时刻第 b类配置电源的出力；In formula (7), T is the operating time of the system; k _{om_b} is the operation management coefficient of the b-class distributed power generation;

Configure the output of the power supply for class b at time t;

The energy exchange cost C _ex between the microgrid and the grid is shown in formula (8):

分别为t时刻购电与售电的电价，

分别为t时刻向配电网购电功率和售电功率；In formula (8),

are the electricity prices for purchasing and selling electricity at time t, respectively,

are the power purchased and sold from the distribution network at time t, respectively;

The charging station construction cost C _cs is shown in formula (9):

C _cs = S _v c _ch (9);

In formula (9), c _charge is the construction cost of the charging station per unit capacity, and S _v is the capacity of the electric vehicle charging station;

The electric vehicle charging cost C _charge is shown in formula (10):

为t时刻购电电价，

为M辆电动汽车在无序充电场景下t时刻的充电负荷大小；In formula (10),

is the electricity purchase price at time t,

is the charging load size of M electric vehicles at time t in the disordered charging scenario;

The load loss cost C _loss is shown in formula (11):

为t时刻系统的失负荷功率。In formula (11), c _loss is the unit load loss cost,

is the unloaded power of the system at time t.

Further, the constraints of the economic cost target f ₁ function and the load fluctuation target f ₂ function include microgrid power balance constraints, distributed power supply capacity upper limit constraints, tie line power upper limit constraints, and energy storage system battery state of charge changes. and constraints, the actual charge and discharge output constraints of the energy storage system, the electric vehicle battery state of charge constraints, the microgrid self-balance rate constraints and the load loss ratio constraints.

Further, the power balance constraint of the microgrid is shown in formula (12):

为风电、光伏、柴油机和储能系统在t时刻输出功率，

为正数，表示储能系统放电，反之，表示充电；

为t时刻联络线的交换功率，

为正值表示微电网从配电网购电，其为负值表示微电网向配电网售电，

为微电网t时刻弃风光量以及失负荷量；In formula (12),

output power for wind power, photovoltaic, diesel engine and energy storage system at time t,

If it is a positive number, it means that the energy storage system is discharged, otherwise, it means charging;

is the exchange power of the tie line at time t,

A positive value indicates that the microgrid purchases electricity from the distribution network, and a negative value indicates that the microgrid sells electricity to the distribution network.

is the amount of wind and solar power abandoned and the amount of lost load at time t of the microgrid;

The upper limit constraint of the distributed power supply capacity is shown in formula (13):

In formula (13), S _WT , S _PV , S _DE , and S _ESS are the configuration capacities of four types of distributed power sources of wind, light, diesel and storage, S _WT-max , S _PV-max , S _DE-max , S _ESS-max is the upper limit of the configuration capacity of the corresponding four types of distributed power sources;

The tie line power cap constraint

In formula (14), _PL-max is the rated power of the tie line;

The state of charge change and constraints of the battery of the energy storage system are shown in formula (15):

为储能系统t时刻的荷电状态，η_ESS为储能系统充放电效率，其取值分别如式(16)和式(17)所示：In formula (15),

is the state of charge of the energy storage system at time t, η _ESS is the charging and discharging efficiency of the energy storage system, and its values are shown in equations (16) and (17) respectively:

In equations (16) and (17), η _c and η _dc are the charging and discharging efficiencies of the energy storage system, and SOC _ESS-min and SOC _ESS-max are the upper and lower limits of the battery state of charge of the energy storage system;

The actual charge and discharge output constraints of the energy storage system are shown in formula (18):

In formula (18), P _cmax and P _dcmax are the maximum charging and discharging power of the energy storage system respectively;

The state-of-charge constraint of the electric vehicle battery is shown in equation (19):

为电池t时刻的荷电状态；In formula (19), SOC _ev-min and SOC _ev-max are the upper and lower limits of the state of charge of the electric vehicle battery,

is the state of charge of the battery at time t;

The self-balancing degree rate constraint of the microgrid is shown in formula (20):

In formula (20), Sa _min and Sa _max are the upper and lower limits of the self-balancing degree, P _load-total is the total load power of the microgrid, and P _buy-total is the total power purchased by the microgrid;

The load loss rate constraint is shown in formula (21):

In formula (21), L _oep-max is the maximum proportion of load shedding, and P _loss-total is the total load shedding power.

Furthermore, the multi-objective particle swarm algorithm is used to solve the optimal configuration problem, and the fuzzy membership function is used to calculate the satisfaction of the economic cost and load fluctuation targets respectively, and the maximum value of the standardized satisfaction is obtained as the optimal compromise solution.

The above optimization model is essentially a dual-objective optimization problem, and it is usually impossible to achieve the optimal situation for both objectives. If a single objective is considered optimal, it may lead to worse results for another objective. Therefore, the present invention selects the multi-objective particle swarm algorithm to solve the above-mentioned optimal configuration problem. Multi-objective particle swarm optimization is widely used as a typical intelligent optimization algorithm. It can find multi-objective non-inferior solution sets, and finally solve a set of Pareto optimal solution sets. The fuzzy membership function is used to calculate the satisfaction of the economic cost and load fluctuation targets respectively, and the maximum value of the standardized satisfaction is obtained as the optimal compromise solution.

Further, the fuzzy membership function is shown in formula (22):

In formula (22), f _i is the solution set of the i-th optimization objective, f _imin is the minimum value of the i-th optimization objective solution, and f _imax is the maximum value of the i-th optimization objective solution;

The calculation of the standardized satisfaction is shown in formula (23):

In formula (23), u is the standardized satisfaction, and N is the number of optimization targets.

Further, before the grid optimization configuration, set the electric vehicle disorder charging strategy and orderly charging strategy:

的大小如式(24)所示：Predict the disordered charging load of charging electric vehicles; the charging demand prediction of each electric vehicle is an independent event, and the charging demand of a single electric vehicle can be cyclically superimposed by the Monte Carlo method to obtain the charging load of N electric vehicles in the disordered charging scenario

The size of is shown in formula (24):

为M辆电动汽车在无序充电场景下t时刻的充电负荷大小；

为第i辆电动汽车在无序充电场景下t时刻的充电负荷大小；In formula (24),

is the charging load size of M electric vehicles at time t in the disordered charging scenario;

is the charging load of the i-th electric vehicle at time t in the disordered charging scenario;

The method of grid-guided orderly charging is to guide electric vehicle load to conduct orderly charging during valley through the difference in electricity price between peaks and valleys, and change the charging behavior of electric vehicles.

The peak electricity price is mostly concentrated from 17:00 to 23:00, and the valley electricity price is mostly concentrated from 24:00 to 7:00 the next day. During the valley electricity price period, most households and enterprises are in the rest time, and electric vehicles are connected to the grid. Therefore, the method for guiding orderly charging by the power grid of the present invention is to guide the electric vehicle load to perform orderly charging during the valley through the difference in electricity prices between peaks and valleys. The load peak of the small power grid reduces the load fluctuation of the power grid, and at the same time reduces the cost of charging users and the installed capacity of the power grid.

Further, the EV charging behavior used to obtain the charging load is set as follows:

The charging demand of electric vehicle users has great randomness and uncertainty. To obtain a reasonable charging demand curve, it is necessary to analyze the travel habits and driving characteristics of users. In the present invention, the travel end time t ₀ of the electric vehicle is approximated as a normal distribution, and the daily mileage s is approximated as a logarithmic normal distribution. For the electric vehicle user, the present invention simply assumes that the charging and discharging time of the network access is the user's last return trip time t ₀ , that is, the electric vehicle is charged immediately after the last return trip. Combined with the travel habits of the owner, the return trip time is concentrated on the user rush hour. According to the electric vehicle data obtained by the US Department of Energy, the maximum likelihood estimation method is used to obtain the probability density functions f _t (x) and f _s (x) of the charging start time t ₀ and the daily mileage s. Therefore, the charging starting time t ₀ is set as a normal distribution function, t ₀ ～N(μ _t ,σ _t ² ), and the probability density function of the charging starting time t ₀ is:

In formula (25), μ _t =17.6, σ _t =3.4;

The daily mileage s obeys the log-normal distribution, and its probability density function is shown in the following formula (26):

In formula (26), μ _s = 0.88, σ _s = 3.2; due to the constraints of the battery capacity state of charge of the electric vehicle, the maximum daily mileage s _max is the driving corresponding to the battery capacity from the upper limit to the lower limit. milage.

The present invention has the following advantages and beneficial effects:

The present invention has carried out research work on the problem of location selection and optimal configuration of a microgrid system including an electric vehicle charging station, establishing a system location model with the goal of maximizing the service radius of a microgrid system including an electric vehicle charging station, and by guiding the electric vehicle Orderly charging, an optimal configuration model of the microgrid system including electric vehicles is established considering the economics, safety and stability of microgrid planning and operation. Has the following beneficial effects:

1. Based on the Voronoi diagram, the present invention defines the concept of the service radius of the microgrid system with electric vehicle charging station, and takes the maximum service radius as the objective function, and proposes a location method for the microgrid system with electric vehicle charging station. Make the system the widest range of services.

2. The present invention simulates the probability density function of user travel characteristics according to the user behavior characteristics of electric vehicles, formulates charging strategies for disordered charging and orderly charging of electric vehicles, and uses Monte Carlo sampling method to predict the disordered charging behavior of electric vehicles.

3. Connecting the micro-grid system including electric vehicle charging stations to the power grid can, on the one hand, consume renewable energy locally and improve energy utilization; . After the electric vehicles are charged in an orderly manner, the installed cost of the distributed power source is reduced, and the charging cost of the user is also reduced, realizing a win-win economy for the user and the microgrid. And after the optimal dispatch of electric vehicles, it reduces the peak-to-valley difference of the microgrid load, so that the overall load fluctuation of the microgrid is significantly reduced, which is beneficial to improve the safety and stability of the grid operation.

Description of drawings

The accompanying drawings described herein are used to provide further understanding of the embodiments of the present invention, and constitute a part of the present application, and do not constitute limitations to the embodiments of the present invention. In the attached image:

Fig. 1 is the optimized configuration flow chart of the present invention;

Figure 2 shows the sum of the service radius of the microgrid system including electric vehicles in a single scene within 1000 scene simulations;

Fig. 3 is the corresponding Voronoi diagram when the service radius of the microgrid system with electric vehicles is the largest;

Figure 4 shows the disordered charging power demand of 200 electric vehicles;

Figure 5 shows the output of each distributed power source and the transmission power of the tie line when the electric vehicle is charged in disorder;

Figure 6 is the pareto curve of the total planning cost and load fluctuation of the microgrid system;

Figure 7 shows the output of each distributed power source and the transmission power of the tie line when the electric vehicle is charged in an orderly manner;

Figure 8 shows the load curve of the microgrid before and after the optimal dispatch.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and the accompanying drawings. as a limitation of the present invention.

Example 1

This embodiment provides a method for location selection of a microgrid including an electric vehicle charging station, and defines the charging service radius R _s , as shown in formula (1):

In order to ensure the optimal site selection plan, when a certain number of microgrid systems including electric vehicle charging stations are built in a certain municipally planned load concentration area, the charging should take the maximum sum of the service radius of all systems as the optimization goal. Station location planning, as shown in formula (3):

In the formula, M is the number of planned and constructed microgrid systems including electric vehicle charging stations.

A specific example is to set up 10 microgrid systems including electric vehicle charging stations, and the load concentration area for planning and site selection is a square area with a length and width of 100 units. After 1000 random locations of the microgrid system including electric vehicle charging stations to calculate the sum of the charging service radius, the results shown in Figure 2 are obtained.

It can be seen from Fig. 2 that under the above planning conditions, the 226th site selection in the 1000 iteration results has the largest service radius, and the largest service radius is 508.928. The results of this iteration are taken as the optimal site selection scheme for the microgrid system including electric vehicle charging stations. The site selection results under this scheme are shown in Figure 3.

Example 2

On the basis of Embodiment 1, this embodiment provides a demand forecast for disordered charging of electric vehicles:

充电起始时间t₀的概率密度函数：Firstly, the charging behavior of electric vehicles is analyzed, and the charging start time t ₀ is set as a normal distribution function,

Probability density function of charging start time t ₀ :

In formula (25), μ _t =17.6, σ _t =3.4;

The daily mileage s obeys the log-normal distribution, and its probability density function is shown in the following formula (26):

In formula (26), μ _s = 0.88, σ _s = 3.2; due to the constraints of the battery capacity state of charge of the electric vehicle, the maximum daily mileage s _max is the driving corresponding to the battery capacity from the upper limit to the lower limit. milage.

的大小如式(24)所示：Then, the disordered charging load of charging electric vehicles is predicted; the charging demand forecast of each electric vehicle is an independent event, and the charging demand of a single electric vehicle can be cyclically superimposed by the Monte Carlo method to obtain N electric vehicles under the disordered charging scenario charging load

The size of is shown in formula (24):

为M辆电动汽车在无序充电场景下t时刻的充电负荷大小；

为第i辆电动汽车在无序充电场景下t时刻的充电负荷大小；In formula (24),

is the charging load size of M electric vehicles at time t in the disordered charging scenario;

is the charging load of the i-th electric vehicle at time t in the disordered charging scenario;

In addition, the method of the power grid to guide orderly charging is to guide the electric vehicle load to perform orderly charging during the valley through the difference in electricity prices between peaks and valleys, so as to change the charging behavior of electric vehicles.

For a specific calculation example, the power consumption per kilometer of an electric vehicle is 0.139kW·h/km, the battery capacity is 17.5kW·h, the upper and lower limits of the battery capacity state of charge are 100% and 20%, respectively, and the charging power is 2kW. The user's charging starting time and daily mileage are obtained by Monte Carlo simulation, and the charging power demand of the electric vehicle during disordered charging is obtained by combining the electric vehicle's power consumption per kilometer and the charging power and other parameters. Figure 4 shows the demand curve for disordered charging of 200 electric vehicles.

Example 3

This embodiment provides an optimal configuration method for a microgrid system including electric vehicles, which comprehensively considers the economic cost objective function f ₁ and the load fluctuation objective function f ₂ ; the economic cost objective function f ₁ :

C=C _i +C _om +C _cs +C _ex +C _charge +C _loss (4);

In the formula, C is the total planned cost, C _i is the construction cost of four types of distributed power sources such as wind power, photovoltaic, diesel engine and energy storage system, C _om is the operation cost, C _cs is the construction cost of the charging station, and C _ex is the microgrid and grid Energy exchange cost, C _charge is the charging cost of electric vehicle users, C _loss is the loss of load cost, the unit is yuan;

Load fluctuation target f2 function _:

为t时刻微电网的基础负荷量；

为M辆电动汽车在无序充电场景下t时刻的充电负荷大小。In the formula, P _{load-fluctuation} is the load fluctuation amount,

is the basic load of the microgrid at time t;

is the charging load of M electric vehicles at time t in the disordered charging scenario.

According to the power balance constraints of the microgrid, the upper limit of the distributed power supply capacity, the upper limit of the tie line power, the changes and constraints of the battery state of charge of the energy storage system, the actual charge and discharge output constraints of the energy storage system, the battery state of charge constraints of the electric vehicle, the microgrid The self-balancing degree rate constraint and the load loss rate constraint conditions; the multi-objective particle swarm algorithm is used to solve the optimal configuration problem, and the fuzzy membership function is used to calculate the satisfaction of the economic cost and load fluctuation objectives respectively, and obtain the standardized satisfaction The maximum value is taken as the optimal compromise solution. The result obtained is as follows:

1. This technology designs the following two scenarios: (1) a microgrid system under an electric vehicle disordered charging strategy (2) a microgrid system under an electric vehicle orderly charging strategy. Optimize the configuration for the two scenarios and conduct a comparative analysis.

The specific calculation example selects one day in each of the four quarters as a typical day for simulation, the unit time length is 1h, and the research time period is one year. It is assumed that there are 200 electric vehicles in the microgrid system. At present, the self-balancing degree of grid-connected microgrid is low, and it mainly depends on the support of the large power grid, so the self-balancing degree constraint range is set to 40% to 60%. Correspondingly, when the distribution network fails and the microgrid operates independently, only the important loads are guaranteed to supply power.

2. Optimization results and analysis

1. Scenario 1: Microgrid system under the disordered charging strategy of electric vehicles

The optimal configuration scheme of the microgrid system in the case of disorderly charging of electric vehicles is as follows: the total planning cost is 6,267,400 yuan, of which the construction cost of distributed power is 5,080,800 yuan, the operating cost is 877,240 yuan, and the electric vehicle charging cost is 309,360 yuan; load fluctuation is 26,198 ( kW) ² ; the configuration capacity is wind power 195kW, photovoltaic 697kW, diesel engine 50kW, battery 350kW·h. Analysis of its optimal configuration results shows that the construction cost accounts for the largest proportion of the total economic cost, accounting for 81.07%, and the expensive installation cost is the main reason for the poor economic benefit of the microgrid at this stage. When the microgrid system is in normal operation, the output of each distributed power source and the transmission power of the tie line are shown in Figure 5.

When electric vehicles are charged in disorder, the charging load is concentrated in the peak load period. Considering the power purchase limit of the tie line, the microgrid system needs to be equipped with distributed power sources with high investment and construction costs to meet the load demand. Analysis of Figure 5 shows that photovoltaics are the main distributed power supply to provide power support for the microgrid. When the output of renewable energy is surplus, the battery can absorb the excess power to achieve peak shaving and valley filling, or sell electricity to the distribution network to obtain a certain income. Therefore, its operating cost is relatively low.

2. Scenario 2: Microgrid system under the orderly charging strategy of electric vehicles

The configuration scheme of the optimal compromise solution in Figure 6 is selected for analysis. The total planned cost is 2,289,410 yuan, of which the annual investment and construction cost of distributed power is 745,977 yuan, the operating cost is 1,381,817 yuan, and the electric vehicle charging cost is 161,616 yuan; The load fluctuation is 8219.2(kW) ² ; the corresponding distributed power configuration capacity is: wind power 195kW, photovoltaic 57kW, diesel engine 50kW, battery 350kW·h. At this time, the photovoltaic configuration capacity is significantly reduced, and the wind turbine is the main distributed power source in the microgrid. When the microgrid system is in normal operation, the output of each distributed power source and the transmission power of the tie line are shown in Figure 7.

In the scenario of orderly charging of electric vehicles, by adjusting the charging load of users, the configuration capacity of distributed power sources can be reduced and the investment and construction costs of distributed power sources can be further reduced. Analysis of Figure 7 shows that the photovoltaic output is relatively low at this time, and the microgrid mainly purchases electricity from the distribution network through the tie line to meet the load demand, so its operating cost is high.

3. Comparative analysis of scenario 1 and scenario 2

(1) Load fluctuation target

The load fluctuation in the disordered charging scenario is 25825kW, and the load fluctuation in the ordered charging scenario is 8961.5kW, which is 65.30% lower than that in the disordered charging, and the overall load fluctuation of the microgrid can be significantly reduced. At the same time, it can be seen from Figure 8 that the 1# curve is more stable than the 2# curve, which shows that through the optimal configuration of the orderly charging of electric vehicles, the load peaks and valleys are well realized, and the original load is avoided. The peak load of electric vehicles and the peak of electric vehicle charging demand are superimposed to form a bad situation of "peak and peak", reducing the impact of charging load on the power grid.

(2) Economic goals

It can be seen from Table 1 that in the case of orderly charging, the installation cost of distributed power and the charging cost of electric vehicles are significantly lower than those in the case of disordered charging, but the cost of charging construction is significantly increased, because orderly charging is only charged in the valley period. Disorderly charging has charging needs throughout the day, so it will lead to higher capacity of charging stations required in the case of orderly charging, and higher construction costs.

Table 1 Comparison of economic indicators after optimized configuration in two scenarios

To sum up, the planning cost of ordered charging is 46.04% lower than that of disordered charging. On the one hand, the optimized configuration of the microgrid including electric vehicle charging stations reduces the peak-to-valley difference of the microgrid load, and saves the user's charging cost while saving the installation cost, realizing a win-win economic for the user and the microgrid.

The specific implementation case provided by the present invention firstly proposes the concept of charging service radius based on the Voronoi diagram, considering the radiation area of the microgrid system and the distance from the service boundary to the system center. Taking this as the objective function, the location coordinates of the system are determined, so that the microgrid system with charging stations has the widest service range.

Then, on the basis of considering the user's car habits, the behavior characteristics of electric vehicle users are analyzed. According to the relevant data of the National Household Vehicle Survey Report, the probability density function of the end time of electric vehicle travel and the daily mileage mileage is simulated, and the daily charging demand of a large number of electric vehicles in the case of disordered charging is obtained by the Monte Carlo random sampling method.

Finally, in view of the limitations of independent research on distributed energy and electric vehicle charging station planning, a charging station with electric vehicle is proposed to reduce the overall economic cost of both the microgrid and electric vehicle users and reduce the total load fluctuation of the microgrid. A dual-objective programming model for microgrid systems. The multi-objective particle swarm algorithm and the introduction of fuzzy membership function are used to solve the objective in the two scenarios of electric vehicle disordered charging and ordered charging, and the optimal configuration results in the two scenarios are compared and analyzed.

The specific embodiments described above further describe the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. The method for selecting the address of the micro-grid comprising the electric vehicle charging station is characterized in that a charging service radius R is defined_sAs shown in formula (1):

in the formula, N_iThe number of the I service area sides of the micro-grid system containing the electric vehicle charging station is counted; omega_nWeight coefficient for the nth side of Voronoi polygon, d_h-nThe Euclidean distance from the head end point of the nth edge to the i; d_t-nThe Euclidean distance from the end point of the nth edge to the system i; l_n、l_kThe lengths of the nth and k edges in the Voronoi polygon respectively.

2. The method for selecting a site of a microgrid with electric vehicle charging stations as claimed in claim 1, characterized in that in the case of constructing a set number of microgrid systems with electric vehicle charging stations in any municipal planned load concentration area, the site selection plan of the charging station with the sum of all system service radii being the maximum optimization target is as shown in formula (3):

in the formula, M is the number of the micro-grid systems which are planned and constructed and contain the electric vehicle charging stations.

3. A microgrid optimal configuration method comprising an electric vehicle charging station is characterized in that any site selection position obtained by site selection by using any site selection method of claims 1 to 2 is optimally configured; and (3) double-target optimization with minimum economic cost and minimum load fluctuation as optimization targets:

economic cost target f₁Function:

C＝C_i+C_om+C_cs+C_ex+C_charge+C_loss(4)；

in formula (4), C is the planned total cost, C_iThe construction cost for four distributed power supplies of wind power, photovoltaic, diesel engine and energy storage system C_omFor operating costs, C_csFor the construction cost of charging stations, C_exFor the cost of energy exchange between the microgrid and the grid, C_chargeCost of charging electric vehicle users, C_lossFor the cost of losing load, the units are yuan;

load fluctuation target f₂Function:

in the formula (5), P_{load-fluctuation}As the amount of fluctuation of the load,

the basic load capacity of the microgrid at the moment t;

the charging load of the M electric automobiles at the time t under the disordered charging scene is obtained.

4. The method of claim 3, wherein the step of optimizing the configuration of the microgrid including electric vehicle charging stations,

said C is_iThe construction cost of four distributed power supplies of wind power, photovoltaic, diesel engine and energy storage system is shown as formula (6):

in the formula (6), B is the type of the distributed power supply; c_bThe installation cost of the b-th type distributed power supply; r is the current rate, usually 8%; l_bThe life cycle of the b-type distributed power supply is shown;

the running cost C_omAs shown in formula (7):

in the formula (7), T is the running time of the system; k is a radical of_{om_b}The operation management coefficient of the b-th type distributed power supply is obtained;

configuring the output of the power supply for the class b at the moment t;

energy exchange cost C between the microgrid and the power grid_exAs shown in formula (8):

in the formula (8), the reaction mixture is,

the electricity prices of electricity purchasing and electricity selling at the time t are respectively,

respectively purchasing electric power and selling electric power from the power distribution network at the time t;

charging station construction cost C_csAs shown in formula (9):

C_cs＝S_vC_charge(9)；

in the formula (9), c_chargeConstruction cost of charging station for unit capacity, S_vCapacity for electric vehicle charging stations;

the charging cost of the electric automobile C_chargeAs shown in equation (10):

in the formula (10), the compound represented by the formula (10),

in order to purchase the electricity price at the time t,

the charging load of the M electric automobiles at the time t under the disordered charging scene is obtained;

the loss of load cost C_lossAs shown in formula (11):

in the formula (11), c_lossIn the form of a unit load loss cost,

the power of the system is lost load at time t.

5. The method of claim 3, wherein the economic cost target f is an economic cost target₁Function and load fluctuation target f₂The constraint conditions of the functions comprise micro-grid power balance constraint, distributed power supply capacity upper limit constraint, tie line power upper limit constraint, energy storage system battery state of charge change and constraint, energy storage system actual charging and discharging output constraint, electric vehicle battery state of charge constraint, micro-grid self-balance rate constraint and load loss rate constraint.

6. The method of claim 5, wherein the step of optimizing the configuration of the microgrid including electric vehicle charging stations,

the microgrid power balance constraint is as shown in formula (12):

in the formula (12), the reaction mixture is,

for wind power, photovoltaic, diesel engine and energy storage system to output power at time t,

positive number indicates that the energy storage system is discharged, and vice versa indicates charging;

for the exchange power of the tie at time t,

a positive value indicates that the micro-grid purchases electricity from the distribution network, a negative value indicates that the micro-grid sells electricity to the distribution network,

abandoning wind and light energy and loss load for the micro-grid at the moment t;

the upper limit constraint of the capacity of the distributed power supply is shown as an equation (13):

in formula (13), S_WT、S_PV、S_DE、S_ESSFor the configuration capacity, S, of wind, light, diesel and storage four types of distributed power_WT-max、S_PV-max、S_DE-max、S_ESS-maxConfiguring the upper limit of the capacity for the corresponding four types of distributed power supplies;

the tie line power upper limit constraint

In formula (14), P_L-maxIs the rated power of the tie line;

the change and constraint of the state of charge of the battery of the energy storage system are shown as the following formula (15):

in the formula (15), the reaction mixture is,

state of charge of the energy storage system at time t, η_ESSThe values of the charge-discharge efficiency of the energy storage system are respectively shown as a formula (16) and a formula (17):

in formulae (16) and (17), η_c、η_dcCharging and discharging efficiency, SOC, for energy storage systems_ESS-min、SOC_ESS-maxThe upper limit and the lower limit of the battery state of charge of the energy storage system are set;

the actual charging and discharging output constraint of the energy storage system is as shown in formula (18):

in the formula (18), P_cmaxAnd P_dcmaxRespectively charging and discharging the energy storage system with maximum power;

the constraint of the state of charge of the battery of the electric automobile is shown as a formula (19):

in the formula (19), SOC_ev-min、SOC_ev-maxThe upper limit value and the lower limit value of the electric automobile battery charge state,

the state of charge of the battery at the moment t;

the self-balance degree rate constraint of the micro-grid is shown as the formula (20):

in formula (20), Sa_min、Sa_maxTo self-balance the upper and lower limits of the degree, P_load-totalFor the total load power, P, of the microgrid_buy-totalPurchasing total power for the micro-grid;

the loss of load rate constraint is as shown in equation (21):

in the formula (21), L_oep-maxFor maximum proportion of load cut, P_loss-totalThe total power for load shedding.

7. The microgrid optimization configuration method comprising an electric vehicle charging station as claimed in claim 3, characterized in that a multi-objective particle swarm algorithm is adopted to solve the optimization configuration problem, the satisfaction degrees of the economic cost and the load fluctuation objective are respectively calculated by using a fuzzy membership function, and the maximum value of the standardized satisfaction degrees is obtained as the optimal compromise solution.

8. The method according to claim 7, wherein the fuzzy membership function is represented by equation (22):

in the formula (22), f_iFor the solution set of the ith optimization objective, f_iminIs the minimum value of the ith optimization target solution, f_imaxThe maximum value of the ith optimization target solution;

the normalized satisfaction is calculated as shown in equation (23):

in the formula (23), u is the normalized satisfaction degree, and N is the number of optimization targets.

9. The method for optimizing the configuration of the microgrid with electric vehicle charging stations according to any one of claims 3 to 8, characterized in that before the optimal configuration of the power grid, an electric vehicle unordered charging strategy and an ordered charging strategy are set:

predicting the disordered charging load of the charging electric automobile to obtain the charging load of the N electric automobiles in the disordered charging scene

Is represented by the formula (24):

in the formula (24), the reaction mixture is,

the charging load of the M electric automobiles at the time t under the disordered charging scene is obtained;

the charging load of the ith electric automobile at the moment t in the disordered charging scene is obtained;

the method for guiding the orderly charging by the power grid is to guide the electric automobile load to carry out orderly charging in the valley through the difference of the electricity prices in the peak valley, so that the charging behavior of the electric automobile is changed.

10. The method of claim 9, wherein the charging behavior of the electric vehicle for obtaining the charging load is set as follows:

setting a charging start time t₀In the form of a normal distribution function,

charging start time t₀Probability density function of (1):

in the formula (25), mu_t＝17.6，σ_t＝3.4；

The daily mileage s follows a lognormal distribution, and the probability density function is shown in the following equation (26):

in the formula (26), mu_s＝0.88，σ_s3.2; the maximum value s of daily mileage is restricted by the battery capacity charge state of the electric vehicle_maxThe number of miles traveled is the number of miles traveled corresponding to the reduction of the battery capacity from the upper limit to the lower limit.

Images