CN115276111A - Construction method of coordination optimization model and power distribution network planning method - Google Patents

Construction method of coordination optimization model and power distribution network planning method Download PDF

Info

Publication number
CN115276111A
CN115276111A CN202210799787.7A CN202210799787A CN115276111A CN 115276111 A CN115276111 A CN 115276111A CN 202210799787 A CN202210799787 A CN 202210799787A CN 115276111 A CN115276111 A CN 115276111A
Authority
CN
China
Prior art keywords
power
load
planning
energy storage
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210799787.7A
Other languages
Chinese (zh)
Inventor
张玮琪
何川
杨钊
王沿胜
陈宝生
车彬
张斌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sichuan University
Economic and Technological Research Institute of State Grid Ningxia Electric Power Co Ltd
Original Assignee
Sichuan University
Economic and Technological Research Institute of State Grid Ningxia Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sichuan University, Economic and Technological Research Institute of State Grid Ningxia Electric Power Co Ltd filed Critical Sichuan University
Priority to CN202210799787.7A priority Critical patent/CN115276111A/en
Publication of CN115276111A publication Critical patent/CN115276111A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a construction method of a coordination optimization model and a power distribution network planning method, which comprises the following steps: step 1: establishing a power distribution network system model and a planning operation model of each device; and 2, step: establishing a deterministic coordination planning model considering wind power plants, photovoltaic power stations, electric vehicle charging stations, energy storage equipment and load demand response by taking the minimum power distribution network system planning cost and the operation cost as objective functions; and 3, step 3: converting the coordination programming model obtained in the step 2 into a mixed integer linear programming model for solving; and 4, step 4: acquiring an uncertainty scene of new energy output and load fluctuation, and establishing a coordination optimization model considering the uncertainty of the new energy; the method comprehensively considers a coordination optimization model of new energy, an electric vehicle charging station, energy storage equipment and a load demand response device, and can research the influence of the coordinated operation of each equipment on the economy and safety of the power system.

Description

Construction method of coordination optimization model and power distribution network planning method
Technical Field
The invention relates to the technical field of optimized operation of power systems, in particular to a construction method of a coordination optimization model and a power distribution network planning method.
Background
With the development of society and times, the concept of sustainable green development is more and more keen. In response to the national call, a new electric power system is constructed, and new energy power generation plays an especially important role in the electric power system. However, the randomness and volatility of new energy output poses a significant challenge to the safety of the power system. In addition, the popularization of electric vehicles makes the travel of people more environment-friendly, but the disordered charging of the electric vehicles also brings certain impact to a power grid. How to solve the influence of uncertain new energy output and disordered electric vehicle charging on the power system is the key problem of the current research.
The influence of uncertainty of new energy output and the influence of disordered charging of the electric automobile on the system are reduced through the coordinated operation of all component devices in the power system. The energy storage equipment and the load demand response have the function of transferring electric quantity, the effects of peak load shifting and supply and demand balance of the system are achieved through electric quantity storage or transfer, and the economy of the system and the reliability of power supply are improved. Due to the influence of new energy output and load fluctuation, the safety of the power system is challenged, most of the domestic and foreign researches on uncertainty treatment adopt a random optimization or robust optimization method, the random optimization method has larger dependence on the probability distribution of random variables, and the decision of the robust optimization method is too conservative, so that the planning economy is reduced.
Disclosure of Invention
The invention provides a construction method of a coordination optimization model and a power distribution network planning method aiming at the prior art.
The technical scheme adopted by the invention is as follows:
a construction method of a coordination optimization model considering uncertainty of new energy comprises the following steps:
step 1: establishing a power distribution network system model and a planning operation model of each device;
and 2, step: establishing a deterministic coordination planning model considering wind power stations, photovoltaic power stations, electric vehicle charging stations, energy storage equipment and load demand response by taking the minimum power distribution network system planning cost and the minimum operation cost as objective functions;
and 3, step 3: converting the coordination programming model obtained in the step 2 into a mixed integer linear programming model for solving;
and 4, step 4: acquiring an uncertainty scene of new energy output and load fluctuation, and establishing a coordination optimization model considering the uncertainty of the new energy;
the coordination optimization model considering the uncertainty of the new energy is a two-stage planning model, and the first stage minimizes the operation cost of a basic scene; the second phase minimizes the penalty cost expectation for considering the loss of load for uncertain scenarios.
Further, the power distribution network model in step 1 is as follows:
Figure BDA0003737057410000021
Figure BDA0003737057410000022
Figure BDA0003737057410000023
Figure BDA0003737057410000024
in the formula: pi (j),Delta (j) is a set of branch head end and tail end nodes with j as tail end and head end nodes in the system, Rij、XijThe resistance value and the reactance value of the line ij are respectively; I.C. Aij,h,btThe current flowing for line ij; pg,h,bt、Qg,h,btRespectively the active power and the reactive power of the generator set; pij,h,bt、Qij,h,btRespectively the active power and the reactive power flowing through the line ij;
Figure BDA0003737057410000025
respectively representing the discharging power and the charging power of the energy storage equipment;
Figure BDA0003737057410000026
the reactive power purchased from a power distribution network to a superior power grid;
Figure BDA0003737057410000027
active power after demand response is carried out on the load;
Figure BDA0003737057410000028
a reactive load that is a load; p ise,h,btActive power for an electric vehicle charging station;
Figure BDA0003737057410000029
the load loss amount is;
Figure BDA00037370574100000210
is the power factor of the load; vi,h,btIs the voltage magnitude of node i, Pjk,h,btFor node j, the magnitude of active power, P, flows outw,h,btFor the actual value of the wind farm output, Pp,h,btIs the actual value of the output of the photovoltaic power station,
Figure BDA00037370574100000211
purchasing electric power, Q, for main networkjk,h,btThe reactive power magnitude flows out for the node j; and each equipment planning operation model comprises an electric vehicle charging station operation model, an energy storage equipment planning operation model and a load demand response planning operation model.
Further, the electric vehicle charging station operation model comprises an electric vehicle operation constraint and an electric vehicle charging station power constraint;
and (3) electric vehicle operation restraint:
Figure BDA00037370574100000212
Figure BDA00037370574100000213
Figure BDA00037370574100000214
Figure BDA00037370574100000215
Figure BDA00037370574100000216
Figure BDA00037370574100000217
wherein v is an index of an electric automobile, Iv,h,btIs a variable of 0 to 1 of the charging and discharging state of the electric automobile,
Figure BDA00037370574100000218
is an access state variable of the electric automobile,
Figure BDA0003737057410000031
is a charge-discharge state variable of the electric automobile,
Figure BDA0003737057410000032
respectively is a 0-1 variable of the electric automobile in the charging and discharging states of the charging station,
Figure BDA0003737057410000033
are respectively the charging power and the discharging power of the electric automobile,
Figure BDA0003737057410000034
rated charging power and rated discharging power of the electric automobile are respectively;
Figure BDA0003737057410000035
Figure BDA0003737057410000036
Figure BDA0003737057410000037
Figure BDA0003737057410000038
in the formula: s. thev,h,btIs the state of charge of the electric automobile at the h moment, Sv,0,btIs the state of charge at the initial moment, M is an infinite positive number, Sv,h-1,btIs the state of charge, eta, of the electric automobile at the h-1 momentcEfficiency of charging electric vehicles, EvIs the battery capacity of the electric vehicle, etadFor the discharge efficiency of electric vehicles, Sv,H,btTo be the state of charge at the moment of departure from the grid,
Figure BDA0003737057410000039
is a variable of 0 to 1 when the electric automobile leaves the power grid,
Figure BDA00037370574100000310
is the minimum value of the v charge state of the electric automobile,
Figure BDA00037370574100000311
the maximum value of the v charge state of the electric vehicle;
electric vehicle charging station power constraint:
Figure BDA00037370574100000312
Figure BDA00037370574100000313
in the formula: p ise,h,btIs the power of the charging station e and,
Figure BDA00037370574100000314
for the commissioning of charging station e, omeganIs a set of regions, PePower for charging station e;
the energy storage equipment planning operation model comprises the following steps:
Figure BDA00037370574100000315
Figure BDA00037370574100000316
Figure BDA00037370574100000317
Figure BDA00037370574100000318
in the formula: es,h,btFor the energy storage of the energy storage device at time h, Es,h-1,btIs the electric quantity, eta, of the energy storage equipment at the moment of h-1inIn order to provide an efficient charging of the energy storage device,
Figure BDA00037370574100000319
for the charging power of the energy storage device at the time h-1,
Figure BDA00037370574100000320
is the discharge power of the energy storage device at the h-1 momentoutIn order to achieve an efficient discharge of the energy storage device,
Figure BDA00037370574100000321
the set-up state for energy storage is provided,
Figure BDA00037370574100000322
respectively, the minimum and maximum electric storage capacity, P, of the energy storage devices in,min、Ps in,maxRespectively minimum and maximum values of charging power, P, of the energy storage devices out,min、Ps out,maxRespectively the minimum and maximum discharge power of the energy storage device;
the load demand response planning operation model is as follows:
Figure BDA0003737057410000041
Figure BDA0003737057410000042
Figure BDA0003737057410000043
Figure BDA0003737057410000044
Figure BDA0003737057410000045
Figure BDA0003737057410000046
Figure BDA0003737057410000047
in the formula:
Figure BDA0003737057410000048
in order to take part in the amount of power required for the demand response,
Figure BDA0003737057410000049
the amount of interruptible load power to participate in the demand response,
Figure BDA00037370574100000410
to participate in the transferable load power size of the demand response,
Figure BDA00037370574100000411
is the size of the power of the node d,
Figure BDA00037370574100000412
the maximum allowed load power level for node d,
Figure BDA00037370574100000413
in response to the proportion of the interruptible load to the system demand,
Figure BDA00037370574100000414
to project state variables for interruptible load demand responses,
Figure BDA00037370574100000415
for the maximum amount of interruptible electrical load allowed by the system,
Figure BDA00037370574100000416
to respond to the proportion of the translatable load by the system demand,
Figure BDA00037370574100000417
and (4) establishing state variables for the transferable load demand response.
Further, the objective function is as follows:
minF＝min(IC+OC+CI·ΔD+CW·ΔW+CL·ΔL)
wherein:
Figure BDA00037370574100000418
Figure BDA00037370574100000419
Figure BDA00037370574100000420
Figure BDA00037370574100000421
Figure BDA0003737057410000051
Figure BDA0003737057410000052
Figure BDA0003737057410000053
Figure BDA0003737057410000054
Figure BDA0003737057410000055
κt＝1/(1+dr)t-1
in the formula: f is the optimal objective function of the system, t and k are the year and the rule to be investedDividing indexes of device models, wherein e, w, p, s and d are indexes of an electric vehicle charging station, a wind power plant, a photovoltaic power station, energy storage equipment and a load respectively, and CE, CW, CP, CS and CDR are candidate investment sets of a planning charging station, a wind power plant, a photovoltaic power station, energy storage equipment and a demand response device respectively; k is a set of planning candidate equipment models, IC is planning investment cost, and OC is total system operation cost; the delta D, the delta W and the delta L are respectively the system load shedding amount, the air abandoning amount and the light abandoning amount; cI、CW、CLUnit punishment cost of load shedding, wind abandoning and light abandoning, kappa is the coefficient of current market value, dr is the current rate, CinvUnit investment cost, y, for each equipment investment constructiontIs a variable of the number of the main chain,
Figure BDA0003737057410000056
Figure BDA0003737057410000057
respectively providing power purchase cost and load demand response cost for the generator set, the electric vehicle charging station, the main network; DThtThe number of days of typical day b of the t year,
Figure BDA0003737057410000058
in order to cut the load capacity,
Figure BDA0003737057410000059
for the predicted value of the output of the wind farm, Pw,h,btIs the actual output value of the wind farm,
Figure BDA00037370574100000510
for the predicted value of the output of the photovoltaic power station, Pp,h,btIs an actual output value of the photovoltaic power station,
Figure BDA00037370574100000511
for fuel costs of the generator set, Fg(. H) is the heat rate curve of the generator set g, ceIn order to keep up the operating costs of the charging station,
Figure BDA00037370574100000512
is a sheetThe cost of purchasing the electricity is determined,
Figure BDA00037370574100000513
the price is compensated for the interruptible load in units,
Figure BDA00037370574100000514
in order to purchase the active power to the upper stage,
Figure BDA00037370574100000515
is interruptible load power; b. h, g and r are indexes of typical day, hour, generator set and substation nodes respectively;
the constraint conditions comprise a commissioning planning constraint, a line power constraint, an electricity purchasing constraint, a new energy output constraint and a load losing constraint;
the building of planning constraints comprises:
Figure BDA00037370574100000516
yw,t-1≤yw,t,w∈CW
yp,t-1≤yp,t,p∈CPV
Figure BDA00037370574100000517
Figure BDA00037370574100000518
Figure BDA00037370574100000519
Figure BDA0003737057410000061
Figure BDA0003737057410000062
Figure BDA0003737057410000063
Figure BDA0003737057410000064
in the formula: n is a radical ofnFor the number of electric vehicle charging stations that can be planned within the area n,
Figure BDA0003737057410000065
is, yw,tIs, yp,tIn order to realize the purpose,
Figure BDA0003737057410000066
in order to realize the purpose,
Figure BDA0003737057410000067
is of SeIn order to build up the capacity of the charging station e,
Figure BDA0003737057410000068
for the charge capacity requirement of all electric vehicles in region N, NwNumber of minimum planning commissioning for wind farm, NpThe minimum planning and construction quantity of the photovoltaic power station is obtained;
line power constraint:
Figure BDA0003737057410000069
Vi min≤Vi,h,bt≤Vi max
Figure BDA00037370574100000610
in the formula:
Figure BDA00037370574100000611
is the maximum flowing power amplitude, V, of the line iji minFor bus voltage V in the systemiMinimum value of (1), Vi maxFor the bus voltage V in the systemiThe maximum value of (a) is,
Figure BDA00037370574100000612
the maximum current amplitude that can flow through the line ij;
and (3) power purchase restraint:
Figure BDA00037370574100000613
Figure BDA00037370574100000614
in the formula: pr min、Pr maxRespectively as the minimum value and the maximum value of the purchased active power,
Figure BDA00037370574100000615
respectively purchasing the minimum value and the maximum value of reactive power;
and (3) new energy output constraint:
Figure BDA00037370574100000616
Figure BDA00037370574100000617
Figure BDA00037370574100000618
in the formula:
Figure BDA00037370574100000619
respectively the maximum values of wind power, photoelectricity and energy storage output,
Figure BDA00037370574100000621
the ratio of the output of the new energy is,
Figure BDA00037370574100000620
is the maximum value of the allowable load d;
the loss of load constraint is:
Figure BDA0003737057410000071
in the formula: alpha (alpha) ("alpha")pIs the proportion of the maximum load to be cut,
Figure BDA0003737057410000072
is the active power of load d.
Further, in the step 3, a second-order cone relaxation method is adopted to perform linearization processing on the coordination planning model, and a power distribution network model in the coordination planning model is processed;
introducing two intermediate variables
Figure BDA0003737057410000073
And
Figure BDA0003737057410000074
and (3) relaxing the node voltage drop equation through the branch current and power relational expression:
Figure BDA0003737057410000075
Figure BDA0003737057410000076
Figure BDA0003737057410000077
Figure BDA0003737057410000078
in the formula: intermediate variables
Figure BDA0003737057410000079
And
Figure BDA00037370574100000710
are each numerically Iij,h,btAnd Vi,,h,btSquare of (d).
Further, the method for acquiring the uncertain scene in step 4 comprises the following steps: and randomly generating l uncertain scenes on the basis of historical data of wind-solar output and load prediction by a Monte Carlo simulation method, and carrying out scene reduction by a synchronous back substitution elimination method to finally obtain the required uncertain scenes.
Further, a distributed robust optimization method is adopted in the step 4, a coordination optimization model considering the uncertainty of the new energy is established, and the coordination optimization model is as follows:
Figure BDA00037370574100000711
s.t.Ax≤e
x∈{0,1}
Cx+Dy≤f
Figure BDA00037370574100000712
in the formula: x is the feasible region satisfied by the decision variable X in the first stage, Y is the feasible region of the variable Y in the second stage, xi0New energy output and xi under basic scenekTo account for the scenario k of uncertainty, the new energy contribution, pkProbability value of uncertain parameter distribution, phi is value-taking domain of uncertain scene probability distribution, deltaTIs the penalty price per unit load-shedding,
Figure BDA00037370574100000713
in order to schedule the operation state variable of each element under the condition of a first-stage variable x in the system under an uncertainty scene k, aTx is the planning and construction cost of each equipment and the start-stop cost of the unit, bTy is the fuel consumption cost of the generator set and the operation and maintenance cost of each device, A is a constant coefficient matrix of the project variable and the state variable of the generator set, C, D and f are coefficient matrices and vectors of the constraint of the inequality of the two-stage variables respectively, and K isrAnd
Figure BDA0003737057410000081
coefficient matrixes and vectors of second-order cone constraint are respectively;
Figure BDA0003737057410000082
in the formula: p is a radical ofkIs the value of the probability distribution for scene k,
Figure BDA0003737057410000083
is the initial probability distribution value of scene k, θ1、θ∞Respectively, 0-1 norm and 0- ∞ allowable upper limit of probability deviation.
A power distribution network planning method adopts a coordination optimization model which is obtained by a construction method and considers uncertainty of new energy resources to plan; inputting the data, equipment parameters and operation parameters of the power distribution network system into a coordination optimization model considering the uncertainty of the new energy, and solving by adopting a solver to obtain an optimal result; and planning the power distribution network by adopting the optimal result.
Further, the power distribution network system data comprise a power distribution network topological structure and a power transmission line; the equipment parameters comprise power, capacity and output upper and lower limits of the generator set, the wind power plant and the photovoltaic power station, and power of the energy storage equipment, the electric automobile and the charging station; the operation parameters comprise load, wind and light predicted values, electricity purchase price of the power supply network, various operation parameters of the equipment, load demand response related data and system punishment price.
The beneficial effects of the invention are:
(1) The coordination planning model of the new energy, the electric vehicle charging station, the energy storage equipment and the load demand response device is comprehensively considered, and the influence of the coordination operation of each equipment on the economy and the safety of the power system can be researched;
(2) The invention adopts a distribution robust method to process uncertainty, relieves the uncertainty of the system by using energy storage equipment and load demand response, and provides a distribution robust planning method for coordination optimization of new energy, electric vehicle charging stations and energy storage, so as to obtain an optimal planning result.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a diagram of a power system with IEEE33 nodes according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a power purchase comparison curve of a system under different planning schemes according to an embodiment of the present invention.
Fig. 4 is a schematic diagram illustrating comparison of net loads of systems under different planning schemes according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
As shown in fig. 1, a method for constructing a coordination optimization model considering uncertainty of new energy includes the following steps:
step 1: establishing a power distribution network system model and a planning operation model of each device;
the power distribution network system model adopts an electric power alternating current power flow model, and due to the fact that the voltage level of a distribution line is low, node power balance constraint is achieved through the DistFlow model by adopting alternating current power flow.
Figure BDA0003737057410000091
Figure BDA0003737057410000092
Figure BDA0003737057410000093
Figure BDA0003737057410000094
In the formula: pi (j) and delta (j) are a branch head end and tail end node set which takes j as a tail end and a head end node in the system, and Rij、XijThe resistance value and the reactance value of the line ij are respectively; I.C. Aij,h,btThe current flowing for line ij; pg,h,bt、Qg,h,btRespectively the active power and the reactive power of the generator set; p isij,h,bt、Qij,h,btRespectively the active power and the reactive power flowing through the line ij;
Figure BDA0003737057410000095
respectively representing the discharging power and the charging power of the energy storage equipment;
Figure BDA0003737057410000096
the reactive power purchased from a power distribution network to a superior power grid;
Figure BDA0003737057410000097
active power after demand response is carried out on the load;
Figure BDA0003737057410000098
a reactive load that is a load; pe,h,btActive power for an electric vehicle charging station;
Figure BDA0003737057410000099
the load loss amount is;
Figure BDA00037370574100000910
is the power factor of the load; vi,h,btIs the magnitude of the voltage at node i, Pjk,h,btFor node j, the magnitude of active power, Pw,h,btFor the actual value of the wind farm output, Pp,h,btIs the actual value of the output of the photovoltaic power station,
Figure BDA00037370574100000911
purchasing power, Q, for main networkjk,h,btThe reactive power for the node j flows out; and each equipment planning operation model comprises an electric vehicle charging station operation model, an energy storage equipment planning operation model and a load demand response planning operation model.
And each equipment planning operation model comprises an electric vehicle charging station operation model, an energy storage equipment planning operation model and a load demand response planning operation model.
The electric automobile station operation model comprises electric automobile operation constraint and electric automobile charging station power constraint; the electric vehicle charging station needs to meet the charging and discharging requirements and the commissioning planning constraints of the electric vehicle.
And (3) electric vehicle operation restraint: the charging of the electric vehicle can be classified into ordered charging and charging-free charging. According to the invention, two charging modes of the electric vehicle are considered, and the influence of planning of the electric vehicle charging station on the economy and safety of the system is researched.
Figure BDA00037370574100000912
Figure BDA00037370574100000913
Figure BDA0003737057410000101
Figure BDA0003737057410000102
Figure BDA0003737057410000103
Figure BDA0003737057410000104
Wherein v is an index of an electric automobile, Iv,h,btThe variable is a 0-1 variable of the charge-discharge state of the electric automobile, the charge-discharge state is 1, otherwise, the variable is 0;
Figure BDA0003737057410000105
the access state variable of the electric automobile is 1 during access, and the other time is 0;
Figure BDA0003737057410000106
the charging and discharging state variable of the electric automobile is 1 hour after the electric automobile is connected to a power grid and leaves the power grid, and the other moments are all 0;
Figure BDA0003737057410000107
respectively is a 0-1 variable of the electric automobile in the charging and discharging states of the charging station,
Figure BDA0003737057410000108
respectively charging and discharging power, P, of the electric vehiclev c,rate、Pv d,rateRated charging and discharging power of the electric automobile are respectively set;
Figure BDA0003737057410000109
Figure BDA00037370574100001010
Figure BDA00037370574100001011
Figure BDA00037370574100001012
in the formula: s. thev,h,btIs powered electricallyState of charge of the vehicle at time h, Sv,0,btIs the state of charge at the initial moment, M is an infinite positive number, Sv,h-1,btIs the state of charge, eta, of the electric automobile at the h-1 momentcEfficiency of charging electric vehicles, EvIs the battery capacity of the electric vehicle, etadFor the discharge efficiency of electric vehicles, Sv,H,btTo be the state of charge at the moment of departure from the grid,
Figure BDA00037370574100001013
the variable is a 0-1 variable when the electric automobile leaves the power grid, the moment of leaving the power grid is equal to 1, and the other moments are 0;
Figure BDA00037370574100001014
is the minimum value of the v charge state of the electric vehicle,
Figure BDA00037370574100001015
and is the maximum value of the v charge state of the electric automobile.
Electric vehicle charging station power constraint:
Figure BDA00037370574100001016
Figure BDA00037370574100001017
in the formula: p ise,h,btFor the purpose of charging the power of the station e,
Figure BDA00037370574100001018
the charging state of the charging station e is set as 1, and the charging state is not set as 0; omeganIs a set of regions, PeIs the power of the charging station e.
The energy storage equipment planning operation model is as follows: the energy storage equipment can improve the consumption of new energy and the effects of staggering peaks and filling valleys and improving the economical efficiency and safety of the system through the function of storing redundant electric quantity in the power system.
Figure BDA0003737057410000111
Figure BDA0003737057410000112
Figure BDA0003737057410000113
Figure BDA0003737057410000114
In the formula: es,h,btFor the energy storage of the energy storage device at time h, Es,h-1,btIs the electric quantity, eta, of the energy storage equipment at the moment of h-1inIn order to provide an efficient charging of the energy storage device,
Figure BDA0003737057410000115
for the charging power of the energy storage device at the time h-1,
Figure BDA0003737057410000116
for the discharge power, eta, of the energy storage device at the moment h-1outIn order to achieve an efficient discharge of the energy storage device,
Figure BDA0003737057410000117
the set-up state for energy storage is provided,
Figure BDA0003737057410000118
respectively, the minimum and maximum electric storage capacity, P, of the energy storage devices in,min、Ps in,maxRespectively minimum and maximum values of charging power, P, of the energy storage devices out,min、Ps out,maxRespectively the minimum and maximum discharge power of the energy storage device;
the load demand response planning operation model is as follows: the load demand response can be divided into interruptible load demand response and transferable load demand response, the economy of the system can be improved by planning the demand response, and the reliability of the system power supply is improved by staggering peaks and filling valleys.
Figure BDA0003737057410000119
Figure BDA00037370574100001110
Figure BDA00037370574100001111
Figure BDA00037370574100001112
Figure BDA00037370574100001113
Figure BDA00037370574100001114
Figure BDA00037370574100001115
In the formula:
Figure BDA00037370574100001116
in order to take part in the amount of power required for the demand response,
Figure BDA00037370574100001117
the amount of interruptible load power to participate in the demand response,
Figure BDA00037370574100001118
the transferable load power for participating in the demand response is transferred out when the transferable load power is larger than zero,a transition is carried out when the value is less than zero;
Figure BDA00037370574100001119
for the power level of node d, the node b,
Figure BDA0003737057410000121
the maximum allowed load power level for node d,
Figure BDA0003737057410000122
in response to the proportion of the interruptible load for the system demand,
Figure BDA0003737057410000123
a state variable is built for interruptible load demand response,
Figure BDA0003737057410000124
for the maximum amount of interruptible electrical load allowed by the system,
Figure BDA0003737057410000125
to respond to the proportion of the translatable load by the system demand,
Figure BDA0003737057410000126
and (4) establishing state variables for the transferable load demand response.
Step 2: establishing a deterministic coordination planning model considering wind power plants, photovoltaic power stations, electric vehicle charging stations, energy storage equipment and load demand response by taking the minimum power distribution network system planning cost and the operation cost as objective functions;
an objective function: the system deterministic coordination planning model takes minimizing the total planning cost and the running cost as the optimization target.
minF＝min(IC+OC+CI·ΔD+CW·ΔW+CL·ΔL)
Wherein:
Figure BDA0003737057410000127
Figure BDA0003737057410000128
Figure BDA0003737057410000129
Figure BDA00037370574100001210
Figure BDA00037370574100001211
Figure BDA00037370574100001212
Figure BDA00037370574100001213
Figure BDA00037370574100001214
Figure BDA00037370574100001215
κt＝1/(1+dr)t-1
in the formula: f is an optimal objective function of the system, t and k are indexes of year and model of planning equipment to be invested respectively, e, w, p, s and d are indexes of an electric vehicle charging station, a wind power plant, a photovoltaic power station, energy storage equipment and a load respectively, and CE, CW, CP, CS and CDR are candidate investment sets of the planning charging station, the wind power plant, the photovoltaic power station, the energy storage equipment and a demand response device respectively; k is a set of planning candidate equipment models, IC is planning investment cost, and OC is total system operation cost; Δ D, Δ W, Δ L pointsRespectively carrying out load shedding, air abandoning and light abandoning on the system; cI、CW、CLUnit punishment cost of load shedding, wind abandoning and light abandoning, kappa is the coefficient of current market value, dr is the current rate, CinvUnit investment cost, y, for each equipment investment constructiontThe variable is 0,1, which represents the investment state of the candidate equipment, if the investment state is 1, otherwise, the variable is 0;
Figure BDA0003737057410000131
respectively providing power purchase cost and load demand response cost for the generator set, the electric vehicle charging station, the main network; DThtThe number of days of typical day b of the t year,
Figure BDA0003737057410000132
in order to cut the load amount,
Figure BDA0003737057410000133
for the predicted value of the output of the wind farm, Pw,h,btIs the actual output value of the wind farm,
Figure BDA0003737057410000134
for the predicted value of the output of the photovoltaic power station, Pp,h,btIs an actual output value of the photovoltaic power station,
Figure BDA0003737057410000135
for the fuel cost of the generator set, FgThe (-) is the heat rate curve of the generator set g and includes the start-stop consumption cost of the generator set; c. CeIn order to keep up with the costs of operating the charging station,
Figure BDA0003737057410000136
the cost of the unit of electricity purchase is,
Figure BDA0003737057410000137
the price can be compensated for the interruptible load in units,
Figure BDA0003737057410000138
in order to purchase the active power to the upper stage,
Figure BDA0003737057410000139
is interruptible load power; b. h, g and r are indexes of typical day, hour, generator set and substation nodes respectively.
The constraint conditions comprise a commissioning planning constraint, a line power constraint, an electricity purchasing constraint, a new energy output constraint and a load loss constraint;
the building of planning constraints comprises: after planning and commissioning of a certain device, the planning state of the certain device is 1, and the planning state of the certain device is 0 if the certain device is not planned and commissioned.
Figure BDA00037370574100001310
yw,t-1≤yw,t,w∈CW
yp,t-1≤yp,t,p∈CPV
Figure BDA00037370574100001311
Figure BDA00037370574100001312
Figure BDA00037370574100001313
Figure BDA00037370574100001314
Figure BDA00037370574100001315
Figure BDA00037370574100001316
Figure BDA00037370574100001317
In the formula: n is a radical ofnFor the number of electric vehicle charging stations that can be planned within the area n,
Figure BDA00037370574100001318
commissioning of state variables, y, for electric vehicle charging stationsw,tCommissioning of State variables, y, for wind farmsp,tState variables are put into operation for the photovoltaic power station,
Figure BDA00037370574100001319
a state variable is put into operation for the energy storage equipment,
Figure BDA00037370574100001320
a state variable is built for load demand response, if the state variable is built, the state variable is 1, otherwise, the state variable is 0, and k is an equipment model index; s. theeIn order to build up the capacity of the charging station e,
Figure BDA00037370574100001321
for the charge capacity requirement of all electric vehicles in region N, NwNumber of minimum planning commissioning for wind farm, NpAnd the minimum planning and construction quantity of the photovoltaic power station is obtained.
Line power constraint: because the transmission capacity of the transmission line has certain limitation, the power transmission of the transmission line, the bus voltage and the flowing current are all restricted to a certain extent.
Figure BDA0003737057410000141
Vi min≤Vi,h,bt≤Vi max
Figure BDA0003737057410000142
In the formula:
Figure BDA0003737057410000143
maximum flowing power amplitude, V, of line iji minFor the bus voltage V in the systemiMinimum value of (1), Vi maxFor the bus voltage V in the systemiThe maximum value of (a) is,
Figure BDA0003737057410000144
the maximum current magnitude that can flow for line ij.
And (3) power purchase restraint: the power purchasing power to the upper main network is restricted by certain upper and lower limits.
Figure BDA0003737057410000145
Figure BDA0003737057410000146
In the formula: pr min、Pr maxRespectively as the minimum value and the maximum value of the purchased active power,
Figure BDA0003737057410000147
respectively the minimum value and the maximum value of the purchased reactive power.
And (3) new energy output constraint: the actual output value of the new energy is smaller than the predicted value of the output.
Figure BDA0003737057410000148
Figure BDA0003737057410000149
Figure BDA00037370574100001410
In the formula:
Figure BDA00037370574100001411
respectively the maximum values of wind power, photoelectricity and energy storage output,
Figure BDA00037370574100001417
the output of the new energy accounts for the ratio,
Figure BDA00037370574100001412
is the maximum value of the allowable load d.
The loss of load constraint is: in order to ensure the safe and reliable operation of the system, the load loss amount of the system is limited by certain constraints.
Figure BDA00037370574100001413
In the formula: alpha (alpha) ("alpha")pIn order to be the maximum load-shedding proportion,
Figure BDA00037370574100001414
is the active power of the load d.
And step 3: converting the coordination programming model obtained in the step 2 into a mixed integer linear programming model for solving;
a second-order cone relaxation method is adopted to carry out linearization processing on the coordination planning model, and a power distribution network model in the coordination planning model is processed;
introducing two intermediate variables
Figure BDA00037370574100001415
And
Figure BDA00037370574100001416
and (3) carrying out relaxation treatment on the node voltage drop equation through the branch current and power relational expression:
Figure BDA0003737057410000151
Figure BDA0003737057410000152
Figure BDA0003737057410000153
Figure BDA0003737057410000154
in the formula: intermediate variables
Figure BDA0003737057410000155
And
Figure BDA0003737057410000156
are each numerically Iij,h,btAnd Vi,,h,btSquare of (d).
And 4, step 4: acquiring an uncertainty scene of new energy output and load fluctuation, and establishing a coordination optimization model considering the uncertainty of the new energy;
5000 uncertain scenes are randomly generated by a Monte Carlo simulation method on the basis of historical data of wind-solar output and load prediction, scene reduction is carried out by a synchronous back-substitution elimination method, and finally 5 uncertain scenes are obtained.
The coordination optimization model considering the uncertainty of the new energy is a two-stage planning model, and the first stage minimizes the operation cost of a basic scene; the second stage minimizes the penalty cost expectation for load shedding considering uncertain scenarios.
And establishing a coordination optimization two-stage model considering the uncertainty of the new energy by adopting a distributed robust optimization method. And decomposing the optimization problem into a main problem and a sub problem through a column and constraint generation CCG algorithm, and repeatedly and iteratively solving.
Considering a coordination optimization two-stage planning model of new energy uncertainty: considering the uncertainty of new energy, quantifying the load shedding penalty under the uncertainty scene into the total cost by a distributed robust method, and solving an optimal planning method, wherein a two-stage planning model comprises the following steps:
Figure BDA0003737057410000157
s.t.Ax≤e
x∈{0,1}
Cx+Dy≤f
Figure BDA0003737057410000158
in the formula: x is the feasible region satisfied by the decision variable X in the first stage, Y is the feasible region of the variable Y in the second stage, xi0New energy output and xi under basic scenekTo account for the scenario k of uncertainty, the new energy contribution, pkIs the probability value of the distribution of the uncertain parameters, phi is the value-taking domain of the probability distribution of the uncertain scenes, deltaTIs the penalty price per unit load-shedding,
Figure BDA0003737057410000159
in order to schedule the operation state variable of each element under the condition of a first-stage variable x in the system under an uncertainty scene k, aTx is the planning and construction cost of each equipment and the start-stop cost of the unit, bTy is the fuel consumption cost of the generator set and the operation and maintenance cost of each device, A is a constant coefficient matrix of the project variable and the state variable of the generator set, C, D and f are coefficient matrices and vectors of the constraint of the inequality of the two-stage variables respectively, and K isrAnd
Figure BDA0003737057410000161
coefficient matrixes and vectors of second-order cone constraint are respectively;
Figure BDA0003737057410000162
in the formula: p is a radical ofkIs the value of the probability distribution for scene k,
Figure BDA0003737057410000163
is the initial probability distribution value of scene k, θ1、θ∞Respectively, 0-1 norm to 0-infinity allowed upper limit of probability deviation.
The invention also discloses a power distribution network planning method, which adopts the established coordination optimization model considering the uncertainty of the new energy to plan the power distribution network, inputs the data, equipment parameters and operation parameters of the power distribution network system into the coordination optimization model considering the uncertainty of the new energy, and adopts a solver to solve (the solver is a commercial solver Gurobi) to obtain an optimal result; and planning by adopting the optimal result.
The power distribution network system data comprises a power distribution network topological structure and a power transmission line; the equipment parameters comprise the power, capacity and upper and lower output limits of the generator set, the wind power plant and the photovoltaic power station, and the power of the energy storage equipment, the electric automobile and the charging station; the operation parameters comprise load, wind and light predicted values, electricity purchase price of the power supply network, various operation parameters of the equipment, load demand response related data and system punishment price.
The invention is further illustrated by the following specific examples.
An example analysis using an IEEE33 node power system is shown in fig. 2. The figure shows candidate nodes for planning and commissioning of part of the equipment, the solid line indicates that the equipment exists in the system, and the dotted line indicates that the equipment is to be commissioned in the system. Wherein GT is the generator set of the system, WT, PV are wind farms, photovoltaic power plants, S is energy storage equipment, and the planning candidates for the load demand response apparatus are given in table 1.
TABLE 1 load demand response device planning candidates
Figure BDA0003737057410000164
In order to better compare the commissioning conditions of each calculation example, an electric vehicle charging station, a wind power plant, a photovoltaic power plant, energy storage equipment and a load demand response device can be represented by E, W, P, S and D respectively, subscripts are used for representing node index numbers of candidate commissioning equipment, and if S12 is used for representing that the energy storage equipment is planned and commissioned at 12 nodes, wherein the commissioning prices of electric vehicle charging stations are all 50 ten thousand yuan/piece; the cost of the W32 of the wind power plant is 50 ten thousand yuan per unit, and the W6 and the W8 are both 200 ten thousand yuan per unit; the photovoltaic power station is set up at a price of 100 ten thousand yuan per unit; the construction price of the energy storage equipment S2 is 15 ten thousand yuan per unit, and the rest is 10 ten thousand yuan per unit. The entire example test tool used Matlab2019a programming software and the GUROBI9.1 commercial solver.
Setting examples 1-4 for verifying and considering the effectiveness of a new energy, an electric vehicle charging station, energy storage equipment and a load demand response coordination planning model; in order to study and consider the influence of uncertainty of new energy on the system planning result, examples 5-6 are set, and specific planning schemes are given in table 2.
TABLE 2 planning scheme for each example
Figure BDA0003737057410000171
Table 3 shows a comparison of the results of the different planning schemes without taking into account the uncertainty of the new energy contribution. It is possible to obtain: along with the planning and the construction of the equipment, the operation cost is continuously reduced, the total cost is also continuously reduced, the load shedding amount can be reduced and the consumption of new energy can be promoted by the coordinated operation of the equipment, and the economical efficiency of the system is improved.
The power purchase comparison curves of the embodiments 1 to 4 are shown in the attached figure 3, and it is obvious from the figure that the main grid power purchase amount in the embodiment 1 is relatively large, while the power purchase amount in the embodiment 2 is remarkably reduced after the wind power plant and the photovoltaic power station are added and built, but in the early morning, because the generator set is not started and the output of the photovoltaic power station is 0, the main grid power purchase amount is increased in comparison with the embodiment 1. Example 3 through the construction of energy storage, reduce and abandon the amount of wind, improved the ability of accepting of wind-powered electricity generation, and then reduce the electric quantity of purchasing of major network. Comparative example 4 increases the main online electricity purchase amount at the valley time and decreases the electricity purchase amount at the peak time compared with comparative example 3 due to the effect of the demand response.
Fig. 4 shows a net load comparison graph, in which the net load of calculation example 2 is compared with the basic net load, and the addition of the electric vehicle increases the load demand, but at the same time, due to the existence of the electric vehicle V2G, the electric vehicle discharges in a time period with a higher basic load, such as 20, and plays a role of certain peak staggering. And after the energy storage is considered, the net load is not changed much compared with the net load in the embodiment 2, the net load is increased only in certain time periods with smaller load in the daytime, for example, 14 hours, and the function of the electric vehicle V2G is increased through the energy storage and electricity storage capacity. Example 4 after considering the commissioning of the demand response device, the net load has a significant change due to the capacity of the demand response to transfer load, and when the base load is in the valley, for example, 0 to 8, the net load is increased, and when the base load is larger, for example, 16 to 22, the net load of example 4 is reduced compared with that of example 3, thereby improving the economy and safety of the system.
TABLE 3 planning results for EXAMPLES 1-4
Figure BDA0003737057410000181
Table 4 shows that the total cost of the system is significantly increased after the uncertainty of the new energy output is considered, and the planning result reduces the construction of the wind farm W8, but increases the construction of the energy storage device S2 and the demand response devices of a plurality of loads, thereby greatly improving the safety of the system.
TABLE 4 comparison of the costs of example 4 and example 6
Figure BDA0003737057410000182
The distributed robust method which gives consideration to the advantages of both the random optimization method and the robust optimization method is adopted, and the distributed robust method has important significance in researching a coordinated optimization planning method considering the uncertainty of new energy. According to the invention, the influence of uncertainty of new energy output and load fluctuation on the power system is researched by a distributed robust method, so that the optimal planning result of the system is obtained. The distributed robust method considering the coordination and optimization of new energy, an electric vehicle charging station, energy storage equipment and load demand response has important significance for improving the economy and the safety of the power system.

Claims (9)

1. A construction method of a coordination optimization model considering uncertainty of new energy is characterized by comprising the following steps:
step 1: establishing a power distribution network system model and a planning operation model of each device;
step 2: establishing a deterministic coordination planning model considering wind power stations, photovoltaic power stations, electric vehicle charging stations, energy storage equipment and load demand response by taking the minimum power distribution network system planning cost and the minimum operation cost as objective functions;
and 3, step 3: converting the coordination programming model obtained in the step 2 into a mixed integer linear programming model for solving;
and 4, step 4: acquiring an uncertainty scene of new energy output and load fluctuation, and establishing a coordination optimization model considering the uncertainty of the new energy;
the coordination optimization model considering the uncertainty of the new energy is a two-stage planning model, and the first stage minimizes the operation cost of a basic scene; the second stage minimizes the penalty cost expectation for load shedding considering uncertain scenarios.
2. The method for constructing a coordination optimization model considering uncertainty of new energy according to claim 1, wherein the power distribution network model in step 1 is as follows:
Figure FDA0003737057400000011
Figure FDA0003737057400000012
Figure FDA0003737057400000013
Figure FDA0003737057400000014
in the formula: pi (j) and delta (j) are a branch head end and tail end node set which takes j as a tail end and a head end node in the system, and Rij、XijThe resistance value and the reactance value of the line ij are respectively; i isij,h,btThe current flowing for line ij; pg,h,bt、Qg,h,btRespectively the active power and the reactive power of the generator set; pij,h,bt、Qij,h,btRespectively the active power and the reactive power flowing through the line ij;
Figure FDA0003737057400000015
respectively representing the discharging power and the charging power of the energy storage equipment;
Figure FDA0003737057400000016
reactive power purchased from a power distribution network to a superior power grid;
Figure FDA0003737057400000017
active power after demand response is carried out on the load;
Figure FDA0003737057400000018
a reactive load that is a load; p ise,h,btActive power for an electric vehicle charging station;
Figure FDA0003737057400000019
is the loss of load;
Figure FDA00037370574000000110
a power factor of the load; vi,h,btIs the voltage magnitude of node i, Pjk,h,btFor node j, the magnitude of active power, Pw,h,btFor the actual value of the wind farm output, Pp,h,btIs the actual value of the output of the photovoltaic power station,
Figure FDA00037370574000000111
purchasing power, Q, for main networkjk,h,btFor node j to flow outThe magnitude of the work power; and the equipment planning operation model comprises an electric vehicle charging station operation model, an energy storage equipment planning operation model and a load demand response planning operation model.
3. The method of claim 1, wherein the electric vehicle charging station operation model comprises an electric vehicle operation constraint and an electric vehicle charging station power constraint;
and (3) electric vehicle operation restraint:
Figure FDA0003737057400000021
Figure FDA0003737057400000022
Figure FDA0003737057400000023
Figure FDA0003737057400000024
Figure FDA0003737057400000025
Figure FDA0003737057400000026
wherein v is an index of an electric vehicle, Iv,h,btIs a variable of 0-1 of the charge-discharge state of the electric automobile,
Figure FDA0003737057400000027
is an access state variable of the electric automobile,
Figure FDA0003737057400000028
is a charge-discharge state variable of the electric automobile,
Figure FDA0003737057400000029
are respectively the 0-1 variable of the charging and discharging state of the electric automobile at the charging station,
Figure FDA00037370574000000210
are respectively the charging power and the discharging power of the electric automobile,
Figure FDA00037370574000000211
rated charging power and rated discharging power of the electric automobile are respectively;
Figure FDA00037370574000000212
Figure FDA00037370574000000213
Figure FDA00037370574000000214
Figure FDA00037370574000000215
in the formula: sv,h,btIs the state of charge of the electric automobile at the h moment, Sv,0,btIs the state of charge at the initial moment, M is an infinite positive number, Sv,h-1,btIs the state of charge, eta, of the electric automobile at the h-1 momentcEfficiency of charging electric vehicles, EvIs the battery capacity of the electric vehicle, etadFor the discharge efficiency of electric vehicles, Sv,H,btFor the state of charge at the moment of departure from the grid,
Figure FDA00037370574000000216
is a 0-1 variable when the electric automobile leaves the power grid,
Figure FDA00037370574000000217
is the minimum value of the v charge state of the electric vehicle,
Figure FDA00037370574000000218
the maximum value of the v charge state of the electric vehicle;
electric vehicle charging station power constraint:
Figure FDA00037370574000000219
Figure FDA00037370574000000220
in the formula: p ise,h,btFor the purpose of charging the power of the station e,
Figure FDA00037370574000000221
for the state of commissioning of the charging station e, ΩnIs a set of regions, PePower for charging station e;
the energy storage equipment planning operation model comprises the following steps:
Figure FDA0003737057400000031
Figure FDA0003737057400000032
Figure FDA0003737057400000033
Figure FDA0003737057400000034
in the formula: es,h,btFor the energy storage of the energy storage device at time h, Es,h-1,btIs the electric quantity, eta, of the energy storage equipment at the moment of h-1inIn order to provide an efficient charging of the energy storage device,
Figure FDA0003737057400000035
for the charging power of the energy storage device at the time h-1,
Figure FDA0003737057400000036
for the discharge power, eta, of the energy storage device at the moment h-1outIn order to achieve an efficient discharge of the energy storage device,
Figure FDA0003737057400000037
for the commissioning state of the energy storage device,
Figure FDA0003737057400000038
respectively, the minimum and maximum electric energy storage capacity, P, of the energy storage devices in,min、Ps in,maxRespectively minimum and maximum values of charging power, P, of the energy storage devices out ,min、Ps out,maxRespectively the minimum and maximum discharge power of the energy storage device;
the load demand response planning operation model is as follows:
Figure FDA0003737057400000039
Figure FDA00037370574000000310
Figure FDA00037370574000000311
Figure FDA00037370574000000312
Figure FDA00037370574000000313
Figure FDA00037370574000000314
Figure FDA00037370574000000315
in the formula:
Figure FDA00037370574000000316
in order to take part in the amount of power required for the demand response,
Figure FDA00037370574000000317
the amount of interruptible load power to participate in the demand response,
Figure FDA00037370574000000318
to participate in the transferable load power size of the demand response,
Figure FDA00037370574000000319
is the size of the power of the node d,
Figure FDA00037370574000000320
the maximum allowed load power level for node d,
Figure FDA00037370574000000321
in response to the proportion of the interruptible load to the system demand,
Figure FDA00037370574000000322
a state variable is built for interruptible load demand response,
Figure FDA00037370574000000323
for the maximum amount of interruptible electrical load allowed by the system,
Figure FDA00037370574000000324
in response to system demand the proportion of the translatable load,
Figure FDA00037370574000000325
and establishing state variables for the transferable load demand response.
4. The method according to claim 3, wherein the objective function is as follows:
minF＝min(IC+OC+CI·ΔD+CW·ΔW+CL·ΔL)
wherein:
Figure FDA0003737057400000041
Figure FDA0003737057400000042
Figure FDA0003737057400000043
Figure FDA0003737057400000044
Figure FDA0003737057400000045
Figure FDA0003737057400000046
Figure FDA0003737057400000047
Figure FDA0003737057400000048
Figure FDA0003737057400000049
κt＝1/(1+dr)t-1
in the formula: f is an optimal objective function of the system, t and k are indexes of year and model of planning equipment to be invested respectively, e, w, p, s and d are indexes of an electric vehicle charging station, a wind power plant, a photovoltaic power station, energy storage equipment and a load respectively, and CE, CW, CP, CS and CDR are candidate investment sets of the planning charging station, the wind power plant, the photovoltaic power station, the energy storage equipment and a demand response device respectively; k is a set of planning candidate equipment models, IC is planning investment cost, and OC is total system operation cost; Δ D, Δ W and Δ L are respectively the system load shedding amount, the abandoned air amount and the abandoned light amount; cI、CW、CLUnit punishment cost of load shedding, wind abandoning and light abandoning, kappa is the coefficient of current market value, dr is the current rate, CinvUnit investment cost, y, for each equipment investment constructiontIs a variable, Ct Gen、Ct Es、Ct Ele,in、Ct DRRespectively providing power purchase cost and load demand response cost for the generator set, the electric vehicle charging station, the main network; DTbtDays b typical day of year t; ,
Figure FDA00037370574000000410
in order to cut the load amount,
Figure FDA00037370574000000411
for the predicted value of the output of the wind farm, Pw,h,btIs an actual output value of the wind farm,
Figure FDA00037370574000000412
for the predicted value of the output of the photovoltaic power station, Pp,h,btIs an actual output value of the photovoltaic power station,
Figure FDA00037370574000000413
for fuel costs of the generator set, Fg(. Is) the heat rate curve of the generator set g, ceIn order to keep up with the costs of operating the charging station,
Figure FDA00037370574000000414
the cost of the unit of electricity purchase is,
Figure FDA00037370574000000415
the price is compensated for the interruptible load in units,
Figure FDA00037370574000000416
in order to purchase the active power to the upper stage,
Figure FDA00037370574000000417
is interruptible load power; b. h, g and r are indexes of typical day, hour, generator set and substation nodes respectively;
the constraint conditions comprise a commissioning planning constraint, a line power constraint, an electricity purchasing constraint, a new energy output constraint and a load loss constraint;
the building of planning constraints comprises:
Figure FDA0003737057400000051
yw,t-1≤yw,t,w∈CW
yp,t-1≤yp,t,p∈CPV
Figure FDA0003737057400000052
Figure FDA0003737057400000053
Figure FDA0003737057400000054
Figure FDA0003737057400000055
Figure FDA0003737057400000056
Figure FDA0003737057400000057
Figure FDA0003737057400000058
in the formula: n is a radical ofnFor the number of electric vehicle charging stations that can be planned within the area n,
Figure FDA0003737057400000059
commissioning of state variables, y, for electric vehicle charging stationsw,tCommissioning of State variables, y, for wind farmsp,tState variables are put into operation for the photovoltaic power station,
Figure FDA00037370574000000510
a state variable is put into operation for the energy storage equipment,
Figure FDA00037370574000000511
setting up a state variable for the load demand response, wherein if the state variable is set up, the state variable is 1, otherwise, the state variable is 0, and k is an equipment model index; s. theeIn order to build up the capacity of the charging station e,
Figure FDA00037370574000000512
for the charge capacity requirement of all electric vehicles in region N, NwNumber of minimum planning commissioning for wind farm, NpThe minimum planning and building quantity of the photovoltaic power station is obtained;
line power constraint:
Figure FDA00037370574000000513
Vi min≤Vi,h,bt≤Vi max
Figure FDA00037370574000000514
in the formula:
Figure FDA00037370574000000515
maximum flowing power amplitude, V, of line iji minFor bus voltage V in the systemiMinimum value of (1), Vi maxFor the bus voltage V in the systemiThe maximum value of (a) is,
Figure FDA00037370574000000516
the maximum current amplitude that can flow through the line ij;
power purchase constraint:
Figure FDA00037370574000000517
Figure FDA0003737057400000061
in the formula: p isr min、Pr maxRespectively the minimum and maximum values of the purchased active power, Qr min、Qr maxRespectively the minimum value and the maximum value of the purchased reactive power;
and (3) new energy output constraint:
Figure FDA0003737057400000062
Figure FDA0003737057400000063
Figure FDA0003737057400000064
in the formula:
Figure FDA0003737057400000065
respectively the maximum values of wind power, photoelectricity and energy storage output, theta is the output ratio of new energy,
Figure FDA0003737057400000066
is the maximum value of the allowable load d;
the loss of load constraint is:
Figure FDA0003737057400000067
in the formula: alpha (alpha) ("alpha")pIs the proportion of the maximum load to be cut,
Figure FDA0003737057400000068
is the active power of load d.
5. The method for constructing a coordination optimization model considering uncertainty of new energy according to claim 4, wherein in step 3, a second-order cone relaxation method is adopted to perform linearization processing on the coordination planning model, and a power distribution network model in the coordination planning model is processed;
introducing two intermediate variables
Figure FDA0003737057400000069
And
Figure FDA00037370574000000610
and (3) relaxing the node voltage drop equation through the branch current and power relational expression:
Figure FDA00037370574000000611
Figure FDA00037370574000000612
Figure FDA00037370574000000613
Figure FDA00037370574000000614
in the formula: intermediate variables
Figure FDA00037370574000000615
And
Figure FDA00037370574000000616
are each numerically Iij,h,btAnd Vi,,h,btSquare of (d).
6. The method for constructing the coordination optimization model considering the uncertainty of the new energy according to claim 5, wherein the uncertainty scene obtaining method in the step 4 is as follows: and randomly generating l uncertain scenes on the basis of historical data of wind-solar output and load prediction by a Monte Carlo simulation method, and carrying out scene reduction by a synchronous back substitution elimination method to finally obtain the required uncertain scenes.
7. The method for constructing the coordination optimization model considering the uncertainty of the new energy according to claim 6, wherein a distributed robust optimization method is adopted in the step 4 to establish the coordination optimization model considering the uncertainty of the new energy, and the coordination optimization model is as follows:
Figure FDA0003737057400000071
s.t.Ax≤e
x∈{0,1}
Cx+Dy≤f
Figure FDA0003737057400000072
in the formula: x is the feasible region satisfied by the decision variable X in the first stage, Y is the feasible region of the variable Y in the second stage, xi0New energy output and xi under basic scenekTo account for the scenario k of uncertainty, the new energy contribution, pkIs uncertainProbability value of parameter distribution, phi is value-taking domain of probability distribution of uncertainty scene, deltaTIs the penalty price per unit of load shedding,
Figure FDA0003737057400000073
in order to schedule the operation state variable of each element under the condition of a first-stage variable x in the system under an uncertainty scene k, aTx is the planning and construction cost of each equipment and the start-stop cost of the unit, bTy is the fuel consumption cost of the generator set and the operation and maintenance cost of each device, A is a constant coefficient matrix of the project variable and the state variable of the generator set, C, D and f are coefficient matrix and vector of the constraint of the inequality of the two-stage variables respectively, and K isrAnd
Figure FDA0003737057400000074
coefficient matrixes and vectors of second-order cone constraint are respectively;
Figure FDA0003737057400000075
in the formula: p is a radical of formulakIs the value of the probability distribution for scene k,
Figure FDA0003737057400000076
is the initial probability distribution value of scene k, θ1、θ∞Respectively, 0-1 norm and 0- ∞ allowable upper limit of probability deviation.
8. A power distribution network planning method is characterized in that a coordination optimization model which is obtained by the construction method according to claims 1-7 and takes uncertainty of new energy into consideration is adopted for planning: and inputting the data, the equipment parameters and the operation parameters of the power distribution network system into a coordination optimization model considering the uncertainty of the new energy, solving by adopting a solver to obtain an optimal result, and planning the power distribution network by adopting the optimal result.
9. The power distribution network planning method according to claim 8, wherein the power distribution network system data comprises a power distribution network topology and power transmission lines; the equipment parameters comprise power, capacity and output upper and lower limits of the generator set, the wind power plant and the photovoltaic power station, and power of the energy storage equipment, the electric automobile and the charging station; the operation parameters comprise load, wind and light predicted values, electricity purchase price of the superior network, various operation parameters of the equipment, load demand response related data and system punishment price.
CN202210799787.7A 2022-07-08 2022-07-08 Construction method of coordination optimization model and power distribution network planning method Pending CN115276111A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210799787.7A CN115276111A (en) 2022-07-08 2022-07-08 Construction method of coordination optimization model and power distribution network planning method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210799787.7A CN115276111A (en) 2022-07-08 2022-07-08 Construction method of coordination optimization model and power distribution network planning method

Publications (1)

Publication Number Publication Date
CN115276111A true CN115276111A (en) 2022-11-01

Family

ID=83764496

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210799787.7A Pending CN115276111A (en) 2022-07-08 2022-07-08 Construction method of coordination optimization model and power distribution network planning method

Country Status (1)

Country Link
CN (1) CN115276111A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115882523A (en) * 2023-02-08 2023-03-31 四川大学 Optimal operation method, system and equipment for power system with distributed energy storage
CN117039884A (en) * 2023-08-22 2023-11-10 四川大学 Flexible interconnection planning method of low-voltage distribution network suitable for improving power supply capacity of multiple scenes
CN117391311A (en) * 2023-12-07 2024-01-12 国网湖北省电力有限公司经济技术研究院 Charging station and power distribution network collaborative planning method considering carbon emission and uncertainty
CN117879016A (en) * 2024-03-11 2024-04-12 国网江西省电力有限公司经济技术研究院 Charging station configuration optimization method and system based on optical charge integration

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115882523A (en) * 2023-02-08 2023-03-31 四川大学 Optimal operation method, system and equipment for power system with distributed energy storage
CN117039884A (en) * 2023-08-22 2023-11-10 四川大学 Flexible interconnection planning method of low-voltage distribution network suitable for improving power supply capacity of multiple scenes
CN117391311A (en) * 2023-12-07 2024-01-12 国网湖北省电力有限公司经济技术研究院 Charging station and power distribution network collaborative planning method considering carbon emission and uncertainty
CN117391311B (en) * 2023-12-07 2024-03-08 国网湖北省电力有限公司经济技术研究院 Charging station and power distribution network collaborative planning method and device considering carbon emission and uncertainty
CN117879016A (en) * 2024-03-11 2024-04-12 国网江西省电力有限公司经济技术研究院 Charging station configuration optimization method and system based on optical charge integration

Similar Documents

Publication Publication Date Title
CN110739725B (en) Optimal scheduling method for power distribution network
CN115276111A (en) Construction method of coordination optimization model and power distribution network planning method
Leou Optimal charging/discharging control for electric vehicles considering power system constraints and operation costs
CN103840457B (en) Consider DG Optimal Configuration Method in the power distribution network that electric automobile discharge and recharge affects
CN111626527B (en) Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle
CN112598224A (en) Interactive game scheduling method for park comprehensive energy system group and power grid
CN114139780A (en) Coordinated optimization method and system for virtual power plant and power distribution network containing distributed power supply
CN111769543B (en) Regional power distribution network autonomous cooperative operation optimization method containing multiple micro-grids
CN112491036A (en) Multi-subject interactive game power distribution network peak regulation operation control method and system
CN115001038A (en) Wind-solar-diesel storage capacity ratio optimization method based on stochastic programming
CN115577909A (en) Campus comprehensive energy system scheduling method considering price type demand response and V2G
CN114050609B (en) Adaptive robust day-ahead optimization scheduling method for high-proportion new energy power system
CN113361078B (en) Power distribution network cloud edge cooperative regulation and control method considering photovoltaic power generation uncertainty
Waqar et al. Multi-objective analysis of DER sizing in microgrids using probabilistic modeling
CN112182915A (en) Optimized scheduling method and system for cooperatively promoting wind power consumption
CN116128193A (en) Park micro-grid dispatching method and dispatching system based on block chain technology
CN115693737A (en) Method for participating in power distribution network scheduling based on V2G electric vehicle aggregation
CN115759589A (en) Power distribution network data-driven robust extension planning method containing high-proportion new energy
Mao et al. A new schedule-controlled strategy for charging large number of EVs with load shifting and voltage regulation
Kermani et al. Optimal operation of a real power hub based on PV/FC/GenSet/BESS and demand response under uncertainty
CN114977271A (en) Novel power system scheduling method considering social factors
Arcos-Aviles et al. Fuzzy control-based energy management system for interconnected residential microgrids using the forecasts of power generation and load demand
Li et al. A Cooperative Scheduling Strategy of Renewable Energy and Electric Vehicle Considering Reactive Power Response
Guoliang et al. Stochastic Optimization of a Microgrid Considering Classification of Electric Vehicles
Li et al. Multi-objective robust optimization of microgrid system with electric vehicles and new renewable energy

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination