CN116454903A - Optimal scheduling method considering operation of electric vehicle charging station in virtual power plant - Google Patents
Optimal scheduling method considering operation of electric vehicle charging station in virtual power plant Download PDFInfo
- Publication number
- CN116454903A CN116454903A CN202310074491.3A CN202310074491A CN116454903A CN 116454903 A CN116454903 A CN 116454903A CN 202310074491 A CN202310074491 A CN 202310074491A CN 116454903 A CN116454903 A CN 116454903A
- Authority
- CN
- China
- Prior art keywords
- representing
- vpp
- wind
- cost
- power plant
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000004044 response Effects 0.000 claims abstract description 10
- 238000009434 installation Methods 0.000 claims description 14
- 230000005611 electricity Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims 1
- 230000015556 catabolic process Effects 0.000 abstract description 3
- 238000006731 degradation reaction Methods 0.000 abstract description 3
- 230000005284 excitation Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 239000002803 fossil fuel Substances 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses an optimal scheduling method considering the operation of an electric vehicle charging station in a virtual power plant, which comprises the following steps: the VPP cost is minimized as an objective function, and a VPP optimal scheduling model consisting of a conventional power plant, a wind farm, an electric automobile and a feeder line and combining with a demand response is established; constructing operation constraints of power plants and feeder lines in the VPP, so that the cost generated by the VPP is minimum; constructing charge and discharge constraint of the electric automobile, so that the electric automobile cost in the VPP is minimum; and under the constraint condition, solving the VPP optimal scheduling model to obtain an optimal scheduling scheme. The invention considers the degradation characteristic of distributed storage, provides an optimal scheduling model of the VPP, introduces a Demand Response (DR) concept based on the VPP to optimize the load distribution of the power system, and provides a method for considering the demand response of the electric automobile user side in the VPP, thereby fully utilizing wind power, reducing the cost of the VPP and further improving the competitiveness of the VPP.
Description
Technical Field
The invention belongs to the field of virtual power plant optimal scheduling and user demand side management of an electric power system, and particularly relates to an optimal scheduling method considering operation of an electric vehicle charging station in a virtual power plant.
Background
The paradigm shift from fossil fuel driven energy systems to renewable energy driven energy systems is increasingly speeding up. The rapid reduction of fossil fuels, coupled with the dramatic increase in global warming, has made the use of renewable energy sources to generate electricity a focus of attention. In addition to combating dangerously high pollution, distributed power generation may be cheaper, providing more flexibility to the power system, and improving power quality. The world is guiding their energy policies to increase the use of distributed power generation. However, integration into current power systems is very complex due to its low capacity and inherent sporadic nature. The concept of Virtual Power Plants (VPPs) has attracted considerable attention in this respect because of its ability to eliminate drawbacks and to provide additional advantages. It is defined as an aggregate of distributed generation, conventional power plants, energy storage systems and flexible loads, which can then interact as a single entity with the power market and power system operators. Along with the development of national economy, load demands are in an obvious rising trend, so that great challenges are brought to the supply and demand balance of a power system, and a demand response technology is used as one of main applications of a smart grid, and load reduction can be carried out from a user side so as to ensure the supply and demand balance.
Disclosure of Invention
The invention aims to: in order to overcome the defects in the prior art, an optimal scheduling method for considering the operation of an electric vehicle charging station in a virtual power plant is provided, the degradation characteristic of distributed storage is considered, an optimal scheduling model of VPP is provided, and a Demand Response (DR) concept based on the VPP is introduced to optimize the load distribution of an electric power system.
The technical scheme is as follows: in order to achieve the above purpose, the invention provides an optimized dispatching method considering the operation of an electric vehicle charging station in a virtual power plant, comprising the following steps:
s1: the VPP cost is minimized as an objective function, and a VPP optimal scheduling model consisting of a conventional power plant, a wind farm, an electric automobile and a feeder line and combining with a demand response is established;
s2: constructing operation constraints of power plants and feeder lines in the VPP, so that the cost generated by the VPP is minimum; constructing charge and discharge constraint of the electric automobile, so that the electric automobile cost in the VPP is minimum;
s3: and under the constraint condition of the step S2, solving the VPP optimal scheduling model to obtain an optimal scheduling scheme.
Further, the objective function in the step S1 is constructed with the cost minimization of VPP, which includes the charging cost of electric vehicles, the installation cost of VPP wind farm and feeder line, and the operation cost of conventional power plant and wind farm, and the expression of the VPP optimization scheduling model is as follows:
Min P＝P s +P r +P EV
where Min represents the lowest cost of the VPP model, P s Indicating the installation cost of the VPP, P r Indicating the operation cost of VPP, P EV Indicating the charge rate of the electric vehicle.
Further, the installation cost P of the VPP of the step S1 s The expression of (2) is as follows:
wherein P is s wind Representing the installation cost of the wind power plant, sigma rebate wind Sum sigma defined wind Representing an initial investment return and an initial determined cost of occurrence of the wind farm, u l Is a decision variable for determining whether to establish a wind power plant, Q l,power wind Indicating rated power of installed wind farm, P s feeder Representing the installation cost of the feeder line, sigma rebate feeder Sum sigma defined feeder Representing initial investment return of feeder and initial determination cost occurring, x m Is a decision variable for deciding whether to lay feeder lines, P s,m feeder Representing the total cost incurred in installing the feeder, S wind and S wind indicating the desire of the wind farm and feeder.
Further, the operation cost P of the VPP of the step S1 r The expression of (2) is as follows:
wherein P is r con Representing the operating cost of a conventional power plant, P r wind Representing the running cost of the wind power plant, N u Representing the number of conventional power plants, Q u,i Representing the power output of a conventional power plant u at i time, a u ，b u ，c u Representing the operating parameters of a conventional power plant u, Q l,i wind The power output of the wind farm l at time i is indicated.
Further, the charging cost P of the electric vehicle in step S1 EV The expression of (2) is as follows：
Wherein i represents a time period, j represents an electric car number, and α i Indicating the time-of-use electricity price, ρ j,i P The charging power of the electric vehicle j in the i period is represented.
In the invention, in the step S2, the charge and discharge constraint of the electric automobile is constructed, so that the electric automobile cost in the VPP is minimized, the overall VPP cost is minimized, the electric automobile participating in the VPP is optimally scheduled, the cost is reduced to the greatest extent, and the safety of the electric automobile is ensured; and the operation constraint of the power plant and the feeder line in the VPP is constructed, so that the cost generated by the VPP is reduced to the greatest extent on the basis of ensuring the safe and stable operation of the power plant.
Further, in the step S2, the electric vehicle charging and discharging constraint is constructed as follows:
0≤D j,i τ j,i ≤C max,j
wherein D is j,i Decision variable tau representing electric automobile j in period i j,i Representing the charging time of the electric automobile j in the period i, C max,j Representing the maximum charging power of the electric automobile j;
β j,min ≤β j ≤β j,max
wherein beta is j Represents the state of charge (SOC, the stateofcharging), β of the electric vehicle j j,min And beta j,max Representing the upper and lower limits of the SOC of the electric automobile j;
wherein beta is j,in Representing the SOC state of the electric automobile when entering the charging pile, and uploading the SOC state of the electric automobile to the VPP when entering the charging pile;
wherein beta is j,out Indicating the SOC state when the electric automobile leaves the charging pile, wherein the SOC state cannot exceed the upper SOC limit when the electric automobile leaves the charging pile;
wherein beta is j,i The SOC state of the electric vehicle j in the period i is represented, and R represents the rated battery capacity of the electric vehicle, and this formula represents the SOC state change of the electric vehicle.
Further, the operation constraint of the power plant and feeder line in the VPP in the step S2 is constructed as follows:
Q u min ≤Q u,i ≤Q u max
in which Q u,i Representing the output power of a conventional power plant u at the moment i, Q u min And Q u max Representing upper and lower limits of output power of the conventional power plant u;
-Y u down ≤Q u,i -Q u,i-1 ≤Y u up
wherein Y is u down And Y u up Representing the power drop and rise magnitudes, respectively, of a conventional power plant, both positive values;
0≤Q l,i wind ≤Q l,i for,wind
in which Q l,i Representing the output power of the wind power plant l at the moment i, Q l,i for,wind Representing the maximum output power of the wind farm l at the moment i;
the power balance constraint of feeder power flow is shown in the above formula, nl represents the number of wind farms, nl represents the number of nodes, and Q L,h,i Representing the fixed load of node h at time i, Q z,h,i Indicating the charging load of the electric vehicle at the point in time h，V h,i Representing the voltage at node h at time i, V f,i Represents the voltage at node f, g at time i hf And b hf Represents the conductance and susceptance, θ, of the feed line hf hf,i Representing the phase angle of the feed line hf;
Q hf,i ＝χ hf [V h,i 2 g hf -V h,i V f,i (g hf cosθ hf,i +b hf sinθ hf,i )]
in which Q hf,i Representing the power flow of the feed line hf at the instant i, χ hf A 0-1 variable indicating whether a feeder hf is present;
-Q hf,max ≤Q hf,i ≤Q hf,max
in which Q hf,max Representing the maximum power flow of the feed line hf;
V h,min ≤V h,i ≤V h,max
wherein V is h,max And V h,min The upper and lower voltage limits representing node h are indicated.
Further, in the step S3, a CPLEX solver under a MATLAB environment is used, and the optimal charging plan and the minimum VPP cost of the electric vehicle are obtained by solving the objective function of the step S1 and the constraint condition of the step S2.
Further, the step S2 is to limit the voltage phase angle and the sum of the output powers of the wind farm:
θ ini ＝0
in θ ini Representing a voltage phase angle, defining the voltage angle as 0;
Q wind min ≤Q wind,i ≤Q wind max
in which Q wind,i Representing the output power of all wind farms at the moment i, Q wind min And Q wind max Representing the upper and lower limits of the sum of all wind farm output powers.
The beneficial effects are that: compared with the prior art, the invention considers the degradation characteristic of distributed storage, provides an optimal scheduling model of the VPP, introduces a Demand Response (DR) concept based on the VPP to optimize the load distribution of the power system, and considers the demand response of the electric automobile user side in the VPP, thereby fully utilizing wind power, reducing the cost of the VPP and further improving the competitiveness of the VPP.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention;
FIG. 2 is a 13 bus IEEE system
FIG. 3 is a graph of predicted output power for a wind farm;
FIG. 4 is a graph of an electric vehicle charging load profile;
FIG. 5 is a graph of wind output for 4 quadrants of node 3 a year.
Detailed Description
The present invention is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the invention and not limiting of its scope, and various modifications of the invention, which are equivalent to those skilled in the art upon reading the invention, will fall within the scope of the invention as defined in the appended claims.
The invention provides an optimal scheduling method considering the operation of an electric vehicle charging station in a virtual power plant, which is shown in fig. 1 and comprises the following steps:
s1: with VPP cost minimization as an objective function, a VPP optimal scheduling model consisting of conventional power plants, wind farms, electric automobiles and feeder lines and combining with demand response is established:
the objective function is constructed with the cost minimization of the VPP, wherein the cost of charging the electric automobile, the installation cost of a wind farm and a feeder line of the VPP and the operation cost of a conventional power plant and a wind farm are included, and the expression of the VPP optimal scheduling model is as follows:
Min P＝P s +P r +P EV
where Min represents the lowest cost of the VPP model, P s Indicating the installation cost of the VPP, P r Indicating the operation cost of VPP, P EV Indicating the charge rate of the electric vehicle.
Installation cost P of VPP s The expression of (2) is as follows:
wherein P is s wind Representing the installation cost of the wind power plant, sigma rebate wind Sum sigma defined wind Representing an initial investment return and an initial determined cost of occurrence of the wind farm, u l Is a decision variable for determining whether to establish a wind power plant, Q l,power wind Indicating rated power of installed wind farm, P s feeder Representing the installation cost of the feeder line, sigma rebate feeder Sum sigma defined feeder Representing initial investment return of feeder and initial determination cost occurring, x m Is a decision variable for deciding whether to lay feeder lines, P s,m feeder Representing the total cost incurred in installing the feeder, S wind and S wind indicating the desire of the wind farm and feeder.
Operating cost P of VPP r The expression of (2) is as follows:
wherein P is r con Representing the operating cost of a conventional power plant, P r wind Representing the running cost of the wind power plant, N u Representing the number of conventional power plants, Q u,i Representing the power output of a conventional power plant u at i time, a u ，b u ，c u Representing the operating parameters of a conventional power plant u, Q l,i wind The power output of the wind farm l at time i is indicated.
Charging cost P of electric automobile EV The expression of (2) is as follows:
wherein i represents a time period, j represents an electric car number, and α i Representing time-sharing electricityValence, ρ j,i P The charging power of the electric vehicle j in the i period is represented.
S2: constructing operation constraints of power plants and feeder lines in the VPP, so that the cost generated by the VPP is minimum; the charge and discharge constraint of the electric automobile is constructed, so that the cost of the electric automobile in the VPP is minimum:
the charge and discharge constraint of the electric automobile is constructed, so that the electric automobile cost in the VPP is minimized, the overall VPP cost is minimized, the electric automobile participating in the VPP is optimally scheduled, the cost is reduced to the maximum extent, and meanwhile, the safety of the electric automobile is ensured; and the operation constraint of the power plant and the feeder line in the VPP is constructed, so that the cost generated by the VPP is reduced to the greatest extent on the basis of ensuring the safe and stable operation of the power plant.
The electric automobile charge-discharge constraint is constructed as follows:
0≤D j,i τ j,i ≤C max,j
wherein D is j,i Decision variable tau representing electric automobile j in period i j,i Representing the charging time of the electric automobile j in the period i, C max,j Representing the maximum charging power of the electric automobile j;
β j,min ≤β j ≤β j,max
wherein beta is j Represents the state of charge (SOC, the state of charging), β of the electric vehicle j j,min And beta j,max Representing the upper and lower limits of the SOC of the electric automobile j;
wherein beta is j,in Representing the SOC state of the electric automobile when entering the charging pile, and uploading the SOC state of the electric automobile to the VPP when entering the charging pile;
wherein beta is j,out Representing the SOC state of an electric vehicle when leaving a charging pile, and the SOC state of an electric vehicle when leaving a charging pileThe state cannot exceed the upper SOC limit;
wherein beta is j,i The SOC state of the electric vehicle j in the period i is represented, and R represents the rated battery capacity of the electric vehicle, and this formula represents the SOC state change of the electric vehicle.
The operational constraints of the power plants and feeders within the VPP are constructed as follows:
Q u min ≤Q u,i ≤Q u max
in which Q u,i Representing the output power of a conventional power plant u at the moment i, Q u min And Q u max Representing upper and lower limits of output power of the conventional power plant u;
-Y u down ≤Q u,i -Q u,i-1 ≤Y u up
wherein Y is u down And Y u up Representing the power drop and rise magnitudes, respectively, of a conventional power plant, both positive values;
0≤Q l,i wind ≤Q l,i for,wind
in which Q l,i Representing the output power of the wind power plant l at the moment i, Q l,i for,wind Representing the maximum output power of the wind farm l at the moment i;
the above represents the power balance constraint of feeder power flow, N l Representing the number of wind power plants, N L Represent the number of nodes, Q L,h,i Representing the fixed load of node h at time i, Q z,h,i Represents the charging load of the electric vehicle at the point in time h, V h,i Representing the voltage at node h at time i, V f,i Represents the voltage at node f, g at time i hf And b hf Representing the conductance and susceptance of the feed line hf，θ hf,i Representing the phase angle of the feed line hf;
Q hf,i ＝χ hf [V h,i 2 g hf -V h,i V f,i (g hf cosθ hf,i +b hf sinθ hf,i )]
in which Q hf,i Representing the power flow of the feed line hf at the instant i, χ hf A 0-1 variable indicating whether a feeder hf is present;
-Q hf,max ≤Q hf,i ≤Q hf,max
in which Q hf,max Representing the maximum power flow of the feed line hf;
V h,min ≤V h,i ≤V h,max
wherein V is h,max And V h,min The upper and lower voltage limits representing node h are indicated.
Limiting the voltage phase angle and the wind farm output power sum:
θ ini ＝0
in θ ini Representing a voltage phase angle, defining the voltage angle as 0;
Q wind min ≤Q wind,i ≤Q wind max
in which Q wind,i Representing the output power of all wind farms at the moment i, Q wind min And Q wind max Representing the upper and lower limits of the sum of all wind farm output powers.
S3: and obtaining an optimal charging plan and the minimum VPP cost of the electric automobile by solving the objective function of the step S1 and the constraint condition of the step S2 by using a CPLEX solver under the MATLAB environment.
Based on the above scheme, in order to verify the effectiveness of the method of the present invention, the above scheme is applied as an example, specifically as follows:
this embodiment verifies the feasibility of the proposed optimized scheduling method on the 13-bus IEEE system in MATPOWER7.1, as shown in fig. 2. The test case system includes 5 conventional power plant units, 12 existing feeders, 3 electric vehicle charging stations and 3 wind farms. They are both connected to node 3 and node 6, near the fixed load on the system. The predicted yield of the wind farm is shown in fig. 3.
It is assumed that all wind power plants are identical. The rated charge power of the electric vehicle charging station is 3kW. The battery capacity of the single electric vehicle is 24kWh, and the charging efficiency is 95%.
The number of electric vehicles connected to each charging station optimally scheduled is 30. The SOC of the electric automobile entering the charging station is randomly distributed within the range of 0.2-0.3. The time-of-use electricity price excitation of the electric automobile charging setting optimization schedule is shown in table 1.
TABLE 1 excitation of different time-of-use electricity prices
| Time | Time-of-use electricity price | Excitation |
| 06:00-09:00 | Middle peak | 0.194 |
| 09:00-12:00 | Peak | 0.217 |
| 12:00-17:00 | Low peak | 0.135 |
| 17:00-19:00 | Peak | 0.217 |
| 19:00-21:00 | Middle peak | 0.194 |
| 21:00-00:00 | Low peak | 0.135 |
| 00:00-06:00 | Trough of low grain | 0.073 |
Fig. 4 depicts an optimized schedule of electric vehicle charging stations over a period of a day. When the time-of-use electricity price is implemented under the management of the user demand side, the charging load of the electric automobile is concentrated in the valley period of 00:00-06:00. Therefore, the optimal scheduling method provided by the invention can obviously improve the benefit of the VPP. In order to meet the increasing load demands of fixed loads such as charging loads of electric automobiles, three wind power generator sets are required to be built and connected into the system. The effect of the optimal scheduling of the night charging load of the electric automobile is due to the increased demand of the wind generating set when the wind generating set is expected to generate maximum power at night. The output of the wind turbine generator (connected system node 3) in the 00:00-06:00 valley period is shown in the following figure 5 which shows four quarters in one year, and the feasibility of the method is verified. The research results clearly define the positive effects of the optimal scheduling of the charging load of the electric vehicle and the VPP on the overall cost of reducing VPP. The optimal scheduling of the electric vehicle charging station and the optimal scheduling modeling of the VPP ensure that the working characteristics of the wind power plant, the electric vehicle and the distribution feeder line are improved.
The feasibility of the proposed solution was verified by example analysis. The result shows that wind power can be fully utilized by optimizing the electric vehicle charging station dispatching, and the cost of VPP is reduced. This can further increase VPP competitiveness by participating in various other power markets, such as the auxiliary services market, and provide opportunities for future research.
Claims (9)
1. An optimized dispatching method considering the operation of an electric vehicle charging station in a virtual power plant is characterized by comprising the following steps:
s1: the VPP cost is minimized as an objective function, and a VPP optimal scheduling model consisting of a conventional power plant, a wind farm, an electric automobile and a feeder line and combining with a demand response is established;
s2: constructing operation constraints of power plants and feeder lines in the VPP, so that the cost generated by the VPP is minimum; constructing charge and discharge constraint of the electric automobile, so that the electric automobile cost in the VPP is minimum;
s3: and under the constraint condition of the step S2, solving the VPP optimal scheduling model to obtain an optimal scheduling scheme.
2. The method according to claim 1, wherein the objective function in step S1 is constructed with a minimum cost of VPP, and the objective function includes a charge cost of the electric vehicle, a installation cost of a VPP wind farm and feeder line, and an operation cost of a conventional power plant and wind farm, and the expression of the VPP optimization scheduling model is as follows:
Min P＝P s +P r +P EV
where Min represents the lowest cost of the VPP model, P s Indicating the installation cost of the VPP, P r Indicating the operation cost of VPP, P EV Indicating the charge rate of the electric vehicle.
3. The optimal scheduling method considering operation of the electric vehicle charging station in the virtual power plant according to claim 2, wherein the VPP installation cost P of step S1 s The expression of (2) is as follows:
wherein P is s wind Representing a wind farmInstallation cost sigma of (2) rebate wind Sum sigma defined wind Representing an initial investment return and an initial determined cost of occurrence of the wind farm, u l Is a decision variable for determining whether to establish a wind power plant, Q l,power wind Indicating rated power of installed wind farm, P s feeder Representing the installation cost of the feeder line, sigma rebate feeder Sum sigma defined feeder Representing initial investment return of feeder and initial determination cost occurring, x m Is a decision variable for deciding whether to lay feeder lines, P s,m feeder Representing the total cost incurred in installing the feeder, S wind and S wind indicating the desire of the wind farm and feeder.
4. The optimal scheduling method considering operation of the electric vehicle charging station in the virtual power plant according to claim 2, wherein the operation cost P of the VPP of step S1 r The expression of (2) is as follows:
wherein P is r con Representing the operating cost of a conventional power plant, P r wind Representing the running cost of the wind power plant, N u Representing the number of conventional power plants, Q u,i Representing the power output of a conventional power plant u at i time, a u ，b u ，c u Representing the operating parameters of a conventional power plant u, Q l,i wind The power output of the wind farm l at time i is indicated.
5. The optimal scheduling method considering operation of the electric vehicle charging station in the virtual power plant according to claim 2, wherein the charging cost P of the electric vehicle in step S1 EV The expression of (2) is as follows:
wherein i represents a time period, j represents an electric car number, and α i Indicating the time-of-use electricity price, ρ j,i P The charging power of the electric vehicle j in the i period is represented.
6. The optimal scheduling method considering the operation of the electric vehicle charging station in the virtual power plant according to claim 1, wherein the construction of the electric vehicle charging and discharging constraint in step S2 is as follows:
0≤D j,i τ j,i ≤C max,j
wherein D is j,i Decision variable tau representing electric automobile j in period i j,i Representing the charging time of the electric automobile j in the period i, C max,j Representing the maximum charging power of the electric automobile j;
β j,min ≤β j ≤β j,max
wherein beta is j Indicating the state of charge, beta, of electric vehicle j j,min And beta j,max Representing the upper and lower limits of the SOC of the electric automobile j;
wherein beta is j,in Representing the SOC state of the electric automobile when entering the charging pile, and uploading the SOC state of the electric automobile to the VPP when entering the charging pile;
wherein beta is j,out Indicating the SOC state when the electric automobile leaves the charging pile, wherein the SOC state cannot exceed the upper SOC limit when the electric automobile leaves the charging pile;
wherein beta is j,i The SOC state of the electric vehicle j in the period i is represented, and R represents the rated battery capacity of the electric vehicle, and this formula represents the SOC state change of the electric vehicle.
7. The method according to claim 1, wherein the operation constraints of the power plant and feeder line in the VPP in step S2 are constructed as follows:
Q u min ≤Q u,i ≤Q u max
in which Q u,i Representing the output power of a conventional power plant u at the moment i, Q u min And Q u max Representing upper and lower limits of output power of the conventional power plant u;
-Y u down ≤Q u,i -Q u,i-1 ≤Y u up
wherein Y is u down And Y u up Representing the power drop and rise magnitudes, respectively, of a conventional power plant, both positive values;
in which Q l,i Representing the output power of the wind power plant l at the moment i, Q l,i for,wind Representing the maximum output power of the wind farm l at the moment i;
the above represents the power balance constraint of feeder power flow, N l Representing the number of wind power plants, N L Represent the number of nodes, Q L,h,i Representing the fixed load of node h at time i, Q z,h,i Represents the charging load of the electric vehicle at the point in time h, V h,i Representing the voltage at node h at time i, V f,i Represented at iVoltage at time node f, g hf And b hf Represents the conductance and susceptance, θ, of the feed line hf hf,i Representing the phase angle of the feed line hf;
Q hf,i ＝χ hf [V h,i 2 g hf -V h,i V f,i (g hf cosθ hf,i +b hf sinθ hf,i )]
in which Q hf,i Representing the power flow of the feed line hf at the instant i, χ hf A 0-1 variable indicating whether a feeder hf is present;
-Q hf,max ≤Q hf,i ≤Q hf,max
in which Q hf,max Representing the maximum power flow of the feed line hf;
V h,min ≤V h,i ≤V h,max
wherein V is h,max And V h,min The upper and lower voltage limits representing node h are indicated.
8. The optimal scheduling method considering the operation of the electric vehicle charging station in the virtual power plant according to claim 1, wherein in the step S3, a CPLEX solver under the MATLAB environment is utilized to obtain the optimal charging plan and the minimum VPP cost of the electric vehicle by solving the objective function of the step S1 and the constraint condition of the step S2.
9. The optimal scheduling method considering operation of the electric vehicle charging station in the virtual power plant according to claim 7, wherein the step S2 is to limit the sum of the voltage phase angle and the output power of the wind farm:
θ ini ＝0
in θ ini Representing a voltage phase angle, defining the voltage angle as 0;
Q wind min ≤Q wind,i ≤Q wind max
in which Q wind,i Representing the output power of all wind farms at the moment i, Q wind min And Q wind max Representing all wind farmsUpper and lower limits of the sum of the output powers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310074491.3A CN116454903A (en) | 2023-02-07 | 2023-02-07 | Optimal scheduling method considering operation of electric vehicle charging station in virtual power plant |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310074491.3A CN116454903A (en) | 2023-02-07 | 2023-02-07 | Optimal scheduling method considering operation of electric vehicle charging station in virtual power plant |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116454903A true CN116454903A (en) | 2023-07-18 |
Family
ID=87127867
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310074491.3A Pending CN116454903A (en) | 2023-02-07 | 2023-02-07 | Optimal scheduling method considering operation of electric vehicle charging station in virtual power plant |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116454903A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117239810A (en) * | 2023-11-09 | 2023-12-15 | 南方电网数字电网研究院有限公司 | Virtual power plant electric energy scheduling scheme acquisition method, device and equipment |
-
2023
- 2023-02-07 CN CN202310074491.3A patent/CN116454903A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117239810A (en) * | 2023-11-09 | 2023-12-15 | 南方电网数字电网研究院有限公司 | Virtual power plant electric energy scheduling scheme acquisition method, device and equipment |
| CN117239810B (en) * | 2023-11-09 | 2024-03-26 | 南方电网数字电网研究院有限公司 | Virtual power plant electric energy scheduling scheme acquisition method, device and equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108039722B (en) | Distributed renewable energy system optimal configuration method suitable for alternating current and direct current mixing | |
| CN111626527B (en) | Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle | |
| CN110751368A (en) | Electric vehicle storage and charging station planning method considering flexibility of charging load | |
| Zeng et al. | Model predictive control for energy storage systems in a network with high penetration of renewable energy and limited export capacity | |
| Sawle et al. | Techno-economic scrutiny of HRES through GA and PSO technique | |
| Datta et al. | The strategies of EV charge/discharge management in smart grid vehicle-to-everything (V2X) communication networks | |
| Lorestani et al. | Energy management in smart home including PV panel, battery, electric heater with integration of plug-in electric vehicle | |
| CN116454903A (en) | Optimal scheduling method considering operation of electric vehicle charging station in virtual power plant | |
| Saberbari et al. | Net-zero energy building implementation through a grid-connected home energy management system | |
| CN115333161A (en) | Capacity optimization configuration method for power supply system of green water plant | |
| Bracco et al. | Energy Management System for the Optimal Operation of a Grid-Connected Building with Renewables and an Electric Delivery Vehicle | |
| Aktaş | The importance of energy storage in solar and wind energy, hybrid renewable energy systems | |
| CN110571868B (en) | Optimal configuration method of micro-grid | |
| Senthilkumar et al. | Profit maximization in hybrid microgrid system incorporating demand response and plug in EV | |
| CN117114281A (en) | Determination method of flexible resource multi-stage planning scheme | |
| CN114491997B (en) | Virtual power plant operation optimization method and system considering demand response and electric automobile | |
| Harsh et al. | A priority-ordered incentive-based smart charging strategy of electric vehicles to determine the optimal size of solar power plant at the charging stations | |
| Tripathy et al. | Voltage profile enhancement of a 33 bus system integrated with renewable energy sources and electric vehicle | |
| Lei et al. | Coordinated scheduling of electric vehicles and wind power generation considering vehicle to grid mode | |
| Julianto et al. | Confronting the Duck Curve Problem Using Dynamic Economic Emission Dispatch with CAES. | |
| Ihm et al. | Optimal Scenarios of Renewables and Chargers for an Electric Vehicle Charging Station using Public Data | |
| CN111062532A (en) | Incremental distribution park power grid capacity configuration optimization method considering V2G | |
| CN112785048A (en) | Direct-current micro-grid economic dispatching method considering user requirements of electric vehicle | |
| Yang et al. | Optimal economic dispatch of microgrid with battery energy storage considering uncertainty of renewable generation | |
| Dutta | Design of a coordinated electric vehicle charging system to flatten the duck curve |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |