CN111064181A - Power supply and charging station configuration method based on charging load space schedulable characteristic - Google Patents

Power supply and charging station configuration method based on charging load space schedulable characteristic Download PDF

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CN111064181A
CN111064181A CN201911144300.6A CN201911144300A CN111064181A CN 111064181 A CN111064181 A CN 111064181A CN 201911144300 A CN201911144300 A CN 201911144300A CN 111064181 A CN111064181 A CN 111064181A
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power
charging
cost
power supply
micro gas
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叶刚进
王骏海
胡伟
徐强
商佳宜
杨翾
吴梦遥
赵天煜
毛西吟
郑亚敏
任晓勇
王浩国
陈琳
李飞
刘冰旖
黄晟
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State Grid Corp of China SGCC
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Corp of China SGCC
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/64—Optimising energy costs, e.g. responding to electricity rates
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a power supply and charging station configuration method based on the spatial schedulable characteristic of a charging load. The method comprises the following steps: firstly, collecting data of each part in the system, modeling loads of a photovoltaic power station, a micro gas turbine and an electric vehicle in the system, secondly, determining the optimal installation position and the installation capacity of a distributed power supply and an electric vehicle charging station in the power distribution system by taking the minimum annual social total cost as a target, then representing the relation among various state quantities in the system by a linear Distflow power flow equation, applying a second-order cone relaxation technology to process branch current constraint, and finally presenting a hybrid integer second-order cone planning model which can be solved in polynomial time. The invention has the beneficial effects that: the operation parameters of the power distribution network are obtained, the objective function is solved, the space distribution condition of the load of the power distribution system is effectively improved, and the power distribution system provides help for safe and economic operation.

Description

Power supply and charging station configuration method based on charging load space schedulable characteristic
Technical Field
The invention belongs to the field of power distribution of an electric power system, and particularly relates to a power supply and charging station configuration method based on the spatial schedulability characteristic of a charging load.
Background
With the popularization of mobile intelligent terminals such as mobile phones and tablet computers and the development of wireless communication technology, more and more car owners rely on real-time navigation technology to determine own driving and parking behaviors. For the electric automobile, the real-time navigation technology can guide a proper charging place for the electric automobile according to the distribution condition of the electric automobile charging pile around the destination, so that the charging demand of the electric automobile becomes a schedulable object to a certain extent and within a certain space range. From the perspective of the distribution system operator, the place where the charging action of the electric vehicle occurs determines the position where the corresponding charging load is connected to the distribution system. The guiding effect of the real-time navigation technology is reasonably utilized, the charging load of the electric automobile is guided to be connected into the power distribution system through the appropriate bus, the space distribution condition of the load of the power distribution system can be effectively improved, and the safety and economic operation of the power distribution system are helped.
The real-time navigation system guides the electric automobile to go to which charging station to charge, the space distribution condition of the load of the power distribution system is obviously influenced, the optimization problem of a plurality of factors, such as the running state of the power distribution system, the number of idle charging piles in each charging station, the cost for scheduling the extra driving distance of the electric automobile and the like, is an optimization problem, and the optimization configuration scheme of related facilities and equipment in the power distribution system is obviously influenced. In a power distribution system with an electric vehicle charging station, a distributed power supply with proper capacity is connected, so that the local stabilization of load fluctuation can be effectively promoted, the impact of a high-power charging load on a power distribution network is relieved, and the new trend of planning and running of the power distribution system gradually becomes.
Disclosure of Invention
Based on this, a distributed power supply and electric vehicle charging station combined configuration model considering the spatial schedulable characteristic of the charging load is constructed in this chapter, and in order to achieve the technical purpose and achieve the technical effect, the technical scheme of the invention is as follows:
a power supply and charging station configuration method based on the spatial schedulable characteristic of charging load comprises
Step one, collecting historical operation data of all parts required in the system, and respectively constructing a photovoltaic power station output model, a micro gas turbine output model and an electric automobile load model;
step two, the lowest annual social total cost of the distributed power supply and the electric vehicle charging station is taken as a target function, and the investment construction cost, the operation maintenance cost, the network loss cost, the fuel cost, the carbon emission cost and the electricity purchasing cost are taken as constraint conditions of the target function;
step three, representing the relation among all state quantities in the system by using a linearized Distflow power flow equation, and taking an equivalent load, a branch current, a voltage amplitude, a distributed power supply capacity discrete type and an output equation as constraint conditions;
and step four, processing the branch current constraint by adopting a second-order cone relaxation technology, introducing an auxiliary variable to represent the branch current constraint as a second-order cone constraint and a linear constraint, processing the optimization model, and converting the optimization model into a mixed integer second-order cone programming model for solving.
Optionally, the building of the photovoltaic power station output model includes:
the piecewise function shown in the formula (1) is used for representing the relation between the active power output of the photovoltaic power station and the solar illumination intensity,
Figure BDA0002281756080000021
wherein, PsRepresenting the active power output, P, of the photovoltaic plant at a solar irradiance of ss-ratedAnd sratedRespectively representing rated power and rated illumination intensity of the photovoltaic power station;
when the solar illumination intensity is s, the reactive power regulation range of the photovoltaic power station is shown as a formula (2),
Figure BDA0002281756080000022
wherein Q issRepresenting the reactive power output of the photovoltaic power station when the solar illumination intensity is S, SPV-ratedIs the capacity of the photovoltaic inverter.
Optionally, constructing the micro gas turbine output modeling includes:
the micro gas turbine is used as a pure active power source which can be completely dispatched, and the actual output of the micro gas turbine is determined by a dispatching scheme of a power distribution system operator in a corresponding rated power range; the active power output range of the micro gas turbine is shown as formula (3):
0≤PMT≤SMT-rated(3)
wherein, PMTIs the active power output, S, of the micro gas turbineMT-ratedThe installed capacity of the micro gas turbine.
Optionally, the constructing an electric vehicle load model includes:
use of EVkIndicating the electric vehicle with number k, EVkThe time of occupying the charging pile in the charging process
Figure BDA0002281756080000031
Represented by formula (4):
Figure BDA0002281756080000032
therein, SOCkIs EVkState of charge, CapkIs EVkThe capacity of the battery of (a) is,
Figure BDA0002281756080000033
is EVkThe expected residence time of the gas mixture in the reactor,
Figure BDA0002281756080000034
the rated charging power of the electric automobile is obtained.
Optionally, the minimizing the total annual social cost of the distributed power supply related to the electric vehicle charging station as an objective function includes:
comprehensively considering the concerns of all interest bodies, the lowest annual social total cost related to the distributed power supply and the electric vehicle charging station is taken as an objective function, and the annual investment construction cost C is specifically includedIAnnual operating and maintenance costs CO&MAnnual fuel cost for micro gas turbines CFAnnual carbon emission cost of micro gas turbines CCAnnual power purchasing cost C to superior power gridPAnnual system loss charge CLAnd extra traffic cost C generated by scheduling electric automobile load every yearTAnd (3) waiting for 7 aspects; the specific form of the objective function is shown in equation (5):
min Cost＝CI+CO&M+CF+CC+CP+CL+CT(5)。
the method for solving the flexibility range of the power exchanged between the power distribution network and the main network based on the alternating current and direct current mixing has the advantages that:
(1) in a power distribution system comprising an electric vehicle charging station, a distributed power supply with proper capacity is connected, so that local stabilization of load fluctuation can be effectively promoted, and impact of high-power charging load on a power distribution network can be relieved. From the aspect of flexibility, the invention obtains the optimal configuration scheme of a plurality of elements related to the output of the distributed power supply, the charging requirement of the electric automobile, the load requirement of the power consumer and the like, and can provide reference for operation scheduling personnel of the main network and the power distribution network.
(2) The second-order cone relaxation technology is applied to processing branch current constraint, the branch current constraint is presented as a mixed integer second-order cone programming model which can be solved in polynomial time, and the model can be effectively simplified, so that efficient and rapid solution is realized.
Drawings
Fig. 1 is a schematic flow chart of a power supply and charging station configuration method based on spatial schedulable characteristics of charging loads according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a load modeling process of an electric vehicle according to the present embodiment.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
As shown in fig. 1, the method for configuring a power supply and a charging station based on spatial schedulable characteristics of charging loads includes the following steps:
step one, collecting historical operation data of all parts required in the system, and respectively constructing a photovoltaic power station output model, a micro gas turbine output model and an electric automobile load model;
step two, the lowest annual social total cost of the distributed power supply and the electric vehicle charging station is taken as a target function, and the investment construction cost, the operation maintenance cost, the network loss cost, the fuel cost, the carbon emission cost and the electricity purchasing cost are taken as constraint conditions of the target function;
step three, representing the relation among all state quantities in the system by using a linearized Distflow power flow equation, and taking an equivalent load, a branch current, a voltage amplitude, a distributed power supply capacity discrete type and an output equation as constraint conditions;
and step four, processing the branch current constraint by adopting a second-order cone relaxation technology, introducing an auxiliary variable to represent the branch current constraint as a second-order cone constraint and a linear constraint, processing the optimization model, and converting the optimization model into a mixed integer second-order cone programming model for solving.
In an implementation, in step one, the photovoltaic contribution is modeled as follows:
Figure BDA0002281756080000051
wherein, PsRepresenting the active power output, P, of the photovoltaic plant at a solar irradiance of ss-ratedAnd sratedRespectively representing the rated power and the rated illumination intensity of the photovoltaic power station. In the optimization configuration problem of a long-time scale, influences of secondary factors such as temperature and illumination angles on active power output of the photovoltaic power station are ignored, and the relation between the active power output of the photovoltaic power station and the solar illumination intensity can be represented by using a piecewise function shown in formula (1).
When the solar illumination intensity is s, the reactive power regulation range of the photovoltaic power station is shown as a formula (4-2):
Figure BDA0002281756080000052
wherein Q issRepresenting the reactive power output of the photovoltaic power station when the solar illumination intensity is S, SPV-ratedIs the capacity of the photovoltaic inverter.
Micro gas turbine output was modeled as follows:
the micro gas turbine is taken as a type of fully schedulable pure active power source, and the actual output of the micro gas turbine is determined by a scheduling scheme of a power distribution system operator within a corresponding rated power range. The active power output range of the micro gas turbine is shown as formula (3):
0≤PMT≤SMT-rated(3)
wherein, PMTIs the active power output, S, of the micro gas turbineMT-ratedThe installed capacity of the micro gas turbine.
Electric vehicle load modeling process as shown in figure 2,
use of EVkIndicating the electric vehicle with number k, EVkThe time of occupying the charging pile in the charging process
Figure BDA0002281756080000061
Can be represented by formula (4):
Figure BDA0002281756080000062
therein, SOCkIs EVkState of charge, CapkIs EVkThe capacity of the battery of (a) is,
Figure BDA0002281756080000063
is EVkThe expected residence time of the gas mixture in the reactor,
Figure BDA0002281756080000064
the rated charging power of the electric automobile is obtained.
Optionally, in step two, the establishing of the objective function is included:
comprehensively considering the concerns of all interest bodies, the lowest annual social total cost related to the distributed power supply and the electric vehicle charging station is taken as an objective function, and the annual investment construction cost C is specifically includedIAnnual operating and maintenance costs CO&MAnnual fuel cost for micro gas turbines CFAnnual carbon emission cost of micro gas turbines CCAnnual power purchasing cost C to superior power gridPAnnual system loss charge CLAnd extra traffic cost C generated by scheduling electric automobile load every yearTAnd 7 aspects are carried out. The specific form of the objective function is shown in equation (5):
min Cost＝CI+CO&M+CF+CC+CP+CL+CT(5)
(1) annual investment and construction costs:
Figure BDA0002281756080000071
wherein the content of the first and second substances,
Figure BDA0002281756080000072
respectively represents the investment construction cost of unit capacity/quantity of the photovoltaic power station, the micro gas turbine and the electric automobile charging pile,
Figure BDA0002281756080000073
respectively showing the capacity/quantity, N, of the charging piles of the photovoltaic power station, the micro gas turbine and the electric automobile which are arranged at the node ibusRepresenting the total number of nodes in the system, RPV、RMT、RCFThe method is a group of auxiliary variables and is used for carrying out annual calculation of investment and construction cost, and the expression is as follows:
Figure BDA0002281756080000074
Figure BDA0002281756080000075
Figure BDA0002281756080000076
wherein d represents the discount rate, yPV、yMT、yCFRespectively show the economic life of photovoltaic power plant, miniature gas turbine, electric automobile fill electric pile.
(2) Annual operating maintenance costs:
Figure BDA0002281756080000077
wherein the content of the first and second substances,
Figure BDA0002281756080000078
and
Figure BDA0002281756080000079
respectively represents the operation and maintenance costs corresponding to the unit power generation of the photovoltaic power station and the micro gas turbine,
Figure BDA00022817560800000710
the annual operation and maintenance cost of the charging pile of the unit quantity of the electric automobiles,
Figure BDA00022817560800000711
respectively representing the active power output of the photovoltaic power plant and the micro gas turbine at the node i under the time section t in the typical day of the working day of the season s,
Figure BDA0002281756080000081
respectively representing the active power output of the photovoltaic power station and the micro gas turbine at the node i under a time section t in a typical day on the weekend of the season s, and delta t is the duration of the time section
(3) Annual fuel costs for micro gas turbines:
Figure BDA0002281756080000082
wherein the content of the first and second substances,
Figure BDA0002281756080000083
indicating the fuel cost per unit power generation of the micro gas turbine.
(4) Annual carbon emission costs for micro gas turbines:
Figure BDA0002281756080000084
wherein the content of the first and second substances,
Figure BDA0002281756080000085
tax, rho, corresponding to unit carbon dioxide emissionemThe emission amount of carbon dioxide corresponding to the unit power generation amount of the micro gas turbine.
(5) The electricity purchasing cost to the superior power grid is as follows:
Figure BDA0002281756080000086
wherein omegaSRepresenting a set of substation nodes, u (i) representing a set of all nodes connected to and downstream of node i, cPRepresenting the cost of purchasing a unit of electricity from an upper grid,
Figure BDA0002281756080000087
the active power flowing through branch ij in time section t during a typical day of the working day and a typical day of the weekend of season s is shown respectively.
(6) Annual system loss costs:
Figure BDA0002281756080000088
wherein, cLIn terms of the cost per unit of network loss,
Figure BDA0002281756080000091
representing branches ij in a typical day of the weekday and a typical day of the weekend of season s, respectivelySquare of the current flowing in time section t, RijIs the resistance of branch ij.
(7) The additional traffic cost due to the scheduling of electric vehicle loads is as follows:
Figure BDA0002281756080000092
wherein, cTThe cost incurred for scheduling the electric vehicle to travel the unit distance in addition,
Figure BDA0002281756080000093
respectively represents the number of electric vehicles with the land block at the node i as the destination in the working day and the weekend typical day of the season s, omegaCFTo configure a candidate node set of electric vehicle charging stations, dijIs the geographic distance between node i and node j,
Figure BDA0002281756080000094
is a 0-1 variable used for representing the load space scheduling condition of the electric automobile.
Optionally, in step three, constraints of the joint configuration model are included. Under the premise of no ambiguity, the constraint related to the working day scene and the constraint related to the weekend scene are characterized by using a unified expression.
(1) And (3) system power flow constraint:
Figure BDA0002281756080000095
wherein u (j)/v (j) represents a set of all nodes connected to and downstream/upstream of node j, Ps,t,ij、Qs,t,ijRespectively representing the active power and the reactive power, U, of the branch ij flowing through the season s time section ts,t,iIs the voltage amplitude of node i at season s time section t,
Figure BDA0002281756080000096
respectively representing the equivalent active load and reactive load of the node j in the season s time section t,Xijreactance of branch ij, ΩN、ΩLRespectively a node set and a branch set, U, in the systemsubThe voltage amplitude of the substation node is usually 1.0p.u. Equations (16) - (18) together form a linearized Distflow power flow equation, and nonlinear terms in the traditional Distflow power flow equation are approximated and ignored. When the radial power distribution system operates around the voltage level of 1.0p.u., the power flow result error caused by approximation and neglect is very small, and the obvious influence on the combined configuration scheme of the distributed power supply and the electric vehicle charging station is not generated.
(2) Equivalent load equation:
Figure BDA0002281756080000101
wherein the content of the first and second substances,
Figure BDA0002281756080000102
respectively representing the active load and the reactive load of the power user at the node j in the season s time section t,
Figure BDA0002281756080000103
respectively representing the active power output and the reactive power output of the photovoltaic power station at the node j in the season s time section t,
Figure BDA0002281756080000104
is the active power output of a micro gas turbine station at a node j in a season s time section t,
Figure BDA0002281756080000105
the active load of the electric vehicle charging station at the node j in the season s time section t is shown.
(3) Voltage amplitude constraint:
Figure BDA0002281756080000106
in the formula of UminAnd UmaxIs the set voltage fluctuation allowable rangeThe lower limit and the upper limit of (1).
(4) And (3) branch current constraint:
Figure BDA0002281756080000107
Figure BDA0002281756080000108
in the formula Iij,maxThe maximum current allowed to flow on branch ij.
(5) Discrete constraint of distributed power capacity:
Figure BDA0002281756080000109
Figure BDA00022817560800001010
wherein the content of the first and second substances,
Figure BDA00022817560800001011
and
Figure BDA00022817560800001012
respectively representing the installation number of the photovoltaic module and the micro gas turbine at the node i,
Figure BDA00022817560800001013
and
Figure BDA0002281756080000111
respectively representing the unit capacity, omega, of the photovoltaic module and of the micro gas turbine of the type employedPV、ΩMTRespectively, candidate node sets for installing the photovoltaic power station and the micro gas turbine in the system.
(6) And (3) output constraint of the distributed power supply:
Figure BDA0002281756080000112
Figure BDA0002281756080000113
in the formula
Figure BDA0002281756080000114
Is a parameter related to the intensity of solar radiation, on any time section in any season
Figure BDA0002281756080000115
The value can be calculated by equation (2).
(7) Electric vehicle load space scheduling constraint:
Figure BDA0002281756080000116
Figure BDA0002281756080000117
in the formula, Bs,i,k,jIs composed of
Figure BDA0002281756080000118
And
Figure BDA0002281756080000119
in a unified characterization format. In the spatial dispatching process of the electric vehicle load, any electric vehicle can only select one charging station to complete charging, as shown in formula (28). In addition, considering the convenience of owners of electric vehicles, the electric vehicles are considered to be only capable of receiving short-distance scheduling, and the upper limit of the distance is dlimThis is shown in formula (29).
(8) Characterization of electric vehicle charging station load:
Figure BDA00022817560800001110
in the formula (I), the compound is shown in the specification,
Figure BDA00022817560800001111
season of expressionAnd the typical day of the section s takes the node i as a destination, and the staying period of the section s comprises the electric vehicle serial number set of the time section t.
(9) Charging pile installation quantity constraint:
Figure BDA00022817560800001112
for any electric vehicle charging station, the number of charging piles installed in the station needs to meet the charging requirement of electric vehicles on any time section.
By combining the objective function and the constraint condition described in this section, a joint configuration model of the distributed power supply and the electric vehicle charging station considering the spatial schedulable characteristic of the charging load can be obtained, as shown in formula (32). The model has a linear objective function, and most of the constraints are linear equality and inequality, and only equation (22) is a quadratic equality constraint.
Figure BDA0002281756080000121
Optionally, step four includes processing the optimization model using a second order cone relaxation technique.
The relaxation process for the optimization model is performed in two steps.
Step 1: the equal sign in the formula (22) is relaxed to be more than or equal to the equal sign, as shown in the formula (33).
Figure BDA0002281756080000122
The step is equivalent processing, the distribution condition of the optimal solution of the optimization model cannot be influenced, and the equivalence can be proved by a back-up method. The right part of the formula (33) above or equal to is denoted by
Figure BDA0002281756080000123
Step 2: introducing an auxiliary variable Au, and characterizing equation (33) as a second order cone constraint and a linear constraint, as shown in equations (34) and (35):
Figure BDA0002281756080000124
Au＝1 (35)
the constructed combined configuration model of the distributed power supply and the electric vehicle charging station is converted into a standard mixed integer second-order cone planning model, which comprises a linear objective function, linear equality constraint, inequality constraint and second-order cone constraint, and the specific form is shown as a formula (36):
Figure BDA0002281756080000131
the above embodiments are merely illustrative of the technical ideas of the present invention, and the description thereof is specific and detailed, but the scope of the present invention should not be limited thereby, and any modifications made on the basis of the technical ideas proposed by the present invention are within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (5)

1. A power supply and charging station configuration method based on charging load spatial schedulable characteristics is characterized by comprising the following steps:
step one, collecting historical operation data of all parts required in the system, and respectively constructing a photovoltaic power station output model, a micro gas turbine output model and an electric automobile load model;
step two, the lowest annual social total cost of the distributed power supply and the electric vehicle charging station is taken as a target function, and the investment construction cost, the operation maintenance cost, the network loss cost, the fuel cost, the carbon emission cost and the electricity purchasing cost are taken as constraint conditions of the target function;
step three, representing the relation among all state quantities in the system by using a linearized Distflow power flow equation, and taking an equivalent load, a branch current, a voltage amplitude, a distributed power supply capacity discrete type and an output equation as constraint conditions;
and step four, processing the branch current constraint by adopting a second-order cone relaxation technology, introducing an auxiliary variable to represent the branch current constraint as a second-order cone constraint and a linear constraint, processing the optimization model, and converting the optimization model into a mixed integer second-order cone programming model for solving.
2. The method of claim 1, wherein the constructing the photovoltaic power plant output model comprises:
the piecewise function shown in the formula (1) is used for representing the relation between the active power output of the photovoltaic power station and the solar illumination intensity,
Figure RE-FDA0002343687340000011
wherein, PsRepresenting the active power output, P, of the photovoltaic plant at a solar irradiance of ss-ratedAnd sratedRespectively representing rated power and rated illumination intensity of the photovoltaic power station;
when the solar illumination intensity is s, the reactive power regulation range of the photovoltaic power station is shown as a formula (2),
Figure RE-FDA0002343687340000021
wherein Q issRepresenting the reactive power output of the photovoltaic power station when the solar illumination intensity is S, SPV-ratedIs the capacity of the photovoltaic inverter.
3. The power supply and charging station configuration method based on spatial schedulable characteristics of charging load of claim 1, wherein said constructing a micro gas turbine output modeling comprises:
the micro gas turbine is used as a pure active power source which can be completely dispatched, and the actual output of the micro gas turbine is determined by a dispatching scheme of a power distribution system operator in a corresponding rated power range; the active power output range of the micro gas turbine is shown as formula (3):
0≤PMT≤SMT-rated(3)
wherein, PMTIs the active power output, S, of the micro gas turbineMT-ratedThe installed capacity of the micro gas turbine.
4. The power supply and charging station configuration method based on spatial schedulable characteristics of charging load according to claim 1, wherein said constructing the electric vehicle load model comprises:
use of EVkIndicating the electric vehicle with number k, EVkThe time of occupying the charging pile in the charging process
Figure RE-FDA0002343687340000022
Represented by formula (4):
Figure RE-FDA0002343687340000023
therein, SOCkIs EVkState of charge, CapkIs EVkThe capacity of the battery of (a) is,
Figure RE-FDA0002343687340000024
is EVkThe expected residence time of the gas mixture in the reactor,
Figure RE-FDA0002343687340000025
the rated charging power of the electric automobile is obtained.
5. The power supply and charging station configuration method based on spatial schedulable characteristics of charging load according to claim 1, wherein said minimizing annual total social costs of distributed power supplies associated with electric vehicle charging stations as an objective function comprises:
comprehensively considering the concerns of all interest bodies, the lowest annual social total cost related to the distributed power supply and the electric vehicle charging station is taken as an objective function, and the annual investment construction cost is specifically includedCIAnnual operating and maintenance costs CO&MAnnual fuel cost for micro gas turbines CFAnnual carbon emission cost of micro gas turbines CCAnnual power purchasing cost C to superior power gridPAnnual system loss charge CLAnd extra traffic cost C generated by scheduling electric automobile load every yearTAnd (3) waiting for 7 aspects; the specific form of the objective function is shown in equation (5):
min Cost＝CI+CO&M+CF+CC+CP+CL+CT(5)。
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CN111682538A (en) * 2020-06-29 2020-09-18 华北电力大学 Charging demand management method and system considering space-time characteristics
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