CN110348610A - A kind of power distribution network congestion management method based on poly-talented virtual plant - Google Patents
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Abstract
The present invention discloses a kind of power distribution network congestion management method based on poly-talented virtual plant, comprising the following steps: constructs the VPP economic load dispatching model for being up to optimization aim with VPP profit according to initial data, solves the model and obtain the scheduling scheme of each polymerized unit；Load flow calculation Optimized model is constructed, the voltage and phase angle of each node of power distribution network is obtained, and calculate each Line Flow, judges whether each Line Flow is out-of-limit；VPP Security corrective Optimized model is established if generation is out-of-limit to adjust number of nodes and the minimum optimization aim of system call interception amount, the single-object problem with priority is converted by multi-objective optimization question by introducing weight and maximum, obtains each polymerized unit correcting value.Such method can effectively eliminate backlog problem.
Description
Technical field
The invention belongs to electric power system power source scheduling field more particularly to a kind of power distribution networks based on poly-talented virtual plant
Congestion management method.
Background technique
With the fast development of science and technology, distributed generation resource ratio shared in area power grid is also increasing,
Influence of the unstability of output power to power grid is consequently increased.And virtual plant (Virtual Power Plant, VPP)
A kind of effective form exactly to solve the above problems.Distributed energy power generation has extremely strong fluctuation, and with per family with itself
Economic interests are target making generation schedule, will lead to load large-scale aggregating, keep distribution line trend out-of-limit, and route resistance occurs
Plug, influences the safe and stable operation of entire power grid.Therefore, it is necessary to study the Security corrective problems of VPP, when trend occurs for route
It more prescribes a time limit, carries out the congestion management of power distribution network in time.
When the requirement of distribution power Transmission, which is greater than actual fed, to be required, it may occur that power distribution network choking phenomenon influences user
Normal electricity trading program destroys the safe and highly efficient operation of power distribution network.Therefore, how power distribution network congestion management is carried out, to realization
The safe and economic operation of VPP is most important.And VPP can polymerize each distributed generation resource of distribution net side, and provide quick response
Ancillary service, therefore study how to eliminate power distribution network obstructing problem with real value by the way of VPP.
Summary of the invention
Goal of the invention: in view of the above problems, the present invention proposes a kind of power distribution network obstruction pipe based on poly-talented virtual plant
Reason method, this method are adjusted by the scheduling scheme to each polymerized unit of VPP, can effectively eliminate system line overload,
And cutting load amount can be reduced as far as possible, to reduce the economic loss of VPP.
Technical solution: to achieve the purpose of the present invention, the technical scheme adopted by the invention is that: one kind being based on poly-talented void
The power distribution network congestion management method of quasi- power plant, includes the following steps:
Step 1, initial data is set, uses Monte Carlo Method to generate photovoltaic scene uncertain to describe photovoltaic power output；
Building is up to the VPP economic load dispatching model of optimization aim with VPP profit；Construct model constraint condition；The raw data packets
It includes: with network parameters, each polymerized unit parameter of VPP, market guidance parameter and photovoltaic power generation output forecasting data；The model is solved to obtain
The scheduling scheme of each polymerized unit of VPP；
Step 2, using the quadratic sum minimum of the unbalanced power amount of all nodes of power distribution network as objective function, trend is constructed
Calculation optimization model；Construct model constraint condition；The voltage and phase angle of each node of power distribution network are obtained by Load flow calculation, and is calculated
Each Line Flow judges whether each Line Flow is out-of-limit；
Step 3, if generation is out-of-limit, VPP safety is established to adjust number of nodes and the minimum optimization aim of system call interception amount
Correct Optimized model；Construct model constraint condition；VPP Security corrective Optimized model is solved, each polymerized unit correction of VPP is obtained
Amount, is adjusted each polymerized unit scheduling scheme of VPP in step 1 according to the correcting value, to eliminate system line overload.
Further, constraint condition described in step 1 include: gas turbine constraint, interruptible load constraint, EV charging station about
Beam, the constraint of transferable load, VPP transaction Constraint, VPP power-balance constraint.
Further, constraint condition described in step 2 includes: the known quantity constraint of power flow equation constraint, Load flow calculation.
Further, constraint condition described in step 3 includes: power-balance constraint, the constraint of node controlled variable, load bus power
Factor constraint, system safety operation constraint, trading volume deviation constraint.
Further, step 1 building is up to the VPP economic load dispatching model of optimization aim with VPP profit；Construct model
Constraint condition；The following steps are included:
The optimization aim of the step 1.1:VPP owner is that whole profit is maximum, including participation Day-ahead electricity market is resulting
The objective function of income, the operation of gas turbine and start-up and shut-down costs and interruptible load cost, VPP economic load dispatching model indicates
Are as follows:
Wherein, number of segment when T is one day total；nsFor photovoltaic power output scene number, π (s) is the general of s group photovoltaic power output scene
Rate；λtFor the Electricity Price of period t；Gs,tFor s group photovoltaic contribute scene lower period t VPP power market transaction amount,
Its value, which is positive, indicates VPP to electricity market sale of electricity, and value, which is negative, indicates VPP from electricity market power purchase；For gas turbine when
The operating cost of section t；For Boolean variable, indicate whether gas turbine starts, when starting sets 1, and 0 is set when not starting；Sf
For the start-up cost of gas turbine；For controllable burden cost；
The operating cost of gas turbine is indicated with piecewise linear function:
Wherein, u is the fixed cost of gas turbine；It for Boolean variable, indicates whether gas turbine works, works
WhenWhen not workingZ is cost of electricity-generating curve segmentation number；kjIt is oblique for gas turbine jth section cost of electricity-generating
Rate；It contributes for t period gas turbine in the jth section that s group photovoltaic is contributed under scene；
Interruptible load cost is the interruptible load reimbursement for expenses that VPP is paid to user, it is contemplated that different interruptible load amounts
Influence to user is different, will interrupt making up price and load rejection grade is linked up with, interrupt level is higher, the compensation of required payment
Price is higher, indicates are as follows:
In formula: nmFor interrupt level number；For m grades of interruptible load making up prices；It is interrupted for m grades of the t period negative
Lotus amount is decision variable；
Step 1.2: the constraint condition of building VPP economic load dispatching model, the constraint condition include:
(1) constraint condition of VPP gas turbine:
Wherein,WithRespectively t period and t-1 period gas turbine always going out under s group photovoltaic power output scene
Power；rampd,rampuThe respectively downwardly and upwardly climbing rate of gas turbine；Respectively gas turbine minimum and
Maximum output；For Boolean variable, indicate whether t-1 period gas turbine works, when workIt does not work
When
(2) interruptible load constraint condition:
In formula:For m grades of interruptible load amounts；For the m grades of interruptible load amount upper limits；WithRespectively
For total interruptible load amount of t period and t-1 period；Lc,maxFor the interruptible load maximum interruption amount in continuous time period；
(3) electric car (EV) charging station constraint condition:
In formula:The respectively charge capacity bound of EV charging station；WithRespectively the t period and
The charge capacity of t-1 v EV charging stations of period；Respectively v EV charging station charge/discharge
Power and its upper limit；The efficiency for charge-discharge of respectively v EV charging stations；For Boolean variable, table
Show EV charging station whether charge and discharge；
(4) transferable load constraint condition:
In formula,Pload,max、Pload,minPower load and its bound are supplied for VPP；eloadFor intraday minimum
Workload demand；
(5) VPP transaction Constraint condition:
-Gmax≤Gs,t≤Gmax
In formula, GmaxFor VPP electricity market the trading volume upper limit；
(6) VPP power-balance constraint condition:
In formula, gs,tIt contributes for photovoltaic plant under t period s kind scene；WithRespectively always the filling of EV charging station/
Discharge power；It is VPP to load electricity sales amount.
Further, the step 2 establishes Load flow calculation Optimized model, comprising the following steps:
Step 2.1: using the quadratic sum minimum of the unbalanced power amount of all nodes of power distribution network as objective function, target letter
Number indicates are as follows:
In formula, f is the quadratic sum of amount of unbalance；ΔPi、ΔQiIndicate the unbalanced power amount of node i；nbFor power distribution network section
Points；
Step 2.2: the constraint condition of building Load flow calculation Optimized model, the constraint condition include:
(1) power flow equation constraint condition:
In formula, Pi=PGi-PDiIndicate the active injection power of node i, value is node generated power power output PGiWith section
Point burden with power PDiDifference；Qi=QGi-QDiIndicate the idle injecting power of node i, value is node generator reactive power output
QGiWith node load or burden without work QDiDifference；Ui、UjThe respectively voltage magnitude of node i and node j；θij=θi-θjFor node i and
The phase difference of voltage of node j, θiFor the voltage phase angle of node i, θjFor the voltage phase angle of node j；GijAnd BijRespectively node is led
Receive the real and imaginary parts of the i-th row jth column element in matrix；
(2) Load flow calculation known quantity constraint condition:
In formula,Respectively indicate Ui、θiAnd PGiGiven value；NPV、NPHIt respectively indicates by PV node, put down
The set that the node serial number of node that weighs forms.
Further, the step 3 establishes VPP Security corrective Optimized model, comprising the following steps:
Step 3.1: establishing VPP Security corrective optimization mould to adjust number of nodes and the minimum optimization aim of system call interception amount
Type, model objective function indicate are as follows:
In formula, f1For the sum of adjustment number of nodes；f2For the sum of system call interception amount；Boolean variable biCharacterize the adjustment shape of node i
State: bi=0 expression node i is not involved in adjustment, bi=1 indicates that node i participates in adjustment；dPi、dQiRespectively represent the active tune of node i
Whole amount and idle adjustment amount；
It is divided into generator node and load bus by the connect load type of node, generator node regulator generator is contributed,
Load bus adjusts cutting load amount, assigns generator node the weight different with load bus, to guarantee the preferential of Security corrective
Property；Objective function can convert are as follows:
In formula, f3For the sum of meter and adjustment number of nodes of weight coefficient；f4For meter and weight coefficient system call interception amount it
With；Wi、Wi' it is respectively the weight coefficient that node i adjusts state and adjustment amount；
Adjustment amount is advanced optimized on the basis of optimization determines minimum adjustment number of nodes, is introduced using Maximum Approach very big
Value M makes multi-objective optimization question be converted into the optimization problem with priority, objective function conversion are as follows:
In formula, f5For the objective function of single-object problem；
Step 3.2: the constraint condition of building VPP Security corrective Optimized model, the constraint condition include:
(1) power-balance constraint condition:
In formula,Indicate that the initial active power of node i, value are the initial active power output of node generatorWith the initial burden with power of nodeDifference；Indicate that the initial reactive power of node i, value are section
Put generator initially idle power outputWith the initial load or burden without work of nodeDifference；
(2) node controlled variable constraint condition:
In formula, Pi The respectively adjustable active power bound of node i, if node i is generator node, value point
Not Wei node i the active power output upper limitAnd lower limitPGi ； Qi The respectively adjustable reactive power bound of node i, if section
Point i is generator node, and value is respectively the idle power output upper limit of node iAnd lower limitQGi ；For load bus, WithValue by actual conditions set；
(3) load bus power factor constraint condition:
(4) system safety operation constraint condition:
In formula, PijIndicate the effective power flow of route i-j； Pij Respectively indicate the upper lower limit value of route i-j effective power flow； Ui Respectively indicate the bound of node i voltage magnitude； θi Respectively indicate the bound of node i voltage phase angle；
(5) trading volume deviation constraint condition:
-hd·G0≤dG≤hd·G0
In formula, hd is trading volume tolerance；G0For the VPP Market clearing quantity before Security corrective；dGFor departure of trading.
The utility model has the advantages that compared with prior art, technical solution of the present invention has technical effect beneficial below: the present invention
The power distribution network congestion management method containing VPP is proposed, the power distribution network congestion management model based on VPP is established, can effectively eliminate
System line overload, and ensure that VPP is certain at least for optimization aim with the number of nodes of adjustment needed for Security corrective and adjustment amount
Economic benefit.
Detailed description of the invention
Fig. 1 is VPP pilot project test macro schematic diagram；
Fig. 2 be per period interruptible load amount and transferable load spirogram；
Fig. 3 is Electricity Price figure；
Fig. 4 is photovoltaic power output scene figure；
In the case of Fig. 5 is operating condition 1 and operating condition 2, the correcting value of each polymerized unit of VPP compares figure；
In the case of Fig. 6 is operating condition 1 and operating condition 3, the correcting value of each polymerized unit of VPP compares figure；
Fig. 7 is flow chart of the invention.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawings and examples.
A kind of power distribution network congestion management method based on poly-talented virtual plant of the present invention, as shown in fig. 7, comprises
Following steps:
Step 1, initial data is set, uses Monte Carlo Method to generate photovoltaic scene uncertain to describe photovoltaic power output；
Building is up to the VPP economic load dispatching model of optimization aim with VPP profit；Construct model constraint condition；The raw data packets
It includes: with network parameters, each polymerized unit parameter of VPP, market guidance parameter and photovoltaic power generation output forecasting data；The constraint condition packet
Include: gas turbine constraint, interruptible load constraint, EV charging station constraint, transferable load constraint, VPP transaction Constraint,
VPP power-balance constraint；It solves the model and obtains the scheduling scheme of each polymerized unit of VPP；
Step 2, using the quadratic sum minimum of the unbalanced power amount of all nodes of power distribution network as objective function, trend is constructed
Calculation optimization model；Construct model constraint condition；The constraint condition includes: the known quantity of power flow equation constraint, Load flow calculation
Constraint；The voltage and phase angle of each node of power distribution network are obtained by Load flow calculation, and calculates each Line Flow, judges each Line Flow
It is whether out-of-limit；
Step 3, if generation is out-of-limit, VPP safety is established to adjust number of nodes and the minimum optimization aim of system call interception amount
Correct Optimized model；Construct model constraint condition；The constraint condition includes: power-balance constraint, the constraint of node controlled variable, bears
The constraint of lotus node power factor, system safety operation constraint, trading volume deviation constraint；VPP Security corrective Optimized model is solved, is obtained
To each polymerized unit correcting value of VPP, each polymerized unit scheduling scheme of VPP in step 1 is adjusted according to the correcting value, from
And eliminate system line overload.
The present embodiment calls GAMS software to solve VPP Security corrective Optimized model, the results showed that the model can effectively eliminate
System line overload, and with the number of nodes of adjustment needed for Security corrective and adjustment amount at least for optimization aim, by the way that weight is arranged
Coefficient can reduce cutting load amount to a certain extent, to reduce the economic loss of VPP.
The step 1 establishes VPP economic load dispatching model, establishes virtual plant economic load dispatching objective function and constraint condition,
The following steps are included:
The optimization aim of the step 1.1:VPP owner is that whole profit is maximum, including participation Day-ahead electricity market is resulting
The objective function of income, the operation of gas turbine and start-up and shut-down costs and interruptible load cost, VPP economic load dispatching model indicates
Are as follows:
Wherein, number of segment when T is one day total；nsFor photovoltaic power output scene number, π (s) is the general of s group photovoltaic power output scene
Rate；λtFor the Electricity Price of period t；Gs,tFor s group photovoltaic contribute scene lower period t VPP power market transaction amount,
Its value, which is positive, indicates VPP to electricity market sale of electricity, and value, which is negative, indicates VPP from electricity market power purchase；For gas turbine when
The operating cost of section t；For Boolean variable, indicate whether gas turbine starts, when starting sets 1, and 0 is set when not starting；Sf
For the start-up cost of gas turbine；For controllable burden cost；
The operating cost of gas turbine is indicated with piecewise linear function:
Wherein, u is the fixed cost of gas turbine；It for Boolean variable, indicates whether gas turbine works, works
WhenWhen not workingZ is cost of electricity-generating curve segmentation number；kjIt is oblique for gas turbine jth section cost of electricity-generating
Rate；It contributes for t period gas turbine in the jth section that s group photovoltaic is contributed under scene；
Interruptible load cost is the interruptible load reimbursement for expenses that VPP is paid to user, it is contemplated that different interruptible load amounts
Influence to user is different, will interrupt making up price and load rejection grade is linked up with, interrupt level is higher, the compensation of required payment
Price is higher, indicates are as follows:
In formula: nmFor interrupt level number；For m grades of interruptible load making up prices；It is interrupted for m grades of the t period negative
Lotus amount is decision variable；
Step 1.2: the constraint condition of building VPP economic load dispatching model, the constraint condition include:
(1) constraint condition of VPP gas turbine:
Wherein,WithRespectively t period and t-1 period gas turbine always going out under s group photovoltaic power output scene
Power；rampd,rampuThe respectively downwardly and upwardly climbing rate of gas turbine；Respectively gas turbine minimum and
Maximum output；For Boolean variable, indicate whether t-1 period gas turbine works, when workIt does not work
When
(2) interruptible load constraint condition:
In formula:For m grades of interruptible load amounts；For the m grades of interruptible load amount upper limits；WithRespectively
For total interruptible load amount of t period and t-1 period；Lc,maxFor the interruptible load maximum interruption amount in continuous time period；
(3) electric car (EV) charging station constraint condition:
In formula:The respectively charge capacity bound of EV charging station；WithThe respectively t period
With the charge capacity of t-1 v EV charging stations of period；Respectively v EV charging station discharge/charge
Electrical power and its upper limit；The efficiency for charge-discharge of respectively v EV charging stations；For Boolean variable, table
Show EV charging station whether charge and discharge；
(4) transferable load constraint condition:
VPP carries out coordination regulation to transferable load can be low to avoid the electricity consumption spiking problems and load of load boom period
The power wastage problem of paddy phase, needs to meet following constraint:
In formula,Pload,max、Pload,minPower load and its bound are supplied for VPP；eloadFor intraday minimum
Workload demand；
(5) VPP transaction Constraint condition:
In view of VPP and major network transimission power are limited, VPP need to meet following constraint formula in the transaction electricity of electricity market:
-Gmax≤Gs,t≤Gmax
In formula, GmaxFor VPP electricity market the trading volume upper limit；
(6) VPP power-balance constraint condition:
In formula, gs,tIt contributes for photovoltaic plant under t period s kind scene；WithRespectively always the filling of EV charging station/
Discharge power；It is VPP to load electricity sales amount.
The step 2 establishes Load flow calculation Optimized model, comprising the following steps:
Step 2.1: using the quadratic sum minimum of the unbalanced power amount of all nodes of power distribution network as objective function, target letter
Number indicates are as follows:
In formula, f is the quadratic sum of amount of unbalance；ΔPi、ΔQiIndicate the unbalanced power amount of node i；nbFor power distribution network section
Points；
Step 2.2: the constraint condition of building Load flow calculation Optimized model, the constraint condition include:
(1) power flow equation constraint condition:
In formula, Pi=PGi-PDiIndicate the active injection power of node i, value is node generated power power output PGiWith section
Point burden with power PDiDifference；Qi=QGi-QDiIndicate the idle injecting power of node i, value is node generator reactive power output
QGiWith node load or burden without work QDiDifference；Ui、UjThe respectively voltage magnitude of node i and node j；θij=θi-θjFor node i and
The phase difference of voltage of node j, θiFor the voltage phase angle of node i, θjFor the voltage phase angle of node j；GijAnd BijRespectively node is led
Receive the real and imaginary parts of the i-th row jth column element in matrix；
(2) Load flow calculation known quantity constraint condition:
In formula,Respectively indicate Ui、θiAnd PGiGiven value；NPV、NPHIt respectively indicates by PV node, put down
The set that the node serial number of node that weighs forms.
The step 3 establishes VPP Security corrective Optimized model, comprising the following steps:
Step 3.1: establishing VPP Security corrective optimization mould to adjust number of nodes and the minimum optimization aim of system call interception amount
Type, model objective function indicate are as follows:
In formula, f1For the sum of adjustment number of nodes；f2For the sum of system call interception amount；Boolean variable biCharacterize the adjustment shape of node i
State: bi=0 expression node i is not involved in adjustment, bi=1 indicates that node i participates in adjustment；dPi、dQiRespectively represent the active tune of node i
Whole amount and idle adjustment amount.
Generator node and load bus two major classes can be divided by the connect load type of node, generator node adjusts power generation
Machine power output, load bus adjust cutting load amount.However, generator output should be optimized and revised in order to guarantee economic benefit.Therefore,
It assigns generator node the weight different with load bus, is taken as 1 and 100, respectively to guarantee the priority of Security corrective.Target
Function can convert are as follows:
In formula, f3For the sum of meter and adjustment number of nodes of weight coefficient；f4For meter and weight coefficient system call interception amount it
With；Wi、Wi' it is respectively the weight coefficient that node i adjusts state and adjustment amount, it is taken as 1 and 0.01 respectively；
This model needs to advanced optimize adjustment amount on the basis of optimization determines minimum adjustment number of nodes, therefore can adopt
Single-object problem is converted by multi-objective optimization question with Maximum Approach, i.e. introducing maximum M, M value is 1000, is made more
Objective optimisation problems are converted into the optimization problem with priority, objective function conversion are as follows:
In formula, f5For the objective function of single-object problem；
Step 3.2: the constraint condition of building VPP Security corrective Optimized model, the constraint condition include:
(1) power-balance constraint condition:
In formula,Indicate that the initial active power of node i, value are the initial active power output of node generatorWith the initial burden with power of nodeDifference；Indicate that the initial reactive power of node i, value are node
The initial idle power output of generatorWith the initial load or burden without work of nodeDifference；
(2) node controlled variable constraint condition:
In formula, Pi The respectively adjustable active power bound of node i, if node i is generator node, value point
Not Wei node i the active power output upper limitAnd lower limitPGi ； Qi The respectively adjustable reactive power bound of node i, if section
Point i is generator node, and value is respectively the idle power output upper limit of node iAnd lower limitQGi ；For load bus, WithValue by actual conditions set；
(3) load bus power factor constraint condition:
(4) system safety operation constraint condition:
In formula, PijIndicate the effective power flow of route i-j, Pij Respectively indicate the upper lower limit value of route i-j effective power flow； Ui Respectively indicate the bound of node i voltage magnitude； θi Respectively indicate the bound of node i voltage phase angle；
(5) trading volume deviation constraint condition:
After Security corrective, VPP is corrected trade contract, it is therefore desirable to guarantee trading volume deviation in certain model
In enclosing:
-hd·G0≤dG≤hd·G0
In formula, hd is trading volume tolerance；G0For the VPP Market clearing quantity before Security corrective；dGFor departure of trading.
The present embodiment chooses somewhere real data as example basis, improves to 56 node power distribution net of somewhere,
Resulting VPP pilot project test macro is as shown in Figure 1.Contain at gas turbine unit 1 in the VPP, it, can at photovoltaic plant 15
At interruptible load 3, at central air conditioner system (transferable load) 2, at EV charging station 2.Gas turbine unit uses CENTAUR40
Model, rated capacity 3.515MW, climbing rate are 1.8MW/h, and start-up and shut-down costs are 18 euro, indicate to fire using piecewise linear function
The operating cost of gas-turbine, wherein the 1st, 2 slope over 10 are respectively 20 euro/MW and 50 euro/MW.The maximum charge and discharge electric work of EV charging station
Rate is 0.65MW, and efficiency for charge-discharge is 90%.The interruptible load amount and transferable load of per period as shown in Fig. 2,
Electricity Price is as shown in figure 3, photovoltaic power output scene is as shown in Figure 4.
Firstly, that is, in the case of operating condition 1, VPP is by carrying out each distributed generation resource when this area's power distribution network operates normally
Coordinated scheduling, to realize the maximum target of itself profit.But when unplanned outage occurs, power distribution network probably generates tide
Flow out-of-limit problem.Therefore, in order to ensure that the safe and stable operation of electric system, needs to the power distribution network in the case of unplanned outage
Load flow calculation is carried out, and judges whether distribution power flow is out-of-limit on this basis.The present embodiment is provided with two kinds and unplanned stops
Transport situation, respectively operating condition 2 and operating condition 3.In the case of operating condition 2, unplanned outage occurs in 11-14h, distribution network line 27-28,
Route 44-51 is computed that there are the out-of-limit problems of trend.In the case of operating condition 3, non-meter occurs in 11-14h, distribution network line 44-51
It draws and stops transport, route 9-13 is computed that there are the out-of-limit problems of trend.Table 1 lists the trend feelings of operating condition 2 and the lower critical circuits of operating condition 3
Condition, thermostabilization limit value.Table 2 lists under two kinds of operating conditions VPP to the Security corrective optimum results of critical circuits.
Table 1
Table 2
As shown in Table 2, for the out-of-limit problem of the trend generated in the case of unplanned outage, the safe school the VPP used herein
Positive Optimized model can effectively eliminate circuit overload.Due to the presence of weight coefficient, which can be one
Determine to reduce cutting load amount in degree and to reduce the economic loss of VPP improves the whole profit of VPP.Meanwhile under two kinds of operating conditions
Correction calculate the time in 2s or so, can satisfy the efficiency requirements in Practical Project.
In addition, the model with the number of nodes of adjustment needed for Security corrective at least for optimization aim, therefore adjust involved in
Number of nodes is less, and general warranty is within 4.But due to the presence of power distribution network power-balance constraint, when VPP is to a certain distribution
When formula power supply coordinate adjustment, necessarily other nodes are impacted, therefore adjust related number of nodes at least two.
To VPP in operating condition 1 and operating condition 2, the correcting value of each polymerized unit compares Fig. 5；Fig. 5 (a) is school
The gas turbine power generation amount of positive front and back；Fig. 5 (b) is correction front and back EV charging station charge volume (node 3)；Fig. 5 (c) is correction front and back
EV charging station charge volume (node 36)；Fig. 5 (d) is correction front and back power market transaction amount.
As shown in Figure 5, at operating condition 2, scheduling scheme and electricity market of the VPP to gas turbine, EV charging station
Trading volume be adjusted, can eliminate route generation overload situations.
To VPP in operating condition 1 and operating condition 3, the correcting value of each polymerized unit compares Fig. 6.Fig. 6 (a) is school
Positive front and back load rejection amount (node 15)；Fig. 6 (b) is correction front and back gas turbine power generation amount.
It will be appreciated from fig. 6 that VPP is adjusted the scheduling scheme of gas turbine, interruptible load at operating condition 3, with
Eliminate the overload situations that route occurs.
Above simulation results show effectiveness of the invention and practicability.The invention can be from the level pair of sacurity dispatching
The Economic Scheduling Policy of VPP is modified and dispatches again, can carry out congestion management to power distribution network, eliminate circuit overload problem,
Guarantee the safe and stable operation of power grid.
The above examples only illustrate the technical idea of the present invention, and this does not limit the scope of protection of the present invention, all
According to the technical idea provided by the invention, any changes made on the basis of the technical scheme each falls within the scope of the present invention
Within.
Claims (7)
1. a kind of power distribution network congestion management method based on poly-talented virtual plant, it is characterised in that: this method includes following step
It is rapid:
Step 1, initial data is set, uses Monte Carlo Method to generate photovoltaic scene uncertain to describe photovoltaic power output；Building
It is up to the VPP economic load dispatching model of optimization aim with VPP profit；Construct model constraint condition；The initial data includes: matching
Each polymerized unit parameter of network parameters, VPP, market guidance parameter and photovoltaic power generation output forecasting data；Solving the model, to obtain VPP each
The scheduling scheme of polymerized unit；
Step 2, using the quadratic sum minimum of the unbalanced power amount of all nodes of power distribution network as objective function, Load flow calculation is constructed
Optimized model；Construct model constraint condition；The voltage and phase angle of each node of power distribution network are obtained by Load flow calculation, and calculates each line
Road trend judges whether each Line Flow is out-of-limit；
Step 3, if generation is out-of-limit, VPP Security corrective is established to adjust number of nodes and the minimum optimization aim of system call interception amount
Optimized model；Construct model constraint condition；VPP Security corrective Optimized model is solved, each polymerized unit correcting value of VPP, root are obtained
Each polymerized unit scheduling scheme of VPP in step 1 is adjusted according to the correcting value, to eliminate system line overload.
2. a kind of power distribution network congestion management method based on poly-talented virtual plant according to claim 1, feature exist
In: constraint condition described in step 1 includes: gas turbine constraint, interruptible load constraint, the constraint of EV charging station, transferable load
Constraint, VPP transaction Constraint, VPP power-balance constraint.
3. a kind of power distribution network congestion management method based on poly-talented virtual plant according to claim 1, feature exist
In: constraint condition described in step 2 includes: the known quantity constraint of power flow equation constraint, Load flow calculation.
4. a kind of power distribution network congestion management method based on poly-talented virtual plant according to claim 1, feature exist
In: constraint condition described in step 3 includes: power-balance constraint, the constraint of node controlled variable, the constraint of load bus power factor, is
System safe operation constraint, trading volume deviation constraint.
5. a kind of power distribution network congestion management method based on poly-talented virtual plant according to claim 1 or 2, feature
Be: step 1 building is up to the VPP economic load dispatching model of optimization aim with VPP profit；Construct model constraint condition；
The following steps are included:
The optimization aim of the step 1.1:VPP owner is that whole profit is maximum, including participates in the resulting income of Day-ahead electricity market,
The objective function of the operation of gas turbine and start-up and shut-down costs and interruptible load cost, VPP economic load dispatching model indicates are as follows:
Wherein, number of segment when T is one day total；nsFor photovoltaic power output scene number, π (s) is the probability of s group photovoltaic power output scene；λt
For the Electricity Price of period t；Gs,tFor the VPP power market transaction amount of s group photovoltaic power output scene lower period t, value is
Positive to indicate VPP to electricity market sale of electricity, value, which is negative, indicates VPP from electricity market power purchase；It is gas turbine period t's
Operating cost；For Boolean variable, indicate whether gas turbine starts, when starting sets 1, and 0 is set when not starting；SfFor combustion gas
The start-up cost of turbine；For controllable burden cost；
The operating cost of gas turbine is indicated with piecewise linear function:
Wherein, u is the fixed cost of gas turbine；For Boolean variable, indicate whether gas turbine works, when workWhen not workingZ is cost of electricity-generating curve segmentation number；kjIt is oblique for gas turbine jth section cost of electricity-generating
Rate；It contributes for t period gas turbine in the jth section that s group photovoltaic is contributed under scene；
Interruptible load cost is the interruptible load reimbursement for expenses that pays to user of VPP, it is contemplated that different interruptible load amounts to
The influence at family is different, will interrupt making up price and load rejection grade is linked up with, interrupt level is higher, the making up price of required payment
It is higher, it indicates are as follows:
In formula: nmFor interrupt level number；For m grades of interruptible load making up prices；For m grades of interruptible loads of t period
Amount is decision variable；
Step 1.2: the constraint condition of building VPP economic load dispatching model, the constraint condition include:
(1) constraint condition of VPP gas turbine:
Wherein,WithThe respectively gross capability of t period and t-1 period gas turbine under s group photovoltaic power output scene；
rampd,rampuThe respectively downwardly and upwardly climbing rate of gas turbine；The respectively minimum and maximum of gas turbine
Power output；For Boolean variable, indicate whether t-1 period gas turbine works, when workWhen not working
(2) interruptible load constraint condition:
In formula:For m grades of interruptible load amounts；For the m grades of interruptible load amount upper limits；WithWhen respectively t
Total interruptible load amount of section and t-1 period；Lc,maxFor the interruptible load maximum interruption amount in continuous time period；
(3) electric car (EV) charging station constraint condition:
In formula:The respectively charge capacity bound of EV charging station；WithRespectively t period and t-1
The charge capacity of v EV charging stations of period；Respectively v EV charging station charge/discharge power
And its upper limit；The efficiency for charge-discharge of respectively v EV charging stations；For Boolean variable, indicate that EV fills
Power station whether charge and discharge；
(4) transferable load constraint condition:
In formula,Pload,max、Pload,minPower load and its bound are supplied for VPP；eloadFor intraday minimum load
Demand；
(5) VPP transaction Constraint condition:
-Gmax≤Gs,t≤Gmax
In formula, GmaxFor VPP electricity market the trading volume upper limit；
(6) VPP power-balance constraint condition:
In formula, gs,tIt contributes for photovoltaic plant under t period s kind scene；WithRespectively total charge/discharge of EV charging station
Power；It is VPP to load electricity sales amount.
6. a kind of power distribution network congestion management method based on poly-talented virtual plant according to claim 1 or 3, feature
Be: the step 2 establishes Load flow calculation Optimized model, comprising the following steps:
Step 2.1: using the quadratic sum minimum of the unbalanced power amount of all nodes of power distribution network as objective function, objective function table
It is shown as:
In formula, f is the quadratic sum of amount of unbalance；ΔPi、ΔQiIndicate the unbalanced power amount of node i；nbFor power distribution network node
Number；
Step 2.2: the constraint condition of building Load flow calculation Optimized model, the constraint condition include:
(1) power flow equation constraint condition:
In formula, Pi=PGi-PDiIndicate the active injection power of node i, value is node generated power power output PGiHave with node
Workload PDiDifference；Qi=QGi-QDiIndicate the idle injecting power of node i, value is node generator reactive power output QGiWith
Node load or burden without work QDiDifference；Ui、UjThe respectively voltage magnitude of node i and node j；θij=θi-θjFor node i and node j
Phase difference of voltage, θiFor the voltage phase angle of node i, θjFor the voltage phase angle of node j；GijAnd BijRespectively node admittance matrix
In the i-th row jth column element real and imaginary parts；
(2) Load flow calculation known quantity constraint condition:
In formula,Respectively indicate Ui、θiAnd PGiGiven value；NPV、NPHIt respectively indicates by PV node, balance section
The set of the node serial number composition of point.
7. a kind of power distribution network congestion management method based on poly-talented virtual plant according to claim 6, feature exist
In: the step 3 establishes VPP Security corrective Optimized model, comprising the following steps:
Step 3.1: establishing VPP Security corrective Optimized model, mould to adjust number of nodes and the minimum optimization aim of system call interception amount
Type objective function indicates are as follows:
In formula, f1For the sum of adjustment number of nodes；f2For the sum of system call interception amount；Boolean variable biCharacterize the adjustment state of node i: bi
=0 expression node i is not involved in adjustment, bi=1 indicates that node i participates in adjustment；dPi、dQiRespectively represent the active power adjustment amount of node i
And idle adjustment amount；
It is divided into generator node and load bus, generator node regulator generator power output, load by the connect load type of node
Node adjusts cutting load amount, assigns generator node the weight different with load bus；Objective function can convert are as follows:
In formula, f3For the sum of meter and adjustment number of nodes of weight coefficient；f4For the sum of meter and system call interception amount of weight coefficient；Wi、
Wi' it is respectively the weight coefficient that node i adjusts state and adjustment amount；
Adjustment amount is advanced optimized on the basis of optimization determines minimum adjustment number of nodes, maximum M is introduced using Maximum Approach
Multi-objective optimization question is set to be converted into the optimization problem with priority, objective function conversion are as follows:
In formula, f5For the objective function of single-object problem；
Step 3.2: the constraint condition of building VPP Security corrective Optimized model, the constraint condition include:
(1) power-balance constraint condition:
In formula,Indicate that the initial active power of node i, value are the initial active power output of node generator
With the initial burden with power of nodeDifference；Indicate that the initial reactive power of node i, value are node hair
The initial idle power output of motorWith the initial load or burden without work of nodeDifference；
(2) node controlled variable constraint condition:
In formula, Pi The respectively adjustable active power bound of node i, if node i is generator node, value is respectively
The active power output upper limit of node iAnd lower limitPGi ； Qi The respectively adjustable reactive power bound of node i, if node i
For generator node, value is respectively the idle power output upper limit of node iAnd lower limitQGi ；For load bus, WithValue by actual conditions set；
(3) load bus power factor constraint condition:
(4) system safety operation constraint condition:
In formula, PijIndicate the effective power flow of route i-j； Pij Respectively indicate the upper lower limit value of route i-j effective power flow； Ui Respectively indicate the bound of node i voltage magnitude； θi Respectively indicate the bound of node i voltage phase angle；
(5) trading volume deviation constraint condition:
-hd·G0≤dG≤hd·G0
In formula, hd is trading volume tolerance；G0For the VPP Market clearing quantity before Security corrective；dGFor departure of trading.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111900728A (en) * | 2020-07-16 | 2020-11-06 | 江苏电力交易中心有限公司 | Block chain-based power distribution network blockage elimination method and system |
| CN112271724A (en) * | 2020-10-13 | 2021-01-26 | 国网上海市电力公司 | Virtual power plant partition construction model and construction method based on voltage regulation |
| CN113077073A (en) * | 2021-03-02 | 2021-07-06 | 上海电力大学 | Power distribution network multiple-blocking control method based on load aggregation quotient optimal grading |
| CN113792995A (en) * | 2021-08-26 | 2021-12-14 | 中国南方电网有限责任公司 | Method, device, equipment and storage medium for determining power resource dominance degree |
-
2019
- 2019-06-20 CN CN201910535212.2A patent/CN110348610A/en not_active Withdrawn
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111900728A (en) * | 2020-07-16 | 2020-11-06 | 江苏电力交易中心有限公司 | Block chain-based power distribution network blockage elimination method and system |
| CN111900728B (en) * | 2020-07-16 | 2023-08-04 | 江苏电力交易中心有限公司 | Block chain-based power distribution network blocking elimination method and system |
| CN112271724A (en) * | 2020-10-13 | 2021-01-26 | 国网上海市电力公司 | Virtual power plant partition construction model and construction method based on voltage regulation |
| CN113077073A (en) * | 2021-03-02 | 2021-07-06 | 上海电力大学 | Power distribution network multiple-blocking control method based on load aggregation quotient optimal grading |
| CN113077073B (en) * | 2021-03-02 | 2022-07-12 | 上海电力大学 | Power distribution network multiple-blocking control method based on load aggregation quotient optimal grading |
| CN113792995A (en) * | 2021-08-26 | 2021-12-14 | 中国南方电网有限责任公司 | Method, device, equipment and storage medium for determining power resource dominance degree |
| CN113792995B (en) * | 2021-08-26 | 2024-02-27 | 中国南方电网有限责任公司 | Method, device, equipment and storage medium for determining power resource dominance degree |
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