CN114336663B - Collaborative planning method and device for power system source network - Google Patents
Collaborative planning method and device for power system source network Download PDFInfo
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Abstract
The invention provides a power system source network collaborative planning method and device, which can be used in the technical field of power systems. The method comprises the following steps: determining inertia support constraint and reactive power balance constraint of the power system according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy unit; establishing a planning model of the power system according to a planning target of the power system, the inertia supporting constraint and the reactive power balance constraint; and solving the planning model by using a data-driven robust optimization method to obtain a planned power system. The device is used for executing the method. According to the power system source network collaborative planning method and device provided by the embodiment of the invention, the reactive power balance and the inertia support of the power system are considered in the planning level, and the planned power system can be ensured to run safely and stably.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a power system source network collaborative planning method and device.
Background
At present, the planning of the existing power system planning technology on the high-proportion new energy power system mainly considers the delivery constraint and the absorption constraint of the power system, and along with the continuous access of the high-proportion new energy, the planning method is difficult to ensure the safe and stable operation of the power system.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a power system source network collaborative planning method and device, which can at least partially solve the problems in the prior art.
In one aspect, the invention provides a power system source network collaborative planning method, which comprises the following steps: determining inertia support constraint and reactive power balance constraint of the power system according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy unit; establishing a planning model of the power system according to a planning target of the power system, the inertia supporting constraint and the reactive power balance constraint; and solving the established planning model of the power system by using a data-driven robust optimization method to obtain the planned power system.
Optionally, the determining the inertia support constraint and the reactive balance constraint of the power system according to the operation characteristic of the thermal power generating unit and the operation characteristic of the new energy generating unit includes: determining an inertia support constraint formula of the power system according to the total inertia provided by the thermal power generating unit and the total inertia provided by the reactive power compensation device; and determining a reactive power balance constraint formula of the power system according to reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit in the system, reactive power output by the reactive power compensation device in the system, total reactive load of the system, total network reactive power loss of the system and reactive power standby of the system.
Optionally, the building the planning model of the power system according to the planning target of the power system, the inertia support constraint and the reactive balance constraint includes: establishing a planning total cost function of the power system according to a planning target of the power system; and establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system.
Optionally, the building the planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system includes: and establishing a planning model of the power system according to a planning total cost function, an inertia supporting constraint formula, a reactive power balance constraint formula, a system load constraint formula, a node balance constraint formula, a candidate line power flow constraint formula, an existing line power flow constraint formula, a thermal power unit power constraint formula, a new energy unit power constraint formula and a reactive power compensation device reactive power constraint formula of the power system.
Optionally, the solving the established planning model of the power system by using the data-driven robust optimization method to obtain the planned power system includes: according to the historical output data of the existing new energy stations of the power system construction nodes, a high-dimensional ellipsoid set based on the historical output data is constructed; performing convex hull scaling on the high-dimensional ellipsoidal collection to obtain an uncertain collection of the historical output limit scene of the new energy station; substituting the output of the new energy station in the uncertain set under the history output limit scene and the load of each node of the power system under the history output limit scene of the new energy station into a planning model of the power system to solve, so as to obtain the planned power system.
Optionally, the inertia support constraint formula of the power system is:
in the method, in the process of the invention,
H sys,t is the total inertia of the system;
∑H G,i,t the total inertia of the thermal power generating unit without carbon capture equipment is set;
the total inertia of the thermal power generating unit is provided with carbon capture equipment;
the total inertia available for the reactive compensation device;
ΔP ctg (t) is a system power deficiency;
Δf max is a limit value of system frequency variation;
f 0 is the initial frequency of the system.
Optionally, the reactive balance constraint formula of the power system is:
Q GC -Q LD -Q L ＝Q res ；
in the method, in the process of the invention,
Q GC the reactive power is the sum of reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit and reactive power output by the reactive compensation device in the system;
Q LD the total reactive load of the system;
Q L reactive power loss is the total network of the system;
Q res reserve reactive power for the system.
Optionally, the planning objective of the power system includes: the peak of carbon is achieved at a first time period at a minimum cost and the neutralization of carbon is achieved at a second time period, wherein the second time period is later than the first time period.
Optionally, the overall cost planning function of the power system is:
in the method, in the process of the invention,
i=1 or 2;
at T for the power system 1 Minimum planning cost for a epoch;
at T for the power system 2 Minimum planning cost for a epoch;
Cost node the construction cost for the system circuit is;
F G the total cost is consumed for the thermal power generating unit;
is T i The installation cost of the carbon capture device in the period;
is T i The period carbon tax cost;
is T i The new energy unit construction cost is used in the period;
is T i And the construction cost of the reactive compensation device in the period.
Optionally, the system load constraint formula is:
in the method, in the process of the invention,
load requirements for each node of the system;
∑P G,i,t,s the method comprises the steps of outputting power of a thermal power unit without carbon capture equipment for the system;
∑P G,i,t,s CCS the thermal power generating unit output of the carbon capture equipment is arranged for the system;
the scheduling value of the new energy unit of the system is obtained.
Optionally, the node balance constraint formula is:
in the method, in the process of the invention,
n (b) is a set of a series of devices connected by node b;
s (l) and r (l) respectively represent a transmitting end node and a receiving end node of the line l;
the output of other power generation equipment i except the new energy station connected with the node b;
is the scheduling value of the new energy electric field w;
is the tide of line l;
a loss load which is the electric load d;
the actual load of the power load d is shown.
Optionally, the candidate line power flow constraint formula is:
in the method, in the process of the invention,
y lt 0 or 1, belonging to a decision variable;
X l a line reactance representing line l;
m is a sufficiently large number;
Is the tide of line l;
P l max the upper limit of the tide of the line l;
CL represents a candidate line set;
and-> t The phase angles of the transmitting end node and the receiving end node of the line l are respectively, and the node phase angle range is that Is a section ofThe upper limit of the phase angle of point b.
Optionally, the existing line power flow constraint formula is:
in the method, in the process of the invention,
is the tide of line l;
X l a line reactance representing line l;
and->The phase angles of the transmitting end node and the receiving end node of the line l are respectively, and the node phase angle range is that
Is the upper bound of the phase angle of node b;
EL represents an existing line set;
P l max is the upper limit of the power flow of line l.
Optionally, the reactive constraint formula of the reactive compensation device is:
in the method, in the process of the invention,
s reactive power provided by the reactive power compensation device of the node i at the time t;
and (3) the reactive power required by the new energy unit of the node i at the time t.
On the other hand, the invention provides a power system source network collaborative planning device, which comprises: the determining module is used for determining inertia supporting constraint and reactive power balance constraint of the power system according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy generating unit; the building module is used for building a planning model of the power system according to the planning target of the power system, the inertia supporting constraint and the reactive balance constraint; and the solving module is used for solving the established planning model of the power system by using a data-driven robust optimization method to obtain the planned power system.
Optionally, the determining module is specifically configured to: determining an inertia support constraint formula of the power system according to the total inertia provided by the thermal power generating unit and the total inertia provided by the reactive power compensation device; and determining a reactive power balance constraint formula of the power system according to reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit in the system, reactive power output by the reactive power compensation device in the system, total reactive load of the system, total network reactive power loss of the system and reactive power standby of the system.
Optionally, the establishing module is specifically configured to: establishing a planning total cost function of the power system according to a planning target of the power system; and establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system.
Optionally, the establishing module establishes the planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system, and the establishing module includes: and establishing a planning model of the power system according to a planning total cost function, an inertia supporting constraint formula, a reactive power balance constraint formula, a system load constraint formula, a node balance constraint formula, a candidate line power flow constraint formula, an existing line power flow constraint formula, a thermal power unit power constraint formula, a new energy unit power constraint formula and a reactive power compensation device reactive power constraint formula of the power system.
Optionally, the solving module solves the established planning model of the power system by using a data-driven robust optimization method, and the obtaining the planned power system includes: according to the historical output data of the existing new energy stations of the power system construction nodes, a high-dimensional ellipsoid set based on the historical output data is constructed; performing convex hull scaling on the high-dimensional ellipsoidal collection to obtain an uncertain collection of the historical output limit scene of the new energy station; substituting the output of the new energy station in the uncertain set under the history output limit scene and the load of each node of the power system under the history output limit scene of the new energy station into a planning model of the power system to solve, so as to obtain the planned power system.
In yet another aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the power system source network collaborative planning method according to any of the embodiments described above when the processor executes the program.
In yet another aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the power system source network collaborative planning method according to any of the embodiments described above.
According to the power system source network collaborative planning method and device, the reactive power balance and the inertia support of the power system are considered in a planning layer, the planning target of the power system is combined, the planning model of the power system is built, uncertain parameters in the planning model of the power system are solved by using a data-driven robust optimization method, the new energy upper limit and the long-term development path of the power system can be obtained, the reactive power balance and the inertia support constraint in the planned power system are guaranteed, and then the safe and stable operation of the planned power system is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a flow chart of a power system source network collaborative planning method according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a portion of a power system source network collaborative planning method according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of a portion of a power system source network collaborative planning method according to an embodiment of the present invention.
Fig. 4 is a schematic flow chart of a portion of a power system source network collaborative planning method according to an embodiment of the present invention.
Fig. 5 is an IEEE-RTS-24 system provided in a test example of the present invention.
FIG. 6 is a schematic diagram of the power generation output versus thermal power output ratio of the new energy source obtained by solving in a test example of the present invention.
FIG. 7 is a schematic diagram of evolution results of a power grid structure based on an initial power grid structure of a test system based on evolution analysis of a source side in a test example of the present invention.
FIG. 8 is a schematic diagram of a system planning result based on total cost optimization after solving using a robust optimization method based on big data driving in a test example of the present invention.
Fig. 9 is a schematic structural diagram of a power system source network collaborative planning apparatus according to an embodiment of the present invention.
Fig. 10 is a schematic physical structure of an electronic device according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be arbitrarily combined with each other.
The execution main body of the power system source network collaborative planning method provided by the embodiment of the invention comprises, but is not limited to, a computer.
Fig. 1 is a flow chart of a power system source network collaborative planning method according to an embodiment of the present invention, and as shown in fig. 1, the power system source network collaborative planning method according to an embodiment of the present invention includes:
s101, determining inertia support constraint and reactive power balance constraint of a power system according to the operation characteristics of a thermal power unit and the operation characteristics of a new energy unit;
in this step, the new energy unit may include a photovoltaic unit, a hydro-generator unit, and a wind turbine unit, and is mainly a wind turbine unit.
The inertia (Inertia of Power System, IPS) of the power system refers to the capability of preventing the frequency change of the voltage and the current of the alternating current power grid, the inertia of the power system shows a resistance effect on the frequency change caused by external disturbance, and the speed of dropping the frequency of the system is slowed down, so that the inertia of the power system is an important guarantee for stabilizing the frequency of the system.
Reactive power balance (Reactive Power Balance of Power System, RPBPS) of the power system is to perform reactive power balance calculation according to a power supply development plan and a power network development plan, so that reactive power generated by a reactive power supply of the power system is balanced with reactive load of the system, and the main purpose of the reactive power balance calculation is to maintain voltage levels of various points of the power network under various operation modes and determine configuration of a reactive compensation device. The basic requirements of reactive power balance of an electric power system are: the reactive power that can be emitted by the reactive power sources in the power system should be greater than or equal to the reactive power required by the load and the reactive losses in the network.
In order to ensure safe and stable operation of the power system, the power supply should provide necessary inertia and reactive support for the system, and in the area with higher specific gravity of new energy grid-connected power generation, the new energy needs to provide inertia and reactive support for the system in the planning and operation stages of the power system.
Analyzing the operation characteristics of the thermal power generating unit and the new energy unit: the synchronous machine of the thermal power unit of the traditional power system is directly connected with a power grid, and has the capability of instantaneously sharing disturbance power due to the voltage source characteristic of the synchronous machine, and the new energy unit can provide less generalized kinetic energy, so that the traditional thermal power unit is required to provide, the inertia of the system is ensured to be larger than the minimum limit value, and the inertia supporting constraint of the power system is determined according to the generalized kinetic energy.
Analyzing the operation characteristics of the thermal power generating unit and the new energy unit: the reactive power output of various reactive power sources in the system should be able to meet the reactive power requirements of the system load and network losses at rated voltage, otherwise the power sources deviate from the rated values. Because of the dynamic reactive power compensation problem of the current large-scale new energy, enough reactive power compensation devices are required to be configured to ensure the reactive power stability of the system, and the reactive power balance constraint of the power system is determined according to the reactive power stability. Compared with a static var compensator SVC, the static var generator SVG has better inhibition effect on transient voltage rise, so that the phase regulator can be used as a reactive power compensation device of a power system.
S102, establishing a planning model of the power system according to a planning target of the power system, the inertia supporting constraint and the reactive power balance constraint;
in this step, the planning objective of the power system may include: the method comprises the steps of achieving a certain target in a certain period, achieving different targets in a plurality of periods respectively, achieving a certain target in a certain period at preset cost, or achieving different targets in a plurality of periods respectively at different cost. For example, the planning objectives are: carbon peaking is achieved during a first period and carbon neutralization is achieved during a second period, which is later than the first period.
And on the basis of the inertia supporting constraint and reactive power balancing constraint of the power system, combining a planning target of the power system to establish a planning model of the power system.
And S103, solving the established planning model of the power system by using a data-driven robust optimization method to obtain the planned power system.
The data-driven robust optimization theory is a mathematical optimization method considering parameter uncertainty, the uncertain parameters in a planning model of the electric power system can be solved by using the data-driven robust optimization method, the new energy upper limit and the long-term development path of the electric power system are calculated, and reactive power balance and inertia support constraint in the electric power system are ensured.
According to the power system source network collaborative planning method provided by the embodiment of the invention, the reactive power balance and the inertia support of the power system are considered in the planning level, the planning target of the power system is combined, the planning model of the power system is established, and then the uncertain parameters in the planning model of the power system are solved by utilizing a data-driven robust optimization method, so that the new energy upper limit and the long-term development path of the power system can be obtained, the reactive power balance and the inertia support constraint in the planned power system are ensured, and the safe and stable operation of the planned power system is further ensured.
As shown in fig. 2, optionally, determining the inertia support constraint and the reactive balance constraint of the power system according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy generating unit includes:
s1011, determining an inertia support constraint formula of the power system according to the total inertia provided by the thermal power unit and the total inertia provided by the reactive compensation device;
in the step, the synchronous machine of the thermal power generating unit of the traditional electric power system is directly connected with a power grid, and has the capability of instantaneously sharing disturbance power due to the voltage source characteristic of the synchronous machine, the shortage (surplus) of system generated power is directly reflected in the sudden increase (decrease) of electromagnetic power, and the rotating mass of the generator responds to the deviation of the electromagnetic power and mechanical power caused by the change of the system power by releasing or absorbing kinetic energy, so that the power balance of the system is supported, and the frequency change is restrained. When unbalanced disturbance occurs in the system, each generator instantly shares disturbance power according to the synchronous power coefficient of the generator and the disturbance point. At this time, the rotor motion state of the ith generator in the system will change according to the rotor motion variance under the action of unbalanced torque:
Wherein H is i,t ,f i,t ,ΔP i,t (t) respectively inertia, frequency and variable power of the i generators at t time, wherein the inertia difference and the shared disturbance power among the units are different to enable the units to change at different rotating speeds, then the synchronous moment acts to enable the rotating speeds to be consistent, and each generator is used for sharing the disturbance power again according to the inertia of the units:
wherein N is G For the number of generators, ΔP L Is the total power ripple. When the system is subject to power disturbance, the system ensures that the initial rate of frequency change (Rate of Change of Frequency, rocofs) satisfies the following constraints
In the method, in the process of the invention,is the frequency change rate of the system; />Limit of frequency variation.
The maximum rocofis therefore determined by the power deficit and the total inertia:
wherein DeltaP ctg (t) is power shortage, H sys,t For the total inertia of the system at time t, f 0 Is the initial frequency of the system.
The minimum inertia required for the system can be expressed as:
then, the overall inertia is calculated and solved, and in general, the generalized inertia constant of the system is:
E sys to the generalized kinetic energy of the system S sys For the total rated capacity of the system, the inertia support constraint formula of the system can be obtained by taking the formula:
in the method, in the process of the invention,generalized kinetic energy of the system of the ith thermal power generating unit at t time,/thermal power generating unit>The system generalized kinetic energy of the jth new energy unit at time t.
As the new energy unit can provide less generalized kinetic energy, the traditional thermal power unit is required to provide, and the inertia of the system is ensured to be larger than the minimum limit. In order to ensure reactive balance of the system, a reactive compensation device is added in the system, and the reactive compensation device can also provide certain inertia; the inertia support constraint formula of the power system can be expressed as:
in the method, in the process of the invention,
H sys,t is the total inertia of the system;
∑H G,i,t the total inertia of the thermal power generating unit without carbon capture equipment is set;
the total inertia of the thermal power generating unit is provided with carbon capture equipment;
the total inertia available for the reactive compensation device;
ΔP ctg (t) is a system power deficiency;
Δf max is a limit value of system frequency variation;
f 0 is the initial frequency of the system.
S1012, determining a reactive power balance constraint formula of the power system according to reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit in the system, reactive power output by the reactive power compensation device in the system, total reactive load of the system, total network reactive loss of the system and reactive power standby of the system.
In this step, the reactive power output of various reactive power sources in the system should be able to meet the reactive power requirements of the system load and network loss at rated voltage, otherwise the power sources deviate from the rated values. The reactive balance constraint formula of the power system can be expressed as:
Q GC -Q LD -Q L ＝Q res (9)
In the method, in the process of the invention,
Q GC the reactive power is the sum of reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit and reactive power output by the reactive compensation device in the system; q (Q) LD The total reactive load of the system; q (Q) L Reactive power loss is the total network of the system; q (Q) res For reactive power standby of the system, the reactive power of the system is generally 15% -20%.
When a dimmer is used as a reactive power compensation device for an electrical power system, the dimmer is designed for normal operation under low excitation conditions, allowing 75% of the nominal power to be absorbed, since the dimmer is a salient pole machine. By representing the phase voltage at the motor terminals by E, by E 0 Representing the internal voltage of the motor, using X d Representing the reactance of the motor, the reactive power provided by the synchronous compensator can be expressed as:
by taking into account E 0 The value is constant, resulting in Q when the voltage at the machine terminal decreases (increases) c Is increased (decreased) in value. The reactive power of the synchronous compensator is contained in the power supply side total reactive power Q in equation (9) GC Is a kind of medium.
As shown in fig. 3, optionally, the building a planning model of the electric power system according to the planning target of the electric power system and the inertia support constraint and reactive balance constraint includes:
s1021, establishing a planning total cost function of the power system according to a planning target of the power system;
And when the planning target comprises cost, the step can establish a planning total cost function of the power system according to the planning target of the power system. For example, at the planning target: carbon peaking is achieved at a first time period at a minimum cost, and carbon neutralization is achieved at a second time period, wherein the total cost of planning function for the power system can be expressed as:
in the method, in the process of the invention,
at T for the power system 1 Minimum planning cost for a epoch;
at T for the power system 2 Minimum planning cost for a epoch;
Cost node the construction cost for the system circuit is;
F G the total cost is consumed for the thermal power generating unit;
is T i The installation cost of the carbon capture device in the period;
is T i The period carbon tax cost;
is T i The new energy unit construction cost is used in the period;
is T i And the construction cost of the reactive compensation device in the period.
Optionally, each detailed cost in the planning total cost function of the power system is respectively:
system line construction cost:
wherein I is node(k) 0 or 1, indicating whether a new line is added; phi is the optional line set; mu (mu) node(k) The unit length cost of the newly added line is set; l (L) node(k) Is the length of the newly added line.
The total cost of the thermal power generating unit is:
wherein u is i,t,s The operation state of the thermal power generating unit i in the scene s at the t period; a, a i ，b i ，c i Is the cost coefficient of the thermal power unit i; p (P) G,i,t,s The output of the thermal power generating unit i in the t period under the scene s is obtained.
The new energy unit construction cost is:
in the method, in the process of the invention,the construction quantity of new energy stations in the period of Ti is C wt The method is a single new energy unit cost.
The reactive compensation device has the construction cost that:
in the method, in the process of the invention,the construction quantity of the reactive compensation device in the period Ti is C tx Cost for the reactive power compensation device.
The carbon tax costs are as follows:
step cost mode is implemented in carbon tax trade, wherein sigma is a carbon tax coefficient, omega is a carbon tax exceeding punishment increment coefficient, f is a carbon tax exceeding interval, zeta is a carbon tax exceeding punishment multiple coefficient, C t For actual carbon emission, C D As a reference carbon emission amount, C D +f is the excessive penalty interval,indicating whether this step was performed during this period.
The carbon capture device installation cost is:
in the method, in the process of the invention,for the technical progress, the value range is 0-1, the specific value can be determined according to an empirical method, and the higher the technical progress is, the more the technical progress is>The greater the value of (2); />For reference investment, & lt + & gt>Whether to invest.
And S1022, establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system.
And in the step, the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system form a planning model of the power system.
Optionally, the building the planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system may include:
and establishing a planning model of the power system according to a planning total cost function, an inertia supporting constraint formula, a reactive power balance constraint formula, a system load constraint formula, a node balance constraint formula, a candidate line power flow constraint formula, an existing line power flow constraint formula, a thermal power unit power constraint formula, a new energy unit power constraint formula and a reactive power compensation device reactive power constraint formula of the power system.
In this embodiment, the planning total cost function, the inertia support constraint formula, the reactive power balance constraint formula, the system load constraint formula, the node balance constraint formula, the candidate line power flow constraint formula, the existing line power flow constraint formula, the thermal power unit power constraint formula, the new energy unit power constraint formula and the reactive power constraint formula of the reactive power compensation device form a planning model of the power system.
When the planning model of the power system is established, not only the planning total cost, the inertia support constraint and the reactive balance constraint of the power system are considered, but also the system load constraint, the node balance constraint, the candidate line flow constraint, the existing line flow constraint, the thermal power unit power constraint, the new energy unit power constraint and the reactive power constraint of the reactive compensation device are considered.
Alternatively, the system load constraint formula may be:
in the method, in the process of the invention,
load demand for node b;
P G,i,t,s the thermal power generating unit output of the non-carbon capture equipment related to the node b is provided;
P G,i,t,s CCS the thermal power generating unit output of the carbon capture equipment is arranged relative to the node b;
is the scheduling value of the new energy unit w related to the node b.
Alternatively, the node balancing constraint formula may be:
in the method, in the process of the invention,
n (b) is a set of a series of devices connected by node b;
the output of other power generation equipment i except the new energy station connected with the node b;
s (l) and r (l) respectively represent a transmitting end node and a receiving end node of the line l;
is the scheduling value of the new energy unit w;
is the tide of line l;
a loss load which is the electric load d;
the considered actual load of the power load d is represented.
Optionally, the candidate line power flow constraint formula may be:
in the method, in the process of the invention,
y lt 0 or 1, belonging to a decision variable;
X l a line reactance representing line l;
m is a number large enough to ensure that it is much larger than the value following the inequality sign to ensure y lt The variable is effective, and can be in fact hundreds of thousands;
is the tide of line l;
P l max the upper limit of the tide of the line l;
CL represents a candidate line set;
And->The phase angles of the transmitting end node and the receiving end node of the line l are respectively, and the node phase angle range is that
Is the upper bound of the phase angle of node b.
Optionally, the existing line power flow constraint formula may be:
in the method, in the process of the invention,
for the tide of line lA stream;
X l a line reactance representing line l;
and->The phase angles of the transmitting end node and the receiving end node of the line l are respectively, and the node phase angle range is that Is the upper bound of the phase angle of node b;
EL represents an existing line set;
P l max is the upper limit of the power flow of line l.
Optionally, the reactive constraint formula of the reactive compensation device may be:
in the method, in the process of the invention,
reactive power provided by the reactive power compensation device of the node i at the time t;
and (3) the reactive power required by the new energy unit of the node i at the time t.
Alternatively, the thermal power plant power constraint formula may be expressed as:
(P G,i,t,s s ) min ≤P G,i,t,s ≤(P G,i,t,s s ) max (23)
in the method, in the process of the invention,
P G,i,t,s the output of the thermal power unit i;
(P G,i,t,s s ) max the maximum output of the thermal power unit i;
(P G,i,t,s s ) min is the minimum output of the thermal power generating unit i.
Alternatively, the new energy unit power constraint formula may be expressed as:
in the method, in the process of the invention,
is the scheduling value of the new energy unit w;
WG is a new energy unit scheduling scene set;
the maximum scheduling value of the new energy unit w.
As shown in fig. 4, optionally, the solving the established planning model of the power system by using the data-driven robust optimization method to obtain a planned power system includes:
S1031, constructing a high-dimensional ellipsoid set based on historical output data according to the historical output data of a new energy station existing in a power system construction node;
in this step, the step of constructing a high-dimensional ellipsoid set based on the historical output data according to the historical output data of the new energy station existing in the power system construction node may include the following steps:
(1) The historical output data of the new energy station existing at the construction node (the historical output data of the new energy station existing at the construction node is selected, for example, 10,13,15,17 nodes in the following test example, and the historical output data includes the short-term output data of the new energy station and the short-term output data of the new energy stationHistorical output data of the output of the new energy station, wherein the output data indicates the power of the power, and the unit is W). The collected historical output data is formed into column vectors, a group of historical data is recorded as a historical scene, and the collected historical scene is recorded asWherein N is h Is the number of collected historical scenes.
(2) And constructing a high-dimensional ellipsoid set based on the historical output data. The following preconditions are assumed: when the amount of historical output data collected is sufficiently large, the historical output data is representative of output data for a period of interest (the period of interest is a period of the set of constructed scenes, such as a selected 8760 hours for one year in succession), i.e., if there is a certain closed set that can fully cover the historical output scene, the output data for the period of interest is also in the closed set. The method comprises the steps of firstly solving a high-dimensional ellipsoid to surround all historical scenes by means of a high-dimensional closure ellipsoid algorithm, wherein the form of the obtained ellipsoid is as follows:
The ellipsoid is a generalized n-dimensional ellipsoid, wherein ω is an n-dimensional vector represented by an uncertainty parameter, i.e. there are n random variables, R n Is an n-order real number domain; wherein the matrix Q is a positive definite matrix, represents the angle of the high-dimensional ellipsoid deviating from the positive direction of the coordinate axis and the length of each symmetry axis, the vector c represents the coordinate of the center point of the high-dimensional ellipsoid, and both Q and c are known quantities.
Solving the high-dimensional ellipsoid parameters Q and c is equivalent to solving the following optimization:
wherein ρ is n Is a constant representing the volume, ω, of an n-dimensional unit sphere h,1 ,ω h,2 … is the historical output data collected in step (1). The optimization is in the form of convex optimization, and can be quickly solved in polynomial time.
S1032, performing convex hull scaling on the high-dimensional ellipsoidal collection to obtain an uncertain collection of the historical output limit scene of the new energy station;
this step assumes that the resulting uncertainty set is as follows:
wherein omega e,i For the limit scene obtained by the expansion and contraction of the convex hull, the vertex omega ' of the axial ellipsoid E ' and the vertex omega ' of the high-dimensional ellipsoid E ' can be obtained by combining the inverse transformation formula of the translational rotation change equation and the corrected vertex coordinate expression of the axial ellipsoid ' v,i The relationship is as follows:
ω e,i ＝c+k * P -1 ω′ v,i (28)
wherein k is * The magnification factor in convex hull scaling is obtained.
As the distance between the scene and the convex hull increases, the coefficient k * And will become larger therewith. The following optimizations can be established to determine the positional relationship of the historical scene to the convex hull:
for N h There are all optimizations of the form above, and there is no correlation between optimizations, so N can be considered h The optimization merges as follows:
for N h From the above, N can be obtained from the historical scene h A sequence formed by the amplification factors, wherein the maximum value in the sequence is convexMagnification k in packet scaling * . The final data-driven robust uncertainty set is that described above is employedIs represented by a polyhedron of (c). Furthermore due to->The scaling coefficient k in (a) * The method is self-determined, and according to the robustness optimization related knowledge, the larger the area contained in the uncertainty set is, the more conservative the decision is made, so that the scaling factor can be corrected to a certain extent according to the current requirement.
S1033, substituting the output of the new energy station in the uncertain set under the history output limit scene and the load of each node of the power system under the history output limit scene of the new energy station into a planning model of the power system to solve, and obtaining the planned power system.
Specifically, for each history output limit scene in the uncertain set, substituting the output (refer to output power) of a new energy station and the load (refer to load power) of each node of the power system in the history output limit scene into a planning total cost function, an inertia support constraint formula, a reactive balance constraint formula, a system load constraint formula and other formulas in a planning model of the power system to solve, so that the upper limit of the new energy proportion of the power system, the total planning cost of the system, the running cost and the total carbon emission amount of the power system can be obtained, and the source load balance, reactive balance and inertia support constraint in the power system are ensured. For example, four fan sites in North China schedule operation load data in 8760 hours in 2019, and the operation load data is taken as load data of an IEEE-RTS-24 node new energy site, and the 8760 hour load data is seen in an IEEE-RTS-24 node file. And (3) carrying out data-driven robust optimization models, and carrying out the results into a planning model. The planning model is written by a Yalmip platform, cplex is taken as a solver, and MILP solving is carried out on the planning model.
The solving process of the power system planning model is a solving process of a multi-element equation set, and can be automatically executed by a computer; for example, when the planning model of the power system is formed by the planning total cost function, the inertia supporting constraint formula, the reactive balance constraint formula, the system load constraint formula, the node balance constraint formula, the candidate line power flow constraint formula, the existing line power flow constraint formula, the thermal power unit power constraint formula, the new energy unit power constraint formula and the reactive constraint formula of the reactive compensation device, for each historical output limit scene in the uncertain set, the solving process of substituting the output of the new energy station and the load of each node of the power system in the historical output limit scene into the planning model of the power system may be as follows:
(1) The method comprises the steps of substituting the output of a new energy station, the load of each node of a power system and fixed parameters of the power system (inherent parameters of an existing and planned line and equipment of the power system) into a system load constraint formula, a node balance constraint formula, a candidate line load flow constraint formula, an existing line load flow constraint formula, a reactive power constraint formula of a reactive power compensation device, a thermal power unit power constraint formula, a new energy unit power constraint formula, an inertia support constraint formula and a reactive balance constraint formula.
Specifically, the sum of the loads of all nodes of the power system is taken as a system load constraint formulaTaking the sum of the output of each new energy station as +.>Substituting a system load constraint formula; in the system load constraint formula, Σp G,i,t,s 、∑P G,i,t,s CCS Respectively optimizing variables;
taking the sum of the output of the new energy station in each node in the power system as the node balance constraint formulaAccording to the sectionPoint load and fixed parameters of the power system, determining the total loss load of the nodeTotal actual load->(this calculation method is a conventional technique in the art and will not be described here in detail), the calculated +. >And->Substituting the node balance constraint formula; determining the power flow of each line of the node according to the output of the new energy station of the node, the load of the node and the inherent parameters of the power system>The calculated tide of each line is +.>Substituting the node balance constraint formula; in the node balance constraint formula, < >>Is an optimization variable;
determining the line reactance X of each candidate line planned in advance according to the inherent parameters of the power system l On-line P of tide l max Upper limit of node phase angle of the candidate lineAnd the phase angle of the transmitting node of the candidate line +.>And phase angle of the receiving node +.>Substituting the calculated parameters into a power flow constraint formula of the candidate line; optimization variable +.of candidate lines in the node balance constraint equation>The flow constraint formula of the candidate line needs to be satisfied.
Similarly, the line reactance X of each line existing in the system is determined according to the inherent parameters of the power system l On-line P of tide l max Upper limit of node phase angle of the candidate lineAnd the phase angle of the transmitting node of the existing line +.>And phase angle of the receiving node +.>Substituting the calculated parameters into a current constraint formula of the existing line; optimization variables of existing lines in the node balance constraint formula >The current constraint formula of the existing line needs to be satisfied.
Calculating the reactive power provided by the reactive power compensation device in the system according to the planning quantity (optimization variable) of the reactive power compensation device and the intrinsic parameters of the reactive power compensation device (the intrinsic parameters of the power system comprise the intrinsic parameters of the reactive power compensation device); calculating reactive power required by the new energy unit in the system according to the planning quantity (optimization variable) of the new energy unit and the inherent parameters (inherent parameters belonging to the power system) of the new energy unit; substituting the reactive power provided by the reactive power compensation device and the reactive power required by the new energy unit into a reactive power constraint formula of the reactive power compensation device.
Determining the maximum output and the minimum output of the thermal power unit according to the inherent parameters of the thermal power unit (the inherent parameters of the power system comprise the inherent parameters of the thermal power unit), and substituting the maximum output and the minimum output of each thermal power unit into a power constraint formula of the thermal power unit; optimization variable Sigma P in the system load constraint formula G,i,t,s Sum sigma P G,i,t,s CCS The power constraint of the thermal power generating unit needs to be met.
Similarly, determining the maximum scheduling value of each new energy unit according to the inherent parameters of the new energy unit (the inherent parameters of the power system comprise the inherent parameters of the new energy unit), and substituting the maximum scheduling value of the new energy unit into a power constraint formula of the new energy unit; the scheduling value of each new energy unit in the system load constraint formula and the node balance constraint formula should meet the power constraint formula of the new energy unit.
According to the planning condition (optimization variable) of the carbon capture equipment and the fixed parameters of the electric power system, calculating the total inertia Sigma H of the thermal power unit without the carbon capture equipment in the system G,i,t Thermal power generating unit total inertia provided with carbon capture equipmentAccording to the planning condition (optimization variable) of the reactive compensation device and the fixed parameters of the power system, calculating the total inertia which can be provided by the reactive compensation device in the system>And substituting the three inertias into an inertias support constraint equation. It should be understood that, because in this step, the planned number of carbon capturing devices and the planned number of reactive compensation devices are optimization variables, which have not been solved yet, they can be replaced by different expressions, that is, the three inertias obtained by calculation are not determined values, but are an expression, respectively, belonging to the optimization variables; in the inertia support constraint formula, the minimum total inertia of the system is +.>Can be calculated according to the fixed parameters of the power system to be a determined value.
According to the output of the new energy station of the system and the fixed parameters of the power system, Q in the reactive power balance constraint formula is calculated GC The reactive power which can be output by the new energy unit (the reactive power which can be output by the new energy unit is calculated according to the output of the new energy station of the system and the fixed parameters of the power system, which belongs to the conventional technology in the field, and is not repeated here, and the following reactive power calculation is the same); according to the output (optimization variable) of the thermal power unit of the system and the fixed parameters of the power system, Q in the reactive power balance constraint formula is calculated GC The reactive power of the thermal power generating unit can be output; calculating Q in a reactive power balance constraint formula according to the planning quantity (optimization variable) of the reactive power compensation device and the fixed parameters of the power system GC Reactive power output by the reactive power compensation device; wherein Q in reactive power balance constraint formula LD 、Q L 、Q res The calculation method can be calculated according to the load of each node of the power system and the fixed parameters of the power system, and belongs to the conventional technology in the field, and is not described herein.
So far, the method completes substituting the output of the new energy station, the load of each node of the power system and the fixed parameters of the power system into the planning model of the power system.
(2) And then according to the planning total cost function, taking the planning total cost as a target, taking the constraint formulas as constraints, and solving the number of candidate line construction of the power system, the number of carbon capture equipment installation, the number of new energy units, the number of reactive compensation device construction, the output of the thermal power unit and the like. The above parameters can be solved by adopting an MILP mixed integer linear programming method, and it should be understood that each parameter obtained by solving can be a fixed value, a numerical range or a plurality of discrete values. According to the parameters obtained by solving, the upper limit of the new energy proportion, the running cost, the total carbon emission and the like of the electric power system can be calculated.
To verify the power system provided by the inventionThe validity of the source network collaborative planning method is tested by adopting an improved IEEE-RTS-24 (see fig. 5) as a testing system, the power system evolution and future development forms are simulated and tested by taking every five years as a planning period, the whole planning period is from 2020 to 2060, 8 planning stages are all adopted, and 2030 (carbon peak, namely T) 1 2020-2030) and 2060 (carbon neutralization, i.e., T 2 2030-2060) as planning period critical nodes. The test system has 34 lines in the reference year, 11 generator sets, the capacity of the total assembly machine is 4250MW, and the load is 3550MW. According to the carbon reaching peak requirement, the carbon emission of the test system reaches a peak value in 2030, and then the carbon emission is decreased year by year until 2060, so as to realize carbon neutralization. And the testing system gradually installs new energy units according to the planning period and carries out carbon capture modification on the thermal power unit to form a source side power system of the new energy power generation and carbon capture power plant. The load demand increases at 20% per year, 13 power transmission corridors are newly built on the basis of the original power transmission corridors according to the power generation capacity and the load, and 26 lines are to be selected.
The power system test is solved by the power system source network collaborative planning method considering reactive power balance and inertia support.
The total power generation load of the new energy planned on the power supply side is 9040MW, the thermal generator set is 3470MW, the new energy output has obvious fluctuation, and the average power generation output of the new energy must meet the fluctuation lower limit constraint requirement of the actual load; the lowest point of the new energy power generation output curve and the maximum output of the thermal power must meet the highest point of the whole load curve. Meanwhile, due to the problems of the absorption channel and the dynamic reactive support, the actual output of the new energy power generation is constrained by the problems of reactive support and frequency response. Due to the limitation, the maximum limit value M exists in the ratio of the new energy power generation output to the thermal power output wind Maximum limit M of system available with actual test data wind . The annual new energy power generation output and the thermal power output obtained through solving are shown in a figure 6.
On the basis of evolution analysis on the source side, the evolution result of the power grid structure is shown in fig. 7 on the basis of the initial power grid structure of the test system. Compared with the original IEEE-24 nodes, new energy power generation is used as a main bearing object of system base load instead of thermal power, and a large number of power transmission corridors are constructed around four groups of new energy concentrated construction nodes of 10 nodes, 13 nodes, 15 nodes and 18 nodes and other nodes with concentrated loads.
As shown in fig. 8, from the perspective of testing the system specific cost, after solving by using the robust optimization method driven by big data, the system planning result based on the total cost optimization is obtained. Before 2035, the cost of the overall system is rapidly increased due to the large number of wind power plants built. After 2035, although the total amount of wind power construction has dropped, construction costs have remained fluctuated smoothly due to the large number of applications of CCUS (carbon capture) technology. With the progressive saturation of construction, after 2055 years, the construction costs fall back.
It can be seen that the solution results verify the effectiveness of the planning method provided by the above embodiment.
Fig. 9 is a schematic structural diagram of a power system source network collaborative planning apparatus according to an embodiment of the present invention, and as shown in fig. 9, the power system source network collaborative planning apparatus according to an embodiment of the present invention includes: a determining module 21, configured to determine an inertia supporting constraint and a reactive balance constraint of the power system according to an operation characteristic of the thermal power generating unit and an operation characteristic of the new energy generating unit; a building module 22, configured to build a planning model of the power system according to a planning target of the power system and the inertia support constraint and reactive balance constraint; and the solving module 23 is used for solving the established planning model of the power system by using a data-driven robust optimization method to obtain a planned power system.
According to the power system source network collaborative planning device provided by the embodiment of the invention, the reactive power balance and the inertia support of the power system are considered in a planning layer, the planning target of the power system is combined, a planning model of the power system is established, and uncertain parameters in the planning model of the power system are solved by using a data-driven robust optimization method, so that the new energy upper limit and the long-term development path of the power system can be obtained, the reactive power balance and the inertia support constraint in the planned power system are ensured, and the safe and stable operation of the planned power system is further ensured.
Optionally, the determining module is specifically configured to: determining an inertia support constraint formula of the power system according to the total inertia provided by the thermal power generating unit and the total inertia provided by the reactive power compensation device; and determining a reactive power balance constraint formula of the power system according to reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit in the system, reactive power output by the reactive power compensation device in the system, total reactive load of the system, total network reactive power loss of the system and reactive power standby of the system.
Optionally, the establishing module is specifically configured to: establishing a planning total cost function of the power system according to a planning target of the power system; and establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system.
Optionally, the establishing module establishes the planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system, and the establishing module includes: and establishing a planning model of the power system according to a planning total cost function, an inertia supporting constraint formula, a reactive power balance constraint formula, a system load constraint formula, a node balance constraint formula, a candidate line power flow constraint formula, an existing line power flow constraint formula, a thermal power unit power constraint formula, a new energy unit power constraint formula and a reactive power compensation device reactive power constraint formula of the power system.
Optionally, the solving module solves the established planning model of the power system by using a data-driven robust optimization method, and the obtaining the planned power system includes: according to the historical output data of a new energy station of a power system construction node, constructing a high-dimensional ellipsoid set based on the historical output data; performing convex hull scaling on the high-dimensional ellipsoidal collection to obtain an uncertain collection of the historical output limit scene of the new energy station; substituting the output of the new energy station in the uncertain set under the historical output limit scene and the historical output data of each node of the power system under the historical output limit scene of the new energy station into a planning model of the power system to solve, so as to obtain the planned power system.
The embodiment of the apparatus provided in the embodiment of the present invention may be specifically used to execute the processing flow of each method embodiment, and the functions thereof are not described herein again, and may refer to the detailed description of the method embodiments.
Fig. 10 is a schematic physical structure of an electronic device according to an embodiment of the present invention, as shown in fig. 10, the electronic device may include: processor 301, communication interface (Communications Interface) 302, memory (memory) 303 and communication bus 304, wherein processor 301, communication interface 302, memory 303 accomplish the communication between each other through communication bus 304. The processor 301 may call logic instructions in the memory 303 to perform the following method: determining inertia support constraint and reactive power balance constraint of the power system according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy unit; establishing a planning model of the power system according to a planning target of the power system, the inertia supporting constraint and the reactive power balance constraint; and solving the established planning model of the power system by using a data-driven robust optimization method to obtain the planned power system.
Further, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The present embodiments disclose a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the method embodiments described above.
The present embodiment provides a computer-readable storage medium storing a computer program that causes the computer to execute the methods provided by the above-described method embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description of the present specification, reference to the terms "one embodiment," "one particular embodiment," "some embodiments," "for example," "an example," "a particular example," or "some examples," etc., means 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, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (18)
1. The power system source network collaborative planning method is characterized by comprising the following steps of:
determining inertia support constraint and reactive power balance constraint of the power system according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy unit;
establishing a planning model of the power system according to a planning target of the power system, the inertia supporting constraint and the reactive power balance constraint;
solving the established planning model of the power system by using a data-driven robust optimization method to obtain a planned power system;
according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy unit, determining the inertia supporting constraint and the reactive power balancing constraint of the power system comprises:
determining an inertia support constraint formula of the power system according to the total inertia provided by the thermal power generating unit and the total inertia provided by the reactive power compensation device;
Determining a reactive power balance constraint formula of the power system according to reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit in the system, reactive power output by the reactive power compensation device in the system, total reactive load of the system, total network reactive power loss of the system and reactive power standby of the system;
the inertia support constraint formula of the power system is as follows:
in the method, in the process of the invention,
H sys,t is the total inertia of the system;
∑H G,i,t the total inertia of the thermal power generating unit without carbon capture equipment is set;
the total inertia of the thermal power generating unit is provided with carbon capture equipment;
available for reactive compensation arrangementsTotal inertia;
ΔP ctg (t) is a system power deficiency;
Δf max is a limit value of system frequency variation;
f 0 is the initial frequency of the system.
2. The method of claim 1, wherein the establishing a planning model of the power system based on a planning objective of the power system and the inertia support constraints and reactive balance constraints comprises:
establishing a planning total cost function of the power system according to a planning target of the power system;
and establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system.
3. The method of claim 2, wherein the building a planning model of the power system from the total cost of planning function, the inertia support constraint formula, and the reactive balance constraint formula of the power system comprises:
and establishing a planning model of the power system according to a planning total cost function, an inertia supporting constraint formula, a reactive power balance constraint formula, a system load constraint formula, a node balance constraint formula, a candidate line power flow constraint formula, an existing line power flow constraint formula, a thermal power unit power constraint formula, a new energy unit power constraint formula and a reactive power compensation device reactive power constraint formula of the power system.
4. A method according to claim 2 or 3, wherein the solving the established planning model of the power system by using the data-driven robust optimization method to obtain a planned power system comprises:
according to the historical output data of the existing new energy stations of the power system construction nodes, a high-dimensional ellipsoid set based on the historical output data is constructed;
performing convex hull scaling on the high-dimensional ellipsoidal collection to obtain an uncertain collection of the historical output limit scene of the new energy station;
Substituting the output of the new energy station in the uncertain set under the history output limit scene and the load of each node of the power system under the history output limit scene of the new energy station into a planning model of the power system to solve, so as to obtain the planned power system.
5. A method according to any one of claims 1 to 3, characterized in that the reactive balance constraint formula of the power system is:
Q GC -Q LD -Q L ＝Q res ；
in the method, in the process of the invention,
Q GC the reactive power is the sum of reactive power output by the thermal power generating unit in the system, reactive power output by the new energy generating unit and reactive power output by the reactive compensation device in the system;
Q LD the total reactive load of the system;
Q L reactive power loss is the total network of the system;
Q res reserve reactive power for the system.
6. A method according to any one of claims 1 to 3, wherein the planning objective of the power system comprises: the peak of carbon is achieved at a first time period at a minimum cost and the neutralization of carbon is achieved at a second time period, wherein the second time period is later than the first time period.
7. The method of claim 6, wherein the overall cost of planning function for the power system is:
in the method, in the process of the invention,
i=1 or 2;
At T for the power system 1 Minimum planning cost for a epoch;
at T for the power system 2 Minimum planning cost for a epoch;
Cost node the construction cost for the system circuit is;
F G the total cost is consumed for the thermal power generating unit;
is T i The installation cost of the carbon capture device in the period;
is T i The period carbon tax cost;
is T i The new energy unit construction cost is used in the period;
is T i And the construction cost of the reactive compensation device in the period.
8. A method according to claim 3, wherein the system load constraint formula is:
in the method, in the process of the invention,
load requirements for each node of the system;
∑P G,i,t,s the method comprises the steps of outputting power of a thermal power unit without carbon capture equipment for the system;
∑P G,i,t,s CCS the thermal power generating unit output of the carbon capture equipment is arranged for the system;
the scheduling value of the new energy unit of the system is obtained.
9. A method according to claim 3, wherein the node balancing constraint formula is:
in the method, in the process of the invention,
n (b) is a set of a series of devices connected by node b;
s (l) and r (l) respectively represent a transmitting end node and a receiving end node of the line l;
the output of other power generation equipment i except the new energy station connected with the node b;
is the scheduling value of the new energy electric field w;
is the tide of line l;
a loss load which is the electric load d;
the actual load of the power load d is shown.
10. A method according to claim 3, wherein the candidate line power flow constraint formula is:
in the method, in the process of the invention,
y lt 0 or 1, belonging to a decision variable;
X l a line reactance representing line l;
m is a sufficiently large number;
is the tide of line l;
P l max the upper limit of the tide of the line l;
CL represents a candidate line set;
and->The phase angles of the transmitting end node and the receiving end node of the line l are respectively, and the node phase angle range is that Is the upper bound of the phase angle of node b.
11. A method according to claim 3, wherein the existing line flow constraint formula is:
in the method, in the process of the invention,
is the tide of line l;
X l a line reactance representing line l;
and->The phase angles of the transmitting end node and the receiving end node of the line l are respectively, and the node phase angle range is that
Is the upper bound of the phase angle of node b;
EL represents an existing line set;
P l max is the upper limit of the power flow of line l.
12. A method according to claim 3, characterized in that the reactive constraint formula of the reactive compensation device is:
in the method, in the process of the invention,
reactive power provided by the reactive power compensation device of the node i at the time t;
and (3) the reactive power required by the new energy unit of the node i at the time t.
13. The utility model provides a power system source net collaborative planning device which characterized in that includes:
The determining module is used for determining inertia supporting constraint and reactive power balance constraint of the power system according to the operation characteristics of the thermal power generating unit and the operation characteristics of the new energy generating unit;
the building module is used for building a planning model of the power system according to the planning target of the power system, the inertia supporting constraint and the reactive balance constraint;
the solving module is used for solving the established planning model of the power system by utilizing a data-driven robust optimization method to obtain a planned power system;
the determining module is specifically configured to:
determining an inertia support constraint formula of the power system according to the total inertia provided by the thermal power generating unit and the total inertia provided by the reactive power compensation device;
determining a reactive power balance constraint formula of the power system according to reactive power output by thermal power units in the system, reactive power output by new energy units in the system, reactive power output by reactive compensation devices in the system, total reactive load of the system, total network reactive loss of the system and reactive power standby of the system;
the inertia support constraint formula of the power system is as follows:
in the method, in the process of the invention,
H sys,t is the total inertia of the system;
∑H G,i,t the total inertia of the thermal power generating unit without carbon capture equipment is set;
The total inertia of the thermal power generating unit is provided with carbon capture equipment;
the total inertia available for the reactive compensation device;
ΔP ctg (t) is a system power deficiency;
Δf max is a limit value of system frequency variation;
f 0 is the initial frequency of the system.
14. The apparatus of claim 13, wherein the means for establishing is specifically configured to:
establishing a planning total cost function of the power system according to a planning target of the power system;
and establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive balance constraint formula of the power system.
15. The apparatus of claim 14, wherein the building module builds the planning model of the power system from a total cost of planning function, an inertia support constraint formula, and a reactive balance constraint formula for the power system, comprising:
and establishing a planning model of the power system according to a planning total cost function, an inertia supporting constraint formula, a reactive power balance constraint formula, a system load constraint formula, a node balance constraint formula, a candidate line power flow constraint formula, an existing line power flow constraint formula, a thermal power unit power constraint formula, a new energy unit power constraint formula and a reactive power compensation device reactive power constraint formula of the power system.
16. The apparatus of claim 14 or 15, wherein the solving module solves the established planning model of the power system by using a data-driven robust optimization method, and the obtaining the planned power system includes:
according to the historical output data of the existing new energy stations of the power system construction nodes, a high-dimensional ellipsoid set based on the historical output data is constructed;
performing convex hull scaling on the high-dimensional ellipsoidal collection to obtain an uncertain collection of the historical output limit scene of the new energy station;
substituting the output of the new energy station in the uncertain set under the history output limit scene and the load of each node of the power system under the history output limit scene of the new energy station into a planning model of the power system to solve, so as to obtain the planned power system.
17. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 12 when the computer program is executed by the processor.
18. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 12.
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