GB2623600A

GB2623600A - Method and system for micro-grid planning of expressway service areas considering energy source self-consistency rate

Info

Publication number: GB2623600A
Application number: GB2300943.4A
Authority: GB
Inventors: Xia Shiwei; Zhang Xiaolong; Wang Zizheng; Li Chen; Wang Peng
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2022-09-26
Filing date: 2023-01-23
Publication date: 2024-04-24
Also published as: CN115239032B; CN115239032A; GB202300943D0

Abstract

A model which accounts micro-grid equipment, power consumption and EV ownership use at service stations to plan new electric vehicle chargers. First, a multi-objective optimization model is established. The multi-objective optimization model includes multi-objective functions and constraint conditions. The multi-objective functions include annual investment benefit objective function, micro-grid operation benefit objective function, user queuing waiting time objective function, and micro-grid energy source self-consistency rate objective function. The constraint conditions include power balance constraint, distributed power output constraint, energy storage device output constraint, electric vehicle charge constraint, and device construction constraint. Then, optimization solution is performed on the multi-objective optimization model by a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid. A planning model may be established having an objective function with a minimum number of charging stations and constraint condition of satisfied charging demand. A solution may be performed to obtain installation positions of the charging stations. The model balances user access/convenience, profitability and local grid resource utilisation to provide recommended installation locations.

Description

METHOD AND SYSTEM FOR MICRO-GRID PLANNING OF EXPRESSWAY

SERVICE AREAS CONSIDERING ENERGY SOURCE SELF-CONSISTENCY RATE

TECHNICAL FIELD

[0001] The present invention relates to the technical field of expressway micro-grid planning, and in particular, to a method and system for micro-grid planning of expressway service areas considering energy source self-consistency rate.

BACKGROUND

[0002] In recent years, the shortage of fossil fuels and environmental pollution are becoming serious increasingly. In order to achieve the goals of peak carbon dioxide emissions and carbon neutralization, more and more countries have begun to vigorously promote electric vehicles and renewable energy sources. Electric vehicles take clean electric power as an energy source, which will produce no emission during use and has very low noise. The generation of electricity by using the renewable energy sources can realize zero emission in an electricity generating process, which reduces the emissions and non-renewable energy source consumption in the generation of electricity by conventional energy sources.

100031 Power battery technologies of the electric vehicles develop slowly, the endurance mileage of the electric vehicles is not as long as that of fuel vehicles and conventional charging requires a longer time, so it is an inevitable option for promoting the electric vehicles by using a fast charging technology to improve user experiences. In particular, in the condition of traveling on an expressway, an electric vehicle has a high speed, and the electricity consumption of the electric vehicle is also high, and thus the electric vehicle needs to be supplemented with electric energy for one or more times to reach a destination. The planning of charging stations in existing expressway service areas has become a key to meet the travel demands of electric vehicle users for an intercity travel. However, the expressway service areas are generally far away from a power grid, and the electric energy of the power grid is generally transmitted over a long distance from the power grid to users so as to cause a large loss of electric energy, which is uneconomical. Therefore, it is desired to achieve energy source self-consistency of micro-grid in service areas by using rich wind power devices and photovoltaic devices in the suburbs in combination with energy storage devices and micro-gas turbines.

SUMMARY

[0004] An objective of the present invention is to provide a method and system for micro-grid planning of expressway service areas considering energy source self-consistency rate, so as to plan the micro-grid reasonably and achieve energy source self-consistency.

100051 In order to achieve the abovementioned objective, the present invention provides the following solutions: [0006] A method for micro-grid planning of expressway service areas considering energy source self-consistency rate includes: 100071 establishing a multi-objective optimization model for micro-grid planning; wherein the multi-objective optimization model comprises multi-objective functions and constraint conditions; the multi-objective functions comprise an annual investment benefit objective function, a micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function; and the constraint conditions comprise a power balance constraint, a distributed power output constraint, an energy storage device output constraint, an electric vehicle charge constraint, and a device construction constraint; and [0008] performing optimization solution on the multi-objective optimization model by using a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid; wherein the devices comprise micro-gas turbines, photovoltaic devices, wind power devices, energy storage devices, and charging piles, and the configuration solution comprises models of the micro-gas turbines, the photovoltaic devices, the wind power devices and the energy storage devices, and the number of the charging piles installed in each service area within a planning range.

[0009] A system for micro-grid planning of expressway service areas considering energy source self-consistency rate includes: 100101 a multi-objective optimization model establishment module, configured to establish a multi-objective optimization model for micro-grid planning, wherein the multi-objective optimization model comprises multi-objective functions and constraint conditions; the multi-objective functions comprise an annual investment benefit objective function, a micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function; and the constraint conditions comprise a power balance constraint, a distributed power output constraint, an energy storage device output constraint, an electric vehicle charge constraint, and a device construction constraint; and [0011] an optimization solution module, configured to perform optimization solution on the multi-objective optimization model by using a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid, wherein the devices comprise micro-gas turbines, photovoltaic devices, wind power devices, energy storage devices, and charging piles, and the configuration solution comprises models of the micro-gas turbines, the photovoltaic devices, the wind power devices, and the energy storage devices and the number of the charging piles installed in each service area within a planning range.

[0012] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: [0013] The present invention provides a method and system for micro-grid planning of expressway service areas considering energy source self-consistency rate. First, a multi-objective optimization model for micro-grid planning is established. The multi-objective optimization model includes multi-objective functions and constraint conditions. The multi-objective functions include an annual investment benefit objective function, a micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function. The constraint conditions include a power balance constraint, a distributed power output constraint, an energy storage device output constraint, an electric vehicle charge constraint, and a device construction constraint. Then, optimization solution is performed on the multi-objective optimization model by using a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid. Therefore, the micro-grid can be reasonably planned to achieve energy source self-consistency.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To describe the technical solutions in the embodiments of the present invention or in the prior art more clearly, the following briefly describes the drawings required for describing the embodiments. Apparently, the drawings in the following description are merely some embodiments of the present invention, and those of ordinary skill in the art may still derive other drawings from these drawings without creative efforts.

100151 FIG. 1 is a flowchart of a method for micro-grid planning according to Embodiment 1 of the present invention; [0016] FIG. 2 is a schematic diagram of the arrangement of entrances, exits, and service areas of an expressway according to Embodiment 1 of the present invention; and [0017] FIG. 3 is a block diagram of a system for micro-grid planning according to Embodiment 2 of the present invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Apparently, the described embodiments are merely part rather than all of the embodiments of the present invention. On the basis of the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present invention.

[0019] An objective of the present invention is to provide a method and system for micro-grid planning of expressway service areas considering energy source self-consistency rate, so as to plan the micro-grid reasonably and achieve energy source self-consistency.

[0020] In order to make the abovementioned objective, features, and advantages of the present invention more apparent and more comprehensible, the present invention is further described in detail below with reference to the drawings and specific implementations.

100211 Embodiment 1: [0022] The present embodiment provides a method for micro-grid planning of expressway service areas considering energy source self-consistency rate. As shown in FIG. 1, the method for micro-grid planning includes that: [0023] SI: A multi-objective optimization model for micro-grid planning is established. The multi-objective optimization model includes multi-objective functions and constraint conditions. The multi-objective functions include an annual investment benefit objective function, a micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function. The constraint conditions include a power balance constraint, a distributed power output constraint, an energy storage device output constraint, an electric vehicle charge constraint, and a device construction constraint.

100241 At present, it has become a key about how to reasonably plan charging stations in the existing expressway service areas to satisfy the travel demands of electric vehicle users for intercity travel. Therefore, the present embodiment provides a preferred implementation, which is to establish a charging demand model for expressway service areas based on various parameters of an electric vehicle, and then establish a planning model with a constraint of a satisfied charging demand and with an objective of a minimum number of charging stations, and perform solution on the planning model to obtain installation positions of the charging stations in the expressway service areas, so as to perform location planning of the charging stations and capacity planning of individual devices of the micro-grid in the expressway service areas orderly.

[0025] Specifically, before performing SI, the method for micro-grid planning of the present embodiment further includes that: a planning model for charging station planning is established, where the planning model includes an objective function with a minimum number of charging stations and a constraint condition with a satisfied charging demand; and the planning model is solved to obtain installation positions of the charging stations.

[0026] More specifically, an establishment process of the planning model may include that: [0027] (1) Travel characteristic parameters of an expressway electric vehicle are calculated: [0028] A time interval when an electric vehicle EV enters from the 5th entrance of an expressway is represented as t" which follows the following piecewise normal distribution: 1(f.cm.02 f s(ts) . v7ras exp ( 2c)_. ) , 0 t, /is + 12 vrrasi exp (2 (ts-ors+24))2) 6-.? , lis + 12 < ts. < 24 100291 In formula (1), ps is a mean value of a peak travel time interval of a day for the 5th entrance; c s is a standard deviation of the peak travel time interval of the day for the s'h entrance; and ps and o-, can be input into the formula (1) to calculate a time interval when the electric vehicle enters from the 5th entrance of the expressway.

[0030] The travel volume of EVs entering the expressway from the 0' entrance during the time interval r, is represented as nevs(r), a calculation formula of which is as follows: Tiers (t) = N EV s f s (t f s (its) (2), [0031] In formula (2), NET; indicates the travel volume of the EVs of the sth entrance at the peak travel time interval of a day; and NETT, is input into the formula (2), so that the travel volume of the EVs entering the expressway from the sth entrance during the time interval t, may be obtained. The total travel volume of the EVs at all entrances during the time interval t, is represented as nev(r), which may be expressed as: nev(t) = Enev, (t) [0032] Generally, electric vehicle users will charge before entering an expressway, so a State of Charge (SOC) value is generally high. An initial charge of the electric vehicle i during the time interval i is represented as socrtini, which is simulated by using truncated normal distribution, as shown in the following formula: (socri-m.soc) ; f s(socfYini) = exp 0.5 < soc < 1 (3); 2o-Lc [0033] In formula (3), psoi. and cr,0. are respectively a mean value and a standard deviation of the initial charge; and p", and as, are input into the formula (3), so that the initial charge of each electric vehicle may be calculated.

[0034] (2) According to the travel characteristic parameters of the expressway electric vehicle, a set of the charging demand points of the electric vehicle can be obtained through a Monte Carlo simulation.

[0035] As shown in FIG. 2, according to a service area construction standard, there is generally one service area every 50 km. Assuming that the total length of the expressway is /. , the number of service areas is Nser = LL/soi. The symbol I_ represents rounding down. (2) may include (2.1)-(2.3): [0036] (2.1) One day is divided into a plurality of time intervals.

[0037] (2.2) For each time interval, the number of electric vehicles entering the expressway through each entrance during the time interval is determined by formula (2), and the numbers through respective entrances are summed to obtain a total number of the electric vehicles entering the expressway during the time interval. For each electric vehicle, the initial charge of the electric vehicle is determined by formula (3), and a starting point and an end point are determined by the Monte Carlo simulation method. Specifically, assuming that the expressway has N entrances and exits in total, a starting point is randomly selected from an entrance set composed of first N -1 entrances and exits of the expressway, and an end point is randomly selected from an exit set composed of the entrances and exits following the starting point. According to the starting point and the end point, a travel path of the electric vehicle on the expressway from the starting point to the end point is acquired by a shortest path algorithm, and positions of the charging demand points of the electric vehicle are marked on the travel path according to the initial charge amount. Specifically, in the travel process of the electric vehicle along the travel path, the initial charge is taken as an initial value. When the charge drops to 20%, the current position is marked as a first charging demand point. Assuming that the charge is supplemented to 90% at the first charging demand point and the electric vehicle continues traveling, when the charge drops to 20% again, the current position is marked as a second charging demand point, and the charge is supplemented to 90% at the second charging demand point and the electric vehicle continues traveling until the end point is reached.

[0038] (2.3) The positions of the charging demand points of the electric vehicles of all the time intervals are combined to obtain the set of the charging demand points.

100391 (3) A position planning model of charging stations of the expressway service areas is established based on the set of the charging demand points of the electric vehicles.

100401 The set of the charging demand points is defined as i G Cid', and a set of the service areas is j E Cr" . When the electric vehicle at the charging demand point i can travel to the service area j by virtue of the remaining charge, then it is thought that the service area j can cover the charging demand point i. A binary variable Tjj is used for indicating whether the service area j can cover the charging demand point i, then Tji I 1, the service area] can cover the demand point i (4); to, the service area] cannot cover the demand point i [0041] A binary variable xi' is used for indicating whether the service area j is installed with a charging station, then a location problem of the charging station can be solved by the following planning model. The planning model is as follows: min Ejen,,. xis XCs > 1 V i jefts".:T ji=l ' [0042] In the above formula, Sr" is the set of the service areas; x" is the binary variable indicating whether the service area j is installed with a charging station; TJ, is the binary variable indicating whether the service area j can cover the charging demand point i; and Cid' is the set of the charging demand points.

[0043] The abovementioned planning model includes an objective function with the minimum number of charging stations and a constraint condition with the satisfied charging demand of all charging demand points, that is, at least one of the service areas accessible for the charging demand point I is installed with the charging station. The abovementioned planning model is solved by a linear solver, so as to obtain the installation position of the charging station.

[0044] On the basis of the determination of the installation position of the charging station, the present embodiment establishes the multi-objective function with the best operation benefit, the shortest waiting time for the electric vehicle user, and the highest energy source self-consistency rate of the micro-grid, establishes the constraint conditions simultaneously considering a power balance constraint, a distributed power and an energy storage output constraint, an electric vehicle charge constraint, and a device construction constraint to obtain the multi-objective optimization model, and performs solution on the multi-objective optimization model by a multi-objective optimization algorithm to obtain configuration information on models of micro-gas turbines, photovoltaic devices, wind power devices, and energy storage devices and the number of charging piles installed [0045] The multi-objective optimization model constructed by the present embodiment includes multi-objective functions and constraint conditions.

[0046] The multi-objective functions include an annual investment benefit objective function, a (5); E ndem (6), micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function, which are as follows specifically: 100471 (1) The annual investment benefit objective function is: finv CPvPmaxPvXP. v Enternt Cmtp..47,tx57,7,,i -FE (i±dr_i_LJEnser mErP" m Pn evtpmaxwtxvyt v st St )Xj,m, Xi (C EMErwt 7t1 IJITIErst M uuut-tittaXin m pmaxmstl st cs cs f ccs,cnis)) (7); 100481 In formula (7), fm" is the annual investment benefit; r is an annualized return; 1' is a planned service life; D."' is a set of the service areas; Init is an optional model set of the micro-gas turbines; cm' is a unit power construction coefficient of the micro-gas turbine; Pmaxalt is a maximum power of the micro-gas turbine with the model in; xrmt is a binary variable indicating whether the service area is installed with the micro-gas turbine with the model in * FP' is an optional model set of the photovoltaic devices; d'y is a unit power construction coefficient of the photovoltaic device; Pmaxm" is a maximum power of the photovoltaic device with the model In; X717vn is a binary variable indicating whether the service area * is installed with the photovoltaic device with the model m; {Wt is an optional model set of the wind power devices; c't is a unit power construction coefficient of the wind power device; pwt is a maximum power of the wind power device with the model m; xy."c is a binary variable indicating whether the service area is installed with the wind power device with the model; I' is an optional model set of the energy storage devices; cmeist is a unit power construction coefficient of the energy storage device with the model m; capni"xsmt is a st p cm capacity of the energy storage device with the model m is a unit power construction coefficient of the energy storage device with the model in, st is a maximum I3maxm charging/discharging power of the energy storage device with the model m; ,c7t is a binary variable indicating whether the service area is installed with the energy storage device with the model m; xy is a binary variable indicating whether the service area is installed with a charging station; crsf is a construction coefficient of a single charging station; ccsiG is a construction coefficient of a single charging pile; and nr is the number of the charging piles in the charging station in the service area 1.

100491 When the abovementioned binary variable of the present embodiment is 1 it indicates "installed"; and when the binary variable is 0, it indicates "uninstalled".

[0050] (2) The micro-grid operation benefit objective function is: [ Cgrid sub fuel p7nttx1 ope "t v24 v e "uj Lt=1ZAjEfiser P j,t CI 17mt Q fuel _t ptschr 1)±se(sociq soci) ,st e,st st mst)x to^ 2 cyclest ME (PM C aP Marin ' CPm Pmax lo 1, [0051] In formula (8), reP e is the micro-grid operation benefit; t is a time interval; clirid is sub i a purchase coefficient of unit electric energy; piit s an active power transmitted to the service area.1 through a power grid substation during the time interval / ; cf"/ is a purchase coefficient of unit natural gas; prtt is an active power of the micro-gas turbine generating electricity in the service area during the time interval I; nnit is a conversion efficiency of the micro-gas turbine fuel is a calorific value of the natural gas, stt is a binary variable indicating a charging state of the energy storage device in the service area, and the binary variable of 1 indicates that the energy storage device is being charged soc is the charge of the energy storage device in the service area 1 during the time interval I * is the charge of the energy storage device in the service area -1 during the time interval stql"hr is a be binary variable indicating a discharging state of the energy storage device in the service area 1, and the binary variable of 1 indicates that the energy storage equipment is discharging currently; and cyclest is a charging-discharging cycle life of the energy storage device, and a complete calculation of charge and discharge is represented as a cycle.

[0052] Where, r, C7t, Cr5, CWt, CCSt, , grid, Clue( , and Cc are all parameters required by the multi-objective optimization model for micro-grid capacity planning. These parameters are determined after an evaluation by planners based on the actual situation of the micro-grid in the expressway service areas.

100531 (3) The user queuing waiting time objective function is: fwait v24 v A. TAMUe J fit v (9); = 36-z-t=i jESI'r [0054] In formula (9), rtut is the user queuing waiting time AL, is a vehicle arrival rate of the service area j during the time interval t; and WIllte is an average waiting time of the charging station of the service area during the time interval [0055] The vehicle arrival rate in the calculation for the user queuing waiting time as shown in formula (9) is calculated by the following formula.

z nie ic( at,id (10), 100561 In formula (10), is a binary variable indicating whether the electric vehicle goes to the service area for charging during the time interval / . When the binary variable city,' is 1, it indicates that the electric vehicle i goes to the service area I for charging.

100571 A calculation formula of an average charging time (h) during the time interval t is as follows: ppiie 1 v4=1 nev(t) cap,"( at/id (0.9-socod) n evt) 4 [0058] In formula (11), /AR indicates the average charging time of the serv 0 0, cc area j during the time interval /; PPile indicates a charging power of each charging pile; cape" indicates a battery capacity of the electric vehicle; and socoj indicates the remaining charge of the battery when the electric vehicle i arrives at the charging station in the service area j during the time interval t.

100591 It is sct that an arrival proccss of a vehicle follows the Poisson distribution with a parameter kit, and the average charging time of each charging pile follows the negative exponential distribution with a parameter /11t. There are n*cs* charging piles in the charging station of the service area j, which forms a multi-service station queuing system The average waiting time can be calculated by a queuing theory.

100601 First, the service intensity of the charging station is calculated by: ALt < 1 (12); Pj,t - jui.t [0061] Formula (12) defines the service intensity pa of the charging station in the service area / during the time interval t. In order to prevent the queue length from increasing unlimitedly, the service intensity should not be greater than 1.

[0062] Then, a balance equation of a charging station service system is given to calculate a probability of electric vehicles in the charging station.

[0063] The balance equation of the charging station service system is as follows: pl /30 nil P 71-1(n +1)p po-ki _) )Pn n n" t -Pn-1 +P * r+1 =fit +ncs u n > n if pn [0064] In formula (13), 11 is the probability of n electric vehicles accepting charging service n.

in the charging station of the service area -1 during the time interval 1; Li is the number of the electric vehicles in the charging station of the service area 1 during the time interval t; and the equation is solved, and the service intensity py is input into formula (13), so as to obtain the probability that the electric vehicle accepts the charging service: n'' 1 nes 1.1 J]-I nes I in "1.1 (p Ifs) Lt p0 n Li * ncs (p)CS Lt nO rft nil > n j (14), [0065] The queue length le of the vehicles in a queue in the charging station of the service area during the time interval t is: 5); [0066] Finally, the average waiting time is calculated, and the average waiting time of the electric vehicle for charging in the charging station of the service area j during the time interval t is: wque eue II = (16); 100671 (4) The micro-grid energy source self-consistency rate objective function is: (13); 131,t =EL k! (pn7) k=0 n j,t n's (t?' ) < n'y ( o n GC CS,)17 1911 e = I (nip cs)p) PP J p0 ") cs!(1-p) n I,t =nes +1 Lt

II

r24 pv wt ser, v Lt=1Pj,t+Pj,t Ti Nser Lijenser v24 ncs s"Ipad 44=11-7,t [0068] In formula (17), Tr". is the micro-grid energy source self-consistency rate, Ns'r is the P7' wt s load are respectively an electricity generation number of service areas; p, , pJ,ano power of the photovoltaic device, an electricity generation power of the wind power device, a consumption power of the charging station, and a consumption power of a conventional load in the service area j during the time interval t [0069] Constraint conditions include a power balance constraint, a distributed power output constraint, an energy storage device output constraint, an electric vehicle charge constraint, and a device construction constraint, which are as follows specifically: [0070] (1) The power balance constraint is: Pjit s ub m t p.113. ,t11 ± (Kip is c r pittc prt pr,tad 100711 In formula (18), pisidischr and pi tic. hr are respectively a discharging power and a charging power of the energy storage device in the service area j during the time interval I. [0072] According to the number of the charging piles and the power of each charging pile in the charging station, py,st can be expressed by the following formula: n" AbtPPile ' AR < ncis i j,t = ,"cs ppile Aj. > 772 "11 1 1,1 j [0073] (2) The distributed power output constraint is: EmErnt P rninmt X?" n t = PTint n Pim*,tt Pjm,mtax m firn = Emervit PmaxmintXijn,mt (20); 73 Li _, np b pvpre v j (21); 0 pLts rj,t,max = (eft LimErPv Prna PvXP v xrn an Pmaxwmt Jm (22); x-, 1 (23); 0 c py,tk P77 max = w tpre LmErwt pvp re < wt pie 0 (pbt _1,0 cobt 100741 In the above formula, Nan": is a minimum power of the micro-gas turbine with the model In; PM in is a minimum output of the micro-gas turbine in the service area J; Prmt pv is a maximum output of the micro-gas turbine in the service area i * P j,t,max is a maximum predicted output of the photovoltaic device in the service area j during the time interval t; pvpre (11 jit is an output coefficient of unit power of the photovoltaic device, which is determined by light intensity of weather forecast, PV",.,:,.x is a maximum predicted output of the wind power wrpre device in the service area j during the time interval t; and is an output coefficient of (17); (18); (19), unit power of the wind power device, which is determined by wind speed of weather forecast. [0075] Formula (20) is upper and lower limit constraints for the output of the micro-gas turbine, which is related to the selected model. Formulas (21) to (23) are upper and lower limit constraints for the output of the photovoltaic device and the wind power device, which are related to the selected models and weather forecast factors, such as the light intensity or the wind speed.

100761 (3) The energy storage device output constraint is: 0 < pIttdischr < , fclischrnPstmax (24); (25); -'1.7,t i (26); 0 < p.ithrSthr tmax < Cp ) bt PI tmax = v. " ma xst,,.st LinErst P al A7 im e fdischr ± "etchr "--1 (27); "Lt j,t - 7Stptchr "striischr P bt SOet = SOCt ± bt (28); ht ht-i stmax st sttnax capj ri cap' capitmax = v.7 _, 12e1N4 capmaxsintx,,,, (29); 0 < sag, < 1 (30); st s sach0 = s0c5t,24 (31); 100771 In the above formulas, pit' is the maximum charging/discharging power of the energy storage device in the service area]; Tr is a charging/discharging efficiency of the energy storage device; capytmax is the capacity of the energy storage device in the service area j; and st st respectively indicate a charge at the beginning and a charge at the end of a soc" and socj,24 day of the energy storage device in the service area j.

[0078] Formulas (24) and (25) are upper and lower limit constraints for charging and discharging power of the energy storage device, and the upper and lower limits of power are determined by the installation models, such as formula (26). Formula (27) indicates that the energy storage device can only choose one from the charging or discharging states within a time interval. Formula (28) is the calculation of the charge of the energy storage device, which is related to the installation model, such as formula (29). Formula (30) is the upper and lower limit constraints of the charge. Formula (31) indicates that the initial charge of energy storage at the beginning of a day should be the same as that at the end of the day, so as to facilitate scheduling. [0079] The abovementioned three constraints are the constraints of a micro-grid operation model of an expressway service area.

[0080] (4) The electric vehicle charge constraint is: ev evini SOCijstart,t = SOC (32); (LO&" -LoCistart,t)x CE ev ev SOC = SOCij_Lt Cap" ev (Locrer-Locr X aev "cs, --11 - "peV (33), 0 socryx 1 (34);

I

cro j = 0, Loc4" < Loc""t or Loci" > Locrt'd i a,,,11, Locf,tart < Loci' < Loct [0081] In the above formula, wherein soc.?? /start,t (35); is the charge of the electric veh cle / when starting from the service area 1 during the time interval 1; societvini is the initial charge of the electric vehicle i during the time interval t; socf,y,, is the charge of the electric vehicle i located in the service area 1 during the time interval 1; is the charge of the electric vehicle located in the service area I during the time interval 1; Loci" is a distance from the service area to the starting point of the expressway, and the service area is numbered from the starting point of the expressway; Loctra" is a distance from an entrance where the electric vehicle / enters the expressway to the starting point of the expressway during the time interval I; o-" is a power consumption per kilometer of the electric vehicle; acji-i is a binary variable indicating whether the electric vehicle i goes to the service area I -I-for charging during the time interval 1; xJ11 is a binary variable indicating whether the service area 1 -1 is installed with the charging station; and Locqnd is a distance from the entrance where the electric vehicle i enters the expressway to the end point of the expressway during the time interval I. 100821 Formula (32) gives the initial charge when the electric vehicle enters the expressway. Formula (33) is a calculation method for charge of the electric vehicle. The change range of the SOC should be limited between an entrance where the electric vehicle enters the expressway and an exit where the electric vehicle leaves away from the expressway. The change of the charge of the electric vehicle is related to the traveling conditions, charging options, and construction of the charging station. Formula (34) gives the upper and lower limit constraints of the charge. Formula (35) stipulates that the electric vehicle can only be charged in the service area during traveling.

[0083] This constraint is the constraint for the charge of the electric vehicle and a charging behavior, which is also the constraint for an electric vehicle model in the expressway service area.

[0084] (5) The device construction constraint is: mEF xrint 1 (36); vpv < 1 (37); EmErP'' tra 1 (38); < 1 (39); Emerwt X jrni -Emerst st 0 < flys < Ncsmax (40); [0085] In the above formula, Ncsmax is a maximum value of the number of the charging piles allowed to be installed in the charging station.

[0086] Formulas (36) to (39) indicate that only one model of the micro-gas turbine, photovoltaic device, wind power device, and energy storage device can be selected in one service area. Formula (40) limits the number of charging piles installed.

[0087] The constraint is a micro-grid device construction constraint in the expressway service area.

100881 S2: Optimization solution is performed on the multi-objective optimization model by using a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid. The devices include micro-gas turbines, photovoltaic devices, wind power devices, energy storage devices, and charging piles. The configuration solution includes the models of the micro-gas turbines, the photovoltaic devices, the wind power devices, and the energy storage devices and the number of the charging piles installed in each service area within a planning range.

100891 Before the optimization solution is performed on the multi-objective optimization model by using the multi-objective optimization algorithm, the method for micro-grid planning of the present embodiment further includes that: the multi-objective functions are optimized to obtain optimized multi-objective functions, and S2 is performed by taking the optimized multi-objective functions as new multi-objective functions.

[0090] The optimization of the multi-objective functions includes that: the annual investment benefit objective function and the micro-grid operation benefit objective function are added together to obtain a first objective function. The user queuing waiting time objective function is taken as a second objective function. The micro-grid energy source self-consistency rate objective function is subtracted from 1 to obtain a third objective function. The first objective function, the second objective function, and the third objective function form the optimized multi-objective functions 100911 Formulas used for optimization are as follows. fiflv

- + tope I 1 (41); (42); (43); 100921 In the above formulas, ,f, is the first objective function; f, is the second objective function, and A is the third objective function.

100931 The present embodiment can further perform normalization on the first objective function, the second objective function, and the third objective function, which is as shown in the following formula: (44); rar imin 100941 In formula (44), if" is the maximum value of a Pareto solution set of the Ph objective function; and rin is the minimum value of the Pareto solution set of the objective function. 100951 However, the normalization is only an option. The performance of the normalization does not affect the implementation of subsequent steps.

100961 The objective functions of the multi-objective optimization model for micro-grid capacity configuration include f, J, and f. The three objective functions are all minimization objective functions, so the solution thereof is a multi-objective optimization problem. The objective functions are all normalized, and are solved by the multi-objective optimization algorithm to obtain the configuration solution.

100971 Preferably, the multi-objective optimization problem is solved by a fast Non-Dominated Sorting Genetic II Algorithm (NSGA-II) to obtain a Pareto front. A decision-maker weighs the objectives and makes a decision to obtain the configuration solution. The fast NSGA-If includes the following steps: first, a population is initialized, and an initial population is obtained through fast non-dominated sorting, selection, crossover and mutation operations, and the number of individuals in the population is N; a parent population and a progeny population are merged, and then individuals of the next generation of population are obtained by sorting and crowding degree calculation; another next generation will continue to be generated according to the genetic operation after a new generation of population is obtained; and the above operations are repeated until the maximum evolution number of generation is reached. Non-dominated sorting and crowding degree calculation are the cores of the algorithm. The non-dominated sorting forms [2 = [wait f3 iiser Rank° by all solution sets that cannot be dominated by any other solution, then eliminates the solutions in Rank0, and forms Rankl by all the remaining solution sets that cannot be dominated by any other solution, and so on. The lower the Rank level, the better the individual therein. The crowding degree calculation is used for calculating the crowding distance between individuals in each Rank solution set. The individuals with larger crowding distance will be preferentially selected to enter the next generation, because it is beneficial to maintain the diversity of the population.

[0098] At present, there are some expressway charging station planning solutions, but they basically only consider the planning of the charging station itself, rarely consider expressway service areas as a micro-grid, and also rarely consider the energy source self-consistency problem of inconvenience in power transmission of a power grid, since an expressway is remote. The present embodiment proposes and solves a multi-objective optimization model from three perspectives of power grid, user, and energy source self-consistency, which achieves a win-win situation in terms of power grid economy and user charging experience, maximizes the use of wind and light renewable resources near the expressway service areas to achieve energy source self-consistency in the service areas, reduces the investment and loss of long-distance transmission, and solves the energy source self-consistency problem of a micro-grid composed of a plurality of distributed power supplies in the expressway service areas far away from the power grid under the situation of satisfying a charging demand of an electric vehicle.

100991 Embodiment 2: [0100] The present embodiment provides a system for micro-grid planning of expressway service areas considering energy source self-consistency rate. As shown in FIG. 3, the system for micro-grid planning includes: 101011 a multi-objective optimization model establishment module Ml, configured to establish a multi-objective optimization model for micro-grid planning, where the multi-objective optimization model includes multi-objective functions and constraint conditions; the multi-objective functions include an annual investment benefit objective function, a micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function; and the constraint conditions include a power balance constraint, a distributed power output constraint, an energy storage device output constraint, an electric vehicle charge constraint, and a device construction constraint; and [0102] an optimization solution module M2, configured to perform optimization solution on the multi-objective optimization model by using a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid, where the devices include micro-gas turbines, photovoltaic devices, wind power devices, energy storage devices, and charging piles, and the configuration solution includes the models of the micro-gas turbines, the photovoltaic devices, the wind power devices, and the energy storage devices and the number of the charging piles installed in each service area within a planning range 101031 Each embodiment in the present specification focuses on describing the differences from other embodiments, and the same and similar parts of various embodiments may be referred to one another. The system disclosed by the embodiment is described relatively simply since it corresponds to the method disclosed by the embodiment, and for relevant points, reference is made to the specification of a method section.

101041 In this specification, specific examples are used to describe the principle and implementation manners of the present invention. The description of the embodiments above is merely intended to help understand the method and core idea of the present invention. In addition, those skilled in the art may make modifications based on the idea of the present invention with respect to the specific implementation manners and the application scope. In conclusion, the contents of the present specification shall not be construed as a limitation to the present invention.

Claims

WHAT IS CLAIMED IS: I. A method for micro-grid planning of expressway service areas considering energy source self-consistency rate, comprising: establishing a multi-objective optimization model for micro-grid planning; wherein the multi-objective optimization model comprises multi-objective functions and constraint conditions; the multi-objective functions comprise an annual investment benefit objective function, a micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function; and the constraint conditions comprise a power balance constraint, a distributed power output constraint, an energy storage device output constraint, an electric vehicle charge constraint, and a device construction constraint; and performing optimization solution on the multi-objective optimization model by using a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid; wherein the devices comprise micro-gas turbines, photovoltaic devices, wind power devices, energy storage devices, and charging piles, and the configuration solution comprises models of the micro-gas turbines, the photovoltaic devices, the wind power devices and the energy storage devices, and the number of the charging piles installed in each service area within a planning range.
2. The method for micro-grid planning according to claim 1, before establishing the multi-objective optimization model for the micro-grid planning, further comprising: establishing a planning model for charging station planning, wherein the planning model comprises an objective function with a minimum number of charging stations and a constraint condition with a satisfied charging demand; and performing solution on the planning model to obtain installation positions of the charging stations.
3. The method for micro-grid planning according to claim 2, wherein the planning model is: CITLfUf §; NiJ tTTNt S 4.3 tufo; wherein J tar is a set of the service areas; V is a binary variable indicating whether the service area.1 is installed with a charging station; F, is a binary variable indicating whether the service area I covers a charging demand point I; and.1 13' is a set of the charging demand points.
4. The method for micro-grid planning according to claim 3, wherein the set of the charging demand points is obtained by: dividing one day into a plurality of time intervals; for each time interval, determining a total number of electric vehicles entering the expressway during the time interval; for each electric vehicle, determining an initial charge of the electric vehicle, and determining a starting point and an end point by a Monte Carlo simulation method; acquiring a travel path of the electric vehicle on the expressway from the starting point to the end point by a shortest path algorithm, and marking positions of the charging demand points of the electric vehicle on the travel path according to the initial charge; and combining the positions of the charging demand points of all electric vehicle of all the time intervals to obtain the set of the charging demand points.
5. The method for micro-grid planning according to claim 1, wherein the annual investment benefit objective function is: I (lWr cwr n-)3.1 (.1 = ),jvC, I. a-CO I NVi j = cjv ti COI *10)1 \S'IN121 03. 0 I. NW 0 jvl r CO (0. ' k"? 1.11f1 I KCSIC-C Wj= I rf w WI rf gicie lAT ^5t-t (ti-rWw t,:;", 1.1.0N)11); wherein u4 is the annual investment benefit; r is an annualized return; T is a planned service life; J lir Is a set of the service areas; v' is an optional model set of the micro-gas turbines; tth I is a unit power construction coefficient of the micro-gas turbine; J "1@ 0) is a maximum power of the micro-gas turbine with the model in; is a binary variable indicating whether the service area j is installed with the micro-gas turbine with the model in; V R is an optional model set of the photovoltaic devices; l" is a unit power construction coefficient of the photovoltaic device; I a, R is a maximum power of the photovoltaic deviceI Rwith the model i NV s a binary variable indicating whether the service area j is installed with the photovoltaic device with the model in; v C is an optional model set of the wind power devices; is a unit power construction coefficient of the wind power device; _I co" is a maximum power of the wind power device with the model in; § is a binary variable indicating whether the service area j is installed with the wind power device with the model in; is an optional model set of the energy storage devices; ultm is a unit power construction coefficient of the energy storage device with the model m; 1I I U is a capacity of the energy storage device withthe model in;iWLT) s a unit power construction coefficient of the energy storage device with the model in; Ef is a maximum charging/discharging power of the energy storage device with the model In;NfAir- is a binary variable indicating whether the service area j is installed with the energy storage device with the model in; 1/45, Nti is a binary variable indicating whether the service area j is installed with a charging station; It144 is a construction coefficient of a single charging station; I."' is a construction coefficient of a single charging pile; 4:0V is the number of the charging piles in the charging station in the service area j the micro-grid operation benefit objective function is: I.1 III U411.1 ms,GE jz,utE( f: T.3j ^.11f tnpisi w \loco tur) ula,f(ci WikatIU, 3)wif.:11-Nriis I3tJ.th\ Lffdr (0 LW 11 m wherein uti is the micro-grid operation benefit; t is a time interval; 1:11.43 is a purchase coefficient of unit electric energy; I a; is an active power transmitted to the service area j through a power grid substation during the time interval t Liquff is a purchase coefficient of unit natural gas; INV is an active power of the micro-gas turbine generating electricity in the service area / during the time interval t; 'sr is a conversion efficiency of the micro-gas turbine; g ' is a calorific value of the natural gas; t fl is a binary variable indicating a charging state of the energy storage device in the service area; U 1Jç is the charge of the energy storage device in the service area / during the time interval t; P1_1-4i3 is the charge of the energy storage device in the service area j during the time interval t -1; trVI-Pr is a binary variable indicating a discharging state of the energy storage device in the service area.1; L11.wk1 is a charging-discharging cycle life of the energy storage device; the user queuing waiting time objective function is: ur I it:.1oN jj cut.a flu wherein at 111 is the user queuing waiting time; vliAv is a vehicle arrival rate of the service area j during the time interval t; and 44u is an average waiting time of the service area j during the time interval t; the micro-grid energy source self-consistency rate objective function is: \FUT Ikv..1-NW0 cur Nit Ti wherein wherein \II:L4 is the micro-grid energy source self-consistency rate; s is the number of service areas; J 14, J J and.1gid 13 are respectively an electricity generation power of the photovoltaic device, an electricity generation power of the wind power device, a consumption power of the charging station, and a consumption power of a conventional load in the service area j during the time interval t.
6. The method for micro-grid planning according to claim 5, wherein the average waiting time of the service arca / during the time interval t is calculated by using a queuing theory.
7. The method for micro-grid planning according to claim 5, wherein the power balance constraint is: R quir c3 I ul,nr) Tw I NV,13; w I (AC w I w I wherein I rfir and.1 Sru-are respectively a discharging power and a charging power of the energy storage device in the service area j during the time interval t; the distributed power output constraint is: J rnr s 0.1 * (1) jevn" aniFm LiW - 1R4 l * RI rtu3, J Rj w03vi.WIT) f T (x) u rroy, . mrsifir.AN, :AI (DI t; Nw %IV f Ichr r LIT K f S N4Vtii COI e, 4. ,-llkirw4. nKilF1u4 LJfl wherein uni, 0 is a minimum power of the micro-gas turbine with the model in; 41, is a minimum output of the micro-gas turbine in the service area j s a maximum output of the micro-gas turbine in the service area j s is a maximum predicted output itdrui.of the photovoltaic device in the service area j during the time interval t; n is an output coefficient of unit power of the photovoltaic device; 414citit is a maximum predicted fr if I rill output of the wind power device in the service area j during the time interval t; and 1NW is an output coefficient of unit power of the wind power device; the energy storage device output constraint is: 0 j Wrl_nr flabir r I; L I trirlir NA.A, T [cc. aNpjytr-TtoicoeNlev, trVbir w tf.14 r; \Fri tcl,nr I ii,:,011.nrWrot B yfr { 1 ra, : W JVIr 1-1 trNV 1.%4 171_1: wherein I r is the maximum charging/discharging power of the energy storage device ji0 I. .;11 in the service area j; ti is a charging/discharging efficiency of the energy storage device; s the capacity of the energy storage device in the service area j; and U 1J and t respectively indicate a charge at the beginning and a charge at the end of a day of the energy storage device in the service area I; the electric vehicle charge constraint is: P tart "Vv: P1.3j4" (31 lir 13 34h,;c1,, r GEI[I tnucv3i...1143 ti I tut (j 1.4C3, ...S(..113 (En.,;(3, w13 (31 1A413311.1i,4r)cfm') . * m7 7TUT t 311_1irf r 31 trr 31 tat 13 1oets'e4- V141 j rEtry 3J -k 3111,13 wherein piaci rrw is the charge of the electric vehicle i when starting from the service area j dining the time interval; p111F"' is the initial charge of the electric vehicle i during the time interval 1; til.,47 is the charge of the electric vehicle i located in the service area j during the time interval t; [UJ3V is the charge of the electric vehicle i located in the service area j -1 during the time interval t; 'nu:-is a distance from the service area j to the starting point of the expressway; 31114" is a distance from an entrance where the electric vehicle i enters the expressway to the starting point of the expressway during the time interval; Wm is a power consumption per kilometer of the electric vehicle; car indicates a battery capacity of the electric vehicle; liaL is a binary variable indicating whether the electric vehicle i goes to the service area j -1 for charging during the time interval t; is a binary variable indicating whether he service area [-1 is installed with the charging station; and 3.1 Ur is a distance from the entrance where the electric vehicle i enters the ( expressway to the end point of the expressway during the time interval t; the device construction constraint is: 4. -1. (0 LsVIR NW1 c 4-e ° sco jos 4' 43,V 4" s Lte,5 wherein o I re" is a maximum value of the number of the charging piles allowed to be installed in the charging station.
8. The method for micro-grid planning according to claim 1, before performing optimization solution on the multi-objective optimization model by using the multi-objective optimization algorithm, further comprising: optimizing the multi-objective functions to obtain optimized multi-objective functions, and taking the optimized multi-objective functions as new multi-objective functions, wherein optimizing the multi-objective functions comprises: adding the annual investment benefit objective function and the micro-grid operation benefit objective function to obtain a first objective finiction; taking the user queuing waiting time objective function as a second objective function; subtracting the micro-grid energy source self-consistency rate objective function from 1 to obtain a third objective function; and forming the optimized multi-objective functions by the first objective function, the second objective function, and the third objective function.
9. The method for micro-grid planning according to claim 1, wherein the multi-objective optimization algorithm is a Non-Dominated Sorting Genetic II Algorithm (NSGA-II).
10. A system for micro-grid planning of expressway service areas considering energy source self-consistency rate, comprising: a multi-objective optimization model establishment module, configured to establish a multi-objective optimization model for micro-grid planning; wherein the multi-objective optimization model comprises multi-objective functions and constraint conditions; the multi-objective functions comprise an annual investment benefit objective function, a micro-grid operation benefit objective function, a user queuing waiting time objective function, and a micro-grid energy source self-consistency rate objective function; and the constraint conditions comprise a power balance constraint, a distributed power output constraint, an energy storage 1-; device output constraint, an electric vehicle charge constraint, and a device construction constraint; and an optimization solution module, configured to perform optimization solution on the multi-objective optimization model by using a multi-objective optimization algorithm to obtain a configuration solution of individual devices of the micro-grid, wherein the devices comprise micro-gas turbines, photovoltaic devices, wind power devices, energy storage devices, and charging piles, and the configuration solution comprises models of the micro-gas turbines, the photovoltaic devices, the wind power devices, and the energy storage devices and the number of the charging piles installed in each service area within a planning range.