CN112952880B - Electric vehicle grid-connected point navigation and charging and discharging control method for improving new energy consumption - Google Patents

Electric vehicle grid-connected point navigation and charging and discharging control method for improving new energy consumption Download PDF

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CN112952880B
CN112952880B CN202110293295.6A CN202110293295A CN112952880B CN 112952880 B CN112952880 B CN 112952880B CN 202110293295 A CN202110293295 A CN 202110293295A CN 112952880 B CN112952880 B CN 112952880B
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charging
grid
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CN112952880A (en
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杨娜
刘亚南
朱刘柱
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Economic and Technological Research Institute of State Grid Anhui 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • 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]
    • 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
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • 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

Abstract

The invention relates to an electric vehicle grid-connected point navigation and charging and discharging control method for improving new energy consumption. The invention comprises the following steps: acquiring related data of an electric vehicle charging node; establishing an electric automobile charging node optimization model; and (5) grid-connected charging and discharging control optimization of the charging nodes. The method effectively controls the grid-connected point navigation and the charging and discharging of the electric automobile based on the collaborative analysis of the new energy consumption, so that the charging and discharging of the electric automobile on each node can be promoted to select proper time and place for charging according to the different electricity prices of the grid-connected nodes, the adjusting capability of a power system is improved, the new energy consumption is promoted, and the mobile energy storage charging and discharging enthusiasm of the electric automobile is mobilized.

Description

Electric vehicle grid-connected point navigation and charging and discharging control method for improving new energy consumption
Technical Field
The invention relates to the technical field of electric vehicle grid-connected points, in particular to an electric vehicle grid-connected point navigation and charging and discharging control method for improving new energy consumption.
Background
At present, China is the world with the largest scale and fastest development of clean energy, and a high proportion of clean energy becomes the outstanding characteristic of the development of the power system in China. Along with large-scale grid connection of intermittent clean energy such as wind power, photovoltaic and the like, the problems of power supply and demand and clean energy consumption are obvious, and a severe 'three-abandon' phenomenon exists in part of regions, so that the sustainable development of a power system in China is restricted.
In the prior art, an electric vehicle ordered charging control method (CN107745650B) based on peak-valley time-of-use electricity price achieves the purpose of optimizing charging cost and suppressing the peak-valley difference of the load of the power grid by establishing an optimization model with the aim of minimizing the total charging cost of the electric vehicle and the peak-valley difference of the load of the power distribution network, and ensures the economy of users and the stability of the power grid. However, the advanced peak regulation of the whole system thermal power generating unit under the new energy grid-connected consumption and the peak regulation pressure and peak regulation cost aggravation of the start-stop peak regulation are not considered, when the peak regulation capability is difficult to guarantee that the new energy is completely consumed by the power grid, the loss caused by the power abandon of the new energy can cause the peak regulation opportunity cost to be greatly increased, and if a low-load advanced peak regulation steering start-stop peak regulation mode is adopted, although the peak regulation amplitude can be increased from less than 50% to 100%, the start-stop cost of the unit is higher, the economical efficiency is poor, and therefore the comprehensive economical efficiency of the power grid advanced peak regulation, the start-stop peak regulation and the new energy power abandon loss needs to be comprehensively considered.
The elastic characteristic of electric automobile charging and discharging can be applied to providing flexible resource allocation, and supply and demand imbalance and power grid blockage are relieved. The charging and discharging of the electric automobile on each node in the electric power spot market are different according to the price of electricity of the respective grid-connected node, so that the electric automobile is promoted to be charged at proper time and place, the adjusting capacity of an electric power system is improved, the consumption of new energy is promoted, and the mobile energy storage charging and discharging enthusiasm of the electric automobile is mobilized.
How to put forward an electric automobile grid-connected navigation and charge-discharge optimization method, thereby relieving new energy wind and light abandonment, frequent start-stop of thermal power generating units and power grid blockage, and optimizing full-system electric automobile grid-connected access points and charge-discharge time sequences becomes a technical problem which needs to be solved urgently.
Disclosure of Invention
The invention aims to solve the defect that grid-connected navigation and charge-discharge optimization of an electric vehicle are difficult to realize to improve new energy consumption in the prior art, and provides a grid-connected point navigation and charge-discharge control method of the electric vehicle for improving new energy consumption to solve the problems.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a grid-connected point navigation and charge-discharge control method for improving new energy consumption of an electric vehicle comprises the following steps:
acquiring related data of the electric vehicle charging node: acquiring new energy wind and light output prediction, load prediction, electric vehicle response total amount prediction, power grid topology and power transmission capacity, thermal power start and stop, climbing, and output upper and lower limit parameter data;
establishing an electric automobile charging node optimization model: constructing an electric automobile charging node optimization model by using new energy at the power supply side and thermal power, grid side and charging node data;
and (3) grid-connected charging and discharging control optimization of the charging node: the method comprises the steps of solving an electric vehicle charging node optimization model by using electric vehicle charging node related data, solving the costs of the whole system whole-day wind abandon light of the operation day and the thermal power start-stop operation, the node marginal electricity price, the electric vehicle grid-connected navigation point and the charging and discharging space-time distribution navigation by using CPLEX programming, and performing control and adjustment.
The establishment of the electric automobile charging node optimization model comprises the following steps:
an optimization target model is established, so that the cost of electricity generation of new energy abandoned electricity and thermal power start-stop operation in a full-system electric power spot market is minimum, and the expression is as follows:
Figure BDA0002983243960000021
wherein: ct,iThe total cost of starting, no-load and operation of the thermal power generating unit i at the moment t;
curwt,wthe abandoned wind electric quantity of the wind power w at the time t is not negative;
wpendaty is a wind curtailment cost coefficient;
curpht,hthe photovoltaic power generation h is the photovoltaic power generation h at the time t, and is not negative;
hpenaly is a light abandon penalty cost coefficient;
llt,sthe load loss amount of the node s at the time t is non-negative;
voll is the power loss load value;
Figure BDA0002983243960000031
in formula (2): x is the number oft,iBinary variables, wherein the unit is 1 when in operation, and is 0 otherwise;
Csui,jstarting cost is given to the thermal power generating unit i after the thermal power generating unit i is shut down for j hours;
Cbi,bb, outputting and quoting the thermal power generating unit i in section b;
Cnolithe no-load cost of the thermal power generating unit i is calculated;
gibt,i,bb, outputting the quoted price of the section b of the thermal power generating unit i at the moment t, wherein the quoted price is not negative;
suct,i,jthe method comprises the following steps that (1) a thermal power generating unit i is started to be 1 after being shut down for j hours at the moment t, and otherwise, the value is 0; setting a power source side constraint expression:
setting a unit start-stop state constraint expression:
yt,i-zt,i＝xt,i-xt-1,i,t>T1 (3)
Figure BDA0002983243960000032
yt,i+zt,i≤1 (5)
in the formula (3-5): x is the number oft,iBinary variables, wherein the unit is 1 when in operation, and is 0 otherwise;
yt,ibinary variable, the unit is 1 when starting, otherwise, the unit is 0;
zt,ibinary variable, 1 when stopping, or 0 otherwise;
the constraint expression of starting and stopping of the unit at the initial moment is as follows:
Figure BDA0002983243960000033
Figure BDA0002983243960000034
Figure BDA0002983243960000035
in the formula (6-8): GTuminiConstraint on starting time of the unit i;
GTdminiconstraint on the shutdown time of the unit i;
Tguministarting a unit i with a minimum duration parameter;
Tgdministopping the unit i for a minimum time parameter;
Tguiniiis an initial time T1Starting up time length parameters of the unit i;
Figure BDA0002983243960000036
is an initial time T1The starting state of the unit is 1, otherwise, the starting state is 0;
setting a minimum start-stop duration constraint expression:
minimum boot time constraint:
Figure BDA0002983243960000037
minimum downtime duration constraint:
Figure BDA0002983243960000041
and (3) restraining the upper and lower limits of the unit output:
Figure BDA0002983243960000042
gt,i≥gmini×xt,i (12)
gibt,i,b≤gmaxi,b×xt,i (13)
in the formula (9-13): gt,iOutputting power for the thermal power generating unit i at the moment t, and carrying out non-negative operation;
unit climbing restraint:
and (3) uphill restriction at the time t: gt,i-gt-1,i≤ramui,t>T1 (14)
Initial uphill restraint:
Figure BDA0002983243960000043
and (3) uphill restriction at the time t: gt-1,i-gt,i≤ramdi,t>T1 (16)
Initial uphill restraint:
Figure BDA0002983243960000044
in the formulae (14 to 17): ramuiLimiting the unit i to ascend a slope;
ramdilimiting the unit i downhill;
g0,isetting an initial output parameter for the unit i;
the unit starting time period constraint expression is as follows:
Figure BDA0002983243960000045
Figure BDA0002983243960000046
in the formulae (18 to 19): j is the total J starting time periods of the unit;
the new energy power abandon constraint and wind abandon constraint are as follows: curwt,w≤gwt,w (20)
Abandon light restraint: curp (curr)t,p≤gpht,p (21)
In the formula (20-21): gw (g) wt,wWind power generation capacity at the moment t;
gpht,pthe photovoltaic power generation capacity at the moment t;
setting a power supply and demand balance expression of a source, a network, a load and a mobile storage at each moment t of an electric vehicle charging node S;
each node S receives the sum of thermal power output, wind power output after wind abandoning and photovoltaic power generation output after light abandoning, deducts the net outflow tide of the line connected with the node, takes the net charge-discharge power of the electric automobile into consideration, and balances the net charge-discharge power with the load supplied by the node:
Figure BDA0002983243960000051
setting a power grid side constraint expression:
power transmission line flow constraint:
Figure BDA0002983243960000052
and (3) current carrying capacity constraint of the power transmission line: -lcap ≦ pft,l≤lcap (24)
Phase angle constraint: -pi ≦ θt,s≤π (25)
In the formulae (23-25): pf (p) oft,lIs the power flow of the transmission line l at the moment t;
Blis the susceptance of line l;
θt,sis the phase angle of the node s at the time t;
the lcap is the maximum transmission capacity parameter of the line;
lmapl,sthe circuit number multiplied by the node number matrix of L multiplied by S dimension, the sending end of the circuit is 1, the receiving end is-1:
Figure BDA0002983243960000053
setting a charge-discharge constraint expression of the electric automobile:
and (3) constraint of charging power: pcs,t≤γ×NEVs×SOCMax (27)
And (3) discharging and charging power constraint: pds,t≤γ×NEVs×SOCMaxs (28)
And (3) state of charge constraint:
Figure BDA0002983243960000054
SOCs,t≤NEVs×SOCMaxs (30)
Figure BDA0002983243960000061
Figure BDA0002983243960000062
SOCs,t≥NEVs×SOC0s (33)
Pcs,t≥0,Pds,t≥0 (34)
in the formula: SOCMaxsThe maximum grid-connected charge state of each electric automobile of the node s is obtained;
SOC0sthe initial grid-connected charge state of each electric automobile is the node s;
NEVsthe number of electric vehicles is connected to the grid for the node s;
SOCs,tthe node s at the time t is the charging state of the grid-connected electric vehicle;
Pcs,tthe charging quantity of the grid-connected electric automobile is a node s at the time t;
Pds,tthe discharge capacity of the electric automobile is connected to the grid for a node s at the time t;
ηcfor charging efficiency, ηc＝0.95；
ηdFor discharge efficiency, ηd＝0.9；
Nmax is the prediction of the maximum grid-connected quantity of the system electric automobile.
The grid-connected charging and discharging control optimization of the charging node comprises the following steps:
reading a data file, wherein the data file contains initial data required by a model;
the optimization model is programmed using GAMS/CPLEX to obtain a gms model file of the optimization problem, the model file including: integrating, parameter, variable, objective function and constraint, and programming the electric vehicle charging node optimization model in GAMS/CPLEX;
inputting the gms model file into a CPLEX solver for optimal solution,
for the minimum mixed integer discrete optimization problem, based on the mixed integer optimization MIP original problem, reducing 0 and 1 binary variable constraints, then training a model solution, changing the original model into a relaxation optimization model S, training a linear relaxation optimization model S solution, if the S optimal solution does not accord with the integer condition of MIP, the optimal objective function value of S must be the lower boundary of the optimal objective function value of MIP, and marking as O;
the objective function value of any feasible solution of the MIP is an upper bound O of the optimal objective function value, the feasible region of the MIP is divided into sub-regions, the lower bound O is gradually increased and the upper bound O is gradually decreased, and finally the optimal objective function value O is obtained;
the percentage GAP formed by the upper and lower bounds is used as an inspection standard of the quality of the target solution, and the optimization solver finds a better solution along with the solution, so that the GAP is reduced;
the optimized running log is divided into two parts, the first part displays solver parameter setting, the second part is a detailed log of each iteration process, Node represents the number of nodes, if yes, an integer feasible solution is represented, and + represents that the solution is found through a heuristic method; nodes left represents the number of reserved Nodes; objective represents an Objective function value; IInf represents the number of integer variables having a fractional value; best Integer represents the Best Integer solution at present; best Bound represents the Best relaxation solution; ItCnt represents the number of iterations; gap shows the current optimality;
the relative optimality of the MIP problem is to train parameters of a CPLEX solver to improve solving precision and optimize large-scale mixed integers, the gap parameter in the solver is used as an inspection standard of target solution quality, the gap in the solver is a relative value, namely the gap is | BP-BF | where BP is an optimal solution obtained after the integers are serialized, and BF is a solution obtained when the integers are solved by the solver at present; when solving, the gap is set to be 0.5 percent, namely, the option optcr is 0.005, whether the optimal solution under the relatively large gap can be obtained is judged, and if the optimal solution exists, the gap parameter training is adjusted downwards until the new energy has no electricity abandonment amount;
and (3) controlling grid-connected charging and discharging of the charging node: after the optimization solution, the electric vehicle charging node obtains node grid-connected information, and grid-connected charging and discharging control is performed according to the node grid-connected information.
Advantageous effects
Compared with the prior art, the grid-connected point navigation and charge-discharge control method for the electric vehicle for improving the new energy consumption effectively controls the grid-connected point navigation and charge-discharge of the electric vehicle based on the synergistic analysis of the new energy consumption, so that the charge-discharge of the electric vehicle on each node can be promoted to select proper time and place for charging according to different electricity prices of respective grid-connected nodes, the regulation capacity of a power system is improved, the new energy consumption is promoted, and the mobile energy storage charge-discharge enthusiasm of the electric vehicle is mobilized.
The invention provides a source-network-load-transfer-storage cooperative optimization method in a power spot market, which is used for realizing the following steps: the new energy source reduces wind and light abandonment, and the traditional thermal power start-stop and operation cost is minimum; the network considers the power grid constraint, and the power grid blockage is relieved; the load is the interaction of the load side and the source network side; the storage is the electric automobile, and the mobile energy storage is low in charging and high in discharging. Meanwhile, the method promotes the consumption of new energy, promotes the transformation of green carbon-reducing energy, optimizes the grid-connected point navigation and charging and discharging time sequence of the electric automobile on the aspect of space-time distribution, systematically optimizes and configures the distribution of flexible energy storage resources, and ensures the safe operation of a power grid and the long-term healthy and ordered operation of the power market.
Drawings
FIG. 1 is a sequence diagram of the method of the present invention;
FIG. 2 is a schematic diagram of the method of the present invention;
FIG. 3 is a log diagram of a CPLEX solver optimization process in the prior art;
FIG. 4 is a comparison graph of cost reduction and efficiency enhancement before and after grid-connected navigation control in practical application of the present invention;
FIGS. 5-1 and 5-2 are node electricity price comparison diagrams of an electric vehicle grid-connected point in practical application of the present invention;
FIGS. 6-1 and 6-2 are comparative diagrams of the state of charge at each moment of each grid-connected point of the system in practical application of the present invention;
FIGS. 7-1 and 7-2 are timing charts comparing the charging amounts of electric vehicles at the grid-connected points of the system in practical application of the present invention;
8-1, 8-2 are timing comparison graphs of the discharge capacity of the electric automobile at each grid-connected point of the system in practical application of the invention;
FIG. 9 shows the quantity and benefits of electric vehicles connected to the grid at each grid connection point of the system in practical application of the present invention.
Detailed Description
So that the manner in which the above recited features of the present invention can be understood and readily understood, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings, wherein:
as shown in fig. 1 and fig. 2, the method for controlling navigation and charging and discharging of the grid-connected point of the electric vehicle for improving new energy consumption according to the present invention includes the following steps:
step one, acquiring related data of an electric automobile charging node: acquiring new energy wind and light output prediction, load prediction, electric vehicle response total amount prediction, power grid topology and power transmission capacity, thermal power start and stop, climbing and output parameter data.
Secondly, establishing an electric automobile charging node optimization model: and constructing an electric automobile charging node optimization model by using the new energy at the power supply side and the thermal power, grid side and charging node data. Comprehensive optimization enables the total system new energy electricity abandonment and thermal power start-stop operation electricity generation cost to be minimum, wherein a penalty cost coefficient is given to the new energy electricity abandonment; in the comprehensive cost of thermal power start-stop, no-load and operation, the practical conditions that the cost for restarting each unit is different after different shutdown time lengths are considered, the operation cost for each unit operating in different power output sections is different, the minimum continuous operation and the minimum start-stop time of each unit are different and the like are considered, binary discrete variable constraint control is respectively carried out on the start-stop and operation multi-states of the units, the minimum start-stop time length and the start time period of the units are constrained on the start-stop of the units, the upper and lower power output limit intervals are constrained on the output of the units, the ramp of the units is constrained on the downslope, the source-network-load-transfer storage power supply and demand balance is carried out on all nodes in the system at every moment, and the power flow constraint and the current carrying capacity constraint are carried out on a power transmission line; charging and discharging power constraint, charge state constraint and the like are carried out on the optional grid-connected points of the electric automobile. The method comprises the following specific steps:
(1) an optimization target model is established, so that the cost of electricity generation of new energy abandoned electricity and thermal power start-stop operation in a full-system electric power spot market is minimum, and the expression is as follows:
Figure BDA0002983243960000091
wherein: ct,iThe total cost of starting, no-load and operation of the thermal power generating unit i at the moment t;
curwt,wthe abandoned wind electric quantity of the wind power w at the time t is not negative;
wpendaty is a wind curtailment cost coefficient;
curpht,hthe photovoltaic power generation h is the photovoltaic power generation h at the time t, and is not negative;
hpenaly is a light abandon penalty cost coefficient;
llt,sthe load loss amount of the node s at the moment t is nonnegative;
voll is the power loss load value;
Figure BDA0002983243960000092
in formula (2): x is the number oft,iBinary variables, wherein the unit is 1 when in operation, and is 0 otherwise;
Csui,jstarting cost is given to the thermal power generating unit i after the thermal power generating unit i is shut down for j hours;
Cbi,bb, outputting and quoting the thermal power generating unit i in section b;
Cnolithe no-load cost of the thermal power generating unit i is calculated;
gibt,i,bb, outputting the quoted price of the section b of the thermal power generating unit i at the moment t, wherein the quoted price is not negative;
suct,i,jand (3) starting the thermal power generating unit i to be 1 after stopping j hours at the moment t according to the binary variable, otherwise, starting the thermal power generating unit i to be 0.
(2) Setting a power source side constraint expression:
A1) setting a constraint expression of the start-stop state of the unit:
yt,i-zt,i＝xt,i-xt-1,i,t>T1 (3)
Figure BDA0002983243960000101
yt,i+zt,i≤1 (5)
in the formula (3-5): x is the number oft,iBinary variables, wherein the unit is 1 when in operation, and is 0 otherwise;
yt,ibinary variable, the unit is 1 when starting, otherwise, the unit is 0;
zt,ibinary variable, 1 when stopping, or 0 otherwise;
A2) the constraint expression of starting and stopping of the unit at the initial moment is as follows:
Figure BDA0002983243960000102
Figure BDA0002983243960000103
Figure BDA0002983243960000104
in the formula (6-8): GTuminiConstraint on starting time of the unit i;
GTdminiconstraint on the shutdown time of the unit i;
Tguministarting a set i with a minimum duration parameter;
Tgdministopping the unit i for a minimum time parameter;
Tguiniiis an initial time T1Starting up time length parameters of the unit i;
Figure BDA0002983243960000105
is an initial time T1The starting state of the unit is 1, otherwise, the starting state is 0;
A3) setting a minimum start-stop duration constraint expression:
minimum boot time constraint:
Figure BDA0002983243960000106
minimum downtime duration constraint:
Figure BDA0002983243960000107
and (3) restraining the upper and lower limits of the unit output:
Figure BDA0002983243960000108
gt,i≥gmini×xt,i (12)
gibt,i,b≤gmaxi,b×xt,i (13)
in the formula (9-13): gt,iOutputting power for the thermal power generating unit i at the moment t, and carrying out non-negative operation;
A4) unit climbing restraint:
and (3) uphill restriction at the time t: gt,i-gt-1,i≤ramui,t>T1 (14)
Initial upslope restraint:
Figure BDA0002983243960000111
and (3) uphill restriction at the time t: gt-1,i-gt,i≤ramdi,t>T1 (16)
Initial uphill restraint:
Figure BDA0002983243960000112
in the formulae (14 to 17): ramuiLimiting the unit i to ascend a slope;
ramdilimiting the unit i downhill;
g0,isetting an initial output parameter for the unit i;
A5) the unit starting time period constraint expression is as follows:
Figure BDA0002983243960000113
Figure BDA0002983243960000114
in the formulae (18-19): j is the total J starting time periods of the unit;
A6) new energy electricity abandoning constraint
Wind abandon restraint: curwt,w≤gwt,w (20)
Abandon light restraint: curp (curr)t,p≤gpht,p (21)
In the formula (20-21): gw (g) wt,wWind power generation capacity at the moment t;
gpht,pand the photovoltaic power generation capacity at the moment t.
(3) Setting a power supply and demand balance expression of a source, a network, a load and a mobile storage at each moment t of an electric vehicle charging node S;
each node S receives the sum of thermal power output, wind power output after wind abandoning and photovoltaic power generation output after light abandoning, deducts the net outflow tide of the line connected with the node, takes the net charge-discharge power of the electric automobile into consideration, and balances the net charge-discharge power with the load supplied by the node:
Figure BDA0002983243960000115
(4) setting a power grid side constraint expression:
power transmission line flow constraint:
Figure BDA0002983243960000116
and (3) current carrying capacity constraint of the power transmission line: -lcap ≦ pft,l≤lcap (24)
Phase angle constraint: -pi ≦ θt,s≤π (25)
In the formulae (23-25): pft,lIs the power flow of the transmission line l at the moment t;
Blsusceptance for line l；
θt,sIs the phase angle of the node s at the time t;
the lcap is the maximum transmission capacity parameter of the line;
lmapl,sthe circuit number multiplied by the node number matrix of L multiplied by S dimension, the sending end of the circuit is 1, the receiving end is-1:
Figure BDA0002983243960000121
(5) setting a charge-discharge constraint expression of the electric automobile:
and (3) constraint of charging power: pcs,t≤γ×NEVs×SOCMax (27)
And (3) discharge power constraint: pds,t≤γ×NEVs×SOCMaxs (28)
And (3) state of charge constraint:
Figure BDA0002983243960000122
SOCs,t≤NEVs×SOCMaxs (30)
Figure BDA0002983243960000123
Figure BDA0002983243960000124
SOCs,t≥NEVs×SOC0s (33)
Pcs,t≥0,Pds,t≥0 (34)
in the formula: SOCMaxsThe maximum grid-connected charge state of each electric automobile of the node s is obtained;
SOC0sthe initial grid-connected charge state of each electric automobile is the node s;
NEVsthe number of electric vehicles is connected to the grid for the node s;
SOCs,tgrid-connected electric vehicle charge state for t moment node s；
Pcs,tThe charging quantity of the grid-connected electric automobile is a node s at the time t;
Pds,tthe discharge quantity of the electric automobile is connected to the grid for a node s at the moment t;
ηcfor charging efficiency, ηc＝0.95；
ηdFor discharge efficiency, ηd＝0.9；
Nmax is the prediction of the maximum grid-connected quantity of the system electric automobile.
And thirdly, grid-connected charging and discharging control optimization of the charging nodes. The method comprises the steps of solving an electric vehicle charging node optimization model by using electric vehicle charging node related data, solving the costs of the whole system whole-day wind abandon light of the operation day and the thermal power start-stop operation, the node marginal electricity price, the electric vehicle grid-connected navigation point and the charging and discharging space-time distribution navigation by using CPLEX programming, and performing control and adjustment. The method comprises the following specific steps:
(1) a data file is read, which contains the initial data required by the model. Taking the reading of the topological data parameters (26) of the power grid network as an example:
$call＝xls2gms r＝lmapl,s！a1:bv121 i＝input.xlsx o＝lmapl,s.inc
$include lmapl,s.inc。
(2) the optimization model is programmed using GAMS/CPLEX to obtain a gms model file of the optimization problem, the model file including: and integrating, parameters, variables, objective functions and constraints, and programming the electric vehicle charging node optimization model in GAMS/CPLEX.
Programming the above equation (1) -equation (34) in GAMS, taking the constraint of equation (29) as an example: constESS (s, t) $ socmax (s.). SOC (s, t) ═ e ═ (ness(s) × SOC0(s)) $ (ord) (t) ═ 1) + SO C (s, t-1) $ (ord (t) >1) + Pc (s, t) × eta _ C-Pd (s, t)/eta _ d.
(3) Inputting the gms model file into a CPLEX solver for optimal solution,
for the minimum mixed integer discrete optimization problem, based on the mixed integer optimization MIP original problem, reducing 0 and 1 binary variable constraints, then training a model solution, changing the original model into a relaxation optimization model S, training a linear relaxation optimization model S solution, if the S optimal solution does not accord with the integer condition of MIP, the optimal objective function value of S must be the lower boundary of the optimal objective function value of MIP, and marking as O;
the objective function value of any feasible solution of the MIP is an upper bound O of the optimal objective function value, the feasible region of the MIP is divided into sub-regions, the lower bound O is gradually increased and the upper bound O is gradually decreased, and finally the optimal objective function value O is obtained;
the percentage GAP formed by the upper and lower bounds is used as an inspection standard of the quality of the target solution, and the optimization solver finds a better solution along with the solution, so that the GAP is reduced;
the optimized running log is divided into two parts, the first part displays solver parameter setting, the second part is a detailed log of each iteration process, Node represents the number of nodes, if yes, an integer feasible solution is represented, and + represents that the solution is found through a heuristic method; nodes left represents the number of reserved Nodes; objective represents an Objective function value; IInf represents the number of integer variables having a fractional value; best Integer represents the Best Integer solution at present; best Bound represents the Best relaxation solution; ItCnt denotes the number of iterations; gap shows the current optimality;
the relative optimality of the MIP problem is to train parameters of a CPLEX solver to improve solving precision, and to perform large-scale mixed integer optimization, as shown in table 1, a gap parameter in the solver is used as an inspection standard of target solution quality, the gap in the solver is a relative value, that is, the gap is | BP-BF |/| BF |, where BP is an optimal solution obtained after integer serialization, and BF is a solution obtained when an integer of the current solver is solved; when the solution is carried out, the gap is set to be 0.5%, namely, the option optcr is 0.005, whether the optimal solution under the relatively large gap can be obtained or not is judged, if the optimal solution exists, the gap parameter training is adjusted downwards until no electricity abandonment amount of the new energy exists, and the optimization process log is shown in a figure 3.
TABLE 1 gap parameter training till no electricity abandonment in new energy contrast table
Figure BDA0002983243960000141
(4) And (3) controlling grid-connected charging and discharging of the charging node: after the optimization solution, the electric vehicle charging node obtains node grid-connected information, and grid-connected charging and discharging control is performed according to the node grid-connected information.
In practical application, the navigation control optimizes and outputs the result: after navigation optimization, the electric automobile is connected with a grid at 14 nodes in total, namely s201, s202, s204, s205, s206, s209, s306, s308, s310, s312, s313, s320, s323 and s 325. Compared before and after grid-connected navigation control, the total cost of new energy and thermal power generation is reduced by 0.46%, the cost of new energy electricity abandonment is reduced by 100%, no wind abandonment is performed, the cost of thermal power starting and stopping is reduced by 0.11%, the cost of thermal power operation is reduced by 0.36%, the number of blocking strips of a transmission line is reduced by 41%, and the cost reduction and efficiency improvement comparison is shown in figure 4. The node electricity prices of the electric vehicle grid-connected points are shown in each node electricity price of the central area 2 in fig. 5-1 and each node electricity price of the south area 3 in fig. 5-2. The electric charge quantity of each grid-connected point at each time point is controlled in a navigation mode, the electric charge quantity of each point in the film area 2 in fig. 6-1 and the electric charge quantity of each point in the film area 3 in fig. 6-2 are shown, the charging control of the electric automobile at each grid-connected point is controlled in the charging control of each point in the film area 2 in fig. 7-1 and the charging control of each point in the film area 3 in fig. 7-2 are shown, the discharging control of each point in the film area 2 in fig. 8-1 and the discharging control of each point in the film area 3 in fig. 8-2 are shown in a discharging control diagram of each grid-connected point of the electric automobile, and the grid-connected quantity control and the benefit of each point of 14 grid-connected points are shown in fig. 9. Taking an electric vehicle owner as an example, firstly, grid connection is carried out at an S202 node, and charging and discharging instructions of the S202 node are controlled according to an S202 time sequence curve in the graphs of 7-1 and 8-1.
The electric vehicle grid-connected point and charging and discharging navigation information can be published through carriers such as automobile navigation and electric power company public app: service reminding, tomorrow weather, temperature and wind power level, recommending an electric vehicle grid-connected point and a charging and discharging time sequence, and improving the consumption contribution degree of green new energy. Therefore, the elastic characteristic aiming at the charging and discharging of the electric automobile can be applied to the characteristic of providing flexible resource allocation, and the power grid blockage at the overload moment and place can be relieved. The charging and discharging of the electric automobile on each bus node can carry out corresponding excitation or punishment according to the electricity price of the respective grid-connected node, so that the electric automobile is enabled to avoid a blocking peak according to the electricity price of the node, proper time and place are selected for charging, the adjusting capacity of a power system is improved, the consumption of new energy resources such as wind power and photovoltaic is promoted, and the mobile energy storage charging and discharging enthusiasm of the electric automobile is mobilized.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (2)

1. A grid-connected point navigation and charge-discharge control method for improving new energy consumption of an electric vehicle is characterized by comprising the following steps:
11) acquiring related data of the electric vehicle charging node: acquiring new energy wind and light output prediction, load prediction, electric vehicle response total amount prediction, power grid topology and power transmission capacity, thermal power start and stop, climbing, and output upper and lower limit parameter data;
12) establishing an electric automobile charging node optimization model: constructing an electric automobile charging node optimization model by using new energy at the power supply side and thermal power, grid side and charging node data;
the establishment of the electric automobile charging node optimization model comprises the following steps:
121) an optimization target model is established, so that the cost of electricity generation of new energy abandoned electricity and thermal power start-stop operation in a full-system electric power spot market is minimum, and the expression is as follows:
Figure FDA0003561826580000011
wherein: ct,iThe total cost of starting, no-load and operation of the thermal power generating unit i at the moment t;
curwt,wthe abandoned wind electric quantity of the wind power w at the time t is not negative;
wpendaty is a wind curtailment cost coefficient;
curpht,hthe photovoltaic power generation h is the photovoltaic power generation h at the time t, and is not negative;
hpenaly is a light abandon penalty cost coefficient;
llt,sthe load loss amount of the node s at the time t is non-negative;
voll is the power loss load value;
Figure FDA0003561826580000012
in formula (2): x is the number oft,iBinary variables, wherein the unit is 1 when in operation, and is 0 otherwise;
Csui,jstarting cost is given to the thermal power generating unit i after j hours of shutdown;
Cbi,bb, outputting and quoting the thermal power generating unit i in section b;
Cnolithe no-load cost of the thermal power generating unit i is calculated;
gibt,i,bb, outputting the quoted price of the section b of the thermal power generating unit i at the moment t, wherein the quoted price is not negative;
suct,i,jthe method comprises the following steps that (1) a thermal power generating unit i is started to be 1 after being shut down for j hours at the moment t, and otherwise, the value is 0;
122) setting a power source side constraint expression:
1221) setting a unit start-stop state constraint expression:
yt,i-zt,i＝xt,i-xt-1,i,t＞T1 (3)
Figure FDA0003561826580000021
yt,i+zt,i≤1 (5)
in the formula (3-5): x is the number oft,iBinary variables, wherein the unit is 1 when running, and is 0 otherwise;
yt,ibinary variable, the unit is 1 when starting, otherwise, the unit is 0;
zt,ibinary variable, stopThe machine time is 1, otherwise, the machine time is 0;
1222) the constraint expression of starting and stopping of the unit at the initial moment is as follows:
Figure FDA0003561826580000022
Figure FDA0003561826580000023
Figure FDA0003561826580000024
in the formula (6-8): GTu miniConstraint on starting time of the unit i;
GTd miniconstraint on the shutdown time of the unit i;
Tgu ministarting a unit i with a minimum duration parameter;
Tgd ministopping the unit i for a minimum time parameter;
Tguiniiis an initial time T1The starting time length parameter of the unit i;
Figure FDA0003561826580000025
is an initial time T1The starting state of the unit is 1, otherwise, the starting state is 0;
1223) setting a minimum start-stop duration constraint expression:
minimum boot time constraint:
Figure FDA0003561826580000026
minimum downtime duration constraint:
Figure FDA0003561826580000027
and (3) restraining the upper and lower limits of the unit output:
Figure FDA0003561826580000028
gt,i≥gmini×xt,i (12)
gibt,i,b≤gmaxi,b×xt,i (13)
in the formula (9-13): gt,iOutputting power for the thermal power generating unit i at the moment t, and carrying out non-negative operation;
224) unit climbing restraint:
and (3) uphill restriction at the time t: g is a radical of formulat,i-gt-1,i≤ramui,t＞T1 (14)
Initial uphill restraint: gT1,i-g0,i≤ramui,t＝T1 (15)
And (3) downhill restraining at time t: gt-1,i-gt,i≤ramdi,t＞T1 (16)
Initial downhill restraint: g0,i-gT1,i≤ramdi,t＝T1 (17)
In the formulae (14 to 17): ramu (r) aiLimiting the unit i to ascend a slope;
ramdilimiting the unit i downhill;
g0,isetting an initial output parameter for the unit i;
1225) the unit starting time period constraint expression is as follows:
Figure FDA0003561826580000031
Figure FDA0003561826580000032
in the formulae (18-19): j is the total J starting time periods of the unit;
1226) new energy electricity abandoning constraint
Wind abandon restraint: curwt,w≤gwt,w (20)
Abandon light restraint: curp (curr)t,p≤gpht,p (21)
In the formula (20-21): gw (g) wt,wWind power generation capacity at the moment t;
gpht,pthe photovoltaic power generation capacity at the moment t;
123) setting a power supply and demand balance expression of a source, a network, a load and a mobile storage at each moment t of an electric vehicle charging node S;
each node S receives the sum of thermal power output, wind power output after wind abandoning and photovoltaic power generation output after light abandoning, deducts the net outflow tide of the line connected with the node, takes the net charge-discharge power of the electric automobile into consideration, and balances the net charge-discharge power with the load supplied by the node:
Figure FDA0003561826580000041
124) setting a power grid side constraint expression:
power transmission line flow constraint:
Figure FDA0003561826580000042
and (3) current carrying capacity constraint of the power transmission line: -lcap ≦ pft,l≤lcap (24)
Phase angle constraint: -pi ≦ θt,s≤π (25)
In the formulae (23-25): pft,lIs the power flow of the transmission line l at the moment t;
Blis the susceptance of line l;
θt,sis the phase angle of the node s at the time t;
the lcap is the maximum transmission capacity parameter of the line;
lmapl,sthe circuit number multiplied by the node number matrix of L multiplied by S dimension, the sending end of the circuit is 1, the receiving end is-1:
Figure FDA0003561826580000043
125) setting a charge-discharge constraint expression of the electric automobile:
and (3) constraint of charging power: pcs,t≤γ×NEVs×SOCMaxs (27)
And (3) discharge power constraint: pds,t≤γ×NEVs×SOCMaxs， (28)
And (3) state of charge constraint:
Figure FDA0003561826580000051
Figure FDA0003561826580000052
SOCs,t≤NEVs×SOCMaxs (30)
Figure FDA0003561826580000053
Figure FDA0003561826580000054
SOCs,t≥NEVs×SOC0s (33)
Pcs,t≥0,Pds,t≥0 (34)
in the formula: SOCMaxsThe maximum grid-connected charge state of each electric automobile of the node s is obtained;
SOC0sthe initial grid-connected charge state of each electric automobile is the node s;
NEVsthe number of electric vehicles is connected to the grid for the node s;
SOCs,tthe node s at the time t is the charging state of the grid-connected electric vehicle;
Pcs,tthe charging quantity of the grid-connected electric automobile is a node s at the time t;
Pds,tthe discharge capacity of the electric automobile is connected to the grid for a node s at the time t;
ηcfor charging efficiency, ηc＝0.95；
ηdFor discharge efficiency, etad＝0.9；
The NEV max is the prediction of the maximum grid-connected quantity of the system electric automobile;
13) and (3) grid-connected charging and discharging control optimization of the charging nodes: the method comprises the steps of solving an electric vehicle charging node optimization model by using electric vehicle charging node related data, solving the costs of the whole system whole-day wind abandon light of the operation day and the thermal power start-stop operation, the node marginal electricity price, the electric vehicle grid-connected navigation point and the charging and discharging space-time distribution navigation by using CPLEX programming, and performing control and adjustment.
2. The grid-connected point navigation and charging and discharging control method for the electric vehicle for improving the new energy consumption according to claim 1, wherein the grid-connected charging and discharging control optimization of the charging node comprises the following steps:
21) reading a data file, wherein the data file contains initial data required by a model;
22) the optimization model is programmed using GAMS/CPLEX to obtain a gms model file of the optimization problem, the model file containing: integrating, parameter, variable, objective function and constraint, and programming the electric vehicle charging node optimization model in GAMS/CPLEX;
23) inputting the gms model file into a CPLEX solver for optimal solution,
for the minimum mixed integer discrete optimization problem, based on the mixed integer optimization MIP original problem, reducing 0 and 1 binary variable constraints, then training a model solution, changing the original model into a relaxation optimization model S, training a linear relaxation optimization model S solution, if the S optimal solution does not accord with the integer condition of MIP, the optimal objective function value of S must be the lower boundary of the optimal objective function value of MIP, and marking as O;
the objective function value of any feasible solution of the MIP is an upper bound O of the optimal objective function value, the feasible region of the MIP is divided into sub-regions, the lower bound O is gradually increased and the upper bound O is gradually decreased, and finally the optimal objective function value O is obtained;
the percentage GAP formed by the upper and lower bounds is used as an inspection standard of the quality of the target solution, and the optimization solver finds a better solution along with the solution, so that the GAP is reduced;
the optimized running log is divided into two parts, the first part displays solver parameter setting, the second part is a detailed log of each iteration process, Node represents the number of nodes, if yes, an integer feasible solution is represented, and + represents that the solution is found through a heuristic method; nodes left represents the number of reserved Nodes; objective represents an Objective function value; IInf represents the number of integer variables having a decimal value; best Integer represents the Best Integer solution at present; best Bound represents the Best relaxation solution; ItCnt denotes the number of iterations; gap shows the current optimality;
the relative optimality of the MIP problem is to train parameters of a CPLEX solver to improve solving precision, and to perform large-scale mixed integer optimization, wherein a gap parameter in the solver is used as an inspection standard of target solution quality, the gap in the solver is a relative value, namely gap ═ BP-BF |/| BF |, wherein BP is an optimal solution obtained after the integers are serialized, and BF is a solution obtained when the integers are solved by the current solver; when solving, the gap is set to be 0.5 percent, namely, the option optcr is 0.005, whether the optimal solution under the relatively large gap can be obtained is judged, and if the optimal solution exists, the gap parameter training is adjusted downwards until the new energy has no electricity abandonment amount;
24) and (3) controlling grid-connected charging and discharging of the charging node: after the optimization solution, the electric vehicle charging node obtains node grid-connected information, and grid-connected charging and discharging control is performed according to the node grid-connected information.
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