CN107887903A - Consider the micro-capacitance sensor robust Optimization Scheduling of element frequency characteristic - Google Patents
Consider the micro-capacitance sensor robust Optimization Scheduling of element frequency characteristic Download PDFInfo
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Abstract
The invention discloses a kind of micro-capacitance sensor robust Optimization Scheduling for considering element frequency characteristic, under conditions of wind-force and the uncertain wave characteristic of photovoltaic generation output is considered, establish the micro-capacitance sensor robust Optimal Operation Model for considering various element frequency response characteristics, and robust Optimal Operation Model is solved using Benders decomposition methods, former PROBLEM DECOMPOSITION is subjected to alternating iteration to obtain robust Optimized Operation scheme into subproblem and primal problem.The object function of Robust Optimization Model is that the micro-capacitance sensor total operating cost under the maximum extreme scenes of via net loss is minimum, constraints includes active Constraints of Equilibrium, diesel engine unit operation characteristic, energy storage device operation characteristic, line frequency characteristic, frequency character of load, voltage security constrains, frequency security constrains, the scheduling scheme of acquisition can ensure system in the uncertain fluctuation range that distributed wind and photovoltaic generation are contributed will not occurrence frequency it is out-of-limit, ensure that the frequency security of micro-capacitance sensor.
Description
Technical field
The present invention relates to dispatching of power netwoks technical field, more particularly to a kind of micro-capacitance sensor robust for considering element frequency characteristic
Optimization Scheduling.
Background technology
The whole world is made a general survey of, most areas are powered by traditional large-scale power grid, and heavy-duty generator group is substantially all
The remote power plant in generation assets area is arranged close to, most loads are with concentrating on the higher economically developed city of the density of population
Area, a large amount of electric power are transported to by load center area from power plant by ultra-high-tension power transmission line.Broken down when in traditional power network
Timely processing is not obtained, major break down may be developed into gradually, the most area of whole power network is had a power failure, causes weight
Big economic loss.In addition, in traditional power network based on thermoelectric generator, coal burning and gas burning, which generates electricity, can produce substantial amounts of carbon dioxide
The atmosphere pollutions such as sulfur dioxide, atmospheric environment is polluted, influence the healthy living of the people.
Different from traditional centralized large-scale power grid, micro-capacitance sensor is the localized fine scale power network of modernization.Contain distribution
The micro-capacitance sensor of energy resource system efficiently solves above-mentioned variety of problems present in bulk power grid, is used widely.It is as one
Individual relatively independent mini-system, can realize self-management, control and defencive function, can be incorporated into the power networks, and and can cuts connection
In autonomous operating state, both contribute to strengthen the stable ability of power system restoration, contribute to mitigate the interference of power network again；Add
Micro-capacitance sensor is generated electricity using different types of distributed energy more and more, such as solar power system, wind generator system,
The discharge capacity of carbon dioxide is significantly decreased, environmental protection, meets the requirement of sustainable development.Due to micro-capacitance sensor can integrate it is more
Kind of the form energy and meet the requirement of load flexible access, additionally providing in case of emergency has in island mode and grid-connected
The good solution of switching capability between pattern nimbly and freely, thus inquire into micro-capacitance sensor traffic control have it is especially important
Practical value.
The intermittent energy source such as distributed wind and photovoltaic plant, which is contributed, has larger uncertainty, and precision of prediction is relatively low,
Qualified voltage and frequency band is maintained to carry out very big challenge when being run to micro-capacitance sensor.Existing micro-capacitance sensor Optimization Scheduling is substantially all
Influence of the scheduling scheme for micro-capacitance sensor frequency is not accounted for, can if the fluctuation of micro-capacitance sensor frequency exceeds safe allowed band
It can cause serious electric power safety accident.Therefore, micro-capacitance sensor dynamically optimized scheduling not only needs to consider wind light generation power
Uncertainty, it is also contemplated that the frequency response characteristic of various elements.
At present, the probabilistic micro-capacitance sensor Optimized Operation of the intermittent energy source such as photovoltaic and wind-powered electricity generation is considered, frequently with random rule
Draw, fuzzy programming and robust optimization etc. uncertainty optimization method.Random optimization passes through probability density function analysis and random mould
Quasi-step matrix mode considers uncertain factor, but stochastic programming needs the accurate probability Distribution Model for knowing uncertain variables, right
Substantial amounts of sampling results carries out statistics calculating, and uncertainty is expressed as fuzzy variable by fuzzy optimization, and constraints is expressed as
Fuzzy set, and characterize constraints satisfaction, but the uncertain membership function of fuzzy programming with fuzzy membership
It is determined that dependent on limited sample data and the experience of policymaker, resultant error is easily caused.Robust optimization passes through " set "
Form describes the uncertain variable in problem, it is not necessary to knows the probability distribution of uncertain variables, has preferable generalization
Energy.
Above-mentioned technology has the drawback that：The existing micro-capacitance sensor Optimized Operation for considering photovoltaic and wind-powered electricity generation, often have ignored bavin
Oil machine group, energy storage device, the frequency characteristic of circuit and load, can when acute variation occurs for load and distributed power generation power
It can cause the frequency out-of-limit problem of micro-grid system, cause potential safety hazard.On the other hand, considered by stochastic programming uncertain
Property, it is necessary to accurately know the probability Distribution Model of uncertain variables, pass through fuzzy optimization and consider uncertain variables, uncertain variables
Membership function experience of the determination dependent on limited sample data and policymaker, easily cause resultant error.
The content of the invention
The defects of for prior art, it is necessary to a kind of micro-capacitance sensor robust Optimization Scheduling is provided, by various elements
Frequency response characteristic takes into account, and has obtained the optimal scheduling scheme of micro-capacitance sensor.
In order to solve the above problems, the technical solution adopted by the present invention is as follows.
A kind of micro-capacitance sensor robust Optimization Scheduling for considering element frequency characteristic, including step：
Under conditions of wind-force and the uncertain wave characteristic of photovoltaic generation output is considered, establish and consider that various element frequencies are rung
Answer the micro-capacitance sensor robust Optimal Operation Model of characteristic；
The micro-capacitance sensor robust Optimal Operation Model is solved using Benders decomposition methods, by the model decomposition into subproblem
Alternating iteration is carried out to obtain micro-capacitance sensor robust Optimized Operation scheme with primal problem,
The micro-capacitance sensor robust Optimal Operation Model includes object function and constraints, and wherein object function damages for network
Consume under maximum scene, the total operating cost of micro-grid system is minimum：
On the right of equation, Part I is the cost of electricity-generating of diesel generating set, and Part II is the depreciation of battery operation
Expense, Part III via net loss expense；Hop count when T is total in dispatching cycle；Pg,j,tRepresent node j diesel generating sets in t
The output of period, ag,jAnd bg,jThe quadratic coefficients and coefficient of first order of the diesel generating set cost of electricity-generating, S are represented respectivelygRepresent
The node set of diesel generating set in micro-capacitance sensor；Pd,j,tAnd Pc,j,tRepresent node j batteries to store energy device in the t periods respectively
Discharge power and charge power, σcAnd σdThe unit depreciation expense that respectively batteries to store energy device is charged and discharged, with electric power storage
Pond state-of-charge SOC is related, and SOC is higher, and the amortization charge of unit charge volume is higher；SOC is lower, the depreciation cost of unit discharge amount
With higher；SbRepresent the node set of batteries to store energy device in micro-capacitance sensor；Represent node j photovoltaic plants having in the t periods
Work(is contributed,Represent node j wind power plants in the active power output of t periods, Pl,j,tRepresent that node j is active in the load of t periods, Cl
For unit via net loss expense；N is the sum of all nodes in micro-capacitance sensor；
The constraints includes：
Node active power balance constraint：
In formula：Vi,tAnd Vj,tRespectively period t node is and node j voltage magnitude；δij,tFor period t node i and node
J phase difference of voltage；GijWith BijFor bus admittance matrix corresponding element；
The operation constraint of diesel generating set：
In formula,P g,jWithThe respectively lower and upper limit of node j diesel generating sets active power output, ruAnd rdRespectively
The creep speed and landslide speed of diesel generating set, Δ T is the time interval of each period；
Active power output-frequency characteristic of diesel generating set：
Pg,j,t=Pg,jN+Kg,j(ft-fN)
In formula, ftAnd fNRespectively frequency and rated frequency of the micro-capacitance sensor in the t periods；Kg,jFor node j diesel generating sets
Frequency mediating effect+6 coefficient；Pg,j,tAnd Pg,jNRespectively node j diesel generating sets the t periods actual output with it is specified go out
Power.In view of the secondary corrective action of frequency, by Pg,jNAs variable；
Batteries to store energy plant running constrains：
In formula,WithThe respectively maximum charge and discharge power of battery, Eb,tFor the charge capacity of period t battery,For the maximum charge capacity of battery, SOCb,tFor the state-of-charge of period t battery,WithSOC bRespectively battery lotus
The bound of electricity condition, ηcFor the charge efficiency of battery；
Active power output-frequency characteristic of batteries to store energy device：
Pd,t=PdN+Kd(ft-fN)
In formula, KdFor the frequency mediating effect+6 coefficient of battery discharging power；PdNFor the nominal discharge power of battery；
The frequency characteristic of load：
Pl=PlN+Kl(ft-fN)
In formula, KlThe respectively frequency mediating effect+6 coefficient of load, PlWith PlNLoad actual power and rated power respectively；
The frequency characteristic of line impedance：
Z=(R0+j2πftL0)l
In formula, Z represents line impedance；L represents the length of circuit；R0And L0The electricity of circuit table timberline road unit length respectively
Resistance and inductance；
System safety operation constrains：
In formula,Vi WithThe respectively lower and upper limit of node i voltage security limitation,fWithRespectively frequency security limits
Lower and upper limit；
Uncertain variables constrain：C is uncertain variables collection, including photovoltaic plant is contributedAnd output of wind electric fieldUsing
The uncertain collection of boxlike, uncertain variables are expressed as two parts of desired value and disturbance
Photovoltaic plant can be determined according to the daily load curve rule of power distribution network photovoltaic plant and wind farm meteorological historical data and statistics
With the desired value and disturbance quantity excursion of wind power plant active power output, and then Uncertainty excursion is obtained, then do not know to become
Duration set C is represented by：
Subproblem is so that the distributed wind and photovoltaic generation output extreme scenes of micro-capacitance sensor via net loss maximum, target
Function is：
In formula, λ is the number of current iteration；
Constraint of the foregoing constraints for including known variables as subproblem, including node active power balance is about
Beam, active power output-frequency characteristic of diesel generating set, active power output-frequency characteristic of batteries to store energy device, the frequency of load
Rate characteristic, the frequency characteristic of line impedance and uncertain variables constraint；
If the result for solving subproblem has optimal solution set U, increase an optimal cut set in primal problem constraints
It is as follows：
In formula,For the auxiliary variable constructed in solution procedure；
If the subproblem solved only has feasible solution V, increase a feasible cut set such as in primal problem constraints
Under：
The operation totle drilling cost of the entitled micro-capacitance sensor in the case of stochastic variable is in extreme scenes of examination in chief is minimum, object function
For：
The constraints of primal problem is except the optimal cut set returned including the subproblem or feasible cut set, in addition to section
Point active power balance constraint, the operation constraint of diesel generating set, active power output-frequency characteristic of diesel generating set, storage
The operation constraint of battery energy storage device, active power output-frequency characteristic of batteries to store energy device, the frequency characteristic of load, circuit
Frequency characteristic and the system safety operation constraint of impedance.
The present invention establishes under conditions of wind-force and the uncertain wave characteristic of photovoltaic generation output is considered and considers various members
The micro-capacitance sensor robust Optimal Operation Model of part frequency response characteristic, and robust Optimized Operation mould is solved using Benders decomposition methods
Type, former PROBLEM DECOMPOSITION is subjected to alternating iteration to obtain robust Optimized Operation scheme into subproblem and primal problem.Robust optimizes mould
The object function of type is that the micro-capacitance sensor total operating cost under the maximum extreme scenes of via net loss is minimum, and constraints includes active
Constraints of Equilibrium, diesel engine unit operation characteristic, energy storage device operation characteristic, line frequency characteristic, frequency character of load, voltage security
Constraint, frequency security constraint, it is not true that the scheduling scheme of acquisition can ensure that system is contributed in distributed wind and photovoltaic generation
Standing wave move scope in will not occurrence frequency it is out-of-limit, ensure that the frequency security of micro-capacitance sensor.
Brief description of the drawings
Fig. 1 is the calculation process that Benders decomposition methods solve robust Optimal Scheduling；
Fig. 2 is island microgrid wiring diagram；
Fig. 3 is changed power curve of each load bus under rated frequency；
Fig. 4 is the photovoltaic power generation output forecasting curve of node 1 and 9；
Fig. 5 is the wind power output prediction curve of node 10；
Fig. 6 is the Plan Curve of diesel-driven generator node in each prioritization scheme；
Fig. 7 is the Plan Curve of each energy storage of prioritization scheme interior joint 11；
Fig. 8 is the Plan Curve of each energy storage of prioritization scheme interior joint 13；
Fig. 9 is that deterministic optimization and the frequency of robust optimization compare under extreme scenes.
Embodiment
The present invention is further elaborated with embodiment below in conjunction with the accompanying drawings.
1.1 consider the micro-capacitance sensor robust Optimal Operation Model of element frequency characteristic
A. object function
Object function is under via net loss maximum scene, and the total operating cost of micro-grid system is minimum.
In formula, Part I is the cost of electricity-generating of diesel generating set, the amortization charge that Part II battery is run, the
Three parts via net loss expense；Hop count when T is total in dispatching cycle；Pg,j,tRepresent node j diesel generating sets going out in the t periods
Power, ag,jAnd bg,jThe quadratic coefficients and coefficient of first order of the diesel generating set cost of electricity-generating, S are represented respectivelygRepresent bavin in microgrid
The node set of fry dried food ingredients group of motors；Pd,j,tAnd Pc,j,tDischarge power of the node j batteries to store energy device in the t periods is represented respectively
And charge power, σcAnd σdThe unit depreciation expense that respectively batteries to store energy device is charged and discharged, with storage battery charge state
(State of Charge, SOC) is related, and SOC is higher, and the amortization charge of unit charge volume is higher；SOC is lower, unit discharge amount
Amortization charge it is higher；SbRepresent the node set of batteries to store energy device in microgrid；Represent node j photovoltaic plants in t
The active power output of section,Represent node j wind power plants in the active power output of t periods, Pl,j,tRepresent loads of the node j in the t periods
It is active, ClFor unit via net loss expense；N is the sum of all nodes in microgrid.
B, constraints
1) node active power balance constraint.The power-balance of each node must is fulfilled in micro-capacitance sensor Optimized Operation, such as formula
(2)。
In formula：Vi,tAnd Vj,tRespectively period t node is and node j voltage magnitude；δij,tFor period t node i and node
J phase difference of voltage；GijWith BijFor bus admittance matrix corresponding element.
2) the operation constraint of diesel generating set.Comprising generated output limit restraint and power Climing constant, such as formula (3).
In formula,P g,jWithThe respectively lower and upper limit of node j diesel generating sets active power output, ruAnd rdRespectively
The creep speed and landslide speed of diesel generating set, the time interval of Δ T each periods.
Active power output-frequency characteristic of diesel generating set, such as formula (4).
Pg,j,t=Pg,jN+Kg,j(ft-fN) (4)
In formula, ftAnd fNRespectively frequency and rated frequency of the micro-capacitance sensor in the t periods；Kg,jFor the frequency of node j diesel generating sets
Rate mediating effect+6 coefficient；Pg,j,tAnd Pg,jNRespectively actual output and nominal output of the node j diesel generating sets in the t periods.
In view of the secondary corrective action of frequency, by Pg,jNAs variable.
3) batteries to store energy plant running constrains.In order to stabilize the uncertain ripple of the renewable energy power generation in micro-capacitance sensor
It is dynamic, batteries to store energy device is introduced, the constraint of its model includes maximum charge-discharge electric power constraint, and energy storage device state-of-charge constrains,
Running status Constraints：Battery each period t in actual motion can be only in the one of which shape of charge or discharge
State.
In formula, Pd,tAnd Pc,tDischarge power and charge power of the battery in the t periods are represented respectively,WithRespectively
The maximum charge and discharge power of battery, Eb,tFor the charge capacity of period t battery,For the maximum charge capacity of battery,
SOCb,tFor the state-of-charge of period t battery,WithSOC bThe respectively bound of storage battery charge state, ηcFor electric power storage
The charge efficiency in pond.
Active power output-frequency characteristic of batteries to store energy device, think that the fluctuation of frequency will not be led during in charged state
Cause the change of charge power；And the active power output of energy storage device and frequency are thought in discharge condition and meets droop control.Such as formula
(6)。
Pd,t=PdN+Kd(ft-fN) (6)
In formula, KdFor the frequency mediating effect+6 coefficient of battery discharging power；PdNFor the nominal discharge power of battery.
4) frequency characteristic of load.
Pl=PlN+Kl(ft-fN) (7)
In formula, KlThe respectively frequency mediating effect+6 coefficient of load, PlWith PlNLoad actual power and rated power respectively.
5) frequency characteristic of line impedance.
Z=(R0+j2πftL0)l (8)
In formula, Z represents line impedance；L represents the length of circuit；R0And L0The electricity of circuit table timberline road unit length respectively
Resistance and inductance.The change of frequency can cause the change of line reactance, so as to change network node admittance matrix, the operation to system
State has an impact.
6) system safety operation constrains.The voltage deviation of each node is no more than safe allowed band, the frequency of micro-capacitance sensor
Deviation is no more than safe allowed band, such as formula (9).
In formula,Vi WithThe respectively lower and upper limit of node i voltage security limitation,fWithRespectively frequency security limits
Lower and upper limit.
7) uncertain variables constrain.C is uncertain variables collection, including photovoltaic plant output, output of wind electric field etc. become at random
Amount, using the uncertain collection of boxlike, uncertain variables can be expressed as to two parts of desired value and disturbanceAccording to power distribution network photovoltaic plant and wind farm meteorological historical data and statistics
Daily load curve rule can determine the desired value and disturbance quantity excursion of photovoltaic plant and wind power plant active power output, Jin Erke
Uncertainty excursion is obtained, then uncertain variables set C is represented by formula (10).
The derivation algorithm of 1.2 robust Optimal Schedulings
Consider excellent containing Min-Max bilayers in the object function of the micro-capacitance sensor robust Optimal Operation Model of element frequency characteristic
Change problem, ready-made solver direct solution can not be used.The key for solving above-mentioned robust Optimal Operation Model is：1) how
In uncertain variables set, 1 group of uncertain variables value is found, it is corresponding with the extreme scenes that network loss is maximum；2) how in decision-making
In variables collection, 1 group of decision variable value is found so that this group of decision variable can expire under any value of uncertain variables
Constraints in sufficient Optimized model, and make it that corresponding object function is minimum under extreme scenes.The present invention uses Benders
Decomposition algorithm solves above-mentioned Robust Optimization Model., can be by this paper Min-Max structures according to the thought of Benders decomposition algorithms
Optimized model is decomposed into primal problem and subproblem two parts.Subproblem finds the uncertain change for make it that micro-capacitance sensor via net loss is maximum
Measure extreme scenes；Primal problem then solves the scheduling scheme for make it that totle drilling cost of the micro-capacitance sensor under extreme scenes operation is minimum.Son is asked
Topic and primal problem are described as follows respectively：
A, subproblem
Asked to find the distributed wind for causing micro-capacitance sensor via net loss maximum and photovoltaic generation output extreme scenes, son
The object function of topic is：
In formula, λ is the number of current iteration.
In subproblem, stochastic variable such as distributed wind and photovoltaic generation output and state variable such as frequency, voltage
As known variables, the decision-making of the generated output of diesel generating set and charge-discharge electric power of energy storage device etc. becomes for amplitude and phase angle
Amount is all used as known quantity, includes constraint of the constraints of known variables as subproblem, including (2) (4) (6) (7) (8)
(10)。
If the result for solving subproblem has optimal solution set U (to include)；Then increase in primal problem constraints
Add an optimal cut set as follows：
In formula,For the auxiliary variable constructed in solution procedure.
If the subproblem solved only has feasible solution V (to include)；Then increase in primal problem constraints
One feasible cut set is as follows：
B, primal problem
The object function of primal problem is to contribute to be in the case of extreme scenes micro-capacitance sensor such as wind light generation in stochastic variable
Totle drilling cost is run, so the object function of primal problem can be set to：
The constraints of primal problem except the optimal cut set formula (12) returned including subproblem or feasible cut set formula (13),
Also include formula (2)~(9).
Calculation process such as Fig. 1 of robust Optimal Scheduling is solved using Benders decomposition algorithms, detailed step is as follows：
1) initialize：Former problem object function lowest limit LB is set to-∞ first, highest boundary UB is set to+∞, not
Determine the desired value scene (P of variables,t,ref, Pw,t,ref) under, deterministic optimization problem is solved, obtains the initial value of decision variable
2) by the initial value of decision variableSubproblem is substituted into as known quantity and is solved, and is obtained
So that the uncertain variables currency that via net loss is maximumMake iterations λ=0.
3) if solving subproblem obtains optimal solution, increase an optimal cut set (12) into primal problem constraints,
If solving subproblem obtains feasible solution, increase a feasible cut set (13) into primal problem constraints.Subproblem is asked
The uncertain variables value that solution obtainsPrimal problem is substituted into as known quantity, primal problem is solved, obtains decision variable
Optimal solutionAnd update lowest limit
4) the decision variable solution for obtaining primal problemSubproblem is substituted into as known quantity, is asked
Solution subproblem obtains uncertain variables optimal solutionHighest boundary UB is updated, such as formula (15).
If 5) UB and LB meet UB-LB≤ε, stop iteration, return to optimal solution.Otherwise, λ=λ+1, return to step are made
3).ε is the convergence criterion constant in Benders decomposition methods, is set to 10-5。
2.1 sample calculation analysis are verified
Simulation analysis are carried out by taking some island microgrid as an example, the micro-capacitance sensor wiring diagram is as shown in Fig. 2 include node 10
Two distributions of two batteries to store energy devices of one diesel generating set, node 11 and node 13, node 1 and node 9
Photovoltaic plant, the distributed wind-power generator of node 12,4 loads of node 6,7,8,10.Cost of losses ClTake 0.65/ (member/
(kWh)).The charge efficiency of battery is 0.85, and the parameter of diesel generating set is as shown in table 1, the parameter such as table 2 of energy storage device
Shown, load data is as shown in table 3, and network-related parameters are as shown in table 4；Power of each load bus under rated frequency is as schemed
Shown in 3, the prediction curve of photovoltaic and wind power output is as shown in Figures 4 and 5.Frequency security limitation range set be 49.5~
50.5Hz。
The relevant parameter of the diesel generating set of table 1
The relevant parameter of the node 11 of table 2 and 13 energy storage devices
The relevant parameter of the load bus of table 3
The relevant parameter of the transformer of table 4
Consider that photovoltaic is contributed and the uncertain fluctuation range of wind power output is ± the 10% of predicted value, contrast micro-capacitance sensor determines
Property prediction scene under Optimized Operation, scene method Optimized Operation and set forth herein robust Optimization Scheduling, obtained optimization knot
Fruit contrast is as shown in table 5；Scheduling scheme contrasts as shown in figs 6-8, and its Scene method is the result curve of 10 error scenes.
Various method simulation result contrasts in the case of the honourable power swing 10% of table 5
As shown in Table 5：
1) in the case of honourable power swing 10%, the cost of losses of robust optimization is 3620.223 yuan, compares certainty
The cost of losses of optimization is 2947.911 yuan big, and the cost of losses also than the optimization of scene method is big, meets robust optimization minimum system
The conclusion of the maximum extreme scenes of cost of losses.Meanwhile the operation totle drilling cost of robust optimization is 11428.216 yuan, compares certainty
The operation totle drilling cost of optimization is 10836.781 yuan big, and the operation totle drilling cost also than the optimization of scene method is big.Because robust Optimization Solution
It is the scheduling result of the minimum operation totle drilling cost under maximum network loss scene, the result goes out in any distributed wind and photovoltaic generation
All meet constraints in the case of fluctuation, conservative is big.
2) contrast the operation totle drilling cost that robust optimization and scene method optimize to obtain to understand, when the number of error scene in scene method
Mesh is more, and operation totle drilling cost is just closer to the system operation totle drilling cost of robust optimization.Because when the number of scenes in scene method takes
Enough to it is more when, equivalent in the set for going out fluctuation in a scene find Optimized Operation result, as a result should meet this
Institute's Constrained in set.And robust optimization is then to find the extreme scenes that scene goes out in fluctuation set, optimal result is solved,
To ensure that result meets institute's Constrained of this set.At this moment the scene method and the core of robust Optimal methods for sampling enough are thought
Think unanimously, therefore their result can approach.
3) the calculating time of deterministic optimization, the optimization of scene method and robust optimization, the calculating time of deterministic optimization are contrasted
Most short, the robust optimization calculating time is longer, and the calculating time of scene method is most long.Because it is single that the scene of deterministic optimization, which is contributed,
Scene is predicted, and it is a set that the scene of robust optimization, which is contributed, scene method is more scenes, it is thus determined that the calculating of property optimization
Time should be minimum.From the calculating time of scene method, with increasing for error number of scenes, the time is calculated also fast
Increase fastly.Because the number of scenes of scene method is more, it is necessary to the double growth of constraints met, amount of calculation increased dramatically,
Program runtime increase.Therefore the calculating time of scene method substantially optimizes more than robust.
4) robust Optimized Operation scheme and other Optimized Operation schemes are compared, the Plan Curve of diesel-driven generator node is as schemed
Shown in 6, the Plan Curve of two energy storage device nodes is as shown in FIG. 7 and 8.From Fig. 7 and Fig. 8, compared with scene method, robust
The energy storage device most of the time output very little of optimum results, photovoltaic and wind power output are tackled to reserve larger pondage
Uncertain fluctuation, exactly because the also output very little of energy storage device, so have to be by diesel generating set in load boom period
It is contribute to power more.
Deterministic optimization scheduling scheme and robust Optimized Operation scheme obtain under the maximum extreme scenes of via net loss
Micro-capacitance sensor frequency compares such as Fig. 9.When uncertain variables photovoltaic and wind power output are all in extreme scenes, deterministic optimization scheduling
Scheme occurs that frequency gets over the situation of lower limit in the period 19, and robust Optimized Operation scheme can then ensure micro-capacitance sensor in each period
Frequency security requirement can be met.Because deterministic optimization scheduling scheme can only ensure to meet respectively in the case where scene contributes prediction scene
Individual constraints, it not necessarily disclosure satisfy that each constraints in the case of the uncertain fluctuation of scene output.So as to demonstrate this
The scheduling scheme that the robust Optimization Scheduling that invention proposes obtains can effectively cope with the uncertain fluctuation of scene output, tool
There is robustness.
To sum up, in the case of known photovoltaic and wind-powered electricity generation prediction power curve and load prediction curve, by solving this hair
The micro-capacitance sensor robust Optimal Operation Model of the consideration element frequency characteristic of bright proposition, can obtain diesel generating set in micro-capacitance sensor
With the planned dispatching scheme of output a few days ago of energy storage device.The scheduling scheme can micro-capacitance sensor via net loss maximum extreme scenes it
Down so that the total operating cost of micro-capacitance sensor is minimum, and system can be ensured in distributed photovoltaic and the uncertain ripple of wind power output
All meet frequency security requirement under any scene in dynamic scope.
Embodiment described above only expresses embodiments of the present invention, and its description is more specific and detailed, but can not
Therefore it is construed as limiting the scope of the patent.It should be pointed out that for the person of ordinary skill of the art,
On the premise of not departing from present inventive concept, various modifications and improvements can be made, these belong to protection scope of the present invention.
Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (2)
1. a kind of micro-capacitance sensor robust Optimization Scheduling for considering element frequency characteristic, it is characterised in that including step：
Under conditions of wind-force and the uncertain wave characteristic of photovoltaic generation output is considered, establish and consider various element frequency response spies
The micro-capacitance sensor robust Optimal Operation Model of property；
The micro-capacitance sensor robust Optimal Operation Model is solved using Benders decomposition methods, by the model decomposition into subproblem and master
Problem carries out alternating iteration to obtain micro-capacitance sensor robust Optimized Operation scheme,
The micro-capacitance sensor robust Optimal Operation Model includes object function and constraints, wherein object function be via net loss most
Under large scene, the total operating cost of micro-grid system is minimum：
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On the right of equation, Part I is the cost of electricity-generating of diesel generating set, and Part II is the amortization charge of battery operation,
Part III via net loss expense；Hop count when T is total in dispatching cycle；Pg,j,tRepresent node j diesel generating sets in the t periods
Contribute, ag,jAnd bg,jThe quadratic coefficients and coefficient of first order of the diesel generating set cost of electricity-generating, S are represented respectivelygRepresent micro-capacitance sensor
The node set of middle diesel generating set；Pd,j,tAnd Pc,j,tElectric discharge of the node j batteries to store energy device in the t periods is represented respectively
Power and charge power, σcAnd σdThe unit depreciation expense that respectively batteries to store energy device is charged and discharged, it is charged with battery
State SOC is related, and SOC is higher, and the amortization charge of unit charge volume is higher；SOC is lower, and the amortization charge of unit discharge amount is got over
It is high；SbRepresent the node set of batteries to store energy device in micro-capacitance sensor；Represent node j photovoltaic plants the t periods it is active go out
Power,Represent node j wind power plants in the active power output of t periods, Pl,j,tRepresent that node j is active in the load of t periods, ClFor list
Position via net loss expense；N is the sum of all nodes in micro-capacitance sensor；
The constraints includes：
Node active power balance constraint：
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In formula：Vi,tAnd Vj,tRespectively period t node is and node j voltage magnitude；δij,tFor period t node i and node j electricity
Press phase angle difference；GijWith BijFor bus admittance matrix corresponding element；
The operation constraint of diesel generating set：
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In formula,P g,jWithThe respectively lower and upper limit of node j diesel generating sets active power output, ruAnd rdRespectively diesel oil
The creep speed and landslide speed of generating set, Δ T is the time interval of each period；
Active power output-frequency characteristic of diesel generating set：
Pg,j,t=Pg,jN+Kg,j(ft-fN)
In formula, ftAnd fNRespectively frequency and rated frequency of the micro-capacitance sensor in the t periods；Kg,jFor the frequency of node j diesel generating sets
Rate mediating effect+6 coefficient；Pg,j,tAnd Pg,jNRespectively actual output and nominal output of the node j diesel generating sets in the t periods,
In view of the secondary corrective action of frequency, by Pg,jNAs variable；
Batteries to store energy plant running constrains：
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</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
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<msub>
<munder>
<mrow>
<mi>S</mi>
<mi>O</mi>
<mi>C</mi>
</mrow>
<mo>&OverBar;</mo>
</munder>
<mi>b</mi>
</msub>
<mo>&le;</mo>
<msub>
<mi>SOC</mi>
<mrow>
<mi>b</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mover>
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<mi>O</mi>
<mi>C</mi>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mi>b</mi>
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</mrow>
</mtd>
</mtr>
<mtr>
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<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>c</mi>
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<msub>
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<mi>d</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula,WithThe respectively maximum charge and discharge power of battery, Eb,tFor the charge capacity of period t battery,For
The maximum charge capacity of battery, SOCb,tFor the state-of-charge of period t battery,WithSOC bThe respectively charged shape of battery
The bound of state, ηcFor the charge efficiency of battery；
Active power output-frequency characteristic of batteries to store energy device：
Pd,t=PdN+Kd(ft-fN)
In formula, KdFor the frequency mediating effect+6 coefficient of battery discharging power；PdNFor the nominal discharge power of battery；
The frequency characteristic of load：
Pl=PlN+Kl(ft-fN)
In formula, KlThe respectively frequency mediating effect+6 coefficient of load, PlWith PlNLoad actual power and rated power respectively；
The frequency characteristic of line impedance：
Z=(R0+j2πftL0)l
In formula, Z represents line impedance；L represents the length of circuit；R0And L0Respectively the resistance of circuit table timberline road unit length and
Inductance；
System safety operation constrains：
<mfenced open = "{" close = "">
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<mtr>
<mtd>
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<msub>
<mi>V</mi>
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</munder>
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<mover>
<mi>f</mi>
<mo>&OverBar;</mo>
</mover>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula,V iWithThe respectively lower and upper limit of node i voltage security limitation,fWithRespectively under frequency security limitation
Limit and the upper limit；
Uncertain variables constrain：C is uncertain variables collection, including photovoltaic plant is contributedAnd output of wind electric fieldUsing boxlike not
It is determined that uncertain variables are expressed as two parts of desired value and disturbance by collection According to
The daily load curve rule of power distribution network photovoltaic plant and wind farm meteorological historical data and statistics can determine photovoltaic plant and wind
The desired value and disturbance quantity excursion of electric field active power output, and then obtain Uncertainty excursion, then uncertain variables collection
C is closed to be represented by：
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Subproblem is so that the distributed wind and photovoltaic generation output extreme scenes of micro-capacitance sensor via net loss maximum, object function
For：
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In formula, λ is the number of current iteration；
Constraint of the foregoing constraints for including known variables as subproblem, including node active power balance constraint,
Active power output-frequency characteristic of diesel generating set, active power output-frequency characteristic of batteries to store energy device, the frequency of load
Characteristic, the frequency characteristic of line impedance and uncertain variables constraint；
If the result for solving subproblem has optimal solution set U, it is as follows to increase an optimal cut set in primal problem constraints：
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<mi>z</mi>
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<mi>l</mi>
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<mi>c</mi>
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In formula,For the auxiliary variable constructed in solution procedure；
If the subproblem solved only has feasible solution V, it is as follows to increase a feasible cut set in primal problem constraints：
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<mi>C</mi>
<mi>l</mi>
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<mi>c</mi>
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The operation totle drilling cost of the entitled micro-capacitance sensor in the case of stochastic variable is in extreme scenes of examination in chief is minimum, and object function is：
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The constraints of primal problem has except the optimal cut set returned including the subproblem or feasible cut set, in addition to node
Work(power-balance constraint, the operation constraint of diesel generating set, active power output-frequency characteristic of diesel generating set, battery
The operation constraint of energy storage device, active power output-frequency characteristic of batteries to store energy device, the frequency characteristic of load, line impedance
Frequency characteristic and system safety operation constraint.
2. the micro-capacitance sensor robust Optimization Scheduling according to claim 1 for considering element frequency characteristic, it is characterised in that
The detailed step that the micro-capacitance sensor robust Optimal Operation Model is solved using Benders decomposition methods is as follows：
Step 1, the object function lowest limit LB of the micro-capacitance sensor robust Optimal Operation Model is set to-∞, highest boundary UB
+ ∞ is set to, in the desired value scene (P of uncertain variabless,t,ref, Pw,t,ref) under, deterministic optimization problem is solved, obtains decision-making
The initial value of variable
Step 2, the initial value by decision variableSubproblem is substituted into as known quantity and is solved, and is made
Obtain the maximum uncertain variables currency of via net lossMake iterations λ=0；
If step 3, solution subproblem obtain optimal solution, increase an optimal cut set into primal problem constraints,
If solving subproblem obtains feasible solution, increase a feasible cut set into primal problem constraints, subproblem is asked
The uncertain variables value that solution obtainsPrimal problem is substituted into as known quantity, primal problem is solved, obtains decision variable
Optimal solutionAnd update lowest limit
Step 4, the decision variable solution for obtaining primal problemSubproblem is substituted into as known quantity, is asked
Solution subproblem obtains uncertain variables optimal solutionUpdate highest boundary
If the highest boundary line UB and minimum boundary line LB after step 5, renewal meet UB-LB≤ε, stop iteration, return to optimal solution,
Otherwise, λ=λ+1, return to step 3 are made, ε is the convergence criterion constant in Benders decomposition methods, is set to 10-5。
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| CN114759616A (en) * | 2022-06-14 | 2022-07-15 | 之江实验室 | Micro-grid robust optimization scheduling method considering characteristics of power electronic devices |
| CN115065069A (en) * | 2022-06-09 | 2022-09-16 | 国网江苏省电力有限公司淮安供电分公司 | Micro-grid optimization scheduling strategy considering frequency stability |
| CN116417994A (en) * | 2023-04-03 | 2023-07-11 | 中国长江电力股份有限公司 | Power system distribution robust optimization scheduling model considering uncertainty of demand response and solving method thereof |
| CN117277428A (en) * | 2023-08-18 | 2023-12-22 | 三峡大学 | Wind power output fluctuation optimization scheduling method considering frequency constraint |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103825279A (en) * | 2014-02-21 | 2014-05-28 | 华南理工大学 | Micro-grid system voltage stability control method based on robust control |
| CN106815661A (en) * | 2017-02-22 | 2017-06-09 | 清华大学 | A kind of composition decomposition dispatching method of CHP system |
| CN106992538A (en) * | 2017-04-17 | 2017-07-28 | 国网浙江省电力公司电力科学研究院 | It is a kind of that micro-capacitance sensor Optimal Configuration Method is exchanged based on the self that Benders is decomposed |
-
2017
- 2017-10-31 CN CN201711045435.8A patent/CN107887903B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103825279A (en) * | 2014-02-21 | 2014-05-28 | 华南理工大学 | Micro-grid system voltage stability control method based on robust control |
| CN106815661A (en) * | 2017-02-22 | 2017-06-09 | 清华大学 | A kind of composition decomposition dispatching method of CHP system |
| CN106992538A (en) * | 2017-04-17 | 2017-07-28 | 国网浙江省电力公司电力科学研究院 | It is a kind of that micro-capacitance sensor Optimal Configuration Method is exchanged based on the self that Benders is decomposed |
Non-Patent Citations (4)
| Title |
|---|
| RODRIGO HIDALGO等: "A review of active distribution networks enabling technologies", 《IEEE PES GENERAL MEETING》 * |
| RUIWEI JIANG等: "Two-Stage Minimax Regret Robust Unit Commitment", 《IEEE TRANSACTIONS ON POWER SYSTEMS》 * |
| 张海鹏等: "考虑光伏不确定性的低压配电网分散无功补偿鲁棒优化配置", 《电网技术》 * |
| 梅生伟等: "一类电力***鲁棒优化问题的博弈模型及应用实例", 《中国电机工程学报》 * |
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