CN110137942A - Multiple Time Scales flexible load rolling scheduling method and system based on Model Predictive Control - Google Patents
Multiple Time Scales flexible load rolling scheduling method and system based on Model Predictive Control Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
The invention discloses a kind of Multiple Time Scales flexible load rolling scheduling method and system based on Model Predictive Control, from scheduling time scale, comprehensively consider the factors such as scheduling parameter otherness and system real-time running state, establishes a few days ago-in a few days rolling scheduling model of meter and flexible load Multiple Time Scales response characteristic；Based on model predictive control method, it is proposed the model solution method of rolling scheduling a few days ago of the hierarchical model PREDICTIVE CONTROL based on decomposition-coordination, and the in a few days rolling scheduling model solution method of the Model Predictive Control based on dynamic rolling optimization, obtain each optimal power output of moment flexible load.Comprehensively consider electric system peak regulation, renewable energy consumption and stabilize renewable energy and go out fluctuation, establishes Multiple Time Scales, the multipair flexible load rolling scheduling model as cooperateing with multiple target, realize Multiobjective Optimal Operation and the coordinated control of flexible load.
Description
Technical field
The invention belongs to operation and control of electric power system fields, and in particular to a kind of more times based on Model Predictive Control
Scale flexible load rolling scheduling method and system.
Background technique
Flexible load has diversity, time variation and uncertainty, and on Object Dimension, different flexible loads are in electric system
There is different adjustable potentiality, the having differences property of scheduling cost between all kinds of flexible loads in management and running；On time dimension, no
With in electric power system dispatching time scale, there is also differences for the dispatching priority and response characteristic of flexible load, give flexible load
Optimized Operation and solution bring difficulty.
Existing electric power system dispatching mostly uses refinement time scale method, is based on discontinuity surface or a time scale at one
Open loop optimal control is carried out, realizes the optimum control sometime put.It is existing as the renewable energy in power grid is increasing
The solution error of open-loop method constantly increases, and can not adapt to dispatching of power netwoks demand.
Summary of the invention
Discontinuity surface or a time ruler when the technical problem to be solved by the present invention is to overcome the prior art to be based only upon one
Degree carries out open loop optimal control, realize the defect of optimum control sometime put, and provides a kind of based on Model Predictive Control
Multiple Time Scales flexible load rolling scheduling method and system.
In order to solve the above technical problems, the invention adopts the following technical scheme:
In one aspect, the present invention provides the Multiple Time Scales flexible load rolling scheduling side based on Model Predictive Control
Method, characterized in that the following steps are included:
In the state variable at current time, the state variable of subsequent time and it is according to all kinds of flexible loads at the k moment
System control variable establishes model to the adjustable resource of all kinds of flexible loads；
Establish Multiple Time Scales flexible load rolling scheduling model, the model includes a few days ago and in a few days two scheduling times
Scale, with the minimum function of regulation goal a few days ago of system operation cost and acquisition, scheduling flexible load has operation plan a few days ago a few days ago
Function power；
It in a few days dispatches in using the scheduling flexible load active power a few days ago of acquisition as the rolling optimization in a few days dispatched
Know reference value, on the basis of the short-term rolling forecast value of renewable energy, determines in a few days regulation goal function and solve acquisition not
Carry out the predicted value of the flexible load active power output in specific time, i.e., in a few days the subsequent time flexible load of scheduling time scale is rung
Answer capacity.
Further, the expression formula for establishing model to the adjustable resource of all kinds of flexible loads at the k moment is as follows:
X (k+1)=Ax (k)+Bu (k) (1),
Wherein, x (k) is current time flexible load state variable, and x (k+1) is subsequent time flexible load state variable,
U (k) is system control variables, and A and B are coefficient matrix.
Further, the system operation cost includes system electric cost, flexible load response cost and renewable energy
Cost is dispatched in source, and the function representation of regulation goal a few days ago is as follows:
Wherein, T1For long time scale dispatching cycle；M and n is respectively flexible load and renewable energy quantity；Cgrid(t)
For power grid tou power price；PgridIt (t) is interconnection active power；CFLIt (t) is flexible load response cost function；CRE(t) being can
The renewable sources of energy dispatch cost.
Still further, flexible load response cost function CFL(t) expression formula are as follows:
Wherein, N is flexible load number of users；For flexible load User Status vector,Indicate the load for being not involved in the user j of interaction,Indicate the load of the user j of participation interaction；For the flexible load active power vector for participating in interaction；ρ1And ρ2To interact cost coefficient；ωFLj
For the participation willingness factor of flexible load user.
Further, in order to reduce the complexity and calculation amount of global optimization problem, the optimization of scheduling model a few days ago is asked
Topic is decomposed into multiple small-scale optimization subproblems, using the hierarchical model forecast Control Algorithm based on decomposition-coordination, and utilizes
Method of Lagrange multipliers solves regulation goal function a few days ago.
Further, in a few days regulation goal function is as follows:
WhereinIt predicts power output a few days ago for renewable energy and in a few days predicts the mistake of power output
Difference, β (i) [x (k+i-1)-xref]2The deviation for referring to capacity for flexible load and actually contributing,For the deviation of flexible load scheduling capacity and real response amount；For the power generation of system a few days ago
Power；For system in a few days generated output；X is flexible load state variable；xrefIt is active for the flexible load of scheduling a few days ago of acquisition
Power,It in a few days contributes predicted value for renewable energy.For time scale renewable energy active power predicted value a few days ago,
α (i), β (i) and γ (i) are weight coefficient, and i is time parameter.
Still further, solving objective function, the specific method is as follows:
A length of N when system rolling window, at the k+i moment, system control variables, that is, flexible load active power output predicted value
Are as follows:
In formula, P0It (k) is flexible load original state；Δ u (k+t | k) it is when predicting following [k+ (t-1), k+t] at the k moment
Flexible load output power variable quantity in section；P (k+i | k) is the flexible load active power at the k+i moment predicted at the k moment；
System active power balance equation is substituted into a few days regulation goal functional expression (4), objective function can be converted to
Following quadratic form optimization problem is solved:
U (k) is control variable vector, uminAnd umaxVariable minimum value and maximum value are respectively controlled, V (k) is target letter
Number, H (k) and fTIt (k) is coefficient matrix, Aineq(k) and BineqIt (k) is the inequality constraints conditional coefficient of control variable, Aeq(k)
BeqIt (k) is control variable equality constraint coefficient.
By solving problem above, the flexible load active power output variable quantity vector { Δ in [k, k+N] rolling window is obtained
U (k+1 | k), Δ u (k+2 | k) ..., Δ u (k+N | k) }, first element in the control Variables Sequence is extracted, it is negative to flexibility
Lotus state is updated, and k+1 moment flexible load output power can be obtained, using the flexible load state at k+1 moment as newly
The initial value of one wheel rolling optimization, that is, carry out the Optimization Solution of subsequent time.
Further, it in order to effectively reduce the deviation that prediction error and system interference generate, obtains in scheduling slot
Closed-loop control best performance, at each moment, the measurand y of detection system reality output simultaneously calculates its error e, to predicted value
Feedback compensation is carried out, is shown below:
Y (k+1)=y (k)+he (k+1) (16), y (k+1) are the measurand at k+1 moment, and y (k) is the k moment
Measurand, h are coefficient, and e (k+1) is the error at k+1 moment.By calculate k when etching system reality output measurand with
The error e (k+1) for the measurand being calculated, is multiplied with coefficient h, obtains the revised measurand y (k+ at k+1 moment
1) feedback compensation, is completed.
The initial value P at the moment is arranged in displacement0(k), and by the optimal solution of objective function in solution formula (14) flexibility is obtained
Load output power variation delta u is carried it into formula (14) and is obtained system control variables to get k+1 moment flexible load is arrived
Output power P (k+1 | k).
On the other hand, the present invention provides a kind of, and the Multiple Time Scales flexible load based on Model Predictive Control rolls tune
Degree system, characterized in that include:
In the state variable at current time, the state variable of subsequent time and it is according to all kinds of flexible loads at the k moment
System control variable establishes the device of model to the adjustable resource of all kinds of flexible loads；
Establish Multiple Time Scales flexible load rolling scheduling model, the model includes a few days ago and in a few days two scheduling times
Scale, with the minimum function of regulation goal a few days ago of system operation cost and acquisition, scheduling flexible load has operation plan a few days ago a few days ago
The device of function power；
It in a few days dispatches in using the scheduling flexible load active power a few days ago of acquisition as the rolling optimization in a few days dispatched
Know reference value, on the basis of the short-term rolling forecast value of renewable energy, determines in a few days regulation goal function and solve acquisition not
Carry out the device of the predicted value of the flexible load active power output in specific time.
Advantageous effects of the invention:
1. the present invention considers that the response characteristic difference that different type flexible load is interacted with power grid, the present invention propose more times
Scale flexible load rolling scheduling model includes a few days ago and in a few days two scheduling time scales；The flexible load of Multiple Time Scales
Rolling scheduling model considers the precision of prediction of renewable energy, improves the ability of system consumption renewable energy, and passes through
Optimization flexible load response capacity stabilizes renewable energy fluctuation, provides in scheduling time scale to flexible load a few days ago and in a few days
Source optimizes coordinated scheduling；
2. the present invention is based on the hierarchical model forecast Control Algorithms of decomposition-coordination to solve scheduling model a few days ago, pass through entirety
Optimization, central controlled mode reduce the complexity and calculation amount of global optimization problem；
3. the present invention is defeated with the intermittent renewable power supply predicted value of Multiple Time Scales when solving in a few days scheduling model
Enter variable, it is negative with the flexibility in the following finite time-domain window using flexible load state as the state variable initial value at current time
Lotus contributes variable quantity as control variable, and in a few days scheduling time scale, the flexible load of scheduling a few days ago obtained in the hope of solution is active
Power is reference value, carries out rolling optimization solution, introduces Model Predictive Control Theory, passes through rolling optimization and feedback compensation mechanism
The deviation that prediction error and system interference generate is effectively reduced, the closed-loop control best performance in a scheduling slot is obtained.
Detailed description of the invention
Fig. 1 is specific embodiment of the invention method general flow chart；
Fig. 2 is in a few days scheduling model solution flow chart of the specific embodiment of the invention based on Model Predictive Control.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and not intended to limit the protection scope of the present invention.
(1) flexible load response model is established, the adjustable resource of each flexible load is modeled at the k moment.I class is flexible
Load aggregation model can be described by following flexible load model:
X (k+1)=Ax (k)+Bu (k) (1),
Wherein, x (k) is current time flexible load state variable, and x (k+1) is subsequent time flexible load state variable,
U (k) is system control variables, and A and B are coefficient matrix.
(2) consider that the response characteristic difference that different type flexible load is interacted with power grid, the present invention propose Multiple Time Scales
Flexible load rolling scheduling model, comprising a few days ago and in a few days two scheduling time scales, the flexible load of operation plan is rung a few days ago
Answer capacity in the rolling optimization in a few days dispatched as known quantity, rolling scheduling process is as shown in Figure 1.Regulation goal is to be a few days ago
Operating cost of uniting minimizes, and obtains and dispatches flexible load active power a few days ago, i.e., scheduling flexible load responds capacity a few days ago.Its
In, system operation cost includes system electric cost, flexible load response cost and renewable energy scheduling cost, is indicated such as
Under:
In formula, T1For long time scale dispatching cycle；M and n is respectively flexible load and renewable energy quantity；Cgrid(t)
For power grid tou power price；PgridIt (t) is interconnection active power；CFLIt (t) is flexible load response cost；CREIt (t) is renewable
Energy scheduling cost.Flexible load cost function are as follows:
In formula, N is flexible load number of users；For flexible load User Status vector,Indicate the load for being not involved in the user j of interaction,Indicate the load of the user j of participation interaction；For the flexible load active power vector for participating in interaction；ρ1And ρ2To interact cost coefficient；ωFLj
For the participation willingness factor of flexible load user.
In a few days in scheduling, on the basis of meter and Load Regulation amount a few days ago, consider that different time scales renewable energy goes out
Power predicts that error realizes the real-time of system power using the short flexible load resource of fast response time, regulating cycle as scheduler object
Dynamic equilibrium.It in a few days dispatches on the basis of the short-term rolling forecast value of renewable energy, it is soft to solve the scheduling a few days ago that formula (2) obtains
Property load active power be reference value, seek following 15 minutes flexible load active power output variable quantities, but only execute scrolling windows
The control instruction at first moment in mouthful, amendment flexible load respond capacity.Wherein the short-term rolling forecast value of renewable energy is silent
Think given data.In a few days shown in regulation goal function such as formula (4):
In formula, first item is that renewable energy predicts power output a few days ago and in a few days predicts the error of power output；Section 2 is flexibility
Load is with reference to capacity and the deviation actually contributed；Section 3 is the deviation of flexible load scheduling capacity and real response amount.For
System generated output a few days ago；For system in a few days generated output；X is flexible load state variable；xrefFor flexible load wattful power
Rate reference value is obtained by dispatching a few days ago, i.e. solution formula (1)；It contributes a few days ago predicted value for renewable energy；For can be again
The raw energy is in a few days contributed predicted value.
(3) the scheduling model method for solving a few days ago based on Model Predictive Control is proposed.
It is proposed the hierarchical model forecast Control Algorithm based on decomposition-coordination, by global optimization, central controlled mode,
Optimization problem is decomposed into multiple small-scale optimization subproblems and is solved, in line interation direct solution optimization problem, is avoided
The inversion calculation of higher dimensional matrix reduces the complexity of global optimization problem.
Multi-variables optimum design problem is rewritten first are as follows:
Define Lagrange multiplierFor giving λT(k), it solvesWherein,
In formula, Δ uMIt (k) is the variable quantity of system control variables,For systematic survey variable,For coordinating factor,
J (k) is objective function,For current time systematic survey variable,For measurand initial value, AijFor constant.
Update coordinating factor λT(k), it can solveIt is optimal to obtain scheduling model a few days ago
Solution.The above objective function is solved by two-stage optimizing algorithm, solution procedure is as follows:
A. it gives for the 1st gradeIt solves
For wherein i-th of subproblem, according to extreme value necessary condition,
Thus it solves:
B. it solvesIt updates coordinating factor λ (k).Coordinating factor is modified by following gradient algorithm:
Wherein, l is the number of iterations, and α (k) is iteration step length, gradient vector byExpression formula convolution (5) obtains:
Repeat iteration, until:
At this point, in system current time control Delta Δ ui,M(k) it can be used to update control variable, thus
To flexible load optimal solution, i.e. flexible load in Fig. 1 responds capacity a few days ago.
(4) the in a few days scheduling model method for solving based on Model Predictive Control is proposed.
Using the intermittent renewable power supply predicted value of Multiple Time Scales as input variable, with flexible load state be it is current when
The state variable initial value at quarter is control variable with the flexible load power output variable quantity in the following finite time-domain window, to solve
The flexible load active power of scheduling a few days ago that formula (2) obtains is reference value, carries out rolling optimization solution, control structure such as Fig. 2 institute
Show.A length of N when system rolling window, at the k+i moment, system control variables, that is, flexible load active power output predicted value are as follows:
In formula, P0It (k) is flexible load original state；Δ u (k+t | k) it is when predicting following [k+ (t-1), k+t] at the k moment
Flexible load output power variable quantity in section；P (k+i | k) is the flexible load active power at the k+i moment predicted at the k moment.
By system active power balance equation substitute into second step formula (4) objective function, objective function can be converted to
Lower quadratic form optimization problem is solved:
By solving problem above, the flexible load active power output variable quantity vector in [k, k+N] rolling window can be obtained
Δ u (k+1 | k), Δ u (k+2 | k) ..., Δ u (k+N | k) }, first element in the control Variables Sequence is extracted, to flexibility
Load condition is updated, and k+1 moment flexible load output power can be obtained.Using the flexible load state at k+1 moment as
The initial value of new round rolling optimization can carry out the Optimization Solution of subsequent time.Process is solved as shown in Fig. 2, in per a period of time
It carving, the measurand y of detection system reality output simultaneously calculates its error e, and feedback compensation is carried out to predicted value, is shown below:
Y (k+1)=y (k)+he (k+1) (16),
The initial value at the moment is arranged in displacement, and obtains control variable by the optimal solution of objective function in solution formula (14) and increase
Δ u is measured, carries it into formula (14) and obtains system control variables, i.e., flexible load active power and exports, obtains current time
Systematic survey variable.
Application model forecast Control Algorithm of the present invention proposes Multiple Time Scales flexible load rolling scheduling model.It is comprehensive
Consider electric system peak regulation, renewable energy consumption and stabilize renewable energy and goes out fluctuation, establish Multiple Time Scales, it is multipair as
With the flexible load rolling scheduling model of multiple target collaboration.By hierarchical model forecast Control Algorithm based on decomposition-coordination and
Based on the model predictive control method of dynamic rolling optimization, a few days ago and in a few days rolling scheduling mould of two time scales is solved respectively
Type focuses the closed-loop control best performance in a scheduling slot, and effectively reduces prediction error and system by feedback compensation
The deviation generated is interfered, solving precision is improved.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (9)
1. the Multiple Time Scales flexible load rolling scheduling method based on Model Predictive Control, characterized in that the following steps are included:
At the k moment according to all kinds of flexible loads in the state variable at current time, the state variable of subsequent time and system control
Variable processed establishes model to the adjustable resource of all kinds of flexible loads；
Establish Multiple Time Scales flexible load rolling scheduling model, the model includes a few days ago and in a few days two scheduling time rulers
Degree, operation plan is obtained with the minimum function of regulation goal a few days ago of system operation cost and dispatches flexible load wattful power a few days ago a few days ago
Rate；
It in a few days dispatches using the scheduling flexible load active power a few days ago of acquisition as the known ginseng in the rolling optimization in a few days dispatched
Value is examined, on the basis of the short-term rolling forecast value of renewable energy, in a few days regulation goal function is determined and solves the following spy of acquisition
The predicted value of flexible load active power output in fixing time, i.e., in a few days the subsequent time flexible load of scheduling time scale responds appearance
Amount.
2. Multiple Time Scales flexible load rolling scheduling method according to claim 1, characterized in that at the k moment to each
The expression formula that resource that class flexible load is adjustable establishes model is as follows:
X (k+1)=Ax (k)+Bu (k) (1),
Wherein, x (k) is current time flexible load state variable, and x (k+1) is subsequent time flexible load state variable, u (k)
For system control variables, A and B are coefficient matrix.
3. Multiple Time Scales flexible load rolling scheduling method according to claim 1, characterized in that the system operation
Cost includes system electric cost, flexible load response cost and renewable energy scheduling cost, the regulation goal letter a few days ago
Number is expressed as follows:
Wherein, T1For long time scale dispatching cycle；M and n is respectively flexible load and renewable energy quantity；CgridIt (t) is electricity
Net tou power price；PgridIt (t) is interconnection active power；CFLIt (t) is flexible load response cost function；CREIt (t) is renewable
Energy scheduling cost.
4. Multiple Time Scales flexible load rolling scheduling method according to claim 3, characterized in that flexible load response
Cost function CFL(t) expression formula are as follows:
Wherein, N is flexible load number of users；For flexible load User Status vector,
Indicate the load for being not involved in the user j of interaction,Indicate the load of the user j of participation interaction；For the flexible load active power vector for participating in interaction；ρ1And ρ2To interact cost coefficient；ωFLj
For the participation willingness factor of flexible load user.
5. Multiple Time Scales flexible load rolling scheduling method according to claim 3, characterized in that mould will be dispatched a few days ago
The optimization problem of type is decomposed into multiple small-scale optimization subproblems, using the hierarchical model PREDICTIVE CONTROL based on decomposition-coordination
Method, and regulation goal function a few days ago is solved using method of Lagrange multipliers, the specific method is as follows:
Multi-variables optimum design problem is rewritten first are as follows:
Define Lagrange multiplierFor giving λT(k), it solvesWherein,
In formula, Δ uMIt (k) is the variable quantity of system control variables,For systematic survey variable,For coordinating factor, J (k)
For objective function,For current time systematic survey variable,For measurand initial value, AijFor constant；It updates
Coordinating factor λT(k), it solvesObtaining scheduling model optimal solution a few days ago is a few days ago
Dispatch flexible load active power.
6. Multiple Time Scales flexible load rolling scheduling method according to claim 1, characterized in that in a few days regulation goal
Function is as follows:
WhereinIt predicts power output a few days ago for renewable energy and in a few days predicts the error of power output, β
(i)[x(k+i-1)-xref]2The deviation for referring to capacity for flexible load and actually contributing,
For the deviation of flexible load scheduling capacity and real response amount；For system generated output a few days ago；It in a few days generates electricity function for system
Rate；X is flexible load state variable；xrefFor the flexible load active power of scheduling a few days ago of acquisition,For renewable energy day
Interior power output predicted value；For time scale renewable energy active power predicted value a few days ago, α (i), β (i) and γ (i) are weight
Coefficient, i are time parameter.
7. Multiple Time Scales flexible load rolling scheduling method according to claim 6, characterized in that solution is in a few days dispatched
The specific method is as follows for objective function:
A length of N when system rolling window, at the k+i moment, system control variables, that is, flexible load active power are as follows:
In formula, P0It (k) is flexible load original state；Δ u (k+t | k) it is to predict following [k+ (t-1), k+t] in the period at the k moment
Flexible load output power variable quantity；P (k+i | k) is the flexible load active power at k+i moment；
System active power balance equation is substituted into a few days regulation goal functional expression (4), is converted objective function to following secondary
Type optimization problem is solved:
U (k) is control variable vector, uminAnd umaxVariable minimum value and maximum value are respectively controlled, V (k) is objective function, H
(k) and fTIt (k) is coefficient matrix, Aineq(k) and BineqIt (k) is the inequality constraints conditional coefficient of control variable, Aeq(k)Beq
It (k) is control variable equality constraint coefficient；
By solving problem above, the flexible load active power output variable quantity vector { Δ u (k+ in [k, k+N] rolling window is obtained
1 | k), Δ u (k+2 | k) ..., Δ u (k+N | k) }, first element Δ u (k+1 | k) in the control Variables Sequence is extracted, it is right
Flexible load state is updated to get to k+1 moment flexible load output power P (k+1 | k), negative with the flexibility at k+1 moment
Initial value of the lotus state as new round rolling optimization carries out the Optimization Solution of subsequent time.
8. Multiple Time Scales flexible load rolling scheduling method according to claim 7, characterized in that at each moment,
The measurand y of detection system reality output simultaneously calculates its error e, carries out feedback compensation to predictive variable, is shown below:
Y (k+1)=y (k)+he (k+1) (16),
Y (k+1) is the measurand at k+1 moment, and y (k) is the measurand at k moment, and h is coefficient, and e (k+1) is the k+1 moment
Error；The measurand of etching system reality output and the error e (k+1) of measurand being calculated when by calculating k and are
Number h is multiplied, and obtains the revised measurand y (k+1) at k+1 moment, completes feedback compensation；
The initial value P at the moment is arranged in displacement0(k), it is defeated and by the optimal solution of objective function in solution formula (14) to obtain flexible load
Power variation Δ u out is carried it into formula (14) and is obtained system control variables, i.e., flexible load active power and exports to obtain the final product
To k+1 moment flexible load output power P (k+1 | k).
9. the Multiple Time Scales flexible load rolling scheduling system based on Model Predictive Control, characterized in that include:
At the k moment according to all kinds of flexible loads in the state variable at current time, the state variable of subsequent time and system control
Variable processed establishes the device of model to the adjustable resource of all kinds of flexible loads；
Establish Multiple Time Scales flexible load rolling scheduling model, the model includes a few days ago and in a few days two scheduling time rulers
Degree, scheduling flexible load is active a few days ago with the minimum function of regulation goal a few days ago of system operation cost and acquisition for operation plan a few days ago
The device of power；
It in a few days dispatches using the scheduling flexible load active power a few days ago of acquisition as the known ginseng in the rolling optimization in a few days dispatched
Value is examined, on the basis of the short-term rolling forecast value of renewable energy, in a few days regulation goal function is determined and solves the following spy of acquisition
The device of the predicted value of flexible load active power output in fixing time.
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| CN110516951A (en) * | 2019-08-22 | 2019-11-29 | 南京工业大学 | A kind of integrated energy system dispatching method of dynamic interval |
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| CN110854932A (en) * | 2019-11-21 | 2020-02-28 | 国网山东省电力公司青岛供电公司 | Multi-time scale optimization scheduling method and system for AC/DC power distribution network |
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