CN114336663A - Novel power system source network collaborative planning method and device - Google Patents

Novel power system source network collaborative planning method and device Download PDF

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CN114336663A
CN114336663A CN202210018102.0A CN202210018102A CN114336663A CN 114336663 A CN114336663 A CN 114336663A CN 202210018102 A CN202210018102 A CN 202210018102A CN 114336663 A CN114336663 A CN 114336663A
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power
power system
reactive
planning
new energy
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CN114336663B (en
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王泽森
赵天骐
夏雪
刘瑛琳
罗婧
郝婧
张涵之
张思琪
梁浩
李�雨
刘苗
谢欢
黄天啸
吴涛
赵志宇
张璐
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State Grid Corp of China SGCC
North China Electric Power Research Institute Co Ltd
Electric Power Research Institute of State Grid Jibei Electric Power Co Ltd
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State Grid Corp of China SGCC
North China Electric Power Research Institute Co Ltd
Electric Power Research Institute of State Grid Jibei Electric Power Co Ltd
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Abstract

The invention provides a novel 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 operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit; establishing a planning model of the power system according to a planning target of the power system and the inertia support 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. The novel power system source network collaborative planning method and device provided by the embodiment of the invention consider the system reactive power balance and inertia support of the power system in the planning aspect, and can ensure the safe and stable operation of the planned power system.

Description

Novel power system source network collaborative planning method and device
Technical Field
The invention relates to the technical field of power systems, in particular to a novel power system source network collaborative planning method and device.
Background
At present, the existing power system planning technology mainly considers the sending constraint and the consumption constraint of a power system for planning a high-proportion new energy power system, and the planning method is difficult to ensure the safe and stable operation of the power system along with the continuous access of the high-proportion new energy.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a novel power system source network collaborative planning method and a novel power system source network collaborative planning device, which can at least partially solve the problems in the prior art.
On one hand, the invention provides a novel 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 operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit; establishing a planning model of the power system according to a planning target of the power system and the inertia support 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, determining the inertia support constraint and the reactive power balance constraint of the power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source 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 the reactive power output by the live power set in the system, the reactive power output by the new energy set in the system, the reactive power output by the reactive power compensation device in the system, the total reactive load of the system, the total network reactive loss of the system and the reactive power reserve of the system.
Optionally, the establishing a planning model of the power system according to the planning target of the power system and the inertia support constraint and the reactive power 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 a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system.
Optionally, the establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive power 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 support 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 constraint formula of a reactive power compensation device 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 historical output data of an existing 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 ellipsoid set to obtain an uncertain set of the historical output limit scene of the new energy station; and substituting the output of the new energy station in the uncertain set under a historical output limit scene and the load 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.
Optionally, the inertia support constraint formula of the power system is as follows:
Figure BDA0003460863540000021
in the formula (I), the compound is shown in the specification,
Hsys,tis the total inertia of the system;
∑HG,i,tthe total inertia of the thermal power generating unit without the carbon capture equipment is obtained;
Figure BDA0003460863540000022
the total inertia of the thermal power generating unit provided with the carbon capture equipment is provided;
Figure BDA0003460863540000023
the total inertia available for the reactive power compensation device;
ΔPctg(t) is the system power deficit;
Δfmaxis the limit value of the system frequency variation;
f0is the initial frequency of the system.
Optionally, the reactive balance constraint formula of the power system is as follows:
QGC-QLD-QL＝Qres；
in the formula (I), the compound is shown in the specification,
QGCthe sum of the reactive power which can be output by the live generator set in the system, the reactive power which can be output by the new energy source set and the reactive power which can be output by the reactive compensation device in the system;
QLDis the total reactive load of the system;
QLthe total network reactive power loss of the system;
Qresspare for the reactive power of the system.
Optionally, the planning objective of the power system includes: carbon peaking is achieved at a first time period and carbon neutralization is achieved at a second time period at a minimum cost, wherein the second time period is later than the first time period.
Optionally, the planned total cost function of the power system is as follows:
Figure BDA0003460863540000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000032
i is 1 or 2;
Figure BDA0003460863540000033
for said power system at T1Minimum planning cost of a time period;
Figure BDA0003460863540000034
for said power system at T2Minimum planning cost of a time period;
Costnodecost of system line construction;
FGthe total cost is consumed for the thermal power generating unit;
Figure BDA0003460863540000035
is TiThe periodic carbon capture device installation cost;
Figure BDA0003460863540000036
is TiThe carbon tax cost of the time period;
Figure BDA0003460863540000037
is TiEstablishing a new energy machine as a cost;
Figure BDA0003460863540000038
is TiTime period of noneAnd the construction cost of the power compensation device.
Optionally, the system load constraint formula is:
Figure BDA0003460863540000039
in the formula (I), the compound is shown in the specification,
Figure BDA00034608635400000310
load requirements of each node of the system;
ΣPG,i,t,sthe output of a thermal power generating unit of which the system is not provided with the carbon capture equipment;
∑PG,i,t,s CCSthe output of a thermal power generating unit with a carbon capture device is installed for the system;
Figure BDA0003460863540000041
and the modulation value is the modulation value of the new energy unit of the system.
Optionally, the node balance constraint formula is:
Figure BDA0003460863540000042
in the formula (I), the compound is shown in the specification,
n (b) a set of a series of devices connected for node b;
s (l) and r (l) respectively represent a sending end node and a receiving end node of a line l;
Figure BDA0003460863540000043
the output of other power generation equipment i except the new energy station connected with the node b;
Figure BDA0003460863540000044
is the modulation value of the new energy electric field w;
Figure BDA0003460863540000045
is the current of line l;
Figure BDA0003460863540000046
a loss load that is an electrical load d;
Figure BDA0003460863540000047
representing the actual load of the electrical load d.
Optionally, the candidate line power flow constraint formula is as follows:
Figure BDA0003460863540000048
Figure BDA0003460863540000049
in the formula (I), the compound is shown in the specification,
yltis 0 or 1, belonging to decision variables;
Xlrepresents the line reactance of line l;
m is a sufficiently large number;
Figure BDA00034608635400000410
is the current of line l;
Pl maxis the upper current limit of the line l;
CL represents a candidate line set;
Figure BDA00034608635400000411
and
Figure BDA00034608635400000412
the phase angles of the transmitting node and the receiving node of the line l are respectively, and the range of the phase angle of the node is
Figure BDA00034608635400000413
The upper limit of the phase angle at node b.
Optionally, the existing line power flow constraint formula is as follows:
Figure BDA0003460863540000051
Figure BDA0003460863540000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000053
is the current of line l;
Xlrepresents the line reactance of line l;
Figure BDA0003460863540000054
and
Figure BDA0003460863540000055
the phase angles of the transmitting node and the receiving node of the line l are respectively, and the range of the phase angle of the node is
Figure BDA0003460863540000056
Is the upper limit of the phase angle of node b;
EL denotes the existing line set;
Pl maxis the upper current limit of the line l.
Optionally, the reactive power constraint formula of the reactive power compensation device is as follows:
Figure BDA0003460863540000057
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000058
providing reactive power for a reactive power compensation device of the node i at the moment t;
Figure BDA0003460863540000059
the new energy source unit of the node i needs reactive power at the time t.
In another aspect, the present invention provides a novel power system source grid collaborative planning apparatus, including: the determining module is used for determining inertia support constraint and reactive power balance constraint of the power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit; the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing a planning model of the power system according to a planning target of the power system and the inertia support constraint and the reactive power 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 the reactive power output by the live power set in the system, the reactive power output by the new energy set in the system, the reactive power output by the reactive power compensation device in the system, the total reactive load of the system, the total network reactive loss of the system and the reactive power reserve 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 a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system.
Optionally, the establishing module establishes a planning model of the power system according to a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system, including: and establishing a planning model of the power system according to a planning total cost function, an inertia support 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 constraint formula of a reactive power compensation device 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 of the planned power system includes: according to historical output data of an existing 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 ellipsoid set to obtain an uncertain set of the historical output limit scene of the new energy station; and substituting the output of the new energy station in the uncertain set under a historical output limit scene and the load 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.
In another aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the novel power system source grid co-planning method according to any of the above embodiments when executing the program.
In another aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the novel power system source grid collaborative planning method according to any of the above embodiments.
According to the novel power system source network collaborative planning method and device provided by the embodiment of the invention, the system reactive power balance and inertia support of the power system are considered in the planning aspect, the planning model of the power system is established by combining the planning target of the power system, and then the uncertain parameters in the planning model of the power system are solved by using a data-driven robust optimization method, so that the upper limit of new energy and the long-term development path of the power system can be obtained, the reactive power balance and inertia support constraint in the planned power system are ensured, and the safe and stable operation of the planned power system is further ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a schematic flow chart of a source grid collaborative planning method of a novel power system according to an embodiment of the present invention.
Fig. 2 is a schematic partial flow chart of a source grid collaborative planning method of a novel power system according to an embodiment of the present invention.
Fig. 3 is a schematic partial flow chart of a source grid collaborative planning method of a novel power system according to an embodiment of the present invention.
Fig. 4 is a schematic partial flow chart of a source grid collaborative planning method of a novel power system 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 a ratio of new energy generated output to thermal power output obtained by solving in a test example of the present invention.
Fig. 7 is a schematic diagram of an evolution result of a power grid structure based on an initial power grid structure of a test system on the basis of evolution analysis performed on a source side in a test example of the present invention.
FIG. 8 is a schematic diagram of a system planning result obtained by solving using a robust optimization method based on big data driving according to a test example of the present invention, wherein the system planning result is optimal based on total cost.
Fig. 9 is a schematic structural diagram of a source grid collaborative planning apparatus of a novel power system according to an embodiment of the present invention.
Fig. 10 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The execution main body of the novel power system source grid collaborative planning method provided by the embodiment of the invention comprises but is not limited to a computer.
Fig. 1 is a schematic flow chart of a novel power system source network collaborative planning method provided in an embodiment of the present invention, and as shown in fig. 1, the novel power system source network collaborative planning method provided in the embodiment of the present invention includes:
s101, determining inertia support constraint and reactive power balance constraint of a power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit;
in this step, the new energy unit may include a photovoltaic unit, a hydroelectric unit, a wind turbine, and mainly a wind turbine.
The Inertia of the Power System (IPS) refers to the ability to stop the frequency change of the voltage and current of the ac Power grid, and the Inertia of the Power System acts as a resistance to the frequency change caused by external disturbance, slows down the frequency drop speed of the System, and is an important guarantee for the frequency stability of the System.
Reactive Power Balance (RPBPS) of an electric Power System is to perform Reactive Power Balance calculation according to Power supply development planning and Power grid development planning to Balance Reactive Power generated by a Reactive Power supply of the electric Power System with Reactive load of the System, and the RPBPS mainly aim at maintaining voltage levels of various points of the electric Power grid in various operation modes and determining configuration of Reactive compensation devices. The basic requirements for reactive power balance of an electric power system are: the reactive power that a reactive power source in the power system can deliver 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 the safe and stable operation of the power system, the power supply provides necessary inertia and reactive support for the system, and in the area with high proportion of new energy grid-connected power generation, new energy needs to provide inertia and reactive support for the system in the planning and operation stages of the power system.
Analyzing the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit: the synchronous machine of the traditional power system thermal power generating unit is directly connected with a power grid, due to the characteristic of a voltage source of the synchronous machine, the synchronous machine has the capacity of instantly sharing disturbance power, the new energy source unit can provide less generalized kinetic energy, the generalized kinetic energy needs to be provided by the traditional thermal power generating unit, the inertia of the system is ensured to be larger than the minimum limit value, and accordingly the inertia support constraint of the power system is determined.
Analyzing the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit: the reactive power output of various reactive power sources in the system should meet the reactive power requirements of system loads and network losses at rated voltage, otherwise the power sources will deviate from the rated values. Because of the dynamic reactive power compensation problem existing in the large-scale new energy at present, enough reactive power compensation devices need to be configured to ensure the reactive power stability of the system, and accordingly, the reactive power balance constraint of the power system is determined. Compared with a Static Var Compensator (SVC) and a Static Var Generator (SVG), a phase modulator has a better inhibition effect on transient voltage rise, so that the phase modulator 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 and the inertia support constraint and the reactive power balance constraint;
in this step, the planning objective of the power system may include: the target is achieved at a certain time, or different targets are achieved at a plurality of times, or the target is achieved at a preset cost at a certain time, or different targets are achieved at different costs at a plurality of times. For example, the planning objectives are: carbon peaking is achieved during a first period of time and carbon neutralization is achieved during a second period of time, the second period of time being later than the first period of time.
On the basis of inertia support constraint and reactive power balance constraint of the power system, a planning model of the power system is established by combining a planning target of the power system.
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 method comprises the following steps that a data-driven robust optimization theory is a mathematical optimization method considering parameter uncertainty, the uncertain parameters in a planning model of the power system can be solved by using the data-driven robust optimization method, the upper limit of new energy and a long-term development path of the power system are calculated, and reactive power balance and inertia support constraint in the power system are guaranteed.
According to the novel power system source network collaborative planning method provided by the embodiment of the invention, the system reactive power balance and inertia support of the power system are considered in the planning aspect, the planning model of the power system is established by combining the planning target of the power system, and then the 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 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 power balance constraint of the power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit includes:
s1011, 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;
in the step, a synchronous machine of a traditional power system thermal power generating unit is directly connected with a power grid, due to the characteristic of a voltage source of the synchronous machine, the capacity of instantly sharing disturbance power is realized, the power shortage (surplus) of the system is directly reflected in the sudden increase (decrease) of electromagnetic power, a rotating mass block of a 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, the power balance of the system is supported, and the frequency change is restrained. When the system generates unbalanced disturbance, each generator instantaneously shares disturbance power according to the synchronous power coefficient of the generator and a disturbance point. At this time, the motion state of the rotor of the ith generator in the system changes according to the motion variance of the rotor under the action of the unbalanced torque:
Figure BDA0003460863540000091
in the formula, Hi,t,fi,t,ΔPi,t(t) i, the inertia, the frequency and the variation power of the generator at t time, the variation of the inertia among the units and the variation of the shared disturbance power lead the units to change at different rotating speeds, then the rotating speeds tend to be consistent under the action of synchronous moment, and each generator shares the disturbance power again according to the inertia of the units:
Figure BDA0003460863540000101
in the formula, NGAs number of generators, Δ PLIs the total power fluctuation. When the system generates power disturbance, the system should ensure that the initial Frequency Change Rate (Rate of Change of Frequency, rocofs) satisfies the following constraint
|Δf|≤Δfmax (3)
In the formula, Δ f is the frequency change rate of the system; Δ fmaxA limit for frequency variation.
The maximum rocef is therefore determined by the power deficit and the total inertia:
Figure BDA0003460863540000102
in the formula,. DELTA.Pctg(t) is the power deficit, Hsys,tTotal inertia of the system at time t, f0Is the initial frequency of the system.
The minimum inertia required for the system can therefore be expressed as:
Figure BDA0003460863540000103
then, the overall inertia is calculated and solved, and generally, the generalized inertia constant of the system is:
Figure BDA0003460863540000104
Esysis the generalized kinetic energy of the system, SsysSubstituting the above equation for the total rated power generation capacity of the system can obtain the inertia support constraint formula of the system:
Figure BDA0003460863540000105
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000106
for the generalized kinetic energy of the system of the ith thermal power generating unit at the time t,
Figure BDA0003460863540000107
and (5) generalized kinetic energy of the system of the jth new energy source unit at the time t.
Because the generalized kinetic energy that new energy unit can provide is less, need traditional thermal power generating unit to provide, guarantee that system inertia is greater than minimum limit. In order to ensure reactive power balance of the system, a reactive power compensation device is added in the system, and the reactive power compensation device can also provide certain inertia; therefore, the inertia support constraint equation of the power system can be expressed as:
Figure BDA0003460863540000111
in the formula (I), the compound is shown in the specification,
Hsys,tis a systemA total inertia;
∑HG,i,tthe total inertia of the thermal power generating unit without the carbon capture equipment is obtained;
Figure BDA0003460863540000112
the total inertia of the thermal power generating unit provided with the carbon capture equipment is provided;
Figure BDA0003460863540000113
the total inertia available for the reactive power compensation device;
ΔPctg(t) is the system power deficit;
Δfmaxis the limit value of the system frequency variation;
f0is the initial frequency of the system.
And S1012, determining a reactive power balance constraint formula of the power system according to the reactive power output by the power generation units in the system, the reactive power output by the new energy source units in the system, the reactive power output by the reactive power compensation devices in the system, the total reactive load of the system, the total network reactive loss of the system and the reactive power reserve of the system.
In this step, the reactive power output of various reactive power sources in the system should meet the requirement of system load and network loss on reactive power under rated voltage, otherwise, the power sources deviate from the rated value. Therefore, the reactive balance constraint equation of the power system can be expressed as:
QGC-QLD-QL＝Qres (9)
in the formula (I), the compound is shown in the specification,
QGCthe sum of the reactive power which can be output by the live generator set in the system, the reactive power which can be output by the new energy source set and the reactive power which can be output by the reactive compensation device in the system; qLDIs the total reactive load of the system; qLThe total network reactive power loss of the system; qresFor the reactive power reserve of the system, 15% -20% of the reactive load of the system is generally achieved.
Using a phase modulator asIn the case of a reactive power compensation device for an electrical power system, the phase modifier is a salient pole machine designed for normal operation under low excitation conditions, allowing absorption of 75% of the nominal power. By denoting the phase voltage at the motor terminals by E, denoted by E0Indicating the internal voltage of the motor by XdRepresenting the reactance of the machine, the reactive power provided by the synchronous compensator can be expressed as:
Figure BDA0003460863540000121
by considering E0The value is constant, resulting in Q being reduced (increased) when the voltage at the machine terminals decreases (increases)cThe value of (c) is increased (decreased). Reactive power of synchronous compensator Power supply side Total reactive Power Q contained in equation (9)GCIn (1).
As shown in fig. 3, optionally, the establishing a planning model of the power system according to the planning target of the power system and the inertia support constraint and the reactive power balance constraint includes:
s1021, establishing a planning total cost function of the power system according to a planning target of the power system;
in this step, when the planning objective includes the cost, a planning total cost function of the power system can be established according to the planning objective of the power system. For example, in the planning objective: achieving carbon peak-to-peak at a first time period and carbon neutralization at a second time period at a minimum cost, wherein the second time period is later than the first time period, the projected total cost function for the power system may be expressed as:
Figure BDA0003460863540000122
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000123
i is 1 or 2;
Figure BDA0003460863540000124
for said power system at T1Minimum planning cost of a time period;
Figure BDA0003460863540000125
for said power system at T2Minimum planning cost of a time period;
Costnodecost of system line construction;
FGthe total cost is consumed for the thermal power generating unit;
Figure BDA0003460863540000126
is TiThe periodic carbon capture device installation cost;
Figure BDA0003460863540000127
is TiThe carbon tax cost of the time period;
Figure BDA0003460863540000128
is TiEstablishing a new energy machine as a cost;
Figure BDA0003460863540000129
is TiAnd the construction cost of the reactive power compensation device in the period.
Optionally, each detailed cost in the total planning cost function of the power system is respectively:
system line construction cost:
Figure BDA0003460863540000131
in the formula Inode(k)0 or 1, indicating whether a new line is added; phi is an optional line set; mu.snode(k)The unit length cost of the newly added line is reduced; l isnode(k)The length of the newly added line.
The thermal power generating unit consumes the total cost:
Figure BDA0003460863540000132
in the formula ui,t,sThe method comprises the steps that the operation state of a thermal power generating unit i at a time t under a scene s is shown; a isi，bi，ciThe cost coefficient is the cost coefficient of the thermal power generating unit i; pG,i,t,sThe output of the thermal power generating unit i at the time t under the scene s is shown.
The new energy machine is established to be the following:
Figure BDA0003460863540000133
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000134
number of new energy station construction in Ti period, CwtThe energy-saving device is a single new energy machine.
The construction cost of the reactive power compensation device is as follows:
Figure BDA0003460863540000135
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000136
number of reactive power compensator constructions for Ti period, CtxThe cost of the reactive power compensation device.
The carbon tax costs are as follows:
Figure BDA0003460863540000137
the carbon tax transaction is implemented in a step fee mode, wherein sigma is a carbon tax coefficient, omega is a carbon tax overproof punishment increment coefficient, f is a carbon tax overproof interval,
Figure BDA00034608635400001314
a carbon tax excess penalty multiple coefficient, CtFor actual carbon emission, CDAs a reference carbon emission, CD+ f is the excess penalty interval,
Figure BDA0003460863540000138
indicating whether this step is performed during that time period.
The installation cost of the carbon capture device is as follows:
Figure BDA0003460863540000139
in the formula (I), the compound is shown in the specification,
Figure BDA00034608635400001310
the value range is 0-1 for the technical progress degree, the specific value can be determined according to an empirical method, the higher the technical progress degree is,
Figure BDA00034608635400001311
the greater the value of (A);
Figure BDA00034608635400001312
the method is used as a benchmark investment,
Figure BDA00034608635400001313
whether to invest or not.
S1022, establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive power balance constraint formula of the power system.
In the step, a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system form a planning model of the power system.
Optionally, the establishing a planning model of the power system according to the planning total cost function, the inertia support constraint formula and the reactive power 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 support 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 constraint formula of a reactive power compensation device 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 power balance constraint of the power system are considered, but also the system load constraint, the node balance constraint, the candidate line power flow constraint, the existing line power flow constraint, the power constraint of the thermal power generating unit, the power constraint of the new energy unit and the reactive power constraint of the reactive power compensation device are considered.
Optionally, the system load constraint formula may be:
Figure BDA0003460863540000141
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000142
is the load demand of node b;
PG,i,t,soutputting power for the thermal power generating unit which is related to the node b and is not provided with the carbon capture equipment;
PG,i,t,s CCSoutputting power for the thermal power generating unit which is related to the node b and is provided with the carbon capture equipment;
Figure BDA0003460863540000143
is the scheduling value of the new energy bank w associated with node b.
Optionally, the node balance constraint formula may be:
Figure BDA0003460863540000144
in the formula (I), the compound is shown in the specification,
n (b) a set of a series of devices connected for node b;
Figure BDA0003460863540000151
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 sending end node and a receiving end node of a line l;
Figure BDA0003460863540000152
is the scheduling value of the new energy unit w;
Figure BDA0003460863540000153
is the current of line l;
Figure BDA0003460863540000154
a loss load that is an electrical load d;
Figure BDA0003460863540000155
representing the considered actual load of the electrical load d.
Optionally, the candidate line power flow constraint formula may be:
Figure BDA0003460863540000156
in the formula (I), the compound is shown in the specification,
yltis 0 or 1, belonging to decision variables;
Xlrepresents the line reactance of line l;
m is a sufficiently large number to ensure that values far behind the non-equal sign are exceeded to ensure that yltThe variable is effective, and can be hundreds of thousands in practice;
Figure BDA0003460863540000157
is the current of line l;
Pl maxis the upper current limit of the line l;
CL represents a candidate line set;
Figure BDA0003460863540000158
and
Figure BDA0003460863540000159
the phase angles of the transmitting node and the receiving node of the line l are respectively, and the range of the phase angle of the node is
Figure BDA00034608635400001510
The upper limit of the phase angle at node b.
Optionally, the existing line power flow constraint formula may be:
Figure BDA00034608635400001511
in the formula (I), the compound is shown in the specification,
Figure BDA00034608635400001512
is the current of line l;
Xlrepresents the line reactance of line l;
Figure BDA0003460863540000161
and
Figure BDA0003460863540000162
the phase angles of the transmitting node and the receiving node of the line l are respectively, and the range of the phase angle of the node is
Figure BDA0003460863540000163
Is the upper limit of the phase angle of node b;
EL denotes the existing line set;
Pl maxis the upper current limit of the line l.
Optionally, the reactive power constraint formula of the reactive power compensation device may be:
Figure BDA0003460863540000164
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000165
providing reactive power for a reactive power compensation device of the node i at the moment t;
Figure BDA0003460863540000166
the new energy source unit of the node i needs reactive power at the time t.
Optionally, the thermal power generating unit power constraint formula may be expressed as:
(PG,i,t,s s)min≤PG,i,t,s≤(PG,i,t,s s)max (23)
in the formula (I), the compound is shown in the specification,
PG,i,t,sthe output of the thermal power generating unit i is obtained;
(PG,i,t,s s)maxthe maximum output of the thermal power generating unit i is obtained;
(PG,i,t,s s)minand the minimum output of the thermal power generating unit i.
Optionally, the new energy unit power constraint formula may be expressed as:
Figure BDA0003460863540000167
in the formula (I), the compound is shown in the specification,
Figure BDA0003460863540000168
is the scheduling value of the new energy unit w;
WG is a new energy unit scheduling scene set;
Figure BDA0003460863540000169
is the maximum modulation 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 the planned power system includes:
s1031, constructing a high-dimensional ellipsoid set based on historical output data according to historical output data of existing new energy station of the power system construction node;
in this step, constructing a set of high-dimensional ellipsoids based on historical output data according to historical output data of existing new energy stations of a power system construction node may include the following steps:
(1) historical output data of the existing new energy station of the construction node (for example, 10,13,15 and 17 nodes in the following test calculation example are selected), wherein the historical output data comprises short-term output data of the new energy station and historical output data of the output of the new energy station, and the output data refers to output power and is in watt W). Forming the collected historical output data into a column vector form, recording a group of historical data as a historical scene, and recording the collected historical scene as a historical scene
Figure BDA0003460863540000171
Wherein N ishIs the number of collected historical scenes.
(2) And constructing a high-dimensional ellipsoid set based on historical output data. The following preconditions are assumed: when the amount of historical output data collected is large enough, the historical output data is representative of the output data for the period of interest (the period of interest is the period of the constructed set of scenarios, such as a selected 8760 hour continuous year), i.e., if there is a closed set that can fully cover the historical output scenario, the output data for the period of interest is also in the closed set. In the step, by means of a high-dimensional closed ellipsoid algorithm, a high-dimensional ellipsoid is firstly solved to surround all historical scenes, and the form of the obtained ellipsoid is as follows:
Figure BDA0003460863540000172
the ellipsoid is a generalized n-dimensional ellipsoid, wherein omega is an n-dimensional vector represented by an uncertain parameter, namely n random variables exist, RnIs an n-order real number domain; the matrix Q is a positive definite matrix and represents the angle of the high-dimensional ellipsoid deviating from the positive direction of the coordinate axis and the length of each symmetric axis, the vector c represents the coordinate of the central point of the high-dimensional ellipsoid, and both Q and c are known quantities.
Solving the high-dimensional ellipsoid parameters Q and c equivalently, and solving the following optimization:
Figure BDA0003460863540000173
s.t.(ωh,1-c)TQ(ωh,1-c)≤1
(ωh,2-c)TQ(ωh,2-c)≤1
… (26)
where ρ isnIs a constant, representing the volume of the unit sphere in dimension n, ωh,1,ωh,2… is the historical force data collected in step (1). The optimization is convex optimizationIn the form of chemometrics, it can be solved quickly in polynomial time.
S1032, performing convex hull scaling on the high-dimensional ellipsoid set to obtain an uncertain set of the historical output limit scene of the new energy station;
in this step, it is assumed that the final uncertainty set is as follows:
Figure BDA0003460863540000181
wherein, ω ise,iThe extreme scene obtained after convex hull scaling is combined with the inverse transformation formula of the translational and rotational variation equation and the modified vertex coordinate expression of the axial ellipsoid to obtain the vertex omega 'of the extreme scene and the high-dimensional ellipsoid E'v,iThe existence relationship is as follows:
ωe,i＝c+k*P-1ω′v,i (28)
wherein k is*I.e. the magnification in the convex hull scaling.
The coefficient k increases as the distance between the scene and the convex hull increases*And will also become larger. Therefore, the following optimization can be established to determine the position relation between the historical scene and the convex hull:
Figure BDA0003460863540000182
for NhThe historical scenes have the optimization in the form of the formula, and no relation exists between the optimizations, so that N can be consideredhThe optimizations are combined as follows:
Figure BDA0003460863540000183
Figure BDA0003460863540000184
for NhA history scenario, fromCan obtain NhA sequence of magnification factors, the maximum value in the sequence being the magnification factor k in the convex hull scaling*. The final data-driven robust uncertainty set is adopted
Figure BDA0003460863540000185
Is represented by a polyhedron of (a). In addition, due to the fact that
Figure BDA0003460863540000186
Scaling factor k in*The method is self-determined, and according to the related knowledge of robust optimization, the larger the area contained in the uncertain set is, the more conservative the decision is, so that the scaling coefficient can be corrected to a certain extent according to the current requirement.
And S1033, substituting the output of the new energy station in the uncertain set in the historical output limit scene and the load of each node of the power system in the historical 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 historical output limit scene in the uncertain set, the output (referred to as output power) of the new energy station and the load (referred to as load power) of each node of the power system in the historical output limit scene are substituted into a planning total cost function in a planning model of the power system and formulas such as an inertia support constraint formula, a reactive power balance constraint formula and a system load constraint formula, so that a new energy proportion upper limit, a system planning total cost, an operation cost and a total carbon emission amount of the power system can be obtained, and source load balance, reactive power balance and inertia support constraint in the power system are ensured. For example, the running load data of the fan stations of four places in North China are scheduled for 8760 hours in 2019 and serve as the load data of the new energy station of the IEEE-RTS-24 node, and the 8760-hour load data is shown in an IEEE-RTS-24 node file. And (4) carrying into a data-driven robust optimization model, and carrying the result into a planning model. The planning model is written by a Yalmip platform and subjected to MILP solution by taking Cplex as a solver in the embodiment.
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 composed of 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, 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 an electric power system and fixed parameters of the electric power system (inherent parameters of existing and planned lines and equipment of the electric power system) into 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 reactive power constraint formula of a reactive power compensation device, a power constraint formula of a thermal power unit, a power constraint formula of a new energy unit, an inertia support constraint formula and a reactive power balance constraint formula.
Specifically, the sum of the loads of each node of the power system is used as the system load constraint formula
Figure BDA0003460863540000191
Taking the sum of the output of each new energy station as the system load constraint formula
Figure BDA0003460863540000192
Substituting into a system load constraint formula; in the system load constraint formula, SIG PG,i,t,s、∑PG,i,t,s CCSRespectively are optimization variables;
taking the sum of the output of the new energy station in each node in the power system as the node balance constraint formula
Figure BDA0003460863540000193
Determining the total loss load of the node according to the load of the node and the fixed parameters of the power system
Figure BDA0003460863540000201
And total actual load
Figure BDA0003460863540000202
(the calculation method is conventional in the art and will not be described herein), and the calculation is performed
Figure BDA0003460863540000203
And
Figure BDA0003460863540000204
substituting into a 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
Figure BDA0003460863540000205
The calculated load flow of each line
Figure BDA0003460863540000206
Substituting into a node balance constraint formula; in the node-balancing constraint formula,
Figure BDA0003460863540000207
to optimize the variables;
determining the line reactance X of each candidate line planned in advance according to the intrinsic parameters of the power systemlTidal current on-line Pl maxUpper limit of nodal phase angle of the candidate line
Figure BDA0003460863540000208
And phase angle of transmitting end node of the candidate line
Figure BDA0003460863540000209
And a receiverPhase angle of end node
Figure BDA00034608635400002010
Substituting the calculated parameters into the load flow constraint formula of the candidate line; optimization variables of candidate lines in the node balance constraint formula
Figure BDA00034608635400002011
The power 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 systemlTidal current on-line Pl maxUpper limit of nodal phase angle of the candidate line
Figure BDA00034608635400002012
And phase angle of transmitting end node of the existing line
Figure BDA00034608635400002013
And phase angle of receiving end node
Figure BDA00034608635400002014
Substituting the calculated parameters into the current constraint formula of the existing line; optimizing variables of existing lines in the node balance constraint formula
Figure BDA00034608635400002015
The current constraint formula of the existing line needs to be satisfied.
Calculating the reactive power provided by the reactive power compensation devices in the system according to the planned number (optimization variable) of the reactive power compensation devices and the intrinsic parameters of the reactive power compensation devices (the intrinsic parameters of the power system comprise the intrinsic parameters of the reactive power compensation devices); calculating the reactive power required by the new energy source unit in the system according to the planned number (optimization variable) of the new energy source unit and the inherent parameters (inherent parameters belonging to the power system) of the new energy source unit; and 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 generating unit according to the intrinsic parameters of the thermal power generating unit (the intrinsic parameters of the power system comprise the intrinsic parameters of the thermal power generating unit), and substituting the maximum output and the minimum output of each thermal power generating unit into a power constraint formula of the thermal power generating unit; the optimization variable sigma P in the system load constraint formulaG,i,t,sSum sigma PG,i,t,s CCSThe power constraint of the thermal power generating unit needs to be met.
Similarly, according to the inherent parameters of the new energy unit (the inherent parameters of the power system include the inherent parameters of the new energy unit), determining the maximum scheduling value of each new energy unit, and substituting the maximum scheduling value of the new energy unit into the power constraint formula of the new energy unit; the scheduling value of each new energy machine set in the system load constraint formula and the node balance constraint formula should satisfy the power constraint formula of the new energy machine set.
Calculating the total inertia sigma H of the thermal power generating unit without the carbon capture equipment in the system according to the planning condition (optimization variable) of the carbon capture equipment and the fixed parameters of the power systemG,i,tAnd total inertia of thermal power generating unit provided with carbon capture equipment
Figure BDA0003460863540000211
Calculating the total inertia available from the reactive compensation device in the system according to the planning condition (optimization variable) of the reactive compensation device and the fixed parameters of the power system
Figure BDA0003460863540000212
And substituting the three inertias into an inertance support constraint formula. It should be understood that, in this step, the planned number of the carbon capture devices and the planned number of the reactive power compensation devices are optimization variables, which have not been solved, and therefore they may be respectively replaced by different expressions, that is, the three calculated inertias are not determined values, but are respectively an expression, and belong to the optimization variables; in the inertia support constraint formula, the minimum total inertia of the system
Figure BDA0003460863540000213
Can be calculated according to the fixed parameters of the power system and is a determined value.
Calculating Q in a reactive balance constraint formula according to the output of the new energy station of the system and the fixed parameters of the power systemGCThe reactive power which can be output by the new energy machine set in (1) (calculating the reactive power which can be output by the new energy machine set according to the output of the new energy station of the system and the fixed parameters of the power system belongs to the conventional technology in the field, and is not described herein again, and the following reactive power calculation is the same; calculating Q in a reactive balance constraint formula according to the output (optimized variable) of the thermal power generating unit of the system and the fixed parameters of the power systemGCThe thermal power generating unit can output reactive power; calculating Q in reactive balance constraint formula according to planned number (optimized variable) of reactive compensation devices and fixed parameters of power systemGCThe reactive power output by the reactive power compensation device in the system; wherein Q in the reactive balance constraint formulaLD、QL、QresThe calculation method is obtained by calculation according to the load of each node of the power system and the fixed parameters of the power system, belongs to the conventional technology in the field, and is not described herein again.
And then, the planning model of the power system is substituted by the output of the new energy station, the load of each node of the power system and the fixed parameters of the power system.
(2) And then according to a planning total cost function, with the lowest planning total cost as a target and the constraint formulas as constraints, solving the number of candidate lines of the power system, the number of carbon capture equipment, the number of new energy machine sets, the number of reactive power compensation device constructions, the output of thermal power generating units and the like. The parameters may be solved by using an MILP mixed integer linear programming method, and it should be understood that each parameter obtained by solving may be a fixed value, a range of values, or a plurality of discrete values. According to the parameters obtained by solving, the new energy ratio upper limit, the operation cost, the total carbon emission and the like of the power system can be calculated.
To verify the originalWith the effectiveness of the proposed novel power system source-grid collaborative planning method in mind, the novel power system source-grid collaborative planning method provided by the above embodiment is tested by adopting improved IEEE-RTS-24 (see FIG. 5) as a test system, and simulation tests are performed on power system evolution and future development forms by taking five years as a planning period, wherein the whole planning period is from 2020 to 2060 years, totally 8 planning stages are provided, and the three planning stages are respectively performed in 2030 (carbon peak, namely T peak, peak value reached by carbon)12020-22030 and 2060) as the planning period key node. The test system has 34 circuits, 11 generator sets in a benchmark year, the total installed capacity is 4250MW, and the load is 3550 MW. According to the carbon peak reaching requirement, in 2030 years, the carbon emission of the test system reaches a peak value, and then the carbon emission is gradually reduced year by year until 2060 year to realize carbon neutralization. The testing system is gradually additionally provided with a new energy unit according to a planning period and carries out carbon capture modification on the thermal power generating unit to form a source side power system of a new energy power generation and carbon capture power plant. The load demand is increased by 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 to be selected are formed.
The power system test is solved by the novel power system source network collaborative planning method considering reactive power balance and inertia support provided by the embodiment.
The total power generation load of the new energy planned at the power supply side is 9040MW, the power generator set is 3470MW, the output of the new energy has obvious fluctuation, and the average output of the new energy power generation 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 problem of a digestion channel and the problem of dynamic reactive support, the actual output of new energy power generation is constrained by the problems of reactive support and frequency response. Due to the limitation, the ratio of the new energy power generation output to the thermal power output has a maximum limit value MwindMaximum limit M of the system for obtaining the actual test datawind. The ratio of the new energy power generation output and the thermal power output obtained year by year through solving is shown in fig. 6.
On the basis of carrying out evolution analysis on the source side, the initial power grid structure of the test system is taken as the basis, and the evolution result of the power grid structure is shown in fig. 7. Compared with the original IEEE-24 node, the new energy power generation is a main bearing object of the system basic load instead of thermal power, four groups of new energy concentrated construction nodes of 10 nodes, 13 nodes, 15 nodes and 18 nodes and other nodes with concentrated loads are surrounded, and a large number of power transmission corridors are constructed.
As shown in fig. 8, from the viewpoint of the specific cost of the test system, after the solution is performed by using the robust optimization method based on big data drive, the optimal system planning result based on the total cost is obtained. Before 2035, the cost of the overall system rose rapidly due to the large number of wind power plants being built. After 2035 years, although the total amount of wind power construction decreased, the construction cost remained gently fluctuating due to the extensive application of the CCUS (carbon capture) technology. With the gradual saturation of construction, after 2055, construction costs fell back.
Therefore, the solution result verifies the effectiveness of the planning method provided by the above embodiment.
Fig. 9 is a schematic structural diagram of the novel power system source grid collaborative planning apparatus according to an embodiment of the present invention, and as shown in fig. 9, the novel power system source grid collaborative planning apparatus according to the embodiment of the present invention includes: the determining module 21 is configured to determine an inertia support constraint and a reactive power balance constraint of the power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit; the establishing module 22 is used for establishing a planning model of the power system according to a planning target of the power system and the inertia support constraint and the reactive power balance constraint; and the solving module 23 is configured to solve 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 novel power system source network collaborative planning device provided by the embodiment of the invention, the system reactive power balance and inertia support of the power system are considered in the planning aspect, the planning model of the power system is established by combining the planning target of the power system, and then the 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 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 the reactive power output by the live power set in the system, the reactive power output by the new energy set in the system, the reactive power output by the reactive power compensation device in the system, the total reactive load of the system, the total network reactive loss of the system and the reactive power reserve 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 a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system.
Optionally, the establishing module establishes a planning model of the power system according to a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system, including: and establishing a planning model of the power system according to a planning total cost function, an inertia support 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 constraint formula of a reactive power compensation device 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 of the planned power system includes: according to 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 ellipsoid set to obtain an uncertain set of the historical output limit scene of the new energy station; and substituting the output of the new energy station in the uncertain set under a 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 configured to execute the processing flows of the above method embodiments, and the functions of the apparatus are not described herein again, and refer to the detailed description of the above method embodiments.
Fig. 10 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 10, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. Processor 301 may call logic instructions in memory 303 to perform the following method: determining inertia support constraint and reactive power balance constraint of the power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit; establishing a planning model of the power system according to a planning target of the power system and the inertia support 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.
In addition, 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 the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute 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), a magnetic disk or an optical disk, and 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, enable the computer to perform the methods provided by the above-described method embodiments.
The present embodiment provides a computer-readable storage medium storing a computer program that causes a computer to execute the method provided by the above-described method embodiments.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 herein, reference to the description of the terms "one embodiment," "a 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, the schematic representations of the terms used above do not necessarily 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.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (21)

1. A novel power system source grid collaborative planning method is characterized by comprising the following steps:
determining inertia support constraint and reactive power balance constraint of the power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit;
establishing a planning model of the power system according to a planning target of the power system and the inertia support 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.
2. The method of claim 1, wherein determining the inertia support constraint and the reactive power balance constraint of the power system based on the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit 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;
and determining a reactive power balance constraint formula of the power system according to the reactive power output by the live power set in the system, the reactive power output by the new energy set in the system, the reactive power output by the reactive power compensation device in the system, the total reactive load of the system, the total network reactive loss of the system and the reactive power reserve of the system.
3. The method of claim 2, wherein building a planning model of the power system based on the planning objectives of the power system and the inertial 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 a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system.
4. The method of claim 3, wherein building the planning model of the power system according to the planning total cost function, the inertia support constraint equation, and the reactive balance constraint equation of the power system comprises:
and establishing a planning model of the power system according to a planning total cost function, an inertia support 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 constraint formula of a reactive power compensation device of the power system.
5. The method according to claim 3 or 4, wherein the solving the established planning model of the power system by using the data-driven robust optimization method to obtain the planned power system comprises:
according to historical output data of an existing 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 ellipsoid set to obtain an uncertain set of the historical output limit scene of the new energy station;
and substituting the output of the new energy station in the uncertain set under a historical output limit scene and the load 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.
6. The method of any of claims 2 to 4, wherein the power system inertia support constraint equation is:
Figure FDA0003460863530000021
in the formula (I), the compound is shown in the specification,
Hsys,tis the total inertia of the system;
∑HG,i,tthe total inertia of the thermal power generating unit without the carbon capture equipment is obtained;
Figure FDA0003460863530000022
the total inertia of the thermal power generating unit provided with the carbon capture equipment is provided;
Figure FDA0003460863530000023
the total inertia available for the reactive power compensation device;
ΔPctg(t) is the system power deficit;
Δfmaxis the limit value of the system frequency variation;
f0is the initial frequency of the system.
7. The method according to any one of claims 2 to 4, wherein the reactive balance constraint equation for the power system is:
QGC-QLD-QL＝Qres；
in the formula (I), the compound is shown in the specification,
QGCthe sum of the reactive power which can be output by the live generator set in the system, the reactive power which can be output by the new energy source set and the reactive power which can be output by the reactive compensation device in the system;
QLDis the total reactive load of the system;
QLthe total network reactive power loss of the system;
Qresspare for the reactive power of the system.
8. The method of any of claims 1 to 4, wherein the planning objective of the power system comprises: carbon peaking is achieved at a first time period and carbon neutralization is achieved at a second time period at a minimum cost, wherein the second time period is later than the first time period.
9. The method of claim 8, wherein the planned total cost function for the power system is:
Figure FDA0003460863530000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003460863530000032
i is 1 or 2;
Figure FDA0003460863530000033
for said power system at T1Minimum planning cost of a time period;
Figure FDA0003460863530000034
for said power system at T2Minimum planning cost of a time period;
Costnodecost of system line construction;
FGthe total cost is consumed for the thermal power generating unit;
Figure FDA0003460863530000035
is TiThe periodic carbon capture device installation cost;
Figure FDA0003460863530000036
is TiThe carbon tax cost of the time period;
Figure FDA0003460863530000037
is TiEstablishing a new energy machine as a cost;
Figure FDA0003460863530000038
is TiTime reactive power compensation deviceAnd setting cost.
10. The method of claim 4, wherein the system load constraint equation is:
Figure FDA0003460863530000039
in the formula (I), the compound is shown in the specification,
Figure FDA00034608635300000310
load requirements of each node of the system;
∑PG,i,t,sthe output of a thermal power generating unit of which the system is not provided with the carbon capture equipment;
∑PG,i,t,s CCSthe output of a thermal power generating unit with a carbon capture device is installed for the system;
Figure FDA00034608635300000311
and the modulation value is the modulation value of the new energy unit of the system.
11. The method of claim 4, wherein the node balancing constraint formula is:
Figure FDA0003460863530000041
in the formula (I), the compound is shown in the specification,
n (b) a set of a series of devices connected for node b;
s (l) and r (l) respectively represent a sending end node and a receiving end node of a line l;
Figure FDA0003460863530000042
the output of other power generation equipment i except the new energy station connected with the node b;
Figure FDA0003460863530000043
is the modulation value of the new energy electric field w;
Figure FDA0003460863530000044
is the current of line l;
Figure FDA0003460863530000045
a loss load that is an electrical load d;
Figure FDA0003460863530000046
representing the actual load of the electrical load d.
12. The method of claim 4, wherein the candidate line flow constraint formula is:
Figure FDA0003460863530000047
Figure FDA0003460863530000048
in the formula (I), the compound is shown in the specification,
yltis 0 or 1, belonging to decision variables;
Xlrepresents the line reactance of line l;
m is a sufficiently large number;
Figure FDA0003460863530000049
is the current of line l;
Pl maxis the upper current limit of the line l;
CL represents a candidate line set;
Figure FDA00034608635300000410
and
Figure FDA00034608635300000411
the phase angles of the transmitting node and the receiving node of the line l are respectively, and the range of the phase angle of the node is
Figure FDA00034608635300000412
Figure FDA00034608635300000413
The upper limit of the phase angle at node b.
13. The method of claim 4, wherein the existing line flow constraint formula is:
Figure FDA00034608635300000414
Figure FDA00034608635300000415
in the formula (I), the compound is shown in the specification,
Figure FDA0003460863530000051
is the current of line l;
Xlrepresents the line reactance of line l;
Figure FDA0003460863530000052
and
Figure FDA0003460863530000053
respectively, of line lThe phase angle of the end node and the receiving node is within the range of
Figure FDA0003460863530000054
Figure FDA0003460863530000055
Is the upper limit of the phase angle of node b;
EL denotes the existing line set;
Pl maxis the upper current limit of the line l.
14. The method according to claim 4, wherein the reactive power constraint equation of the reactive power compensation device is:
Figure FDA0003460863530000056
in the formula (I), the compound is shown in the specification,
Figure FDA0003460863530000057
providing reactive power for a reactive power compensation device of the node i at the moment t;
Figure FDA0003460863530000058
the new energy source unit of the node i needs reactive power at the time t.
15. The utility model provides a novel power system source net is planning in coordination which characterized in that includes:
the determining module is used for determining inertia support constraint and reactive power balance constraint of the power system according to the operating characteristics of the thermal power generating unit and the operating characteristics of the new energy source unit;
the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing a planning model of the power system according to a planning target of the power system and the inertia support constraint and the reactive power 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.
16. The apparatus of claim 15, wherein 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 the reactive power output by the live power set in the system, the reactive power output by the new energy set in the system, the reactive power output by the reactive power compensation device in the system, the total reactive load of the system, the total network reactive loss of the system and the reactive power reserve of the system.
17. The apparatus of claim 16, wherein 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 a planning total cost function, an inertia support constraint formula and a reactive power balance constraint formula of the power system.
18. The apparatus of claim 17, wherein the building module builds the planning model of the power system according to a planning total cost function of the power system, an inertia support constraint equation, and a reactive balance constraint equation, comprising:
and establishing a planning model of the power system according to a planning total cost function, an inertia support 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 constraint formula of a reactive power compensation device of the power system.
19. The apparatus of claim 17 or 18, wherein the solving module is configured to solve the established planning model of the power system by using a data-driven robust optimization method, and the obtaining of the planned power system comprises:
according to historical output data of an existing 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 ellipsoid set to obtain an uncertain set of the historical output limit scene of the new energy station;
and substituting the output of the new energy station in the uncertain set under a historical output limit scene and the load 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.
20. 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 steps of the method of any of claims 1 to 14 are implemented when the computer program is executed by the processor.
21. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 14.
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