CN107862160B - Method and device for generating future power grid evolution model of compressed air energy storage system - Google Patents
Method and device for generating future power grid evolution model of compressed air energy storage system Download PDFInfo
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Abstract
The invention discloses a method and a device for generating a future power grid evolution model of a compressed air energy storage system, wherein the method comprises the following steps: collecting and collecting power grid data, and generating a transmission power distribution factor matrix and a topological structure of a power grid; modeling various power supplies, energy storage equipment and demand side management through multi-time-period production simulation; and establishing a power grid growth evolution model through sensitivity guidance. The method can improve the existing power grid evolution model, and the compressed air energy storage unit is added on the basis of considering the permeability of the high-proportion renewable energy source, so that the power grid evolution is better depicted and simulated under the existing energy storage technical level, and the power grid evolution model is more formal and comprehensive in generation in the future and better accords with the actual situation.
Description
Technical Field
The invention relates to the technical field of new energy power generation systems, in particular to a method and a device for generating a future power grid evolution model of a compressed air energy storage system.
Background
The energy storage technology is considered to be a technology capable of making up for uncertainty of clean energy in time and space, impact of renewable energy on a power grid can be effectively relieved, and the flexible adjustment capability of the power grid is improved. The introduction of the energy storage technology can reduce the requirement of clean energy access on a power grid structure. A CAES (Compressed Air Energy Storage) system has the outstanding advantages of high Energy density, high Energy Storage efficiency, low cost, no geographical limitation, and the like, and is considered to be one of the future Energy Storage technologies with great potential. The advanced adiabatic compressed air energy storage has the advantages of high system efficiency and the like, electric energy can be stored and supplied to users when needed, and waste heat discharged after the system generates electricity can supply heat and cold to the users to realize combined supply of cold, heat and power.
The volatility of renewable energy can cause a series of impacts to the power grid under the high proportion situation, such as wind abandonment, light abandonment, grid connection cost and the like. For the renewable energy unit, the electricity limiting problems such as wind abandonment and light abandonment affect the economic efficiency of the renewable energy unit, and an internal logic for judging whether to carry out wind abandonment and light abandonment on the generated energy of the renewable energy during operation is needed. Furthermore, the integration of the cogeneration sector with the power sector may provide greater flexibility to the power system, thereby enabling cogeneration to remember the bridge between the power system and the thermodynamic system, so that the thermodynamic system provides the maximum range of peak shaving capabilities to the power system as the sector with lower instantaneous power balance requirements, thereby facilitating renewable energy consumption.
However, most existing models only consider the situation of water pumping and energy storage on new energy consumption, do not completely describe the existing energy storage technology, and do not have internal logic for judging whether to carry out wind curtailment and light curtailment on the power generation amount of renewable energy during operation. All of the factors result in that the existing model cannot well depict and simulate the evolution of the power grid under high-proportion clean energy.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, one objective of the present invention is to provide a method for generating a future power grid evolution model of a compressed air energy storage system, which enables the generation of the future power grid evolution model to be more formal and comprehensive and to better meet the actual situation.
The invention further aims to provide a device for generating a future power grid evolution model of the compressed air energy storage system.
In order to achieve the above object, an embodiment of the invention provides a method for generating a future power grid evolution model of a compressed air energy storage system, which includes the following steps: collecting and collecting power grid data, and generating a transmission power distribution factor matrix and a topological structure of a power grid; modeling various power supplies, energy storage equipment and demand side management through multi-time-period production simulation; and establishing a power grid growth evolution model through sensitivity guidance.
According to the method for generating the future power grid evolution model of the compressed air energy storage system, existing production and transmission data of a power and thermal system in the base year can be input according to the existing loading amount, transmission capacity and the like in the base year, next, according to the existing research, the future power and thermal energy consumption, technical cost and performance are assumed, the drive model is set to be optimized year by year, finally, the capacity expansion path of the power and the power generation loading and transmission capacity of the thermoelectric system is output by taking the minimum overall cost of the system as an optimization target, and the development of the power grid under high-proportion clean energy is predicted more reasonably, so that the future power grid evolution model is generated more formally and comprehensively and conforms to the actual situation.
In addition, the method for generating the future power grid evolution model of the compressed air energy storage system according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the generating a transmission power distribution factor matrix and a topology of a power grid further includes: constructing a topological graph of a target power grid, and determining parameters of a generator node, a load node and each line in the power grid; determining a matrix H of the transmission power distribution factor in the power grid, wherein the expression of the matrix H is as follows:
H＝B′B-1＝DC[CTDC]-1，
wherein, B is a reversible power grid admittance matrix, C is a node-branch incidence matrix for cutting out a reference node, and D ═ diag (y)1,…,yl)，yiIs the branch admittance.
Further, in one embodiment of the present invention, the modeling of various power supplies, energy storage devices, and demand side management through multi-time period production simulation includes: constraint conditions of power flow and line capacity:
-F≤H(Agg(t)+All(t))≤F，
wherein, F is a column vector composed of branch capacity, and H is a matrix composed of transmission power distribution factors of the network，Agg (t) is the generated power of the node, All (t) is node load power; a power generation model including a minimum start-up time constraint, a minimum shut-down time constraint, and a ramp-up constraint of the unit; rotating for standby:
wherein the content of the first and second substances,
is the minimum rotational reserve, r, required for the partition K on the time section tiI ∈ K is the rotation reserve contributed by each unit in the partition K, clean energy, energy storage equipment:
wherein, ci(t) is charging power, di (t) is discharging power,
η ci is the charging efficiency of the energy storage device i, the discharging efficiency is η di,
and
respectively the lower and upper energy storage limits, c, of the devicei(0) Initial energy storage of the equipment;
load model and demand side management, including constraints at load nodes and demand side management constraints; an objective function:
C＝Cfuel+Cstart+P1Crigid+P2Cflexible，
wherein, P1And P2As a penalty factor, P1>P2＞＞0，CfuelAs a cost of fuel, CstartThe cost is the start-stop cost; p1CrigidIs a penalty term caused by the removal of the "rigid" load, P2CflexibleIs that the total amount of "flexible" load cannot be met with the resulting penalty term.
Further, in an embodiment of the present invention, the constructing a power grid growth evolution model through sensitivity guidance further includes: screening candidate branches meeting preset conditions from all possible connecting lines between two points in the power grid, solving the sensitivity of each branch, and selecting the branch with the sensitivity larger than a preset threshold value into a candidate branch set; and carrying out basic search on the candidate branch set to obtain an optimal solution of network growth evolution.
Further, in one embodiment of the present invention, the sensitivity is
Wherein λ is newly added admittance, FlTo line capacity, pii(t) is the price of node i; and when the optimal solution of the network growth evolution is obtained, the objective function f (x) is:
wherein Z (x) is annual operating cost, ciIs the construction cost of branch i, A is the reduced age, xiAnd a prejudice value representing the amount of degradation of the objective function caused by the new line i on the basis of x.
In order to achieve the above object, an embodiment of another aspect of the present invention provides an apparatus for generating a future power grid evolution model of a compressed air energy storage system, including: the acquisition module is used for acquiring and collecting power grid data and generating a transmission power distribution factor matrix and a topological structure of a power grid; the simulation module is used for modeling various power supplies, energy storage equipment and demand side management through multi-time-period production simulation; and the generation module is used for establishing a power grid growth evolution model through sensitivity guidance.
According to the device for generating the future power grid evolution model of the compressed air energy storage system, existing production and transmission data of a power and thermal system in the base year can be input according to the existing loading amount, transmission capacity and the like in the base year, next, according to the existing research, the future power and thermal energy consumption, technical cost and performance are assumed, the drive model is set to be optimized year by year, finally, the capacity expansion path of the power and the power generation loading and transmission capacity of the thermoelectric system is output by taking the minimum overall cost of the system as an optimization target, and the development of the power grid under high-proportion clean energy is predicted more reasonably, so that the future power grid evolution model is generated more formally and comprehensively and conforms to the actual situation.
In addition, the device for generating the future power grid evolution model of the compressed air energy storage system according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the acquisition module is further configured to construct a topological graph of a target power grid, determine parameters of a generator node, a load node, and each line in the power grid, and determine a matrix H of the transmission power distribution factor in the power grid, where an expression of the matrix H is:
H＝B′B-1＝DC[CTDC[-1，
wherein, B is a reversible power grid admittance matrix, C is a node-branch incidence matrix for cutting out a reference node, and D ═ diag (y)1,…,yl)，yiIs the branch admittance.
Further, in one embodiment of the invention, the simulation module is further configured to apply to the power flow and line capacity constraints:
-F≤H(Agg(t)+All(t))≤F，
wherein F is a column vector composed of branch capacity, H is a matrix composed of transmission power distribution factors of the network, Agg (t) is the generated power of the node, All (t) is node load power; power generation moduleA profile comprising a minimum start-up time constraint, a minimum shut-down time constraint, and a ramp-up constraint for the unit; rotating for standby:
wherein the content of the first and second substances,
is the minimum rotational reserve, r, required for the partition K on the time section tiI ∈ K is the rotation reserve contributed by each unit in the partition K, clean energy, energy storage equipment:
wherein, ci(t) is charging power, di (t) is discharging power,
η ci is the charging efficiency of the energy storage device i, the discharging efficiency is η di,
and
respectively the lower and upper energy storage limits, c, of the devicei(0) Initial energy storage of the equipment;
load model and demand side management, including constraints at load nodes and demand side management constraints; an objective function:
C＝Cfuel+Cstart+P1Crigid+P2Cflexible，
wherein, P1And P2As a penalty factor, P1>P2＞＞0，CfuelAs a cost of fuel, CstartThe cost is the start-stop cost; p1CrigidIs a penalty term caused by the removal of the "rigid" load, P2CflexibleIs that the total amount of "flexible" load cannot be met with the resulting penalty term.
Further, in an embodiment of the present invention, the generating module is further configured to screen out candidate branches meeting a preset condition from all possible inter-point connections in the power grid, find the sensitivity of each branch, select a branch having a sensitivity greater than a preset threshold into a candidate branch set, and perform basic search on the candidate branch set to obtain an optimal solution of network growth evolution.
Further, in one embodiment of the present invention, the sensitivity is
Wherein λ is newly added admittance, FlTo line capacity, pii(t) is the price of node i; and when the optimal solution of the network growth evolution is obtained, the objective function f (x) is:
wherein Z (x) is annual operating cost, ciIs the construction cost of branch i, A is the reduced age, xiAnd a prejudice value representing the amount of degradation of the objective function caused by the new line i on the basis of x.
Additional aspects and advantages 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 foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow diagram of a method for generating a future power grid evolution model of a compressed air energy storage system according to one embodiment of the present invention;
FIG. 2 is a flow chart of a method of generating a future power grid evolution model of a compressed air energy storage system according to another embodiment of the present invention;
FIG. 3 is a flow diagram of a basic search algorithm according to one embodiment of the present invention;
fig. 4 is a schematic structural diagram of a device for generating a future power grid evolution model of a compressed air energy storage system according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a method and an apparatus for generating a future power grid evolution model of a compressed air energy storage system according to an embodiment of the present invention with reference to the accompanying drawings, and first, a method for generating a future power grid evolution model of a compressed air energy storage system according to an embodiment of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for generating a future power grid evolution model of a compressed air energy storage system according to an embodiment of the present invention.
As shown in fig. 1, the method for generating the future power grid evolution model of the compressed air energy storage system includes the following steps:
in step S101, collecting and collecting power grid data, and generating a transmission power distribution factor matrix and a topology structure of a power grid.
It can be understood that, as shown in fig. 2, in the embodiment of the present invention, a Power Transfer Distribution Factors (PTDF) matrix and a topology structure of a power grid may be generated by collecting power grid data.
Further, in an embodiment of the present invention, generating the transmission power distribution factor matrix and the topology of the power grid further includes: constructing a topological graph of a target power grid, and determining parameters of a generator node, a load node and each line in the power grid; determining a matrix H of transmission power distribution factors in the power grid, wherein the expression of the matrix H is as follows:
H＝B′B-1＝DC[CTDC]-1，
wherein, B is a reversible power grid admittance matrix, C is a node-branch incidence matrix for cutting out a reference node, and D ═ diag (y)1,…,yl)，yiIs the branch admittance.
Specifically, the steps are as follows:
(1) and constructing a topological graph of the target power grid, and determining parameters (impedance, admittance and the like) of a generator node, a load node and each line in the power grid.
(2) Determining a transmission power distribution factor (PTDF) matrix H in the power grid, wherein the expression of the PTDF matrix H is as follows:
H＝B′B-1＝DC[CTDC]-1，
wherein, B is a reversible power grid admittance matrix, C is a node-branch incidence matrix for cutting out a reference node, and D ═ diag (y)1,…,yl)，yiIs the branch admittance.
In step S102, various power supplies, energy storage devices, and demand side management are modeled by a multi-time period production simulation.
It is understood that the embodiment of the invention can perform production simulation for multiple time periods. The model carries out modeling to a certain detailed degree on various power supplies, energy storage equipment and demand side management, and the model can make decisions on unit start-stop, unit output, wind and light abandonment, power collection outside the area, charge and discharge of the energy storage equipment, demand side correspondence and load shedding amount by taking the minimum sum of power collection cost, power generation cost and load loss cost as a target.
Further, in one embodiment of the present invention, modeling various power supplies, energy storage devices, and demand side management through multi-time period production simulation includes: constraint conditions of power flow and line capacity:
-F≤H(Agg(t)+All(t))≤F，
wherein F is a column vector composed of branch capacity, H is a matrix composed of transmission power distribution factors of the network, Agg (t) is the generated power of the node, All (t) is node load power; a power generation model including a minimum start-up time constraint, a minimum shut-down time constraint, and a ramp-up constraint of the unit; rotating for standby:
wherein the content of the first and second substances,
is the minimum rotational reserve, r, required for the partition K on the time section tiI ∈ K is the rotation reserve contributed by each unit in the partition K, clean energy, energy storage equipment:
wherein, ci(t) is charging power, di (t) is discharging power,
η ci is the charging efficiency of the energy storage device i, the discharging efficiency is η di,
and
respectively the lower and upper energy storage limits, c, of the devicei(0) Initial energy storage of the equipment;
load model and demand side management, including constraints at load nodes and demand side management constraints; an objective function:
C＝Cfuel+Cstart+P1Crigid+P2Cflexible，
wherein, P1And P2As a penalty factor, P1>P2＞＞0，CfuelAs a cost of fuel, CstartThe cost is the start-stop cost; p1CrigidIs a penalty term caused by the removal of the "rigid" load, P2CflexibleIs that the total amount of "flexible" load cannot be met with the resulting penalty term.
Specifically, a multi-time-interval production simulation model parameter list 1 based on MILP (Mixed-integer linear programming) is shown. Wherein, Table 1 is a parameter list of a multi-time-interval production simulation model of mixed integer linear programming,
TABLE 1
The embodiment of the invention can model various power supplies, energy storage equipment and demand side management through multi-time-period production simulation, and comprises the following parts:
(1) the constraint conditions of the power flow and the line capacity are as follows:
-F≤H(Agg(t)+All(t))≤F，
wherein F is a column vector composed of branch capacity, H is a matrix composed of transmission power distribution factors (PTDF) of the network obtained in the first step, and Agg (t) is the generated power of the node, All (t) is the node load power.
1Tg(t)+1Tl(t)＝0，
Wherein g (t) ═ g1(t),…,g|G|(t))T，l(t)＝(l1(t),…,l|D|(t))T
(2) Power generation model
The minimum start-up time constraint is:
the minimum down time constraint is:
the climbing restriction of the unit is as follows:
wherein z isi(t), i ∈ G is the start-stop state of each generating set,
and
the active power of the unit has upper and lower limits, ki 1≤ki 2≤…≤ki mIs the slope of each segment, for a total of m segments,
(3) rotate for standby
In the formula (I), the compound is shown in the specification,
the minimum rotation standby quantity required by the partition K on the time section t; r isiI ∈ K is the rotation reserve contributed by each unit in the partition K, which is limited by the unit output and climbing characteristics:
in the formula (II), R'iThe short-time climbing capacity of the unit i is represented and generally different from the climbing capacity Ri per hour. R'iThe 10-minute climbing capacity of the unit can be taken.
(4) Clean energy
Assuming that the power generation cost of wind power, photovoltaic and hydropower is zero, the maximum available power P of the wind power and the photovoltaici maxGiven by the history, Pi minIs always equal to zero. The discarded amount of clean energy is equal to the maximum available generated power Pi maxSubtracting the actual generated power gi(t) of (d). The water energy storage power station and the compressed air energy storage power station are modeled according to energy storage equipment.
(5) Energy storage device
Wherein ci (t) is charging power and di (t) is discharging power
η Ci is the charging efficiency of energy storage device i, the discharging efficiency is η di, Cmaxi and Cminii are the lower energy storage limit and the upper energy storage limit of the device, respectively, Ci (Ci)0) The initial energy storage of the device.
(6) Load model and demand side management
The constraints at the load node are:
demand side management constraints
Wherein lrigidAnd lflexibleNode "rigid" and "flexible" loads, respectively.
(7) Objective function
C＝Cfuel+Cstart+P1Crigid+P2Cflexible，
Wherein, P1And P2Is a penalty factor, P1>P2> 0; cost of fuel
Cost of start and stop
P1CrigidIs a penalty due to the "rigid" load being cut;
P2Cflexibleis the penalty term resulting from the total amount of "flexible" load not being satisfied;
in step S103, a power grid growth evolution model is constructed through sensitivity guidance.
It can be understood that the embodiment of the invention can construct a power grid growth evolution model based on sensitivity guidance, and the model takes the grid frame data in step S101 and the production simulation in step S102 as tools to process the situation of newly added branches.
Further, in an embodiment of the present invention, the constructing a power grid growth evolution model through sensitivity guidance further includes: screening candidate branches meeting preset conditions from all possible connecting lines between two points in the power grid, solving the sensitivity of each branch, and selecting the branches with the sensitivity greater than a preset threshold value into a candidate branch set; and carrying out basic search on the candidate branch set to obtain an optimal solution of network growth evolution.
Optionally, in one embodiment of the invention, the sensitivity is
Wherein λ is newly added admittance, FlTo line capacity, pii(t) is the price of node i; and when the optimal solution of the network growth evolution is obtained, the objective function f (x) is as follows:
wherein Z (x) is annual operating cost, ciIs the construction cost of branch i, A is the reduced age, xiAnd a prejudice value representing the amount of degradation of the objective function caused by the new line i on the basis of x.
Specifically, the embodiment of the invention can construct a power grid growth evolution model through sensitivity guidance, and the method can comprise the following steps:
step 3-1: and (S102) screening feasible and attractive candidate branches from all possible inter-two-point connecting lines in the power grid. And solving the sensitivity of each feasible branch, and selecting the branch with the sensitivity greater than a certain threshold value into a candidate branch set. Wherein the sensitivity of the line reaching the capacity constraint is:
wherein λ is newly added admittance, FlIn order to be the capacity of the line,
is the price of node i.
Step 3-2: after the candidate branch set is given, basic search is performed, and as shown in fig. 3, an optimal solution of network growth evolution is obtained. Wherein the objective function f (x) is the sum of annual operating cost and annual construction cost, i.e. the cost
Wherein Z (x) is the annual operating cost, ciIs the construction cost of branch i, A is the reduced age, xiAnd a prejudice value representing the amount of degradation of the objective function caused by the new line i on the basis of x.
For example, the flow of the basic search algorithm is shown in fig. 3, and includes the following steps:
in step S1, the process starts.
In step S2, an initial iteration point x is given.
In step S3, an objective function value and sensitivity are calculated.
In step S4, it is determined whether f (x) is currently optimal, if so, an optimal solution update is performed, and step S5 is performed; if not, go directly to step S5.
In step S5, the next iteration point is determined.
In step S6, if the search is completed, step S7 is executed; if not, the process returns to continue to step S3.
In step S7, the process ends.
In addition, the embodiment of the invention aims to provide a future power grid evolution model considering a compressed air energy storage system, the existing power grid evolution model is improved, and a compressed air energy storage unit is added on the basis of considering the permeability of high-proportion renewable energy sources, so that the power grid evolution is better described and simulated under the existing energy storage technical level. Meanwhile, aiming at the characteristic that the uncertainty, volatility and transmission demand of a power grid are greatly increased under high-proportion renewable energy, the embodiment of the invention combines the advantages of compressed air energy storage, and the model mainly describes power generation technologies such as renewable energy, energy storage and the like, and considers the combination of a power system and a cogeneration system.
In summary, the embodiment of the invention can provide a model considering the future power grid evolution actual situation of the compressed air energy storage system by combining an optimal power flow model and a complex network evolution model, wherein the optimal power flow model is responsible for calculating the power flow distribution situation of the power grid in the model fast dynamic process, and the power grid topology evolution model is responsible for simulating the multiplication and updating of the actual power grid in time and space scales, so as to describe the evolution rule of the power grid in a longer time range. The new improved model is more consistent with the actual situation of the power system. In the modeling process, various power supplies, energy storage equipment and demand side management are modeled to a certain degree of detail so as to fully reflect the time characteristics of the power grid elements, really incorporate the uncertainty of clean energy into the research of power grid evolution, and well reflect the evolution rule of power grid topology. The improved model in the embodiment of the invention has more formality and comprehensiveness in the process of simulating the cascading failure of the power grid and the upgrading evolution of the power grid, and is more in line with the actual situation.
According to the method for generating the future power grid evolution model of the compressed air energy storage system, the existing production and transmission data of the power and thermal system in the base year can be input according to the existing loading amount, transmission capacity and the like in the base year, next, the drive model after the optimization year is set for year-by-year optimization according to the existing research hypothesis of future power and thermal energy consumption and technical cost and performance, finally, the capacity expansion path of the power and thermal power system power generation loading and transmission capacity is output by taking the minimum overall cost of the system as an optimization target, and the development of the power grid under high-proportion clean energy is more reasonably predicted, so that the future power grid evolution model is generated more formally and comprehensively and is more in line with the actual situation.
Next, a device for generating a future power grid evolution model of a compressed air energy storage system according to an embodiment of the present invention is described with reference to the drawings.
FIG. 4 is a schematic structural diagram of a device for generating a future power grid evolution model of a compressed air energy storage system according to an embodiment of the present invention
As shown in fig. 4, the device 10 for generating a future power grid evolution model of the compressed air energy storage system includes: an acquisition module 100, a simulation module 200 and a generation module 300.
The acquisition module 100 is configured to acquire and collect power grid data, and generate a transmission power distribution factor matrix and a topology structure of a power grid. The simulation module 200 is used to model various power supplies, energy storage devices, and demand side management through multi-time period production simulation. The generation module 300 is used for building a power grid growth evolution model through sensitivity guidance. The device 10 of the embodiment of the invention can improve the existing power grid evolution model, and the compressed air energy storage unit is added on the basis of considering the permeability of the high-proportion renewable energy source, so that the power grid evolution can be better depicted and simulated under the existing energy storage technical level, and the generation of the power grid evolution model in the future is more formal and comprehensive and is more in line with the actual situation.
Further, in an embodiment of the present invention, the acquisition module 100 is further configured to construct a topological graph of a target power grid, determine parameters of a generator node, a load node and each line in the power grid, and determine a matrix H of a transmission power distribution factor in the power grid, where an expression of the matrix H is:
H＝B′B-1＝DC[CTDC]-1，
wherein B is a reversible electric networkAdmittance matrix, C is a node-branch incidence matrix with reference nodes scribed out, D ═ diag (y)1,…,yl)，yiIs the branch admittance.
Further, in one embodiment of the present invention, the simulation module 200 is also used for power flow and line capacity constraints:
-F≤H(Agg(t)+All(t))≤F，
wherein F is a column vector composed of branch capacity, H is a matrix composed of transmission power distribution factors of the network, Agg (t) is the generated power of the node, All (t) is node load power; a power generation model including a minimum start-up time constraint, a minimum shut-down time constraint, and a ramp-up constraint of the unit; rotating for standby:
wherein the content of the first and second substances,
is the minimum rotational reserve, r, required for the partition K on the time section tiI ∈ K is the rotation reserve contributed by each unit in the partition K, clean energy, energy storage equipment:
wherein, ci(t) is charging power, di (t) is discharging power,
η ci is the charging efficiency of the energy storage device i, the discharging efficiency is η di,
and
respectively the lower and upper energy storage limits, c, of the devicei(0) Initial energy storage of the equipment;
load model and demand side management, including constraints at load nodes and demand side management constraints; an objective function:
C＝Cfuel+Cstart+P1Crigid+P2Cflexible，
wherein, P1And P2As a penalty factor, P1>P2＞＞0，CfuelAs a cost of fuel, CstartThe cost is the start-stop cost; p1CrigidIs a penalty term caused by the removal of the "rigid" load, P2CflexibleIs that the total amount of "flexible" load cannot be met with the resulting penalty term.
Further, in an embodiment of the present invention, the generating module 300 is further configured to screen out candidate branches meeting a preset condition from all possible inter-point connections in the power grid, find the sensitivity of each branch, select a branch having a sensitivity greater than a preset threshold into a candidate branch set, perform basic search on the candidate branch set, and obtain an optimal solution of network growth evolution.
Further, in one embodiment of the present invention, the sensitivity is
Wherein λ is newly added admittance, FlTo line capacity, pii(t) is the price of node i; and when the optimal solution of the network growth evolution is obtained, the objective function f (x) is as follows:
wherein Z (x) is annual operating cost, ciIs the construction cost of branch i, A is the reduced age, xiShowing that the new line i is led on the basis of xThe resulting predicted amount of decrease in the objective function.
It should be noted that the explanation of the embodiment of the method for generating the future power grid evolution model of the compressed air energy storage system is also applicable to the device for generating the future power grid evolution model of the compressed air energy storage system of the embodiment, and details are not repeated here.
According to the generation device of the future power grid evolution model of the compressed air energy storage system, the existing production and transmission data of the power and thermal system in the base year can be input according to the existing loading amount, transmission capacity and the like in the base year, the drive model after the optimization year is set for year-by-year optimization according to the existing research hypothesis of future power and thermal energy consumption and technical cost and performance, finally, the expansion path of the power and thermal power system power generation loading and transmission capacity is output by taking the minimum overall cost of the system as the optimization target, and the development of the power grid under high-proportion clean energy is more reasonably predicted, so that the generation of the future power grid evolution model is more formal and comprehensive, and is more in line with the actual situation.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. A method for generating a future power grid evolution model of a compressed air energy storage system is characterized by comprising the following steps:
collecting and collecting power grid data, and generating a transmission power distribution factor matrix and a topological structure of a power grid;
the production simulation through many time quantums models power, energy storage equipment and demand side management, include:
constraint conditions of power flow and line capacity:
-F≤H(Agg(t)+All(t))≤F，
wherein F is a column vector composed of branch capacity, H is a matrix of transmission power distribution factors, and Agg (t) is the generated power of the node, All (t) is node load power;
a power generation model including a minimum start-up time constraint, a minimum shut-down time constraint, and a ramp-up constraint of the unit;
rotating for standby:
wherein the content of the first and second substances,
is the minimum rotational reserve, r, required for the partition K on the time section tiI ∈ K is the rotation reserve contributed by each unit in the partition K;
cleaning energy sources;
an energy storage device:
wherein, ci(t) is charging power, di (t) is discharging power,
for the charging efficiency and the discharging efficiency of the energy storage device i
And
respectively the lower and upper energy storage limits, C, of the devicei(0) Initial energy storage of the equipment;
load model and demand side management, including constraints at load nodes and demand side management constraints;
an objective function:
C＝Cfuel+Cstart+P1Crigid+P2Cflexible，
wherein, P1And P2As a penalty factor, P1>P2＞＞0，CfuelAs a cost of fuel, CstartThe cost is the start-stop cost; p1CrigidIs a penalty term caused by the removal of the "rigid" load, P2CflexibleIs the penalty term resulting from the total amount of "flexible" load not being satisfied; and
and establishing a power grid growth evolution model through sensitivity guidance.
2. The method for generating the future power grid evolution model of the compressed air energy storage system according to claim 1, wherein the generating the transmission power distribution factor matrix and the topology of the power grid further comprises:
constructing a topological graph of a target power grid, and determining parameters of a generator node, a load node and each line in the power grid;
determining a matrix H of the transmission power distribution factor in the power grid, wherein the expression of the matrix H is as follows:
H＝B′B-1＝DC[CTDC]-1，
wherein, B is a reversible power grid admittance matrix, C is a node-branch incidence matrix for cutting out a reference node, and D ═ diag (y)1,…,yl)，yiIs the branch admittance.
3. The method for generating the future power grid evolution model of the compressed air energy storage system according to claim 1, wherein the establishing the power grid growth evolution model through sensitivity guidance further comprises:
screening candidate branches meeting preset conditions from all possible connecting lines between two points in the power grid, solving the sensitivity of each branch, and selecting the branch with the sensitivity larger than a preset threshold value into a candidate branch set;
and carrying out basic search on the candidate branch set to obtain an optimal solution of network growth evolution.
4. The method for generating a future power grid evolution model of a compressed air energy storage system according to claim 3, wherein the sensitivity is
Wherein λ is newly added admittance, FlIn order to be the capacity of the line,πl(t) is the price of node l;
and when the optimal solution of the network growth evolution is obtained, the objective function f (x) is:
wherein Z (x) is annual operating cost, ciIs the construction cost of branch i, A is the reduced age, xiAnd a prejudice value representing the amount of degradation of the objective function caused by the new line i on the basis of x.
5. A generation device of a future power grid evolution model of a compressed air energy storage system is characterized by comprising:
the acquisition module is used for acquiring and collecting power grid data and generating a transmission power distribution factor matrix and a topological structure of a power grid;
the simulation module is used for modeling power supply, energy storage equipment and demand side management through multi-time-period production simulation, and is also used for load flow and line capacity constraint conditions:
-F≤H(Agg(t)+All(t))≤F，
wherein F is a column vector composed of branch capacity, H is a matrix of transmission power distribution factors, and Agg (t) is the generated power of the node, All (t) is node load power;
a power generation model including a minimum start-up time constraint, a minimum shut-down time constraint, and a ramp-up constraint of the unit;
rotating for standby:
wherein the content of the first and second substances,
is the minimum rotational reserve, r, required for the partition K on the time section tiI ∈ K is the machines in partition KThe rotational reserve of the group contribution;
cleaning energy sources;
an energy storage device:
wherein, ci(t) is charging power, di (t) is discharging power,
for the charging efficiency and the discharging efficiency of the energy storage device i
And
respectively the lower and upper energy storage limits, C, of the devicei(0) Initial energy storage of the equipment;
load model and demand side management, including constraints at load nodes and demand side management constraints;
an objective function:
C＝Cfuel+Cstart+P1Crigid+P2Cflexible，
wherein, P1And P2As a penalty factor, P1>P2＞＞0，CfuelAs a cost of fuel, CstartThe cost is the start-stop cost; p1CrigidIs a penalty term caused by the removal of the "rigid" load, P2CflexibleIs the penalty term resulting from the total amount of "flexible" load not being satisfied; and
and the generation module is used for establishing a power grid growth evolution model through sensitivity guidance.
6. The device for generating the future power grid evolution model of the compressed air energy storage system according to claim 5, wherein the acquisition module is further configured to construct a topological graph of a target power grid, determine parameters of a generator node, a load node and each line in the power grid, and determine a matrix H of the transmission power distribution factor in the power grid, wherein an expression of the matrix H is as follows:
H＝B′B-1＝DC[CTDC]-1，
wherein, B is a reversible power grid admittance matrix, C is a node-branch incidence matrix for cutting out a reference node, and D ═ diag (y)1,…,yl)，yiIs the branch admittance.
7. The device for generating the future power grid evolution model of the compressed air energy storage system according to claim 5, wherein the generation module is further configured to screen candidate branches meeting a preset condition from all possible connections between two points in the power grid, find the sensitivity of each branch, select the branch having the sensitivity greater than a preset threshold into a candidate branch set, and perform basic search on the candidate branch set to obtain an optimal solution of network growth evolution.
8. The apparatus for generating a model of future power grid evolution of a compressed air energy storage system according to claim 7, wherein the sensitivity is
Wherein λ is newly added admittance, FlTo line capacity, pii(t) is the price of node i;
and when the optimal solution of the network growth evolution is obtained, the objective function f (x) is:
wherein Z (x) is annual operating cost, ciIs the construction cost of branch i, A is the reduced age, xiAnd a prejudice value representing the amount of degradation of the objective function caused by the new line i on the basis of x.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102355057A (en) * | 2011-09-25 | 2012-02-15 | 国网电力科学研究院 | Computer monitoring method for microgrid system |
| WO2014144078A3 (en) * | 2013-03-15 | 2015-01-22 | General Compression, Inc. | Horizontal actuation compressed air energy storage system |
| CN104933481A (en) * | 2015-06-16 | 2015-09-23 | 国网山东省电力公司经济技术研究院 | Reference power grid model used for power system evaluation and incremental planning, and solving method |
| CN106339773A (en) * | 2016-08-11 | 2017-01-18 | 中国电力科学研究院 | Sensitivity-based active power distribution network distributed power source constant-capacity planning method |
-
2017
- 2017-12-06 CN CN201711279290.8A patent/CN107862160B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102355057A (en) * | 2011-09-25 | 2012-02-15 | 国网电力科学研究院 | Computer monitoring method for microgrid system |
| WO2014144078A3 (en) * | 2013-03-15 | 2015-01-22 | General Compression, Inc. | Horizontal actuation compressed air energy storage system |
| CN104933481A (en) * | 2015-06-16 | 2015-09-23 | 国网山东省电力公司经济技术研究院 | Reference power grid model used for power system evaluation and incremental planning, and solving method |
| CN106339773A (en) * | 2016-08-11 | 2017-01-18 | 中国电力科学研究院 | Sensitivity-based active power distribution network distributed power source constant-capacity planning method |
Non-Patent Citations (4)
| Title |
|---|
| 压缩空气储能技术及其应用探讨;梅生伟等;《南方电网技术》;20160331;第10卷(第3期);第11-15、31页 * |
| 含压缩空气储能的微网复合储能技术及其成本分析;田崇翼等;《电力系统自动化》;20150525;第39卷(第10期);第36-41页 * |
| 基于压缩空气储能的社区微能源网设计;薛小代等;《中国电机工程学报》;20160620;第36卷(第12期);第3306-3313页 * |
| 多时间尺度协调控制的独立微网能量管理策略;郭思琪等;《电工技术学报》;20140228;第29卷(第2期);第122-129页 * |
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