CN111310966A - Micro-grid site selection and optimal configuration method containing electric vehicle charging station - Google Patents
Micro-grid site selection and optimal configuration method containing electric vehicle charging station Download PDFInfo
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Abstract
The invention discloses a microgrid addressing and optimal configuration method comprising an electric vehicle charging station. And determining the site selection coordinate of the system by taking the maximum sum of the radius of the charging service as an objective function, so that the service range of the micro-grid system with the charging stations is widest. Aiming at the limitation of independent research on planning of distributed energy and electric vehicle charging stations, the invention provides a micro-grid system dual-target planning model containing the electric vehicle charging stations, which aims at reducing the overall economic cost of a micro-grid and electric vehicle users and reducing the total load fluctuation of the micro-grid. Effectively overcomes the defects of the prior art and has good economic and social values.
Description
Technical Field
The invention relates to the technical field of power grid configuration, in particular to a micro-grid site selection and optimal configuration method with an electric vehicle charging station.
Background
With the rapid development of economy in China, the problems of energy shortage and environmental pollution are also caused. Therefore, distributed renewable clean energy such as wind power generation and photovoltaic power generation and electric vehicles are receiving wide attention from various fields. Due to the fact that the uncertainty and intermittence of wind and light output can cause serious electric energy quality influence on a power grid, the micro power grid can be generated at the same time. The micro-grid is a regional power system with distributed power supplies, wind and light output is absorbed through regional internal loads, and the influence of wind and light uncertainty on the grid can be effectively relieved. For the electric automobile, according to the energy structure mainly based on coal in China, the electric automobile directly obtains electric quantity from the traditional power grid, and pollutants discharged by power generation cannot 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 load peak value of the micro-grid, so that the load peak-valley difference of the micro-grid is increased, the installed capacity of the distributed power supply is increased, the construction cost of the micro-grid is increased, and the safety and the economy of planning and running of the micro-grid are affected. Therefore, the method has high economic benefit and great social significance for carrying out optimization configuration research on the micro-grid comprising the electric automobile by controlling the charging behavior of the electric automobile.
The aim of the optimal configuration of the micro-grid is to balance the supply and demand relationship between the load and the distributed power supply (the grid-connected micro-grid also needs to consider energy exchange with a large power grid), and the intermittent renewable energy exists in the distributed power supply, so that the load demand and the natural resource condition need to be sufficiently analyzed and predicted. Different from the traditional microgrid planning, the optimal configuration result in the microgrid with the electric vehicle charging station has a high coupling relation with the charging behavior of the electric vehicle, the type and the capacity of the microgrid equipment need to be determined during optimal configuration, the influence of the charging strategy of the electric vehicle on the configuration result needs to be fully considered, and then the comprehensive planning is carried out aiming at a specific optimization target and a constraint condition.
In the existing research, scholars and organizations at home and abroad obtain some achievements on the planning of the electric vehicle charging station in the electric power system. The basic method is to select the site of the electric vehicle rapid charging station in consideration of the aspects of voltage stability margin, system loss and the like, and to evaluate the maximum acceptable charging station capacity based on the reliability of the power grid. In recent years, there has been a study of a charging station planning method for calculating an optimal planned capacity of a charging station in consideration of traffic flow conditions, coupling between a traffic network and a power grid structure, user travel consumption, user charging waiting time, and the like.
The existing research is mainly independently developed aiming at two aspects of optimization configuration of a distributed power supply and an electric vehicle charging station, and the research on the optimization configuration of a micro-grid containing the electric vehicle charging station is less. Most of the researches are mainly from the economy of realizing the optimal configuration of the microgrid, but in the optimal configuration of the microgrid with the electric vehicle charging station, due to the randomness and the volatility of the load demands of the distributed power supply and the electric vehicle, the load fluctuation of the microgrid is frequent, the peak-valley load difference is large, and the economy, the safety and the stability of the microgrid are influenced. In addition, the conventional research may not fully consider the problem of location selection of the charging station.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the invention provides a microgrid site selection and optimal configuration method with an electric vehicle charging station, which solves the problems.
The invention is realized by the following technical scheme:
microgrid addressing method comprising electric vehicle charging station and used for defining charging service radius RsAs shown in formula (1):
in the formula, NiThe number of the I service area sides of the micro-grid system containing the electric vehicle charging station is counted; omeganWeight coefficient for the nth side of Voronoi polygon, dh-nThe Euclidean distance from the head end point of the nth edge to the i; dt-nThe Euclidean distance from the end point of the nth edge to the system i; ln、lkThe lengths of the nth and k edges in the Voronoi polygon respectively.
In order to plan the addressing condition of the microgrid system with the electric vehicle charging station more reasonably and enable the service range covered by the microgrid system to be the widest, the index of the charging service radius is given, and on one hand, the area size of a Voronoi polygon corresponding to the microgrid system with the electric vehicle charging station needs to be considered, and on the other hand, the distance from the service boundary of the microgrid system with the electric vehicle charging station to the service boundary needs to be considered.
Further, in any municipal planned load concentration area, under the condition that a set number of micro-grid systems containing electric vehicle charging stations are built, the site selection plan of the charging station with the maximum sum of service radii of all the systems as an optimization target is shown in the 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.
Based on the site selection method, the microgrid optimization configuration method with the electric vehicle charging station is carried out, and double-target optimization with the minimum economic cost and the minimum load fluctuation as optimization targets is carried out:
economic cost target f1Function:
C＝Ci+Com+Ccs+Cex+Ccharge+Closs(4)；
where C is the planned total cost, CiThe construction cost for four distributed power supplies of wind power, photovoltaic, diesel engine and energy storage system ComFor operating costs, CcsFor the construction cost of charging stations, CexFor the cost of energy exchange between the microgrid and the grid, CchargeCost of charging electric vehicle users, ClossFor the cost of losing load, the units are yuan;
load fluctuation target f2Function:
in the formula, Pload-fluctuationAs 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.
Through reducing load fluctuation, the loss in the electric quantity transmission and distribution process can be reduced on the one hand, and the safe and stable operation of a power grid can be ensured on the other hand. The fluctuation condition of the load of the micro-grid can be represented by adopting the mean square error of the total load, and the smaller the mean square error is, the more stable the load change is.
Further, said CiThe construction cost of the four distributed power supplies of wind power, photovoltaic, diesel engine and energy storage system is shown as formula (6), and the invention can be calculated by adopting an equal annual cost method:
in the formula (6), B is the type of the distributed power supply; cbThe installation cost of the b-th type distributed power supply; r is the discount rate, usuallyTaking 8 percent; lbThe life cycle of the b-type distributed power supply is shown;
the running cost ComAs shown in equation (7), the operation management coefficient is used to calculate:
in the formula (7), T is the running time of the system; k is a radical ofom_bThe 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 gridexAs 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 CcsAs shown in formula (9):
Ccs＝Svcch(9)；
in the formula (9), cchargeConstruction cost of charging station for unit capacity, SvCapacity for electric vehicle charging stations;
the charging cost of the electric automobile CchargeAs 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 ClossAs shown in formula (11):
in the formula (11), clossIn the form of a unit load loss cost,
the power of the system is lost load at time t.
Further, the economic cost target f1Function 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.
Further, 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), SWT、SPV、SDE、SESSFor the configuration capacity, S, of wind, light, diesel and storage four types of distributed powerWT-max、SPV-max、 SDE-max、SESS-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), PL-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, ηESSFor the charge-discharge efficiency of the energy storage system, the values are respectivelyAs shown in formulas (16) and (17):
in formulae (16) and (17), ηc、ηdcCharging and discharging efficiency, SOC, for energy storage systemsESS-min、SOCESS-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), PcmaxAnd PdcmaxRespectively 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), SOCev-min、SOCev-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), Samin、SamaxTo self-balance the upper and lower limits of the degree, Pload-totalFor the total load power, P, of the microgridbuy-totalPurchasing electricity for micro-gridThe total power;
the loss of load rate constraint is as shown in equation (21):
in the formula (21), Loep-maxFor maximum proportion of load cut, Ploss-totalThe total power for load shedding.
Further, the optimization configuration problem is solved by adopting a multi-target particle swarm algorithm, the satisfaction degrees of the economic cost and the load fluctuation target are respectively calculated by using a fuzzy membership function, and the maximum value of the standardized satisfaction degrees is obtained to serve as an optimal compromise solution.
The optimization model is essentially a dual-objective optimization problem, and usually cannot optimize both objectives. If a single target is considered optimal, it may cause another target to have worse results. Therefore, the optimal configuration problem is solved by using a multi-objective particle swarm optimization. The multi-target particle swarm algorithm is widely applied as a typical intelligent optimization algorithm, can find a multi-target non-inferior solution set, and finally solves a group of Pareto optimal solution sets. And respectively calculating the satisfaction degrees of the economic cost and the load fluctuation target by using a fuzzy membership function, and solving the maximum value of the standardized satisfaction degrees as an optimal compromise solution.
Further, the fuzzy membership function is shown as equation (22):
in the formula (22), fiFor the solution set of the ith optimization objective, fiminIs the minimum value of the ith optimization target solution, fimaxThe 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.
Further, before the optimal configuration of the power grid is carried out, an electric vehicle unordered charging strategy and an ordered charging strategy are set:
predicting the disordered charging load of the charging electric automobile; the charging demand prediction of each electric automobile is an independent event, the charging demands of the single electric automobile can be circularly superposed through a Monte Carlo method, and the charging load of the N electric automobiles in the disordered charging scene is obtained
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.
When the peak time electricity price is mostly concentrated on 17 hours to 23 hours and the valley time electricity price is mostly concentrated on 24 hours to the next day 7, most families and enterprises are in rest time in the valley time electricity price period, and the electric automobile is in a grid-connected state. Therefore, the method for guiding the orderly charging by the power grid guides the electric automobile load to carry out orderly charging at the valley time through the difference of the electricity prices at the peak valley time, changes the charging behavior of the electric automobile under the condition of meeting the normal use of most electric automobile users, reduces the load peak value of the power grid, reduces the load fluctuation of the power grid, and simultaneously reduces the charging cost of the users and the installed capacity cost of the power grid.
Further, the electric vehicle charging behavior for acquiring the charging load is set as follows:
the charging requirements of electric vehicle users have high randomness and uncertainty, and the traveling habits and driving characteristics of the users must be analyzed to obtain a reasonable charging requirement curve. The invention relates to the travel ending time t of the electric automobile0The distribution is approximately normal distribution, and the daily driving mileage s is approximately lognormal distribution. For the user of the electric automobile, the invention simply assumes that the charging and discharging time of the network access is the last return time t of the user0Namely, the electric automobile is charged immediately after the last return trip, and the return trip time is concentrated in the off-duty peak period of the user by combining the traveling habit of the automobile owner. According to the electric automobile data obtained by statistics of the United states department of energy, the maximum likelihood estimation method is used for obtaining the charging initial time t0Probability density function f of mileage s of daily drivingt(x) And fs(x) In that respect Thus setting the charging start time t0Is a normal distribution function, t0～N(μt,σt 2) Charging start time t0Probability density function of (1):
in the formula (25), mut＝17.6，σt＝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), mus＝0.88，σs3.2; the maximum value s of daily mileage is restricted by the battery capacity charge state of the electric vehiclemaxThe 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.
The invention has the following advantages and beneficial effects:
the invention develops research work around the problems of site selection and optimal configuration of a micro-grid system containing an electric vehicle charging station, establishes a site selection model of the system with the maximum service radius of the micro-grid system containing the electric vehicle charging station as the target, and establishes an optimal configuration model of the micro-grid system containing the electric vehicle considering the planning operation economy and the safety and stability of the micro-grid by guiding the electric vehicle to be charged in order. Has the following beneficial effects:
1. the invention defines the concept of the service radius of the microgrid system containing the electric vehicle charging station based on a Voronoi diagram, and provides a site selection method of the microgrid system containing the electric vehicle charging station by taking the maximum service radius as an objective function. The service range of the system is widest.
2. According to the method, the probability density function of the user travel characteristic is simulated according to the user behavior characteristic of the electric automobile, the charging strategies of the disordered charging and the ordered charging of the electric automobile are formulated, and the disordered charging behavior of the electric automobile is predicted by adopting a Monte Carlo sampling method.
3. The micro-grid system containing the electric vehicle charging station is connected to a power grid, on one hand, renewable energy can be consumed on the spot, and the energy utilization rate is improved; on the other hand, the fluctuation of wind and light output force can be effectively stabilized, and the operation safety of a power grid is improved. After the electric automobile is charged in order, the installation cost of the distributed power supply is reduced, meanwhile, the charging cost of a user is also reduced, and the win-win situation of the user and the micro-grid economy is realized. And after the electric automobile is optimally scheduled, the peak-valley difference of the load of the micro-grid is reduced, so that the overall load fluctuation of the micro-grid is obviously reduced, and the safety and the stability of the operation of the power grid are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart of an optimized configuration of the present invention;
FIG. 2 is a diagram showing the sum of service radii of the micro-grid system including the electric vehicle in a single scene in 1000 scene simulations;
FIG. 3 is a Voronoi diagram corresponding to the micro-grid system with the electric automobile when the service radius is maximum;
FIG. 4 is a graph of the disordered charging power requirements for 200 electric vehicles;
fig. 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;
FIG. 6 is a pareto curve of total planning cost and load fluctuation of the micro-grid system;
FIG. 7 shows the output of each distributed power source and the transmission power of the tie lines during the sequential charging of the electric vehicle;
fig. 8 is a microgrid load curve before and after optimal scheduling.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
The embodiment provides a microgrid site selection method comprising an electric vehicle charging station, and the microgrid site selection method defines a charging service radius RsAs shown in formula (1):
in order to ensure that the site selection scheme is optimal, under the condition that a certain number of micro-grid systems containing electric vehicle charging stations are built in a certain municipal planned load concentration area, site selection planning of the charging stations with the maximum sum of service radii of all the systems as an optimization target is required, as shown in the 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.
The specific calculation example is that 10 micro-grid systems containing electric vehicle charging stations are arranged, and a load concentration area for planning and site selection is a square area with the length and the width of 100 units. After 1000 times of random addressing calculation of the sum of the charging service radii of the microgrid system with the electric vehicle charging station, the result shown in fig. 2 is obtained.
As can be seen from fig. 2, under the above planning conditions, the 226 th address in the result of 1000 iterations has the largest service radius, which is 508.928. The iteration result is used as an optimal addressing scheme of the micro-grid system with the electric vehicle charging station, and the addressing result under the scheme is shown in the attached figure 3.
Example 2
Based on embodiment 1, the present embodiment provides the demand prediction of the electric vehicle in disorder charging:
firstly, the charging behavior of the electric automobile is analyzed, and the charging starting time t is set0In the form of a normal distribution function,
charging start time t0Probability density function of (1):
in the formula (25), mut＝17.6，σt＝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), mus＝0.88，σs3.2; the maximum value s of daily mileage is restricted by the battery capacity charge state of the electric vehiclemaxFor the capacity of the battery to be reduced from the upper limit to the lower limitCorresponding mileage traveled.
Then, predicting the disordered charging load of the charging electric automobile; the charging demand prediction of each electric automobile is an independent event, the charging demands of the single electric automobile can be circularly superposed through a Monte Carlo method, and the charging load of the N electric automobiles in the disordered charging scene is obtained
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;
in addition, 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.
Specifically, the power consumption of an electric automobile per kilometer is 0.139 kW.h/km, the battery capacity is 17.5 kW.h, the upper limit and the lower limit of the battery capacity charge state are 100% and 20% respectively, and the charging power is 2 kW. And (3) simulating by adopting a Monte Carlo method to obtain the charging starting time and daily driving mileage of the user, and obtaining the charging power requirement of the electric automobile during the disordered charging by combining parameters such as the power consumption and the charging power of the electric automobile per kilometer. Fig. 4 is a graph of the disordered charging demand of 200 electric vehicles.
Example 3
The embodiment provides an optimal configuration method for a micro-grid system comprising electric vehicles, and an economic cost objective function f is comprehensively considered1And load fluctuation objective function f2(ii) a Economic benefitThe objective function f1：
C＝Ci+Com+Ccs+Cex+Ccharge+Closs(4)；
Where C is the planned total cost, CiThe construction cost for four distributed power supplies of wind power, photovoltaic, diesel engine and energy storage system ComFor operating costs, CcsFor the construction cost of charging stations, CexFor the cost of energy exchange between the microgrid and the grid, CchargeCost of charging electric vehicle users, ClossFor the cost of losing load, the units are yuan;
load fluctuation target f2Function:
in the formula, Pload-fluctuationAs 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.
According to the constraint conditions of microgrid power balance, 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 charge and discharge output constraint, electric vehicle battery state of charge constraint, microgrid self-balance rate constraint and loss load rate constraint; and solving the optimal configuration problem by adopting a multi-target particle swarm algorithm, respectively calculating the satisfaction degrees of the economic cost and the load fluctuation target by using a fuzzy membership function, and solving a maximum value of the standardized satisfaction degrees as an optimal compromise solution. The results obtained are as follows:
the technology designs the following two scenes: (1) the microgrid system under the electric vehicle unordered charging strategy (2) is a microgrid system under the electric vehicle ordered charging strategy. And carrying out optimization configuration and comparative analysis on the two scenes.
The specific calculation example selects each day of four seasons as a typical day for simulation, the unit time length is 1h, and the research time period is one year. Suppose that the electric vehicles in the microgrid system have 200 vehicles in total. At present, the self-balancing degree of a grid-connected micro-grid is low, and the grid-connected micro-grid is mainly supported by a large power grid, so that the self-balancing degree constraint range is set to be 40% -60%. Correspondingly, when the power distribution network is in fault and the microgrid operates independently, only important loads are guaranteed to supply power.
Second, optimizing results and analysis
1. Scene one: microgrid system under electric automobile unordered charging strategy
The optimal configuration scheme of the micro-grid system under the condition of disordered charging of the electric automobile is as follows: the total planning cost is 6267400 yuan, wherein the construction cost of the distributed power supply is 5080800 yuan, the operation cost is 877240 yuan, and the charging cost of the electric vehicle is 309360 yuan; the load fluctuation amount is 26198(kW)2(ii) a The configuration capacity condition is 195kW of wind power, 697kW of photovoltaic, 50kW of diesel engine and 350 kW.h of storage battery. The analysis of the optimization configuration result shows that the construction cost accounts for 81.07 percent of the total economic cost in the largest proportion, and the expensive installation cost is the main reason of poor economic benefit of the micro-grid at the present stage. When the microgrid system operates normally, the output of each distributed power supply and the transmission power of the connecting lines are as shown in fig. 5.
When the electric automobile is charged in disorder, the charging load is concentrated in the peak period of the load, and the micro-grid system needs to be configured with a distributed power supply with higher investment and construction cost to meet the load requirement in consideration of the limitation of the electricity purchasing power of a connecting line. As can be seen from fig. 5, the photovoltaic provides power support for the microgrid for the main distributed power source, and when the renewable energy output remains, the storage battery can absorb the excess power to realize peak clipping and valley filling, or to sell power to the distribution grid to obtain a certain benefit, so that the operating cost is relatively low.
2. Scene two: microgrid system under electric automobile ordered charging strategy
Selecting the configuration scheme of the optimal compromise solution in the figure 6 for analysis, wherein the planning total cost is 2289410 yuanThe annual investment construction cost of the distributed power supply is 745977 yuan, the operation cost is 1381817 yuan, and the charging cost of the electric automobile is 161616 yuan; the load fluctuation amount is 8219.2(kW)2(ii) a The corresponding distributed power source configuration capacity is: wind power is 195kW, photovoltaic is 57kW, a diesel engine is 50kW, and a storage battery is 350 kW.h. At the moment, the photovoltaic configuration capacity is obviously reduced, and the wind driven generator is a main distributed power supply in the microgrid. When the microgrid system operates normally, the output of each distributed power supply and the transmission power of the connecting lines are as shown in fig. 7.
Under the scene of orderly charging of the electric automobile, the configuration capacity of the distributed power supply can be reduced by adjusting the charging load of a user, and the investment and construction cost of the distributed power supply is further reduced. As can be seen from the analysis of fig. 7, at this time, the photovoltaic output is relatively low, and the microgrid mainly purchases electricity from the distribution network through the tie line to meet the load demand, so that the operating cost is relatively high.
3. Comparative analysis of scene one and scene two
(1) Load fluctuation target
The load fluctuation amount in the disordered charging scene is 25825kW, the load fluctuation amount in the ordered charging scene is 8961.5kW, 65.30% is reduced compared with the disordered charging, and the overall load fluctuation of the micro-grid is remarkably reduced. Meanwhile, as can be seen from fig. 8, the curve 1 is more stable than the curve 2, which means that peak clipping and valley filling of the load are well realized through the optimized configuration of the orderly charging of the electric vehicle, the severe condition that the original load peak and the charging demand peak of the electric vehicle are superposed to form "peak-to-peak" is avoided, and the impact of the charging load on the power grid is reduced.
(2) Economic objective
It can be seen from table 1 that under the condition of charging in order, distributed generator installation cost and electric automobile charging cost all are more the unordered condition of charging and are showing and reduce, but the construction cost that charges obviously promotes because orderly charging charges only in the valley period, and unordered charging all has the demand of charging in the whole day period, consequently can lead to charging station capacity that needs under the condition of charging in order higher, and the construction cost is also higher.
TABLE 1 comparison of economic indicators after optimal configuration of two scenarios
In summary, the planning cost for ordered charging is reduced by 46.04% compared to the planning cost for unordered charging. The optimization configuration of the micro-grid comprising the electric vehicle charging station reduces the peak-valley difference of the load of the micro-grid on one hand, saves the charging cost of a user under the condition of saving the installation cost, and realizes the win-win situation of the user and the micro-grid in economy.
The specific implementation case provided by the invention is based on a Voronoi diagram, and the concept of the radius of the charging service is provided by considering the radiation area of the microgrid system and the distance from the service boundary to the center of the system. And the address selection coordinates of the system are determined by taking the address selection coordinates as the objective function, so that the service range of the micro-grid system with the charging stations is the widest.
Then, on the basis of considering the habit of the user to use the vehicle, the behavior characteristics of the user of the electric vehicle are analyzed. According to relevant data of the survey report of the domestic vehicle in the whole beauty, probability density functions of the trip finish time and the daily driving mileage of the electric vehicle are simulated, and daily charging requirements of a large number of electric vehicles under the condition of disordered charging are obtained through a Monte Carlo random sampling method.
Finally, aiming at the limitation of independent research on the planning of distributed energy and electric vehicle charging stations, a micro-grid system dual-target planning model containing the electric vehicle charging stations is provided, wherein the micro-grid system dual-target planning model aims at reducing the overall economic cost of a micro-grid and electric vehicle users and reducing the total load fluctuation of the micro-grid. The method comprises the steps of solving a target under two scenes of disordered charging and ordered charging of the electric automobile by adopting a multi-target particle swarm algorithm and introducing a fuzzy membership function, and comparing and analyzing an optimized configuration result under the two scenes.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the 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 definedsAs shown in formula (1):
in the formula, NiThe number of the I service area sides of the micro-grid system containing the electric vehicle charging station is counted; omeganWeight coefficient for the nth side of Voronoi polygon, dh-nThe Euclidean distance from the head end point of the nth edge to the i; dt-nThe Euclidean distance from the end point of the nth edge to the system i; ln、lkThe 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＝Ci+Com+Ccs+Cex+Ccharge+Closs(4)；
in formula (4), C is the planned total cost, CiThe construction cost for four distributed power supplies of wind power, photovoltaic, diesel engine and energy storage system ComFor operating costs, CcsFor the construction cost of charging stations, CexFor the cost of energy exchange between the microgrid and the grid, CchargeCost of charging electric vehicle users, ClossFor the cost of losing load, the units are yuan;
load fluctuation target f2Function:
in the formula (5), Pload-fluctuationAs 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 isiThe 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; cbThe installation cost of the b-th type distributed power supply; r is the current rate, usually 8%; lbThe life cycle of the b-type distributed power supply is shown;
the running cost ComAs shown in formula (7):
in the formula (7), T is the running time of the system; k is a radical ofom_bThe 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 gridexAs 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 CcsAs shown in formula (9):
Ccs＝SvCcharge(9)；
in the formula (9), cchargeConstruction cost of charging station for unit capacity, SvCapacity for electric vehicle charging stations;
the charging cost of the electric automobile CchargeAs 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 ClossAs shown in formula (11):
in the formula (11), clossIn 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), SWT、SPV、SDE、SESSFor the configuration capacity, S, of wind, light, diesel and storage four types of distributed powerWT-max、SPV-max、SDE-max、SESS-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), PL-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, ηESSThe 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), ηc、ηdcCharging and discharging efficiency, SOC, for energy storage systemsESS-min、SOCESS-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), PcmaxAnd PdcmaxRespectively 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), SOCev-min、SOCev-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), Samin、SamaxTo self-balance the upper and lower limits of the degree, Pload-totalFor the total load power, P, of the microgridbuy-totalPurchasing total power for the micro-grid;
the loss of load rate constraint is as shown in equation (21):
in the formula (21), Loep-maxFor maximum proportion of load cut, Ploss-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), fiFor the solution set of the ith optimization objective, fiminIs the minimum value of the ith optimization target solution, fimaxThe 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), mut＝17.6，σt＝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), mus＝0.88，σs3.2; the maximum value s of daily mileage is restricted by the battery capacity charge state of the electric vehiclemaxThe 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.
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