CN111463838B - Two-stage robust optimization scheduling method and system considering energy storage participation in secondary frequency modulation - Google Patents

Two-stage robust optimization scheduling method and system considering energy storage participation in secondary frequency modulation Download PDF

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CN111463838B
CN111463838B CN202010376700.6A CN202010376700A CN111463838B CN 111463838 B CN111463838 B CN 111463838B CN 202010376700 A CN202010376700 A CN 202010376700A CN 111463838 B CN111463838 B CN 111463838B
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孙东磊
李雪亮
马逸然
韩学山
白娅宁
王明强
许易经
杨思
李文博
杨金洪
程佩芬
付一木
魏佳
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Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses a two-stage robust optimization scheduling method and a two-stage robust optimization scheduling system considering energy storage participation in secondary frequency modulation, wherein the two-stage robust optimization scheduling method comprises the following steps: acquiring a power grid AGC signal, decoupling the power of the AGC signal, and distributing the obtained low-frequency change and high-frequency change to a conventional AGC unit and energy storage equipment by using an energy storage system matched with the AGC unit; determining an uncertainty set, taking the minimum operation cost of an outer-layer minimum solving system and the most extreme scene of an inner-layer maximum solving energy storage system in operation as two layers of objective functions, and constructing a two-stage robust self-adaptive optimization scheduling model; and solving the two-stage robust self-adaptive optimization scheduling model in stages to obtain an optimal power base point and an energy storage maximum matching amount of the AGC unit so as to determine an optimal configuration operation strategy of the energy storage participating in secondary frequency modulation. The invention converts the standby requirement of the AGC unit into the requirement of climbing speed and response capability, and makes up the deficiency of the AGC unit by using the energy storage system, thereby enlarging the schedulable coordination area of the system.

Description

Two-stage robust optimization scheduling method and system considering energy storage participation in secondary frequency modulation
Technical Field
The invention relates to the technical field of power automation, in particular to a two-stage robust optimization scheduling method and system considering energy storage and participating in secondary frequency modulation.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Currently, the global energy development situation is undergoing a third significant energy conversion, which indicates that the global power system is undergoing a structural transformation. For the future power grid structure in China, the function of the traditional large unit is not large-scale power generation, but the traditional large unit is developed to a distributed structure, and the main problem is also changed to the dispatching direction for realizing source-load balance. The time of a traditional large unit responding to an automatic generation amount control (AGC) instruction is about tens of seconds, and the climbing capability is poor and is not enough to respond to the fluctuation of a high proportion of renewable energy sources. For example, the climbing capacity of a hydroelectric generating set is 30%/min, the climbing capacity of a gas generating set is 20%/min, and the climbing capacity of a coal generating set is only 2%/min. After the large-scale renewable energy is accessed into the power grid, the high volatility of the large-scale renewable energy increases the demand of the power grid on the climbing rate of the AGC unit.
Secondly, renewable energy sources such as wind, light and the like have intermittent and fluctuating characteristics, so that the problem of uncertainty in source load is more prominent, and the aim of receiving all the renewable energy sources needs to be fulfilled by means of energy storage to cooperate with an AGC unit to stabilize peak-valley difference, so that the energy storage participates in secondary frequency modulation scheduling. When the influence of uncertain factors of source load is responded, how to realize low-carbon economic operation of a power grid in day-ahead scheduling is an important challenge faced by the contemporary power system. The energy storage system has rapid and accurate power response capability, is beneficial to better tracking an automatic power generation control instruction by a power plant, and more efficiently finishes the automatic power generation control target of a power grid. But its power and capacity are limited and cannot solely undertake the peak shaving task. Therefore, the problem of coordinated distribution of the energy storage system and the AGC set is the key to the application of energy storage to the power system.
Three important issues remain to be discussed for multi-stage coordinated planning of large-scale renewable energy sources in cooperation with energy storage systems: firstly, determining the size of a coordination area of an AGC unit to obtain the admission capacity of the whole system; the energy storage is matched with the coordination and complementation capacity of the AGC unit; and thirdly, an efficient solution method for the uncertainty problem.
Disclosure of Invention
In view of the above, the invention provides a two-stage robust optimization scheduling method and system considering that energy storage participates in secondary frequency modulation, which relate the function of eliminating uncertainty in the secondary frequency modulation of the energy storage to the AGC control process, and analyze the control process in economic scheduling, thereby constructing a corresponding scheduling model. The method has the advantages that the decision result can be directly butted with the control process, robustness is provided for all possible realizations of uncertainty, and the advantages of economy and efficiency are guaranteed; the method is suitable for solving the problem of renewable energy consumption of source load storage balance.
In some embodiments, the following technical scheme is adopted:
the two-stage robust optimization scheduling method considering the energy storage participation in the secondary frequency modulation comprises the following steps:
acquiring a power grid AGC signal, decoupling the power of the AGC signal, and distributing the obtained low-frequency change and high-frequency change to a conventional AGC unit and energy storage equipment by using an energy storage system matched with the AGC unit; determining an uncertainty set, taking the minimum operation cost of an outer-layer minimum solving system and the most extreme scene of an inner-layer maximum solving energy storage system in operation as two layers of objective functions, and constructing a two-stage robust self-adaptive optimization scheduling model;
and solving the two-stage robust self-adaptive optimization scheduling model in stages to obtain an optimal power base point and an energy storage maximum matching amount of the AGC unit so as to determine an optimal configuration operation strategy of the energy storage participating in secondary frequency modulation.
In other embodiments, the following technical solutions are adopted:
a two-stage robust optimization scheduling system considering energy storage and participating in secondary frequency modulation comprises:
the device is used for acquiring a power grid AGC signal and decoupling the power of the AGC signal;
means for distributing the obtained low frequency variations and high frequency variations to a conventional AGC unit and an energy storage device, respectively;
the device is used for determining an uncertainty set, taking the minimum running cost of an outer-layer minimum solving system and the most extreme scene of an inner-layer maximum solving energy storage system in running as two layers of objective functions, and constructing a two-stage robust self-adaptive optimization scheduling model;
and the device is used for solving the two-stage robust self-adaptive optimization scheduling model in stages to obtain an optimal power base point and an energy storage maximum coordination amount of the AGC unit so as to determine an optimal configuration operation strategy of the energy storage participating in secondary frequency modulation.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the two-stage robust optimization scheduling method considering the energy storage and participation in the secondary frequency modulation.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the above two-stage robust optimized scheduling method taking into account energy storage involved in secondary frequency modulation.
Compared with the prior art, the invention has the beneficial effects that:
the invention converts the standby requirement of the AGC unit into the requirement of climbing speed and response capability, and makes up the deficiency of the AGC unit by using the energy storage system, thereby enlarging the schedulable coordination area of the system.
The invention provides an optimization scheme of energy storage configuration, improves the flexibility of the system and increases the wind and light absorption capability of the power system.
The invention enhances the robustness of the uncertainty of the renewable energy source, the C & CG algorithm can be suitable for various problems, the calculation iteration times are greatly reduced, and the calculation speed is improved.
Drawings
FIG. 1 is a block diagram of an embodiment of the invention in which an energy storage system participates in scheduling of a long-time-scale power system;
FIG. 2 is a logic block diagram of a two-stage adaptive robust optimization method 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 disclosure. 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 application 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 example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
In one or more embodiments, a two-stage robust optimization scheduling method considering that energy storage participates in secondary frequency modulation is disclosed, and referring to fig. 1, the method specifically includes the following steps:
step one, based on the energy storage system participating in the long-time scale power system scheduling strategy, referring to fig. 1, firstly, a scheduling day plan is determined through prediction, meanwhile, a power generation plan is corrected through ultra-short-term prediction, and then, an instruction is sent to a power generator.
Analyzing an AGC signal of a power grid, decoupling the power of the AGC signal, using an energy storage system to match with an AGC unit, respectively distributing low-frequency change and high-frequency change to a conventional AGC unit and energy storage equipment, and determining a model uncertainty set;
specifically, the energy storage system is matched with an AGC unit to supplement the omission of insufficient regulation rate and overlong response time of the traditional unit; in the robust optimization, an uncertainty set is firstly established, wind-solar output has uncertainty, the fluctuation of wind, light and load is considered as a whole and is used as the uncertainty set, and each uncertainty scene is included in the uncertainty set.
Establishing a two-stage adaptive robust economic dispatching model, wherein U is specifically represented as:
Figure BDA0002480375090000051
Figure BDA0002480375090000052
wherein U is a set of uncertain fluctuation variables, U represents the scene of each uncertain condition,
Figure BDA0002480375090000053
for the vector of uncertain fluctuation at time t,
Figure BDA0002480375090000054
as the amount of fluctuation at the time t,
Figure BDA0002480375090000055
is the amount of fluctuation at time t +1,
Figure BDA0002480375090000056
is the deviation between the fluctuations at each Δ t period, and is also the change slope of the fluctuation amount.
Fluctuation amount in two extremely short time sections
Figure BDA0002480375090000057
Can be viewed as linear, with
Figure BDA0002480375090000058
The range of the uncertain set is increased;
Figure BDA0002480375090000059
the determination of the value means that the deviation from the expected fluctuation is controlled within a certain range, so the resulting solution of robust economic scheduling is more conservative.
Step two, aiming at the problem that the energy storage participates in secondary frequency modulation, a target function is divided into two layers, the minimum operation cost y of an outer-layer minimum solving system is obtained, and the maximum extreme scene x of the inner-layer maximum solving energy storage system in operation is obtained;
specifically, the objective function is specifically:
Figure BDA00024803750900000510
wherein y represents the minimum operation cost of the system, x represents the configuration capacity of the inner-layer energy storage system, U represents any uncertain scene, and F (y, U) represents that the solving variable x meets the constraint condition in any scene of the uncertain set.
The first constraint comprises the upper and lower limit constraints of the AGC unit, the energy storage capacity requirement, the slope constraint and the like.
The second of the constraints includes scheduling related constraints such as energy balance constraints.
The third constraint emphasizes that the network injection of uncertain nodes in the uncertainty model is fixed at the expected value.
Figure BDA0002480375090000061
The method comprises the following steps:
Figure BDA0002480375090000062
Figure BDA0002480375090000063
Figure BDA0002480375090000064
wherein,
Figure BDA0002480375090000065
generating cost of an AGC unit i in the t scheduling period;
Figure BDA0002480375090000066
the power generation cost of the energy storage system k in the t scheduling period is obtained.
Under the worst condition of an uncertain set, namely the scene with the highest power generation and energy storage operation cost, the first-stage decision and the second-stage adaptive scheduling action of uncertain immunity are obtained by minimizing the sum of the output cost and the energy storage cost of a unit.
The second phase problem is modeling the decision after the first phase decision is made and its uncertainty is revealed.
Step three, establishing a two-stage robust adaptive economic dispatching model according to specific problems, and writing constraint conditions;
specifically, the two-stage robust adaptive economic scheduling model is as follows:
Figure BDA0002480375090000071
s.t.Ay≥d y∈Sy
By＝C y∈Sy
F(y,u)＝{x∈Sx:Dx≥E-Gy-hu}
wherein y represents the minimum operation cost of the system, x represents the configuration capacity of the inner-layer energy storage system, U represents a set of uncertainty fluctuation variables, and F (y, U) represents that solving variables x meet constraint conditions in any scene of the uncertainty set; a, B, D, E, F and G are constraint condition coefficient matrixes respectively; c, d, h are constant vectors. Sx、SyRespectively representing a set of inner layer solutions x and a set of outer layer solutions y.
The constraint conditions are specifically as follows:
the active power of the power system should meet the following balance constraints at any time:
Figure BDA0002480375090000072
wherein, Pi G、gk、
Figure BDA0002480375090000073
PLThe power, the energy storage state, the energy storage charging (discharging) energy power and the wind and light output power of the AGC unit shuchu are respectively.
The AGC set is often used to cooperate with the secondary frequency adjustment of the power system, and under the adjustment action of the AGC system, the AGC set automatically undertakes the system power mismatch amount by the participation factor to maintain the qualified system frequency and the regional power exchange plan, which can be expressed as:
Figure BDA0002480375090000074
in the formula,
Figure BDA0002480375090000075
outputting variable quantity for the AGC unit; k is a radical ofiThe participation factor of the unit i;
Figure BDA0002480375090000076
in order to output the fluctuation amount of the wind and light,
Figure BDA0002480375090000077
and outputting the variable quantity for energy storage.
Wherein the climbing constraint is:
Figure BDA0002480375090000081
Figure BDA0002480375090000082
wherein,
Figure BDA0002480375090000083
outputting power for the AGC unit;
Figure BDA0002480375090000084
P i Gthe upper limit and the lower limit of an AGC unit i are set;
Figure BDA0002480375090000085
Figure BDA0002480375090000086
the upper and lower limits of the climbing of the AGC unit i.
The power base value of the energy storage system k in the t scheduling period is
Figure BDA0002480375090000087
The charging and discharging of the stored energy can be expressed as:
Figure BDA0002480375090000088
Figure BDA0002480375090000089
wherein,
Figure BDA00024803750900000810
the upper limit and the lower limit of energy storage charging and discharging are defined;
Figure BDA00024803750900000811
the energy storage charging and discharging variable quantities are the upper limit and the lower limit of the energy storage charging and discharging variable quantities; omegac、ωdControl flags respectively indicating a state of charge and a state of discharge;
Figure BDA00024803750900000812
respectively the charge and discharge power of stored energy.
And step four, introducing Lagrange multipliers lambda and pi as inner layer max-min problem decoupling by a dual theory, carrying out dual transformation on constraint conditions in a feasible domain of the variable, and facilitating problem solving. And converting the minimization problem in the inner layer model into a maximum value problem, and combining the converted maximum value problem with the outer layer model for solving.
Specifically, the inner layer includes two problems, max represents the most extreme scenario when the energy storage system is running, but min represents the least cost strategy for charging and discharging the energy storage. The solution idea of the max-min problem is to change the dualization of the min problem into the max problem, namely changing the min problem into the max-max problem, so that maximization is achieved, and unified solution can be achieved.
Maximization of inner and outer layers by the dual principleThe main problem obtained after merging then becomes: min cTy + η, η represents the inner layer problem after dual conversion, min cTy + η represents that the main problem becomes a linear problem and can be solved.
After duality is taken, x does not affect the constraint polyhedron, and only affects the objective function; introducing uncertainty adjusting parameters, rewriting a scene U, and showing the state of obtaining a boundary value of each period of uncertainty variable;
and step five, generating constraint without using dual solution of the decision problem of the second stage through a C & CG algorithm, and simplifying the constraint into solving an equivalent mixed integer programming main problem. Therefore, the problem comprises a mixed integer programming main problem of outer-layer first-stage decision and a bilinear sub problem related to second-stage inner-layer energy storage scheduling, and then the solution is carried out.
Specifically, referring to fig. 2, the C & CG algorithm is embodied as follows:
1) defining an initial pole set and a pole direction set for solving the main problem, wherein the initial set is a subset of the whole set (or an empty set);
2) converting the inner layer to a maximization problem, get Q (y) max { (E-Gy-hu)TPi is not less than 0, and the optimal solution (y) under the initial pole set and the pole direction set can be obtained by solving*，π*)；
3) Generating a cut plane form: eta is not less than (E-Gy-hu)*)Tπ*
4) Updating the lower bound LB ═ c of the objective functionTy*+η*If the constraint set of the lower limit LB is an empty set
Figure BDA0002480375090000091
Then the objective function has no solution, if not an empty set
Figure BDA0002480375090000092
Then the poles and pole directions of the constraint set can be found;
5) substituting the solution into a subproblem: x obtained*Substituting as a known value into the subproblem, and updating the upper bound UB ═ min { UB, cTy*′+η*′}
x*、η*、u*And w is the inner layer solution, the cutting plane variable introduced by the inner layer, the set of parameters of a certain scene and the solution of the dualization problem respectively.
6) Wherein if the LB optimal objective function value is > -infinity, the optimal solution is y*And the dual optimal solution is denoted as w*If UB-LB is less than or equal to epsilon, the optimal solution (x) is obtained*，y*) The algorithm stops; if no solution exists, adding a constraint in the main problem, wherein eta is more than or equal to bTx*′Update constraint Gy + Dx is not less than E-hu*′Go to the main problem solving Q (y) again*′) And the process loops. The constraint conditions of the main problems are more and more, the optimal solutions of the main problems are larger and larger, and the optimal solutions are closer to the true optimal solutions.
In the method of the fifth step, an equivalent unilateral optimization formula can be constructed by enumerating variables and constraints of each scene in an uncertainty set, so that the optimal multiplier vector and the constraint condition under the optimal solution condition meet the complementary relaxation property, the conservative property of the robust optimization method is ensured, and the energy storage participation secondary frequency modulation optimal configuration operation strategy is obtained.
And finally, obtaining an optimal power base point and an energy storage maximum coordination amount of the AGC unit by solving the model so as to determine an energy storage participation secondary frequency modulation optimal configuration operation strategy.
The optimal power base point and the maximum coordination amount of the energy storage of the AGC unit are quantities for evaluating the relation between the automatic power generation control capability and the energy storage configuration, and the fact that the energy storage participates in secondary frequency modulation and is effective for absorbing renewable energy is explained.
Example two
In one or more embodiments, a two-stage robust optimized scheduling system considering energy storage participation in secondary frequency modulation is disclosed, comprising:
the device is used for acquiring a power grid AGC signal and decoupling the power of the AGC signal;
means for distributing the obtained low frequency variations and high frequency variations to a conventional AGC unit and an energy storage device, respectively;
the device is used for determining an uncertainty set, taking the minimum running cost of an outer-layer minimum solving system and the most extreme scene of an inner-layer maximum solving energy storage system in running as two layers of objective functions, and constructing a two-stage robust self-adaptive optimization scheduling model;
and the device is used for solving the two-stage robust self-adaptive optimization scheduling model in stages to obtain an optimal power base point and an energy storage maximum coordination amount of the AGC unit so as to determine an optimal configuration operation strategy of the energy storage participating in secondary frequency modulation.
The specific working process of the device is realized by adopting the method in the first embodiment, and details are not repeated.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, which includes a server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements a two-stage robust optimal scheduling method in consideration of energy storage participating in secondary frequency modulation in embodiment one when executing the program. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The two-stage robust optimization scheduling method considering the energy storage participation in the secondary frequency modulation in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
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. The two-stage robust optimization scheduling method considering the energy storage participation in the secondary frequency modulation is characterized by comprising the following steps of:
acquiring a power grid AGC signal, decoupling the power of the AGC signal, and distributing the obtained low-frequency change and high-frequency change to a conventional AGC unit and energy storage equipment by using an energy storage system matched with the AGC unit; determining an uncertainty set, taking the minimum operation cost of an outer-layer minimum solving system and the most extreme scene of an inner-layer maximum solving energy storage system in operation as two layers of objective functions, and constructing a two-stage robust self-adaptive optimization scheduling model; the method specifically comprises the following steps:
Figure FDA0003471659070000011
s.t.Ay≥d y∈Sy
By＝C y∈Sy
F(y,u)＝{x∈Sx:Dx≥E-Gy-hu}
y represents the minimum operation cost of the system, x represents the configuration capacity of the inner-layer energy storage system, c represents a first deterministic vector matrix, b represents a second deterministic vector matrix, U represents a set of uncertainty fluctuation variables, and F (y, U) represents that a solving variable x meets constraint conditions in any scene of the uncertain set; a, B, D, E, F and G are constraint condition coefficient matrixes respectively; c, d and h are constant vectors; sx、SyRepresenting a set of inner layer solutions x and a set of outer layer solutions y; the constraint conditions of the two-stage robust self-adaptive optimization scheduling model comprise: the method comprises the following steps of (1) carrying out active power balance constraint, climbing constraint and charge-discharge constraint of an energy storage system on the power system;
solving the two-stage robust self-adaptive optimization scheduling model in stages to obtain an optimal power base point and an energy storage maximum matching amount of the AGC unit so as to determine an optimal configuration operation strategy of energy storage participation in secondary frequency modulation;
the set of uncertainties includes:
Figure FDA0003471659070000021
Figure FDA0003471659070000022
wherein U is a set of uncertain fluctuation variables, U represents the scene of each uncertain condition,
Figure FDA0003471659070000023
for vectors of uncertain fluctuation at time t, L represents the set of all load nodes, L represents the index of the load node, e.g. the first load node, phaseTaking 1 as the corresponding l;
Figure FDA0003471659070000024
the vector of uncertain fluctuation at time t +1,
Figure FDA0003471659070000025
as the amount of fluctuation at the time t,
Figure FDA0003471659070000026
is the amount of fluctuation at time t +1,
Figure FDA0003471659070000027
is the deviation between the fluctuations in each Δ t period, and is also the change slope of the fluctuation amount; rLRepresents a set of real numbers for L;
the AGC set is used for coordinating with the secondary frequency adjustment of the power system, and under the adjustment action of the AGC system, the AGC set automatically undertakes the system power mismatch amount by the participation factor to maintain the qualified system frequency and the regional power exchange plan, which can be expressed as:
Figure FDA0003471659070000028
in the formula,
Figure FDA0003471659070000029
outputting variable quantity for the AGC unit; k is a radical ofiThe participation factor of the unit i; k is a radical ofi,tIs a participation factor of the unit i at the time t;
Figure FDA00034716590700000210
in order to output the fluctuation amount of the wind and light,
Figure FDA00034716590700000211
outputting the variable quantity for the stored energy;
wherein the climbing constraint is:
Figure FDA00034716590700000212
Figure FDA00034716590700000213
wherein,
Figure FDA0003471659070000031
outputting power for the AGC unit;
Figure FDA0003471659070000032
the upper limit and the lower limit of an AGC unit i are set; k is a radical ofi,tIs a participation factor of the unit i at the time t;
Figure FDA0003471659070000033
the upper limit and the lower limit of the climbing of the AGC unit i are set;
the step of solving the two-stage robust adaptive optimization scheduling model in stages comprises the following steps: through the C & CG algorithm, the dual solution of the decision problem in the second stage is not used for generating constraint, and the constraint is simplified into the solution of an equivalent mixed integer programming main problem; the problem comprises a mixed integer programming main problem of outer layer first-stage decision and a bilinear sub problem related to second-stage inner layer energy storage scheduling, and then solving is carried out.
2. The two-stage robust optimal scheduling method taking into account energy storage participation in secondary frequency modulation according to claim 1, wherein the set of uncertainties comprises: actual wind and light output; the fluctuations of wind, light, load are considered as a whole and as a set of uncertainties.
3. The two-stage robust optimal scheduling method considering energy storage participation in secondary frequency modulation according to claim 1, wherein the two-stage robust adaptive optimal scheduling model is solved in stages, specifically: a solving method combining a column constraint generation algorithm adopted by the outer layer and an external approximation algorithm adopted by the inner layer is adopted, the model is iteratively solved, and the robust optimization model is converted into a mixed integer model to be solved;
the mixed integer model solving problem comprises a mixed integer planning main problem of the first-stage outer-layer decision and a bilinear sub problem related to the second-stage inner-layer energy storage scheduling.
4. The two-stage robust optimization scheduling method considering energy storage participation in secondary frequency modulation as claimed in claim 3, characterized in that a dual theory is adopted, lagrange multipliers λ and π are introduced as inner layer max-min problem decoupling, a dual transformation is performed on constraint conditions in a feasible domain of a variable, a minimization problem in an inner layer model is transformed into a maximum value problem, and the transformed minimization problem is combined with an outer layer model for solution.
5. A two-stage robust optimization scheduling system considering energy storage and participating in secondary frequency modulation is characterized by comprising:
the device is used for acquiring a power grid AGC signal and decoupling the power of the AGC signal;
means for distributing the obtained low frequency variations and high frequency variations to a conventional AGC unit and an energy storage device, respectively;
the device for determining the uncertainty set, taking the minimum operation cost of the outer-layer minimum solving system and the most extreme scene of the inner-layer maximum solving energy storage system in operation as two-layer objective functions, and constructing the two-stage robust adaptive optimization scheduling model specifically comprises the following steps:
Figure FDA0003471659070000041
s.t.Ay≥d y∈Sy
By＝C y∈Sy
F(y,u)＝{x∈Sx:Dx≥E-Gy-hu}
wherein y represents the minimum operating cost of the system, x represents the configuration capacity of the inner-layer energy storage system, and U represents notDetermining a set of fluctuation variables, wherein F (y, u) represents that a solving variable x meets constraint conditions under any scene of an uncertain set; a, B, D, E, F and G are constraint condition coefficient matrixes respectively; c, d and h are constant vectors; sx、SyRepresenting a set of inner layer solutions x and a set of outer layer solutions y; the constraint conditions of the two-stage robust self-adaptive optimization scheduling model comprise: the method comprises the following steps of (1) carrying out active power balance constraint, climbing constraint and charge-discharge constraint of an energy storage system on the power system;
the device is used for solving the two-stage robust self-adaptive optimization scheduling model in stages to obtain an optimal power base point and an energy storage maximum matching amount of the AGC unit so as to determine an optimal configuration operation strategy of energy storage participation in secondary frequency modulation;
solving the two-stage robust self-adaptive optimization scheduling model in stages to obtain an optimal power base point and an energy storage maximum matching amount of the AGC unit so as to determine an optimal configuration operation strategy of energy storage participation in secondary frequency modulation;
the set of uncertainties includes:
Figure FDA0003471659070000051
Figure FDA0003471659070000052
wherein U is a set of uncertain fluctuation variables, U represents the scene of each uncertain condition,
Figure FDA0003471659070000053
for the vector of uncertain fluctuation at the time t, L represents a set of all load nodes, and L represents the index of the load node, for example, if the first load node, the corresponding L is 1;
Figure FDA0003471659070000054
the vector of uncertain fluctuation at time t +1,
Figure FDA0003471659070000055
as the amount of fluctuation at the time t,
Figure FDA0003471659070000056
is the amount of fluctuation at time t +1,
Figure FDA0003471659070000057
is the deviation between the fluctuations in each Δ t period, and is also the change slope of the fluctuation amount; rLRepresents a set of real numbers for L;
the AGC set is used for coordinating with the secondary frequency adjustment of the power system, and under the adjustment action of the AGC system, the AGC set automatically undertakes the system power mismatch amount by the participation factor to maintain the qualified system frequency and the regional power exchange plan, which can be expressed as:
Figure FDA0003471659070000058
in the formula,
Figure FDA0003471659070000059
outputting variable quantity for the AGC unit; k is a radical ofiThe participation factor of the unit i; k is a radical ofi,tIs a participation factor of the unit i at the time t;
Figure FDA00034716590700000510
in order to output the fluctuation amount of the wind and light,
Figure FDA00034716590700000511
outputting the variable quantity for the stored energy;
wherein the climbing constraint is:
Figure FDA00034716590700000512
Figure FDA00034716590700000513
wherein,
Figure FDA0003471659070000061
outputting power for the AGC unit;
Figure FDA0003471659070000062
the upper limit and the lower limit of an AGC unit i are set; k is a radical ofi,tIs a participation factor of the unit i at the time t;
Figure FDA0003471659070000063
the upper limit and the lower limit of the climbing of the AGC unit i are set;
the step of solving the two-stage robust adaptive optimization scheduling model in stages comprises the following steps: through the C & CG algorithm, the dual solution of the decision problem in the second stage is not used for generating constraint, and the constraint is simplified into the solution of an equivalent mixed integer programming main problem; the problem comprises a mixed integer programming main problem of outer layer first-stage decision and a bilinear sub problem related to second-stage inner layer energy storage scheduling, and then solving is carried out.
6. A terminal device comprising a processor and a computer-readable storage medium, the processor being configured to implement instructions; the computer-readable storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of any of claims 1-4 for two-stage robust optimized scheduling taking into account energy storage participation in chirp.
7. A computer readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the method of two-stage robust optimized scheduling taking into account energy storage participation in chirp of any of claims 1-4.
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