CN115528736B - Electric power system forward-looking scheduling method and system - Google Patents
Electric power system forward-looking scheduling method and system Download PDFInfo
- Publication number
- CN115528736B CN115528736B CN202211287025.5A CN202211287025A CN115528736B CN 115528736 B CN115528736 B CN 115528736B CN 202211287025 A CN202211287025 A CN 202211287025A CN 115528736 B CN115528736 B CN 115528736B
- Authority
- CN
- China
- Prior art keywords
- scheduling
- energy storage
- model
- thermal power
- constraints
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- 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]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention provides a prospective scheduling method and a prospective scheduling system for an electric power system, which comprise the following steps: establishing a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of a thermal power generating unit and energy storage; adding actual constraints to identify the unit which exits AGC operation aiming at the established model; in a forward looking period, simplifying the AGC dynamic models of the thermal power generating unit and the energy storage power station by adopting a continuous time model; solving the model: firstly, solving the energy storage charging and discharging constraint by adopting a 01 variable complementary relaxation method, then solving the actual constraint for identifying the unit which exits from the AGC operation by utilizing a heuristic method, and finally solving by adopting a Benders decomposition algorithm to obtain the scheduling instructions of the thermal power unit and the energy storage power station.
Description
Technical Field
The invention belongs to the technical field of operation and control of an electric power system, and particularly relates to a method and a system for prospective scheduling of the electric power system.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Along with the continuous improvement of new forms of energy infiltration ratio, electric power system's randomness and volatility are constantly strengthened, and the deviation of active scheduling plan and real-time power generation demand also constantly increases, and the reserve incident of system frequency modulation and insufficient and thermal power unit climbing occasionally takes place, and frequency safety and reserve settlement problem become present research focus.
In order to avoid insufficient ramp-up of the thermal power generating unit in a future period, the current research mainly adopts a prospective economic scheduling mode to optimize a scheduling instruction, and only issues the scheduling instruction for executing the scheduling period based on a model prediction control idea. In order to further consider the influence caused by the uncertainty of the net load, a great deal of research is also developed around a random look-ahead economic scheduling model, a random look-ahead scheduling model based on a robust optimization method is provided in some documents, and the random look-ahead scheduling model is established by adopting a stochastic programming method in some documents. However, related researches still have some defects, which do not consider the real-time operation characteristics of the system and the dynamic frequency modulation characteristics of the thermal power generating units and the energy storage power stations, and may cause insufficient frequency modulation standby of part of the AGC units, resulting in out-of-limit frequency deviation.
In recent years, a single-period economic dispatching model considering AGC dynamic constraint of the thermal power generating unit is researched, and the frequency modulation dynamic characteristic of the thermal power generating unit is represented by an AGC state space expression and a frequency feedback signal. However, the influence of the net load demand in a future period is not considered, the climbing of the thermal power unit is insufficient and the frequency modulation standby is insufficient when the net load uncertainty is large, the frequency stability of the system is influenced, and the actual situation that the thermal power unit exits from AGC operation when the power upper limit and the power lower limit are reached is not considered. If the single-period model is directly expanded, an AGC dynamic constraint model of the thermal power generating unit is established in each period of the forward-looking period, the scale of the problem is obviously increased, and the online operation requirement is difficult to meet. In addition, the frequency modulation dynamic characteristics of the energy storage power station are not considered, and the quick response capability of the energy storage power station is not fully utilized.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a power system prospective scheduling method, which is a discrete-continuous time random prospective scheduling method considering AGC dynamic constraints of a thermal power generating unit and energy storage, solves the charging and discharging constraints of the energy storage and the actual constraints of a scheduling period, and reduces the calculation time.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
in a first aspect, a method for power system look-ahead scheduling is disclosed, which comprises:
establishing a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of a thermal power generating unit and energy storage;
adding actual constraints to identify the unit which quits AGC operation aiming at the established model;
in a forward looking period, simplifying the AGC dynamic models of the thermal power generating unit and the energy storage power station by adopting a continuous time model;
solving a model: firstly, solving the energy storage charging and discharging constraint by adopting a 01 variable complementary relaxation method, then solving the actual constraint for identifying the unit which exits from AGC operation in the scheduling period by utilizing a heuristic method, and finally solving by adopting a Benders decomposition algorithm to obtain the scheduling instructions of the thermal power unit and the energy storage power station.
As a further technical scheme, a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of a thermal power generating unit and energy storage is established based on a multi-scene random planning two-stage optimization model.
As a further technical solution, in the two-stage optimization model of the multi-scenario stochastic programming, the first-stage model optimizes the scheduling instruction of each adjusting resource, including the reference power and the spare capacity, and the second-stage model optimizes the power variation of each adjusting resource under the scheduling instruction in each scenario.
As a further technical solution, an objective function of the look-ahead scheduling model is:
and minimizing the energy cost and the standby cost of each adjusting resource and the adjusting mileage cost under each scene.
As a further technical solution, the constraint conditions of the look-ahead scheduling model include:
a first stage constraint comprising: power balance at the scheduling time;
the method comprises the following steps of (1) power limitation, climbing rate limitation and standby limitation of the output of a thermal power generating unit;
the energy storage device is limited in charging and discharging power, charging and discharging states and energy;
line power flow constraint;
and (4) a power flow calculation formula based on the power transfer distribution factor.
As a further technical solution, the constraint conditions of the look-ahead scheduling model further include:
second stage constraints comprising:
scheduling period constraints;
actual constraints of the scheduling period;
a look-ahead period constraint;
a coupling constraint of the scheduling period and the look-ahead period.
As a further technical solution, the scheduling period constraint includes:
discretization AGC state space expressions of the thermal power generating unit and the stored energy;
frequency response constraints and feedback signal constraints of the system;
the power regulation requirement of the system is distributed between each thermal power generating unit and the energy storage power station;
all output variables of the frequency-modulated (primary and secondary frequency modulation) thermal power generating units are limited by frequency modulation standby;
the climbing rate of the unit output variation is restrained;
the energy storage device is used for charging and discharging power and energy constraint;
line power constraints.
As a further technical solution, the practical constraint of the scheduling period includes:
when the output of the unit reaches a power boundary, the frequency modulation constraint does not work;
at the moment, the output value of the unit is lower/upper power limit;
constraints on the value of the 01 identification variable.
As a further technical solution, the look-ahead period constraint includes:
assuming that the continuous contribution trajectory of each adjustment resource in the look-ahead period can balance the net load demand to simplify the estimation of its reserve capacity demand;
the power and the climbing rate of the output variable quantity of the thermal power unit are restrained;
charging and discharging power and energy constraint of energy storage in a prospective period;
line power constraints.
As a further technical solution, the constraint of coupling the scheduling period and the look-ahead period includes:
the climbing of the fire generator set is restrained within an AGC instruction period delta t;
and energy of energy stored in the AGC command period delta t is restricted, and the linear change of the charge and discharge power of the energy stored in the period is assumed.
In a second aspect, a look-ahead scheduling system for an electrical power system is disclosed, comprising:
a model building module configured to: establishing a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of a thermal power generating unit and energy storage;
adding actual constraints to identify the unit which exits AGC operation aiming at the established model;
a simplification module configured to: in a forward looking period, simplifying the AGC dynamic models of the thermal power generating unit and the energy storage power station by adopting a continuous time model;
a solving module configured to: solving the model: firstly, solving the charging and discharging constraints of the stored energy by adopting a 01 variable complementary relaxation method, then solving the actual constraints of the scheduling time interval for identifying the unit which exits from the AGC operation by adopting a heuristic method, and finally solving by adopting a Benders decomposition algorithm to obtain the scheduling instructions of the thermal power unit and the stored energy.
The above one or more technical solutions have the following beneficial effects:
the model established by the invention not only considers the system frequency characteristic of the scheduling time interval, but also considers the net load demand of the future time interval through the continuous time model, so that more accurate and more reasonable scheduling decision can be made. In addition, the invention also provides a specific solving method, which solves the energy storage charging and discharging constraint and the actual constraint of the scheduling time period and reduces the calculation time.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.
FIG. 1 is a diagram of a scheduling architecture according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a solution according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment discloses a prospective scheduling method of an electric power system, which comprises the following steps:
in a scheduling period, carrying out fine modeling on the thermal power generating unit and the frequency modulation dynamic state of the stored energy by adopting an AGC state space expression, and adding actual constraint to identify the unit which quits AGC operation;
in a forward looking period, an AGC dynamic model of adjusting resources (a thermal power generating unit and an energy storage power station) is simplified by adopting a continuous time model, and the problem scale is reduced.
In order to reduce the solving difficulty, firstly, a 01 variable complementary relaxation method is adopted to solve the energy storage charging and discharging constraint, then a heuristic method is provided to solve the actual constraint that a scheduling time interval is used for identifying the unit which is out of AGC operation, and finally a Benders decomposition algorithm is adopted to solve, so that the solving time is further reduced.
Specifically, the method comprises the following steps:
step 1: and a discrete-continuous time random look-ahead scheduling method and system considering AGC dynamic constraints of a thermal power generating unit and energy storage are provided. The scheduling framework is shown in fig. 1, two-stage modeling is performed in each rolling window (1-3 hours), the first stage optimizes the scheduling instruction for the thermal power generating unit and the energy storage, and the second stage describes the actual operating characteristics of each adjusting resource (the thermal power generating unit and the energy storage) under the scheduling instruction. And (3) optimizing the scheduling instructions in the scheduling period and the forward looking period by taking 15min or 5min as a starting cycle, and only issuing the scheduling instructions for executing the scheduling period.
Step 2: and establishing a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of the thermal power generating unit and the energy storage. The method is based on a multi-scene random planning two-stage optimization model, wherein the first-stage model optimizes scheduling instructions (reference power and spare capacity) of each adjusting resource, and the second-stage model optimizes power variation of the adjusting resource under the scheduling instructions in each scene. The objective function and constraint conditions are as follows:
(one) objective function
And minimizing the energy cost and the standby cost of each adjusting resource and the adjusting mileage cost under each scene, wherein the expression is shown as follows.
In the formula: t is m M respectively represents the length and the number of the scheduling intervals in the rolling window; c G ,C E And respectively representing the energy cost and the standby cost of the thermal power generating unit and the stored energy in the first stage. Ω denotes the set of considered second-stage multi-scenes, pr s Which represents the probability of the occurrence of the scene s,
respectively represents the adjustment mileage cost of the thermal power generating unit and the energy storage in the scheduling period>
And respectively representing the adjustment mileage cost of the thermal power generating unit and the energy storage unit in the look-ahead period.
(II) first stage constraint
In the formula:
respectively represent t m At the moment, the output instruction value of the unit g is reserved upwards/downwards;
respectively representing the charging/discharging power of the energy storage plant e and the up/down standby during charging/discharging; />
Denotes t m The net load value at time node i (load minus uncontrollable new energy output); />
Represents the maximum allowable current capacity of the line l, <' > or>
Representing the stored energy output, function->
Representing line power flow; />
Representing the power transfer factor, N, from node i to line l Gi ,N Ei And respectively representing the thermal power generating unit and the energy storage device at the node i.
Equation (2) represents the power balance at the scheduling time, and Φ in equation (3) G Represents the power limit, the climbing rate limit and the standby limit of the output of the thermal power generating unit, and phi in the formula (4) E And (3) representing the constraints of the charging and discharging power limit, the charging and discharging state and the energy of the energy storage device, wherein the formula (5) represents the line power flow constraint, and the formula (6) represents the power flow calculation formula based on the power transfer distribution factor.
(III) second stage constraints
(1) Scheduling period constraints
In the formula:
respectively representing the reference power variation and the actual output variation, delta f, of the thermal power generating unit under the scene s s (k) Represents a system frequency deviation in scene s>
Representing a corresponding coefficient formed by a prime mover response time constant and a static difference adjustment coefficient of the thermal power generating unit; />
Respectively representing the reference power variation and the actual output variation of the stored energy, m e A droop control coefficient representing the energy storage device participating in frequency modulation; />
Respectively represent->
And &>
OfColumn vector +>
Y sys ,/>
Representing a corresponding coefficient, which is determined by an inertia time constant and a load damping coefficient of the system; />
Representing the system load variation; />
Representing the total regulated power demand of the system, K sys Representing the feedback gain factor, alpha, of the system g ,β j Indicating the distribution coefficient.
The above expression shows the AGC dynamic characteristics of the thermal power generating unit and the energy storage, k shows the control time with an AGC command period delta t (4 s) as an interval, expressions (7) to (8) respectively show discretization AGC state space expressions of the thermal power generating unit and the energy storage, expressions (9) to (10) respectively show frequency response constraints and feedback signal constraints of the system, and expression (11) shows the distribution of the power regulation requirements of the system between each thermal power generating unit and the energy storage power station.
In the formula:
respectively represent the first stage t 0 The upper/lower standby at any time; n is a radical of GC ,N GN Respectively represents the aggregates which take part in secondary frequency modulation and only take part in primary frequency modulation>
R g Representing the climbing power upper/lower limit of the unit g; />
A set of constraints representing energy storage device reference power and output variation; />
Representing the actual output of the thermal power generating unit at k moment under each scene s; />
Representing the actual output of the stored energy; />
Representing the payload value at node i.
The equation (12) represents that the output variation of the thermal power unit participating in secondary frequency modulation is constrained by the frequency modulation standby limit, the equation (13) represents that the output variable of the thermal power unit participating in primary frequency modulation is also constrained by the frequency modulation standby limit, the equation (14) represents the climbing rate constraint of the output variation of the thermal power unit, the equation (15) represents the charge and discharge power and energy constraint of the energy storage device, and the equation (16) represents the line power constraint.
(2) Practical constraints for scheduling periods
In the actual operation process, when the output force of the unit reaches the upper power limit/lower power limit, the unit can quit the AGC operation and does not participate in the frequency modulation process. Therefore, it is necessary to increase the recognition unit output powerAnd the constraint of the upper/lower power limits is achieved, and meanwhile, the corresponding units output power according to the upper/lower power limits, and the output power is not determined through frequency modulation constraint. The invention identifies variables by introducing 01
To identify whether the output of the unit reaches the upper/lower power limit and the upper power limit->
When the lower power limit is reached->
To achieve the above function, equation (7) in the scheduling period constraint is replaced with the following constraint.
In the formula:
represents the unit output at time k>
Respectively representing the lower limit/the upper limit of the output of the unit; />
For two constructed non-negative functions related to the output force of the unit, the value is 0 when the output force of the unit reaches a lower/upper limit, and is larger than 0 otherwise; m is a large positive number and ε is a small positive number.
Equation (17) represents the crashThe group output reaches the power boundary (
Or->
) When the frequency modulation constraint does not work, the formula (18) shows that the output value of the unit is lower/upper power limit, and the formula (19) realizes the constraint on the value of the 01 identification variable: when the output reaches a power limit->
(or +>
) So that->
(or +>
) Based on the fact that the output does not reach the power limit->
(or +>
) So that->
(or +>
)。
(3) Look-ahead period constraint
In the formula:
respectively representing continuous time tracks of thermal power generating units and energy storage output under the scene s, wherein the continuous time tracks are equal to the sum of scheduling output and output variable quantity, and the output variable quantity is respectively represented as
Representing the net load trajectory at node i in the look-ahead period under scene s;
representing a reserve capacity trajectory which is a staircase curve corresponding to the reserve at the first phase scheduling instant, i.e.
And a constraint set representing the variable quantity of the charging and discharging power of the energy storage device.
Because the frequency deviation of the system can be smaller through a reasonable scheduling strategy and a primary and secondary frequency modulation process, and meanwhile, the actual total output of the unit can be regarded as a continuous time change track, as shown in formula (20), the continuous output track of each adjusting resource in a prospective period is assumed to balance the net load requirement so as to carry out simplified estimation on the spare capacity requirement; equations (21) and (22) respectively represent power and climbing rate constraints of the output variation of the thermal power generating unit, equation (23) represents charging and discharging power and energy constraints of stored energy in a look-ahead period, and equation (24) represents line power constraints.
(4) Coupling constraints of scheduling periods and look-ahead periods
In the formula:
represents the output of the thermal power unit at the penultimate moment in the scheduling period, N represents the number of control moments with an interval delta t in the scheduling period, and/or is greater than or equal to>
Representing the output of the thermal power generating unit at the initial moment of the prospective period;
respectively representing the energy storage energy values of the initial moment of the look-ahead period and the second last moment of the scheduling period,
respectively represents the charging and discharging power at the corresponding time point, and>
representing the efficiency coefficient of the stored energy charging/discharging.
Between the look-ahead period model and the scheduling period model, coupling constraint needs to be added to realize the transition of the two-segment models. Since there is a simplification in the modeling process of the look-ahead period continuous-time model, it is assumed here that within the last Δ t of the scheduling period, each adjustment resource adjusts its contribution to achieve power balance at the end time of the scheduling period (i.e., the initial time of the look-ahead period). Equation (25) represents the hill climbing constraint of the internal combustion engine set within Δ t, equation (26) represents the energy constraint of the stored energy within Δ t, and it is assumed that the charge and discharge power of the stored energy in this section changes linearly.
A discrete-continuous time random look-ahead economic scheduling model considering AGC dynamic constraints of thermal power and energy storage is established, a finite-dimension model can be obtained after Bernstein polynomial conversion is adopted on the continuous time model in the look-ahead period, the model is difficult to solve due to the fact that actual constraints exist in the scheduling period, the problem scale is further increased due to energy storage charging and discharging constraints, and therefore the solving method in the step 3 is adopted.
And step 3: and (3) solving the random forward-looking scheduling model in the step (2) to obtain scheduling instructions (reference power and reserve capacity) of the thermal power generating unit and the energy storage power station. Firstly, solving the energy storage charge-discharge constraint by adopting an energy storage variable relaxation method in the step 4, then solving the actual constraint of the scheduling time interval by adopting a heuristic method in the step 5, and finally solving by adopting a Benders decomposition algorithm in the step 6 so as to further accelerate the calculation time. The solving flow chart is shown in fig. 2.
And 4, step 4: energy storage 01 variable relaxation method
The economic scheduling problem of any relaxation of the energy storage 01 variable constraint or the complementary constraint at any time can be expressed as follows (taking one energy storage as an example, a plurality of energy storages are also applicable):
min g(p dis )+f(p ch )+∑h(p g ) (27)
p g ∈G (28)
E(t+1)＝E(t)+(η ch p ch -p dis /η dis )Δt (31)
∑p g +p dis -p ch ＝∑d i ,λ (33)
in the formula: p is a radical of dis ,p ch Respectively representing stored energy discharge/charging power, p g Representing the output of the thermal power generating unit, g (-) and f (-) and h (-) respectively represent corresponding cost functions,
respectively, the maximum charge/discharge power, η ch ,η dis Respectively represents charge-discharge efficiency, E (t) represents energy stored in the accumulator, and>
Erespectively represent upper/lower energy limits, d i Representing the value of the net load at node i, α i ,β i And λ represents each of the dual multipliers.
Equation (27) represents the thermal power output and the cost of energy storage charging and discharging, G in equation (28) represents the power and climbing constraints that the thermal power output should satisfy, equations (29) to (32) represent energy storage charging and discharging power limits and energy limits, and equation (33) represents the power balance constraint. Since it is an LP problem and the charge-discharge cost function is linear, the KKT optimality condition can be expressed as follows, where C ch ,C dis The charge/discharge cost coefficient is expressed, and according to the existing literature, the charge/discharge cost is assumed to be borne by the power grid:
the effectiveness of the relaxation is demonstrated by a back-proof method, i.e. p if the energy storage device is charged and discharged simultaneously ch ＞0,p dis The existence of more than 0 at the same time,then alpha is 1 ＝0,α 3 =0, will be the formula (34) and η ch η dis The addition of multiples of (35) yields:
C ch +η ch η dis C dis +α 2 +η ch η dis α 4 +(1-η ch η dis )λ＝0 (36)
due to C ch +η ch η dis C dis ＞0，η ch η dis If < 1, LMP is not negative under the condition that the transmission line is not congested, (1-eta) ch η dis ) Lambda is greater than or equal to 0, then alpha 2 +η ch η dis α 4 < 0, which contradicts the non-negativity of the dual multiplier, so p ch ＞0,p dis And > 0 cannot exist at the same time, namely, cannot be charged and discharged at the same time.
Therefore, the 01 variable in the energy storage charge-discharge constraint can be relaxed, and a model without the energy storage 01 variable is obtained.
And 5: and solving the actual constraint of the scheduling period by adopting a heuristic method to obtain a local optimal solution.
Firstly, identifying the 01 identification variable of the unit
(or +>
) And (4) all values are assigned to be 1, and the original problem without the formula (19) is solved to obtain the output of the unit as an initial value.
The identified actual constraint subproblem shown in equation (37) is then solved, which introduces non-negative slack variables
Judging whether the unit output reaches a power capacity boundary (upper/lower limit): if the relaxation variable is greater than 0 (i.e. the output of the unit exceeds the power capacity), the 01 identification variable of the corresponding unit is combined with the corresponding unit>
(or +>
) The value is assigned to 0. And (4) solving the original problem without the formula (19) again according to the latest assignment of the identification variable to obtain the unit output. And iterating in the above manner until the output of the unit makes the relaxation variables in the formula (37) all zero, so as to obtain the local optimal solution of the original problem.
Step 6: solving by Benders decomposition algorithm
And (4) decomposing the model converted in the step (4) into a main problem without line constraint and a line constraint sub-problem capable of being calculated in parallel by adopting a Benders decomposition algorithm, wherein the actual constraint of the scheduling time period in the main problem is solved by the heuristic method in the step (5).
Firstly, solving a main problem without line constraint, and determining the planned output of each adjusting resource and the output under each scene. And solving the line constraint sub-problems shown in the formulas (38) to (40), judging whether the line power of the scheduling period and the look-ahead period under the first-stage planning and each scene is out of limit by checking whether a relaxation variable is zero, generating a corresponding Benders feasibility cut if the line power is out of limit, adding the Benders feasibility cut into the main problem, and re-solving the main problem, wherein the Benders feasibility cut is shown in the formulas (41) and (42). And iterating until the output plan determined by the main problem meets all the line constraints in the two stages.
1) First phase line constraint sub-problem
In the formula (I), the compound is shown in the specification,
respectively representing the planned output of the thermal power generating unit and the planned output of the first stage of the energy storage determined by the main problem;
respectively represent corresponding relaxation variables; />
The dual multipliers corresponding to the respective formulae are shown.
2) Scheduling period line constraint sub-problem
In the formula (I), the compound is shown in the specification,
respectively representing the actual output of the thermal power generating unit determined by the main problem and the actual output of the energy storage unit at the k moment in the scheduling time period under the scene s; />
Representing the corresponding slack variable; />
The dual multipliers corresponding to the respective formulae are shown.
3) Look-ahead period line constraint subproblem
In the formula, column vector
The mapping coefficient, the column vector and the output locus of the thermal power generating unit and the energy storage in the look-ahead period under the scene s are determined according to the main problem>
A mapping coefficient representing a payload; />
Indicating a corresponding slack changeMeasuring a column vector of quantities; />
A column vector composed of dual multipliers corresponding to the expressions; 1 F A column vector having a value of 1 and the same dimension is shown.
4) Benders feasibility cut
In the formula, the superscript a is used for distinguishing the line constraint subproblems of the first stage and the scheduling period, and the subscript b is used for representing the line and the time;
expressing all the adjusting resource output variables corresponding to the a-type subproblems in the main problem; />
Representing the corresponding dual multiplier; equation (42) represents the Benders feasibility cut of the look-ahead period line constraint sub-problem>
A mapping coefficient vector representing the output trajectory of each adjustment resource in a look-ahead period in the main problem, device for selecting or keeping>
Representing the corresponding dual multiplier column vector.
And 7: through the steps, the invention provides the discrete-continuous time random look-ahead scheduling method and system considering AGC dynamic constraints of the thermal power generating unit and energy storage, not only considers the system frequency characteristic of the scheduling time period, but also considers the net load requirement of the future time period through the continuous time model, and can make more accurate and more reasonable scheduling decisions. In addition, the invention also provides a solving method in the step 3, which solves the energy storage charging and discharging constraint and the actual constraint of the scheduling time period and reduces the calculation time.
Example two
It is an object of this embodiment to provide a computer device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Example four
The object of this embodiment is to provide a power system look-ahead scheduling system, including:
a model building module configured to: establishing a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of a thermal power generating unit and energy storage;
adding actual constraints to identify the unit which exits AGC operation aiming at the established model;
a simplification module configured to: in a forward looking period, simplifying the AGC dynamic models of the thermal power generating unit and the energy storage power station by adopting a continuous time model;
a solving module configured to: solving the model: firstly, solving the charging and discharging constraints of the stored energy by adopting a 01 variable complementary relaxation method, then solving the actual constraints of the scheduling time interval for identifying the unit which exits from the AGC operation by adopting a heuristic method, and finally solving by adopting a Benders decomposition algorithm to obtain the scheduling instructions of the thermal power unit and the stored energy.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (7)
1. A power system prospective scheduling method is characterized by comprising the following steps:
establishing a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of a thermal power generating unit and energy storage;
for the established model, in a scheduling period, carrying out refined modeling on the thermal power generating unit and the frequency modulation dynamic state of the stored energy by adopting an AGC state space expression, and adding actual constraints to identify the unit which exits AGC operation;
in a forward looking period, simplifying the AGC dynamic models of the thermal power generating unit and the energy storage power station by adopting a continuous time model;
model solving, namely solving energy storage charging and discharging constraints by adopting a 01 variable complementation relaxation method, solving actual constraints for identifying the unit exiting AGC operation in a scheduling period by utilizing a heuristic method, and solving by adopting a Benders decomposition algorithm to obtain scheduling instructions of the thermal power unit and the energy storage unit;
the establishment of the discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of the thermal power generating unit and the energy storage is a two-stage optimization model based on multi-scene random planning, the first-stage model optimizes scheduling instructions of each adjusting resource, wherein the scheduling instructions comprise reference power and standby capacity of each adjusting resource, and the second-stage model optimizes power variation of each adjusting resource under the scheduling instructions in each scene;
the objective function of the look-ahead scheduling model is: minimizing the energy cost and the standby cost of each adjusting resource and the adjusting mileage cost under each scene, wherein the expression is as follows:
in the formula: t is m M denotes the length and number of scheduling intervals within the rolling window, respectively, C G Representing the energy cost and the standby cost of the thermal power generating unit scheduled in the first stage, C E Representing the energy cost and the standby cost of the first-stage scheduling energy storage, omega representing the set of the considered second-stage multi-scenes, pr s Which represents the probability of the occurrence of the scene s,
respectively represents the adjustment mileage cost of the thermal power generating unit and the energy storage in the scheduling period>
And respectively representing the adjustment mileage cost of the thermal power generating unit and the energy storage unit in the look-ahead period.
2. The method according to claim 1, wherein the constraints of the look-ahead scheduling model include:
a first stage constraint comprising: power balance at the scheduling moment;
the power limit, the climbing rate limit and the standby limit of the output of the thermal power generating unit;
the energy storage device is limited in charging and discharging power, charging and discharging states and energy;
line power flow constraint;
and (4) a power flow calculation formula based on the power transfer distribution factor.
3. The method according to claim 2, wherein the constraints of the look-ahead scheduling model further include:
second stage constraints comprising:
scheduling period constraints;
actual constraints of the scheduling period;
a look-ahead period constraint;
a coupling constraint of the scheduling period and the look-ahead period.
4. The method according to claim 3, wherein the scheduling period constraint comprises:
discretization AGC state space expressions of the thermal power generating unit and the stored energy;
frequency response constraints and feedback signal constraints of the system;
the power regulation requirement of the system is distributed between each thermal power generating unit and the energy storage power station;
the output variables of all the frequency-modulated thermal power generating units are limited by frequency modulation standby;
the climbing rate of the unit output variation is restrained;
the energy storage device is used for charging and discharging power and energy;
line power constraints.
5. A power system look-ahead scheduling system is characterized by comprising:
a model building module configured to: establishing a discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of a thermal power generating unit and energy storage;
adding actual constraints to identify the unit which exits AGC operation aiming at the established model;
a simplification module configured to: in a forward looking period, simplifying the AGC dynamic models of the thermal power generating unit and the energy storage power station by adopting a continuous time model;
a solving module configured to: solving the model, namely solving the charge-discharge constraint of the stored energy by adopting a 01 variable complementation relaxation method, solving the actual constraint for identifying the unit which exits the AGC operation by utilizing a heuristic method, and solving by adopting a Benders decomposition algorithm to obtain a scheduling instruction of the thermal power unit and the stored energy;
the establishment of the discrete-continuous time random look-ahead scheduling model considering AGC dynamic constraints of the thermal power generating unit and the energy storage is a two-stage optimization model based on multi-scene random planning, the first-stage model optimizes scheduling instructions of each adjusting resource, wherein the scheduling instructions comprise reference power and standby capacity of each adjusting resource, and the second-stage model optimizes power variation of each adjusting resource under the scheduling instructions in each scene;
the objective function of the look-ahead scheduling model is: minimizing the energy cost and the standby cost of each adjusting resource and the adjusting mileage cost under each scene, wherein the expression is as follows:
in the formula: t is m M denotes the length and number of scheduling intervals within the rolling window, respectively, C G Representing the energy cost and the standby cost of the thermal power generating unit scheduled in the first stage, C E Representing the energy cost and the standby cost of the first-stage scheduling energy storage, omega representing the set of the considered second-stage multi-scenes, pr s Which represents the probability of the occurrence of the scene s,
respectively represents the adjusted mileage cost of the thermal power generating unit and the energy storage in the scheduling period, and>
respectively representing thermal power generating units andthe adjusted mileage cost of stored energy over the look-ahead period.
6. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method as claimed in any one of claims 1 to 4 when executing the program.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the preceding claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211287025.5A CN115528736B (en) | 2022-10-20 | 2022-10-20 | Electric power system forward-looking scheduling method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211287025.5A CN115528736B (en) | 2022-10-20 | 2022-10-20 | Electric power system forward-looking scheduling method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115528736A CN115528736A (en) | 2022-12-27 |
| CN115528736B true CN115528736B (en) | 2023-04-18 |
Family
ID=84703291
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211287025.5A Active CN115528736B (en) | 2022-10-20 | 2022-10-20 | Electric power system forward-looking scheduling method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115528736B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117913920B (en) * | 2024-03-19 | 2024-06-04 | 山东大学 | Scheduling method and system considering unit climbing rate constraint and system initial state |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114938011A (en) * | 2022-07-07 | 2022-08-23 | 中国长江三峡集团有限公司 | Wind-solar-fire-storage system combined operation method considering energy storage optimization configuration |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109472423A (en) * | 2018-12-08 | 2019-03-15 | 国网辽宁省电力有限公司电力科学研究院 | A kind of Unit Combination method of fired power generating unit depth peak regulation under the conditions of consideration wind-electricity integration |
| CN109840636B (en) * | 2019-02-19 | 2020-09-25 | 清华大学 | Newton method-based power system random rolling scheduling method |
| CN111311008B (en) * | 2020-02-21 | 2021-04-30 | 山东大学 | AC/DC large power grid dynamic security risk situation rolling prospective early warning method and system |
| CN112054556B (en) * | 2020-08-26 | 2022-06-10 | 济南大学 | Multi-microgrid distributed interactive operation optimization control method and system |
| CN112713615B (en) * | 2020-12-23 | 2022-07-26 | 山东大学 | Quick coordination scheduling method and system for electricity-gas integrated energy system |
| CN114123235A (en) * | 2021-10-22 | 2022-03-01 | 国网浙江省电力有限公司电力科学研究院 | Cooperative operation method and system for main energy and frequency modulation of fire storage combined power station |
| CN115204056A (en) * | 2022-07-29 | 2022-10-18 | 中国电力科学研究院有限公司 | Power grid prospective optimization scheduling method and system |
-
2022
- 2022-10-20 CN CN202211287025.5A patent/CN115528736B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114938011A (en) * | 2022-07-07 | 2022-08-23 | 中国长江三峡集团有限公司 | Wind-solar-fire-storage system combined operation method considering energy storage optimization configuration |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115528736A (en) | 2022-12-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109103912B (en) | Industrial park active power distribution system scheduling optimization method considering power grid peak regulation requirements | |
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| CN110854932B (en) | Multi-time scale optimization scheduling method and system for AC/DC power distribution network | |
| JP4203602B2 (en) | Operation support method and apparatus for power supply equipment | |
| CN102474105B (en) | Power system demand control equipment, demand control program and recording medium | |
| CN102687033B (en) | Method for controlling secondary battery and power storage device | |
| CN111697578B (en) | Multi-target energy storage-containing regional power grid operation control method | |
| CN108964103B (en) | Microgrid energy storage configuration method considering schedulability of microgrid system | |
| CN112053035B (en) | Power transmission channel and energy storage joint planning method considering economy and flexibility | |
| CN115423207A (en) | Wind storage virtual power plant online scheduling method and device | |
| CN111786417A (en) | Distributed new energy consumption-oriented active power distribution network multi-target interval optimization scheduling method | |
| CN115528736B (en) | Electric power system forward-looking scheduling method and system | |
| CN111092455A (en) | Load recovery optimization method for combined operation of energy storage system and recovered unit | |
| WO2022054442A1 (en) | Power regulation method and power regulation device | |
| CN107591806A (en) | A kind of major network dispatching method a few days ago for considering the active regulating power of active distribution network | |
| CN115395505B (en) | Wind power system-containing black start partitioning method considering scale energy storage | |
| CN113964819B (en) | Power system operation optimization method and device considering participation of wind power plant in frequency modulation | |
| CN115936273A (en) | Power system multi-stage evolution path planning operation optimization method and system | |
| CN115800275B (en) | Power balance regulation and control distribution method, system, equipment and storage medium | |
| CN117394446A (en) | Multi-stage robust unit combination method and device based on sequential evolution of batch scenes | |
| CN115000957B (en) | Resource scheduling optimization method and system considering uncertainty of net load among moments | |
| CN118473021B (en) | Micro-grid optimal scheduling method and system combining CMA-ES algorithm and DDPG algorithm | |
| CN116247743A (en) | Micro-grid real-time scheduling method based on robust model predictive control | |
| CN116187910B (en) | Temperature control warehouse capacity configuration method and device, electronic equipment and storage medium | |
| Wu et al. | Two-Stage Rolling Scheduling Strategy For Hybrid Energy Storage With Wind Power Integration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |