CN108520314B - Active power distribution network scheduling method combined with V2G technology - Google Patents
Active power distribution network scheduling method combined with V2G technology Download PDFInfo
- Publication number
- CN108520314B CN108520314B CN201810224570.7A CN201810224570A CN108520314B CN 108520314 B CN108520314 B CN 108520314B CN 201810224570 A CN201810224570 A CN 201810224570A CN 108520314 B CN108520314 B CN 108520314B
- Authority
- CN
- China
- Prior art keywords
- charging
- distribution network
- period
- power
- discharging
- 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.)
- Active
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 84
- 238000005516 engineering process Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000004146 energy storage Methods 0.000 claims abstract description 64
- 238000007599 discharging Methods 0.000 claims abstract description 57
- 238000010248 power generation Methods 0.000 claims abstract description 33
- 238000005457 optimization Methods 0.000 claims description 24
- 230000005611 electricity Effects 0.000 claims description 7
- 238000010977 unit operation Methods 0.000 claims description 4
- 238000010521 absorption reaction Methods 0.000 abstract 1
- 208000028659 discharge Diseases 0.000 description 16
- 230000008901 benefit Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000002485 combustion reaction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000029305 taxis Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010885 neutral beam injection Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses an active power distribution network scheduling method combined with a V2G technology, and belongs to the technical field of economic scheduling of power systems. The dispatching method adopts a V2G technology to control charging and discharging of the electric automobile, on the basis of regarding the electric automobile as an energy storage system, day-ahead dispatching is carried out on distributed energy and stored energy by considering the influence of the electric automobile access on line outlet active power, then real-time dispatching is carried out on the operated distributed power generation units by combining various distributed energy output costs, real-time dispatching is carried out on the stored energy with the minimum energy storage output fluctuation cost, absorption on the distributed energy is realized, the utilization rate of renewable energy is improved, charging and discharging of the electric automobile are controlled through the V2G system, peak clipping and valley filling are carried out on a power grid on the premise that traveling of electric automobile users is met, the peak-valley difference of the power distribution network is reduced, and the economical efficiency and the stability of power grid operation are improved.
Description
Technical Field
The invention discloses an active power distribution network scheduling method combined with a V2G technology, and belongs to the technical field of economic scheduling of power systems.
Background
With the increasing environmental pollution and the situation of energy shortage, electric vehicles that do not rely on fossil energy are beginning to receive wide attention from society. The electric automobile has the characteristics of high energy efficiency, low noise, zero exhaust emission and the like, is more environment-friendly than the traditional fuel oil automobile, can effectively relieve crises such as energy shortage and environmental pollution by applying the electric automobile on a large scale, and is beneficial to realizing a low-carbon economic target.
Electric vehicles can be currently classified into three major categories, buses, taxis and private cars. The battery capacity of the electric bus and the electric private car is difficult to meet the requirement of one-day operation, so that the electric energy needs to be supplemented by replacing the battery or charging the battery quickly. Private car owners are used for car owners to go on and off duty, charging places of the private car owners are mostly fixed in unit parking lots or cell garages, and due to the fact that daily mileage is small, slow charging is mostly adopted. The large-scale application of the electric automobile can form huge charging load in a specific time period in one day, impact is caused to a power grid, and the safety of the power grid is influenced. The charging time of the three types of electric automobiles is determined by respective trip habits, and the charging time has strong regularity, which means that the charging process of the electric automobiles can be orderly controlled in a certain mode, the charging load is reduced, and the reliability and the safety of a power grid are improved.
The V2G technology is the latest technology in the technical field of electric automobiles, and the core of the technology is that a large number of electric automobiles are regarded as a distributed energy storage system and managed by utilizing the characteristic that storage batteries of the electric automobiles can be charged and discharged, so that discharging in the peak period and charging in the valley period of the load of a power grid are realized, the peak-valley difference rate is reduced, and the safety and the economical efficiency of the power grid are improved. In addition, a large number of storage batteries can be used as buffer of renewable energy sources, and intermittent energy sources such as photovoltaic energy and the like can be consumed.
The active power distribution network has active power flow control capability and load interaction capability, and is a technical solution capable of optimally utilizing distributed energy. The active power distribution network can eliminate the influence of distributed energy on the power grid, and high-efficiency utilization of energy is realized.
The energy storage elements are difficult to be used in large quantities in the distribution network due to their high cost, which also results in that the distributed energy is often difficult to be fully utilized. The electric automobile is connected into the active power distribution network and controlled through a certain algorithm, so that the utilization rate of distributed energy and the economical efficiency of power grid operation can be effectively improved, and the advantages of the active power distribution network are fully exerted.
Disclosure of Invention
The invention aims to provide an active power distribution network dispatching method combined with a V2G technology aiming at the defects of the background technology, an electric automobile is regarded as the load of an active power distribution network, the V2G technology is combined with day-ahead dispatching and implementation dispatching, negative effects brought by large-scale electric automobile access to a power grid are reduced, meanwhile, effective utilization of distributed energy is achieved, and the technical problem that the distributed energy is difficult to fully utilize due to the fact that energy storage elements in the existing active power distribution network dispatching technology are high in cost and difficult to apply in large quantities is solved.
The invention adopts the following technical scheme for realizing the aim of the invention:
the active power distribution network dispatching method combined with the V2G technology adopts the V2G technology to control charging and discharging of the electric automobile, a day-ahead dispatching optimization model of the main power distribution network is established by considering the influence of charging power of the electric automobile connected to the active power distribution network on line outlet power, and day-ahead dispatching values of distributed energy output and stored energy output are optimized in real time.
As a further optimization scheme of the active power distribution network scheduling method combined with the V2G technology, the method for controlling the charging and discharging of the electric automobile by adopting the V2G technology comprises the following steps: the electric automobile in the idle state is charged in the peak-valley load period, and the electric automobile in the idle state discharges to the active power distribution network in the peak-valley load period on the premise of meeting the use requirements of users.
The influence of the charging power of the electric vehicle accessed to the active power distribution network on the line outlet power is determined by the established active power distribution network charging and discharging power model, the active power distribution network power model takes the sum of the charging power of the electric vehicle charged at the load node as the target and is equal to the charging capacity of the load node, and the charging power provided by the load node for each accessed electric vehicle is determined under the load node charging capacity constraint, the electric vehicle charging power constraint and the electric vehicle trip demand constraint.
As a further optimization scheme of the active power distribution network scheduling method combined with the V2G technology, the day-ahead scheduling optimization model of the active power distribution network takes the lowest total cost including the output cost of distributed energy, the charging and discharging cost of energy storage equipment and the charging and discharging cost of electric vehicles connected into the active power distribution network as an optimization target, and optimizes the output of distributed power generation units and the energy storage output under the system balance constraint, the output power constraint of the active power distribution network, the output power constraint of distributed power generation units and the charging and discharging constraint of the energy storage equipment.
As a further optimization scheme of the active power distribution network scheduling method combined with the V2G technology, the method for optimizing the day-ahead scheduling values of distributed energy output and energy storage output in real time comprises the following steps: and correcting the output of the operated distributed power generation unit at the current stage by taking the output cost of the distributed units of different types into consideration and taking the minimum total operating cost of the distributed energy sources as a target, and correcting the stored energy output according to the energy storage operating cost at the current stage and taking the minimum fluctuation cost of the stored energy output as a target.
As a further optimization scheme of the active power distribution network scheduling method combined with the V2G technology, the optimization objective of the day-ahead scheduling optimization model of the active power distribution network is as follows:
wherein f is the number of feeders in the whole area, ce(t) is the electricity price of the e-th line during the period t,
the active power value of the exit of the e line in the t period, n is the number of distributed power generation units, cj(t) is the unit operating cost of the jth distributed generation unit in the t period,
the active power value of the jth distributed power generation unit in the t period, m is the number of energy storage devices, cc(t) and cd(t) charging cost and discharging cost of the energy storage device in the period t, cdThe value of (t) is 0, c when the energy storage device is in a charging statec(t) is 0 when the energy storage device is in a discharged state,
the active power value for charging or discharging the kth energy storage device in the t period,
for the value of the active network loss of the e-th line during the period t,
and
respectively represents the charging and discharging costs of the electric automobile connected to the e-th line in the t period,
and predicting power for charging and discharging of the electric automobile accessed to the e-th line in the t period.
As a further optimization scheme of the active power distribution network scheduling method combined with the V2G technology, the charge and discharge prediction power of the electric vehicle accessed to the e-th line in the t period is predicted by using the following method: and predicting the charging time period and the charging power of the electric automobile according to the travel time probability distribution and the travel mileage probability distribution of the electric automobile, and predicting the discharging power of the electric automobile by adopting constant-power discharging to approximate the actual discharging process of the electric automobile.
As a further optimization scheme of the active power distribution network scheduling method combined with the V2G technology, an objective function for minimizing the total cost of distributed energy operation is as follows:
As a further optimization scheme of the active power distribution network scheduling method combined with the V2G technology, the objective function with the minimum energy storage output fluctuation cost is as follows:
wherein, Pk(t) and P'k(t) energy storage output before and after correction, cc(t) and c'c(t) charging costs of the energy storage device in the day-ahead and real-time phases, respectively, cd(t) and c'd(t) cost of discharge of the energy storage device at the day-ahead stage and the real-time stage, respectively, NtThe time is scheduled in real time.
By adopting the technical scheme, the invention has the following beneficial effects:
(1) the invention combines the electric automobile V2G technology, considers the influence of the electric automobile access on the line outlet active power to carry out day-ahead scheduling on distributed energy and stored energy on the basis of regarding the electric automobile as an energy storage system, and then carries out real-time scheduling on the operated distributed power generation units by combining various distributed energy output costs, so that the stored energy is scheduled in real time with the minimum energy output fluctuation cost, the consumption of the distributed energy is realized, and the utilization rate of renewable energy is improved.
(2) The charging and discharging of the electric automobile are controlled through the V2G system, the power distribution dispatching control of the electric automobile is realized, the negative effects caused by the fact that large-scale electric automobiles are connected into an active power distribution network are effectively improved, peak clipping and valley filling are carried out on the power grid on the premise that the traveling of electric automobile users are met, the peak-valley difference of the power distribution network is reduced, and the economical efficiency and the stability of the power grid operation are improved.
Drawings
Fig. 1 is a detailed structural diagram of an active power distribution network.
Fig. 2 is a specific flowchart of the scheduling method of the present application.
Detailed Description
The technical scheme of the invention is explained in detail in the following with reference to the attached drawings.
An active power distribution network scheduling method combining V2G technology is provided. The method comprises the steps of establishing a charging and discharging power model of the electric automobile, a distributed energy source and an energy storage output model by taking one day as a scheduling cycle and taking one hour as a time interval, realizing adjustment of distributed energy source and energy storage output through an active power distribution network according to charging and discharging requirements of the electric automobile in different time intervals on the premise of meeting traveling requirements of the electric automobile, and realizing peak clipping and valley filling through a V2G technology to improve the utilization rate of renewable energy sources.
The active power distribution network scheduling method combining the V2G technology, which is related by the invention, is shown in FIG. 2, and the specific flow is as follows:
step 1: establishing a charge-discharge power model of an electric automobile
The charging mode of the electric automobile can be divided into three modes of conventional charging, quick charging and battery replacement. The charging power in the conventional charging mode is small, and the full charge of a general electric automobile needs 6-8 hours. The fast charge mode and the battery replacement mode are generally used for taxis, buses and the like, wherein the fast charge mode can fully charge the battery within 1-2 hours. Since the V2G technology requires that the electric vehicle be in a stationary state for a long time, the present invention is mainly directed to the case that the electric private car adopting the conventional charging mode is connected to the active power distribution network.
1.1 electric vehicle charging power model
Considering the travel rule of private cars and the current working time of China, the charging time of the electric car is 9:00 to 19: 00. Since the randomness of the private car traveling is high, the private car traveling is assumed to follow a normal distribution. The probability density function is as follows:
wherein x is the travel time of the electric automobile, fs1(x)、fs2(x) Probability distribution of travel time, mu, of electric vehicle at the beginning of charging at 9:00 and 19:00 respectivelys1、σs1Respectively the mathematical expectation and standard deviation mu of the travel time of the electric automobile when the electric automobile starts to be charged at 9:00s1＝9，σs1＝0.5，μs2、σs2Respectively the mathematical expectation and standard deviation mu of the travel time of the electric automobile at the beginning of charging at 19:00s2＝19，σs2＝1.5。
According to the 2001 survey results of the mileage of domestic vehicles in the United states department of transportation, the daily mileage of private vehicles satisfies the lognormal distribution:
in the above formula, y is the daily mileage of the electric vehicle, fDc(y) is the probability distribution of the daily mileage of the electric vehicle, muDc、σDcRespectively the mathematical expectation and standard deviation mu of the daily mileage of the electric automobileDc＝3.196，σDc＝0.844。
In conclusion, a charging power model of the electric vehicle can be obtained.
1.2 electric vehicle discharge power model
Electric vehicles typically employ lithium batteries. Because the lithium battery is a capacity type battery, the power discharged to the power grid by the electric automobile can be converted by the power conversion system according to the actual situation. According to the load demand of the power distribution network during peak hours, the V2G system is used for controlling the electric automobile to release the residual electric quantity to the power distribution network in a centralized manner, wherein the residual electric quantity refers to the residual electric quantity after the consumption of the electric automobile on the way of going to work and the maximum discharge depth is met. For convenience of calculation, the constant-power discharge is adopted to approximate the actual discharge process.
1.3 Power distribution network charging and discharging power model
Taking the charging power of the electric automobile as the load of the power distribution network, and establishing a charging power model of each charging load node of the power distribution network in each time period:
wherein, Pi,tRepresenting the charging capacity, P, of the load node i during the period ti,t,nRepresents the charging power, N, of the nth electric vehicle charged at the load node i during the period ti,tAnd the number of the electric vehicles accessed by the load node i in the t period is represented.
The power distribution network charging power model meets the following constraint conditions:
constraint conditions of charge capacity of load node i:
constraint conditions of electric vehicle charging power are as follows:
Pi,t,n≤Pmax(5)，
SOCmin≤SOCt≤SOCmax(6)，
the constraint conditions of the travel requirements of the electric automobile are as follows:
SOCend≥SOCDrive(7)，
wherein the content of the first and second substances,
representing the rated capacity of the transformer at the load node i of the distribution network,
representing the original load, P, of the load node i in the time period tmaxIndicating the maximum charging power, SOC, of the electric vehicleendIndicating the state of charge, SOC, of the electric vehicle at the end of chargingDriveSOCdriveRepresents the minimum state of charge, SOC, that meets the user's travel needsmaxAnd SOCminRespectively represent the highest state of charge and the lowest state of charge of the electric vehicle.
Equivalent load N of load node i in t periodi,tExpressed as:
in conclusion, a power distribution network charge and discharge power model is established.
Step 2: establishing distributed energy scheduling optimization model under active power distribution network
Day-ahead scheduling optimization model
Under the current electric power market environmental condition, the cost of the day-ahead scheduling optimization of the distributed energy in the active power distribution network mainly comprises the power generation cost of the distributed energy, the power purchasing cost to the main network, the network loss cost, the energy storage cost, the charging and discharging cost of the electric automobile and the like, so that the day-ahead scheduling objective function of the distributed energy in the active power distribution network can be expressed as follows:
wherein f is the number of feeders in the whole area, ce(t) is the electricity price of the e-th line during the period t,
the active power value of the exit of the e line in the t period, n is the number of distributed power generation units, cj(t) is the unit operating cost of the jth distributed generation unit in the t period,
the active power value of the jth distributed power generation unit in the t period, m is the number of energy storage devices, cc(t) and cd(t) charging cost and discharging cost of the energy storage device in the period t, cdThe value of (t) is 0, c when the energy storage device is in a charging statec(t) is 0 when the energy storage device is in a discharged state,
the active power value for charging or discharging the kth energy storage device in the t period,
for the value of the active network loss of the e-th line during the period t,
and
respectively represents the charging and discharging costs of the electric automobile connected to the e-th line in the t period,
and predicting power for charging and discharging of the electric automobile accessed to the e-th line in the t period.
wherein λ iszAnd (t) is the comprehensive network loss value of the node z at the time t.
The constraint conditions of the day-ahead scheduling model are as follows:
system power balance constraint:
restraint of output power of the active power distribution network:
constraint of output power of distributed generation unit:
constraint conditions of the energy storage device:
wherein the content of the first and second substances,
total active power output for the system during the period t, NBIs a set of nodes, ez(t) and fz(t) real and imaginary parts of the node z voltage, e, respectively, during t periodsτ(t) and fτ(t) real and imaginary parts of node τ voltage at time t，GzτAnd BzτRespectively the mutual conductance and the mutual susceptance between the node z and the node tau,
for the reactive power value at the exit of the e-th line for the time period t,
the reactive power value of the jth distributed generation unit in the t period,
the value of reactive power for charging or discharging the kth energy storage device in the t period,
for the value of the reactive network loss of the e-th line in the t period,
for the total reactive power, P, output by the system during the period tgrid,max(t) is the maximum value of the total active power output by the active power distribution network in the period t,
and
respectively the maximum voltage and the minimum voltage of the node z,
and
respectively the minimum active power and the maximum active power of the distributed generation unit during the period t,
respectively the charging power and the discharging power of the energy storage device in the time period t,
respectively the maximum charging power and the maximum discharging power of the energy storage device in the time period t,
minimum and maximum values of the charging or discharging power, E, respectively, of the energy storage device during the period tESS(0) For scheduling the energy storage value of the energy storage device at the initial moment, NtTo schedule the end time, EESS(Nt) For the energy storage value of the energy storage device at the end of the scheduling,
respectively representing the charging and discharging states of the energy storage device k in a period t,
respectively, the charging and discharging states of the energy storage device k during the period t-1, η1、η2The maximum charging times and the maximum discharging times of the energy storage device in the scheduling period are respectively.
Real-time scheduling optimization model
And (3) on the basis of day-ahead scheduling, the running state of each current distributed energy unit is timely adjusted by combining ultra-short-term load prediction, namely, the output of the distributed energy units and the stored energy is corrected.
Operation correction of the distributed energy resource unit:
the method is characterized in that the output of the distributed power supply operated at the current stage is adjusted or corrected by taking the minimum operation cost of the distributed energy as a target, and the target function is as follows:
wherein, P'DG,α(t) is a corrected value of t-time output of the controllable distributed units excluding the photovoltaic power generation units and the fan units, and cDG(t) is the unit operating cost, P ', of the controllable distributed units excluding the photovoltaic power generation unit and the fan unit in the period t'S,β(t) is a photovoltaic power generation unit tCorrection of the segment force, cS(t) is the unit operating cost of the photovoltaic power generation unit over period t, P'W,γ(t) is a corrected value of the output of the fan unit at t time period, cW(t) is the unit operation cost of the fan set in the period of t, r is the total number of the controllable distributed units which are operated in the current stage except the photovoltaic power generation units and the fan set, p is the total number of the photovoltaic power generation units which are operated in the current stage, and q is the total number of the fan set which is operated in the current stage.
And ii, correcting the output of the energy storage system:
taking the minimum fluctuation cost of the energy storage output as an objective function:
wherein the content of the first and second substances,
scheduling the total energy storage operation cost for the day before the time t,
a corrected value of the energy storage operation total cost is scheduled in real time; pk(t) and P'k(t) energy storage output before and after correction, cc(t) and c'c(t) costs of the day-ahead phase and the real-time charging phase, respectively, cd(t) and c'd(t) costs of the day-ahead stage and the real-time discharge stage, respectively, NtThe time is scheduled in real time.
The corrected distributed energy and the corrected stored energy output meet the following constraint conditions:
and (3) the corrected constraint conditions of the output of the energy storage equipment are as follows:
and (3) the corrected constraint conditions of the distributed power supply output are as follows:
and (3) system total load balance constraint:
other constraints of the real-time phase still need to satisfy the relevant constraints of the day-ahead phase.
And step 3: electric vehicle charging and discharging scheduling strategy
The charging and discharging control strategies of the electric vehicle are generally divided into two types: direct control and indirect control. The direct control means that the charging and discharging time of the electric automobile is limited, such as charging in valley time and discharging in peak time; the indirect control means that the charge and discharge behavior is guided by formulating the peak-to-valley time electricity price, the charge and discharge electricity price, and the like.
The invention combines direct control and indirect control, and controls the charging and discharging behaviors of the electric automobile on the basis of reasonable peak-valley time-of-use price and charging and discharging price. The peak-valley load time period of the power distribution network is determined according to historical data statistics, the V2G system is required to charge the electric automobile in the idle state in the peak-valley load time period, the electric automobile in the idle state participates in the peak-valley load time period to participate in the power system peak shaving service, and the step of participating in the power system peak shaving by the electric automobile is specifically that the electric automobile only discharges to the power distribution network on the premise of meeting the use of users.
The single electric vehicle charging and discharging scheduling strategy is as follows:
the electric automobile is in a static state and is just in a load valley period at the moment, and if the SOC of the electric automobile at the momenttNot satisfying SOCt≥SOCDriveControlling the charging of the electric automobile by using the V2G system;
II, if the state of charge of the electric automobile meets the SOCt≥SOCDriveAnd the electric vehicle is still in the load valley period, the V2G system controls the electric vehicle to continue charging until the charging period is over or the charging period is reached
SOCt＝SOCmax；
The electric vehicle is at rest and during peak load periods, if the electric vehicle is soState of charge at time of satisfying SOCt≥SOCmin+SOCDriveAnd controlling the electric automobile to discharge to the end of the load peak period or when the SOC is reached by using the V2G systemt＝SOCmin+SOCDriveThe discharge is terminated.
And controlling the charging and discharging of the electric automobile by using a V2G system according to the determined electric automobile charging and discharging scheduling strategy, thereby realizing the scheduling control of the charging and discharging of the electric automobile.
In the electric vehicle charge-discharge control method based on the V2G technology, a preferable scheme is designed by combining practical situations, namely the maximum charge state and the minimum charge state of the electric vehicle are respectively taken as
SOCmax＝1,SOCmin＝0.2。
The technical features and effects of the present invention will be further described with reference to the following detailed description.
Example 1:
the example analysis is carried out by taking a modified IEEE33 node power distribution network system as an example. In combination with the current development situation of electric vehicles at home and abroad, the embodiment makes the following assumptions for private electric vehicles in the example analysis:
the power consumption of the electric automobile is only related to the driving mileage and is unrelated to factors such as vehicle-mounted electronic equipment, road condition information and the like, the hundred-kilometer energy consumption of each electric automobile is 15 kW.h, and the battery capacity is uniformly distributed within 50-60 kW.h.
Secondly, the charging mode adopts conventional charging, and the charging current multiplying power is 0.2C.
The calculation is carried out by using a 33-node power distribution network shown in FIG. 1. The distributed energy configuration unit is shown as a table:
| serial number | Connection node | Type (B) | Rated capacity |
| 1 | A4 | Photovoltaic system | 300kW |
| 2 | A6 | Energy storage | 250kW·h |
| 3 | A8 | Wind power | 500kW |
| 4 | A9 | Photovoltaic system | 300kW |
| 5 | A10 | Gas combustion | 500kW |
| 6 | A12 | Energy storage | 250kW·h |
| 7 | A13 | Gas combustion | 500kW |
| 8 | A15 | Photovoltaic system | 250kW |
| 9 | A16 | Wind power | 300kW |
| 10 | A18 | Energy storage | 250kW·h |
| 11 | A19 | Photovoltaic system | 250kW |
| 12 | A21 | Wind power | 300kW |
| 13 | A23 | Photovoltaic system | 300kW |
| 14 | A25 | Energy storage | 500kW·h |
| 15 | A26 | Energy storage | 500kW·h |
| 16 | A28 | Photovoltaic system | 250kW |
| 17 | A29 | Wind power | 300kW |
| 18 | A30 | Gas combustion | 500kW |
| 19 | A32 | Energy storage | 500kW·h |
Through simulation calculation, the capacity of the active power distribution network for consuming renewable energy is obviously improved by combining the V2G technology, the load fluctuation of the power distribution network is stabilized, the economic benefit is improved by 8.69% through day-ahead scheduling and real-time scheduling optimization, and the photovoltaic energy consumption rate is improved by 13.71%.
In conclusion, the active power distribution network distributed energy control method combined with the V2G technology comprehensively considers factors such as peak-valley electricity price and the like under the condition of considering the charging and discharging factors of the electric automobile, improves the utilization rate of distributed energy on the premise of meeting the traveling requirements of the electric automobile, ensures effective charging in the valley period and participates in the peak clipping function of the system in the peak period; in the valley period, the output of the main network is reduced to a great extent by increasing the output of the wind power and the photovoltaic, so that the electricity purchasing cost of the power grid is reduced. The next day system operation strategy can be obtained through system day-ahead scheduling optimization, distributed energy output can be further reasonably arranged according to the current unit operation state through real-time scheduling optimization, the distributed energy utilization rate can be effectively improved, the system operation cost is reduced, and economic benefits are improved.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (7)
1. An active power distribution network scheduling method combined with a V2G technology is characterized in that the V2G technology is adopted to control charging and discharging of electric automobiles, a day-ahead scheduling optimization model of a main power distribution network is established by considering the influence of charging power of the electric automobiles connected into the active power distribution network on line outlet power, day-ahead scheduling values of distributed energy output and stored energy output are optimized in real time, the day-ahead scheduling optimization model of the active power distribution network takes the lowest total cost including distributed energy output cost, energy storage equipment charging and discharging cost and charging and discharging cost of the electric automobiles connected into the active power distribution network as an optimization target, and the distributed power generation unit output and the stored energy output are optimized under system balance constraint, active power distribution network output power constraint, distributed power generation unit output power constraint and energy storage equipment charging and discharging constraint, wherein,
the optimization target of the day-ahead scheduling optimization model of the active power distribution network is as follows:
wherein f is the number of feeders in the whole area, ce(t) is the electricity price of the e-th line during the period t,
the active power value of the exit of the e line in the t period, n is the number of distributed power generation units, cj(t) is the unit of the jth distributed generation unit in the t periodThe cost of the operation is reduced, and the operation cost is reduced,
the active power value of the jth distributed power generation unit in the t period, m is the number of energy storage devices, cc(t) and cd(t) charging cost and discharging cost of the energy storage device in the period t, cdThe value of (t) is 0, c when the energy storage device is in a charging statec(t) is 0 when the energy storage device is in a discharged state,
the active power value for charging or discharging the kth energy storage device in the t period,
for the value of the active network loss of the e-th line during the period t,
and
respectively represents the charging and discharging costs of the electric automobile connected to the e-th line in the t period,
and predicting power for charging and discharging of the electric automobile accessed to the e-th line in the t period.
2. The active power distribution network scheduling method combining the V2G technology of claim 1, wherein the method for controlling the charging and discharging of the electric vehicle by adopting the V2G technology comprises the following steps: the electric automobile in the idle state is charged in the peak-valley load period, and the electric automobile in the idle state discharges to the active power distribution network in the peak-valley load period on the premise of meeting the use requirements of users.
3. The active power distribution network dispatching method combining the V2G technology of claim 1, wherein an influence of charging power of electric vehicles connected to the active power distribution network on line outlet power is determined by an established active power distribution network charging and discharging power model, the active power distribution network power model is targeted at that a sum of charging power of electric vehicles charged at a load node is equal to a charging capacity of the load node, and the charging power provided by the load node for each connected electric vehicle is determined under load node charging capacity constraints, electric vehicle charging power constraints, and electric vehicle travel demand constraints.
4. The active power distribution network scheduling method in combination with the V2G technology of claim 1, wherein the method for optimizing the day-ahead scheduling values of distributed energy output and stored energy output in real time comprises: and correcting the output of the operated distributed power generation unit at the current stage by taking the output cost of the distributed units of different types into consideration and taking the minimum total operating cost of the distributed energy sources as a target, and correcting the stored energy output according to the energy storage operating cost at the current stage and taking the minimum fluctuation cost of the stored energy output as a target.
5. The active power distribution network scheduling method combining the V2G technology of claim 1, wherein the predicted charging and discharging power of the electric vehicle connected to the e-th line in the t period is predicted by using the following method: and predicting the charging time period and the charging power of the electric automobile according to the travel time probability distribution and the travel mileage probability distribution of the electric automobile, and predicting the discharging power of the electric automobile by adopting constant-power discharging to approximate the actual discharging process of the electric automobile.
6. The active power distribution network scheduling method combining the V2G technology according to claim 4, wherein the objective function for minimizing the total cost of distributed energy operation is as follows:
7. The active power distribution network scheduling method combining the V2G technology of claim 4, wherein the objective function with the minimum energy storage output fluctuation cost is as follows:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810224570.7A CN108520314B (en) | 2018-03-19 | 2018-03-19 | Active power distribution network scheduling method combined with V2G technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810224570.7A CN108520314B (en) | 2018-03-19 | 2018-03-19 | Active power distribution network scheduling method combined with V2G technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108520314A CN108520314A (en) | 2018-09-11 |
| CN108520314B true CN108520314B (en) | 2020-10-09 |
Family
ID=63433816
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810224570.7A Active CN108520314B (en) | 2018-03-19 | 2018-03-19 | Active power distribution network scheduling method combined with V2G technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108520314B (en) |
Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109149574A (en) * | 2018-09-27 | 2019-01-04 | 青岛特锐德电气股份有限公司 | Charging method and system based on electricity price |
| CN109768561A (en) * | 2018-12-17 | 2019-05-17 | 珠海格力电器股份有限公司 | A kind of Control of Electric Vehicles method and system |
| CN109710882B (en) * | 2018-12-21 | 2023-06-09 | 重庆大学 | Off-grid micro-grid electric vehicle ordered charge-discharge load modeling and solving method based on optimized operation |
| CN109918798B (en) * | 2019-03-11 | 2023-06-02 | 三峡大学 | Electric vehicle charging mode optimization method based on charging power level |
| CN109969011A (en) * | 2019-04-15 | 2019-07-05 | 国网山西省电力公司大同供电公司 | A kind of flexible multimode regulating switch regulating system for charging vehicle power distribution network |
| CN110303931B (en) * | 2019-05-29 | 2021-03-23 | 北京航盛新能科技有限公司 | Electric automobile intelligent charging method suitable for V2G |
| CN110429598A (en) * | 2019-09-10 | 2019-11-08 | 周封 | Power grid energy adjusts and the distributed flexible energy cell system of consumption |
| CN110739690A (en) * | 2019-10-31 | 2020-01-31 | 山东大学 | Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility |
| CN110943475A (en) * | 2019-11-08 | 2020-03-31 | 广东电网有限责任公司 | Distribution network collaborative optimization scheduling method considering distributed photovoltaic and electric automobile |
| CN111126765B (en) * | 2019-11-22 | 2022-05-06 | 广东电网有限责任公司 | Electric automobile group joint optimization method and system |
| CN110920459A (en) * | 2019-12-18 | 2020-03-27 | 福建星云电子股份有限公司 | Distributed energy storage method, device, equipment and medium for electric automobile |
| CN111469682B (en) * | 2020-04-02 | 2021-09-17 | 国网上海市电力公司 | Electric automobile real-time intelligent charging method based on day-ahead plan |
| CN111483331B (en) * | 2020-05-27 | 2021-07-09 | 南京德睿能源研究院有限公司 | Charging load aggregation scheduling control method and system based on scheduling instruction |
| CN111738518B (en) * | 2020-06-24 | 2022-02-11 | 国家电网公司西南分部 | Electric vehicle charging and discharging scheduling method based on average discharge rate |
| CN111969602B (en) * | 2020-08-14 | 2022-02-01 | 山东大学 | Day-ahead random optimization scheduling method and device for comprehensive energy system |
| CN112109580B (en) * | 2020-08-19 | 2022-04-05 | 同济大学 | Micro-grid electric automobile charge and discharge control system with electric quantity self-distribution function |
| CN112319304B (en) * | 2020-10-21 | 2022-10-14 | 国网天津市电力公司 | V2G double-layer charging and discharging control method for electric automobile |
| CN112801447A (en) * | 2020-12-22 | 2021-05-14 | 北京新能源汽车技术创新中心有限公司 | Intelligent charging network system and electric vehicle charging scheduling method based on same |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9054532B2 (en) * | 2012-03-02 | 2015-06-09 | Alstom Technology Ltd. | Dispatching vehicle-to-grid ancillary services with discrete switching |
| CN103679372B (en) * | 2013-12-18 | 2017-01-11 | 国家电网公司 | Hierarchical and coordinating charging control method for electric bus charging stations |
| CN105046371A (en) * | 2015-08-19 | 2015-11-11 | 东南大学 | Electric vehicle charge-discharge scheduling method based on demand side bidding |
| CN106160091B (en) * | 2016-07-25 | 2018-08-28 | 东南大学 | Promote the electric automobile charging station charge and discharge dispatching method of regenerative resource consumption |
-
2018
- 2018-03-19 CN CN201810224570.7A patent/CN108520314B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN108520314A (en) | 2018-09-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108520314B (en) | Active power distribution network scheduling method combined with V2G technology | |
| Wang et al. | Two-stage optimal scheduling strategy for large-scale electric vehicles | |
| CN110661246B (en) | Capacity optimization configuration method for urban rail transit photovoltaic energy storage system | |
| CN109217290B (en) | Microgrid energy optimization management method considering electric vehicle charging and discharging | |
| Yu et al. | A real time energy management for EV charging station integrated with local generations and energy storage system | |
| CN103679299A (en) | Electric automobile optimal peak-valley time-of-use pricing method giving consideration to owner satisfaction degree | |
| CN111626527B (en) | Intelligent power grid deep learning scheduling method considering fast/slow charging/discharging form of schedulable electric vehicle | |
| CN111310966A (en) | Micro-grid site selection and optimal configuration method containing electric vehicle charging station | |
| CN110126679B (en) | Method for acquiring optimal operating point of fuel cell | |
| Ivanova et al. | Coordinated charging of electric vehicles connected to a net-metered PV parking lot | |
| CN105958498A (en) | Electric-vehicle-considered unit commitment and time-of-use power price joint optimization method | |
| CN110868134A (en) | Photovoltaic power station three-time-period energy management method based on time-of-use electricity price and load characteristics | |
| CN107732937B (en) | Peak clipping and valley filling method for grid-connected micro-grid containing wind-light-storage-electric automobile | |
| CN115411756A (en) | Light storage charging station electric vehicle three-stage optimization method based on wolf algorithm | |
| CN113799640A (en) | Energy management method suitable for microgrid comprising electric vehicle charging pile | |
| CN114036459A (en) | Energy green degree calculation method for electric vehicle based on V2G scheduling response | |
| Yi et al. | Power demand side response potential and operating model based on EV mobile energy storage | |
| CN111681127A (en) | Orderly charging and discharging control method for electric automobile in residential community | |
| Zhiyong et al. | Optimization of orderly charge and discharge scheduling of electric vehicles and photovoltaic in industrial par | |
| CN109583136B (en) | Electric vehicle charging, replacing and storing integrated station model building method based on schedulable potential | |
| CN112072713A (en) | Power distribution network scheduling method and storage medium | |
| CN117353355A (en) | Wind power system optimization scheduling method considering regulation potential of electric automobile | |
| CN115882494B (en) | Hierarchical V2G scheduling optimization method considering time length difference of vehicle grid-connected service | |
| CN113852073B (en) | Day-ahead optimal scheduling method based on excitation-response charging decision estimation | |
| Lei et al. | Coordinated scheduling of electric vehicles and wind power generation considering vehicle to grid mode |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |