CN112085362A - New energy power distribution network planning system considering flexible resource adjustment potential - Google Patents

New energy power distribution network planning system considering flexible resource adjustment potential Download PDF

Info

Publication number
CN112085362A
CN112085362A CN202010894511.8A CN202010894511A CN112085362A CN 112085362 A CN112085362 A CN 112085362A CN 202010894511 A CN202010894511 A CN 202010894511A CN 112085362 A CN112085362 A CN 112085362A
Authority
CN
China
Prior art keywords
power
new energy
typical
scene
distribution network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010894511.8A
Other languages
Chinese (zh)
Other versions
CN112085362B (en
Inventor
丁岩
任洲洋
李秋燕
孙义豪
姜云鹏
罗潇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
Original Assignee
Chongqing University
Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University, Economic and Technological Research Institute of State Grid Henan Electric Power Co Ltd filed Critical Chongqing University
Priority to CN202010894511.8A priority Critical patent/CN112085362B/en
Publication of CN112085362A publication Critical patent/CN112085362A/en
Application granted granted Critical
Publication of CN112085362B publication Critical patent/CN112085362B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/388—Islanding, i.e. disconnection of local power supply from the network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure

Abstract

The invention discloses a new energy distribution network planning system considering flexible resource adjustment potential, which comprises a data acquisition module, a typical scene generation module, a new energy planning module and a database, wherein the data acquisition module is used for acquiring a typical scene; the invention excavates the flexibility resource adjustment potential to optimize the network flow, and improves the flexibility of the power grid, thereby improving the new energy installed capacity of the power grid. In addition, the N-1 expected fault state of the system operation is simulated in the new energy power supply planning stage, and the safety of the system operation is effectively guaranteed. The model and the method provided by the invention can provide good guidance for future high-proportion new energy development planning.

Description

New energy power distribution network planning system considering flexible resource adjustment potential
Technical Field
The invention relates to the field of new energy power systems, in particular to a new energy power distribution network planning system considering flexible resource adjustment potential.
Background
With the increasing development of fossil energy crisis and environmental pollution problems, renewable energy sources such as wind energy, solar energy and the like are widely developed and utilized. However, the unreasonable access of the new energy power supply will also have adverse effects on the safe operation of the power grid, resulting in the problems of voltage out-of-limit, short-circuit current increase, line overload and the like. The influence degree of the new energy power supply on the operation of the power distribution network and the capacity of the new energy power supply connected to the power distribution network are closely related, so that in order to avoid negative influence of the new energy power supply connected to the power distribution network, reasonable planning needs to be carried out on wind power and photovoltaic power supplies connected to the power distribution network.
The existing research aims at economy and technology to carry out comprehensive research on new energy power supply planning, but the flexibility of water, electricity and a network frame is not comprehensively considered in the planning, the flexibility resource adjustment potential in a power grid cannot be fully exploited, and the installed capacity of the new energy power supply is underestimated. In addition, the new energy power supply planning only considers the normal operation state of the system, and the N-1 safety criterion is an important criterion for power grid planning and is ignored in the new energy power supply planning, so that the new energy power supply planning result does not necessarily meet the safety requirement. Therefore, a new energy distribution network planning system considering flexible resource adjustment potential is lacked at present.
Disclosure of Invention
The invention aims to provide a new energy distribution network planning system considering flexible resource adjustment potential, which comprises a data acquisition module, a typical scene generation module, a new energy planning module and a database.
The data acquisition module acquires a typical day of power grid operation in each season in the past year, and divides the typical day of power grid operation into three periods of peak, flat and valley according to the load of the power grid.
And the data acquisition module sends three periods of peak, flat and valley of each power grid operation typical day to the typical scene generation module.
The typical scene generation module clusters power grid operation scenes in three periods of peak, flat and valley to obtain a plurality of typical scenes, and sends the typical scenes to the new energy planning module.
The steps for generating a typical scene are as follows:
1) sampling and analyzing the typical daily source load power data, and respectively establishing original scenes in each time period of a peak, a flat and a valley by taking the sampling time as a sequence, wherein the original scenes are shown as a formula (1); and setting the number of clusters, fuzzy parameters and an iteration termination threshold value, initializing a cluster center V, and making the iteration number b equal to 0.
Figure BDA0002658011080000021
In the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000022
and
Figure BDA0002658011080000023
respectively an original scene in a peak-valley period;
Figure BDA0002658011080000024
and
Figure BDA0002658011080000025
respectively obtaining source load power at the nth sampling point at the sampling time t in the peak-to-valley period, wherein n is 1,2, …, k, and k is the total number of the sampling points; t istop、TlevAnd TlowRespectively, the total number of sampling instants within the peak-to-valley plateau period.
2) And calculating a membership matrix and updating a clustering center. If V | |b+1-VbIf | is less, stopping iteration; otherwise, let b be b +1, go back to step 2), and continue the iteration. Vb+1Is the cluster center at b +1 iteration, VbIs the cluster center at the b-th iteration.
3) Output Vtop、VlevAnd VlowClass Vtop、VlevAnd VlowIndividual cluster centers and scene occurrence probability.
The new energy planning module stores a new energy planning model considering flexibility.
Target function maxS of new energy planning model considering flexibilityenergyAs follows:
Figure BDA0002658011080000026
in the formula, Cwind.iAnd Csolar.jRespectively the installed capacity of the ith wind power plant and the jth photovoltaic power plant. N is a radical ofwindAnd NsolarThe total number of the wind power plant and the total number of the photovoltaic power station in the power distribution network.
The constraint conditions of the new energy planning model considering the flexibility comprise power balance constraint, wind and light resource constraint, hydropower station operation constraint, network topology constraint, gateway power constraint, normal operation state constraint and N-1 safety constraint.
Wherein the power balance constraint is as follows:
Figure BDA0002658011080000027
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000028
and
Figure BDA0002658011080000029
and the power values are respectively the power values output and received by the superior power grid to the power distribution network in the s-th scene.
Figure BDA00026580110800000210
And
Figure BDA00026580110800000211
and output per unit values of the ith wind power plant and the jth photovoltaic power station in the s scene are respectively.
Figure BDA00026580110800000212
And
Figure BDA00026580110800000213
and the power values of the ith hydropower station, the ith thermal power plant and the ith load under the s scene are respectively. N is a radical ofwt、NtherAnd NloadRespectively the total number of hydropower stations, thermal power plants and loads in the power distribution network.
The wind and light resource constraints are as follows:
Figure BDA0002658011080000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000032
and
Figure BDA0002658011080000033
the maximum load-bearing installed scale of wind and light resources of the power distribution network is respectively.
The hydropower station operation constraints are as follows:
Figure BDA0002658011080000034
where η is the total efficiency of the hydropower station.
Figure BDA0002658011080000035
And
Figure BDA0002658011080000036
respectively the water head height and the generating flow of the ith hydropower station under the s scene.
Figure BDA0002658011080000037
And
Figure BDA0002658011080000038
the water inflow and the water abandonment of the ith hydropower station under the s scene are respectively.
Figure BDA0002658011080000039
And
Figure BDA00026580110800000310
respectively the minimum and maximum capacity of the ith hydropower station reservoir.
Figure BDA00026580110800000311
The initial reservoir capacity of the ith hydropower station under the s scene is respectively.
Figure BDA00026580110800000312
And
Figure BDA00026580110800000313
the upper limit and the lower limit of the generating flow of the ith hydropower station are respectively.
The network topology constraints are as follows:
T∈Tτ。 (6)
in the formula, T is the current operation structure of the power distribution networkτTo satisfy the radial grid structure set.
The gateway power constraint is as follows:
Figure BDA00026580110800000314
in the formula (I), the compound is shown in the specification,
Figure BDA00026580110800000315
and
Figure BDA00026580110800000316
and respectively injecting upper and lower limits of the power distribution network for the superior power grid.
Figure BDA00026580110800000317
And
Figure BDA00026580110800000318
and respectively injecting upper and lower limits of power of a superior power grid into the power distribution network.
The normal operating conditions are constrained as follows:
Figure BDA00026580110800000319
in the formula (I), the compound is shown in the specification,
Figure BDA00026580110800000320
the apparent power flowing through branch i is the s-th scene in the normal operation state.
Figure BDA00026580110800000321
Is the transmission capacity limit of branch i.
The N-1 security constraint is as follows:
Figure BDA00026580110800000322
in the formula (I), the compound is shown in the specification,
Figure BDA00026580110800000323
the apparent power flowing through branch i for the s scenario in the f envisioned fault condition.
And the new energy planning module inputs the typical scene into the new energy planning model considering the flexibility, and calculates to obtain the maximum installed capacity of the new energy meeting the constraint conditions of the new energy planning model considering the flexibility.
And the new energy planning module calculates the maximum installed capacity of the new energy as CPLEX.
The database stores data of the data acquisition module, the typical scene generation module and the new energy planning module.
The technical effect of the invention is undoubted, and in order to promote the healthy and stable development of high-proportion new energy, the invention provides a new energy power distribution network planning system considering flexible resource adjustment potential, and the system can explore the flexible resource adjustment potential to optimize network trend, improve the flexibility of a power grid, and further improve the installed capacity of new energy of the power grid. In addition, the N-1 expected fault state of the system operation is simulated in the new energy power supply planning stage, and the safety of the system operation is effectively guaranteed. The model and the method provided by the invention can provide good guidance for future high-proportion new energy development planning.
Drawings
Fig. 1 is a schematic flow diagram of a new energy distribution network planning system taking into account flexible resource adjustment potential;
FIG. 2 is a grid structure of an actual power grid in a certain area;
FIG. 3(a) is a typical scene I generated by typical day clustering in spring;
FIG. 3(b) is a typical scene II generated by typical day clustering in spring;
FIG. 3(c) is a typical scene III generated by typical clustering of spring days;
FIG. 4(a) is a typical scene I generated by a summer typical day cluster;
FIG. 4(b) is a typical scene II generated by a summer typical day cluster;
FIG. 4(c) is a typical scene III generated by a summer typical day cluster;
FIG. 5(a) is a typical scene I generated by a typical day cluster in autumn;
FIG. 5(b) is a typical scene II generated by a typical autumn day cluster;
FIG. 5(c) is a typical scene III generated by a typical autumn day cluster;
FIG. 6(a) is a typical scene I generated by a winter typical daily cluster;
FIG. 6(b) is a typical scenario II generated by a winter typical daily cluster;
fig. 6(c) is a typical scene III generated by a winter typical daily cluster.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1 to 2, a new energy distribution network planning system considering flexible resource adjustment potential includes a data acquisition module, a typical scenario generation module, a new energy planning module, and a database.
The data acquisition module acquires a typical day of power grid operation in each season in the past year, and divides the typical day of power grid operation into three periods of peak, flat and valley according to the load of the power grid.
And the data acquisition module sends three periods of peak, flat and valley of each power grid operation typical day to the typical scene generation module.
The typical scene generation module clusters power grid operation scenes in three periods of peak, flat and valley to obtain a plurality of typical scenes, and sends the typical scenes to the new energy planning module.
The steps for generating a typical scene are as follows:
1) sampling and analyzing the typical daily source load power data, and respectively establishing original scenes in each time period of a peak, a flat and a valley by taking the sampling time as a sequence, wherein the original scenes are shown as a formula (1); and setting the number of clusters, fuzzy parameters and an iteration termination threshold value, initializing a cluster center V, and making the iteration number b equal to 0.
Figure BDA0002658011080000051
In the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000052
and
Figure BDA0002658011080000053
respectively an original scene in a peak-valley period;
Figure BDA0002658011080000054
and
Figure BDA0002658011080000055
respectively obtaining source load power at the nth sampling point at the sampling time t in the peak-to-valley period, wherein n is 1,2, …, k, and k is the total number of the sampling points; t istop、TlevAnd TlowRespectively, the total number of sampling instants within the peak-to-valley plateau period.
2) And calculating a membership matrix and updating a clustering center. If V | |b+1-VbIf | is less, stopping iteration; otherwise, let b be b +1, go back to step 2), and continue the iteration. Vb+1Is the cluster center at b +1 iteration, VbIs the cluster center at the b-th iteration. And | | represents a norm.
3) Output Vtop、VlevAnd VlowClass Vtop、VlevAnd VlowThe cluster centers and the probability of occurrence of each scene in each cluster.
The new energy planning module stores a new energy planning model considering flexibility.
The objective function of the new energy planning model taking into account the flexibility is as follows:
Figure BDA0002658011080000056
in the formula, Cwind.iAnd Csolar.jRespectively the installed capacity of the ith wind power plant and the jth photovoltaic power plant. N is a radical ofwindAnd NsolarThe total number of the wind power plant and the total number of the photovoltaic power station in the power distribution network. MaxSenergyIs the maximum output energy.
The constraint conditions of the new energy planning model considering the flexibility comprise power balance constraint, wind and light resource constraint, hydropower station operation constraint, network topology constraint, gateway power constraint, normal operation state constraint and N-1 safety constraint.
Wherein the power balance constraint is as follows:
Figure BDA0002658011080000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000062
and
Figure BDA0002658011080000063
and the power values are respectively the power values output and received by the superior power grid to the power distribution network in the s-th scene.
Figure BDA0002658011080000064
And
Figure BDA0002658011080000065
and output per unit values of the ith wind power plant and the jth photovoltaic power station in the s scene are respectively.
Figure BDA0002658011080000066
And
Figure BDA0002658011080000067
and the power values of the ith hydropower station, the ith thermal power plant and the ith load under the s scene are respectively. N is a radical ofwt、NtherAnd NloadRespectively the total number of hydropower stations, thermal power plants and loads in the power distribution network.
The wind and light resource constraints are as follows:
Figure BDA0002658011080000068
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000069
and
Figure BDA00026580110800000610
the maximum load-bearing installed scale of wind and light resources of the power distribution network is respectively.
The hydropower station operation constraints are as follows:
Figure BDA00026580110800000611
where η is the total efficiency of the hydropower station.
Figure BDA00026580110800000612
And
Figure BDA00026580110800000613
respectively the water head height and the generating flow of the ith hydropower station under the s scene.
Figure BDA00026580110800000614
And
Figure BDA00026580110800000615
the water inflow and the water abandonment of the ith hydropower station under the s scene are respectively.
Figure BDA00026580110800000616
And
Figure BDA00026580110800000617
respectively the minimum and maximum capacity of the ith hydropower station reservoir.
Figure BDA00026580110800000618
The initial reservoir capacity of the ith hydropower station under the s scene is respectively.
Figure BDA00026580110800000619
And
Figure BDA00026580110800000620
the upper limit and the lower limit of the generating flow of the ith hydropower station are respectively.
The network topology constraints are as follows:
T∈Tτ。 (6)
in the formula, T is the current operation structure of the power distribution networkτTo satisfy the radial grid structure set.
The gateway power constraint is as follows:
Figure BDA0002658011080000071
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000072
and
Figure BDA0002658011080000073
and respectively injecting upper and lower limits of the power distribution network for the superior power grid.
Figure BDA0002658011080000074
And
Figure BDA0002658011080000075
and respectively injecting upper and lower limits of power of a superior power grid into the power distribution network.
The normal operating conditions are constrained as follows:
Figure BDA0002658011080000076
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000077
the apparent power flowing through branch i is the s-th scene in the normal operation state.
Figure BDA0002658011080000078
Is the transmission capacity limit of branch i.
The N-1 security constraint is as follows:
Figure BDA0002658011080000079
in the formula (I), the compound is shown in the specification,
Figure BDA00026580110800000710
the apparent power flowing through branch i for the s scenario in the f envisioned fault condition.
And the new energy planning module inputs the typical scene into the new energy planning model considering the flexibility, and calculates to obtain the maximum installed capacity of the new energy meeting the constraint conditions of the new energy planning model considering the flexibility.
And the new energy planning module calculates the maximum installed capacity of the new energy as CPLEX.
The database stores data of the data acquisition module, the typical scene generation module and the new energy planning module.
The database is stored in a computer readable storage medium.
Example 2:
a method for using a new energy distribution network planning system considering flexible resource adjustment potential mainly comprises the following steps:
1) based on the actual running state of the power distribution network, typical days are selected in spring, summer, autumn and winter respectively, and each typical day is divided into peak time periods, flat time periods and valley time periods.
Further, typical day selection steps in spring, summer, autumn and winter seasons and the main steps of typical day peak, plateau and valley time period division are as follows:
1.1) based on regularity and similarity presented by power output and load requirements in the same season, respectively selecting a typical day in spring, summer, autumn and winter to reflect the annual operation state of the power grid.
1.2) dividing each typical day into peak, flat and valley periods according to the load state in the typical day of different seasons.
2) And clustering the scenes in the peak period, the flat period and the valley period into a specified number of typical scenes based on a fuzzy C-means clustering algorithm.
Further, the main steps of generating a typical scene in typical daily peak, plateau and valley periods are as follows:
2.1) sampling and analyzing the typical daily load power data, and establishing original scenes in each time period of a peak, a plateau and a valley by taking the sampling time as a sequence, wherein the original scenes are shown as a formula (1); and setting the number of clusters, fuzzy parameters and an iteration termination threshold value, initializing a cluster center V, and making the iteration number b equal to 0.
Figure BDA0002658011080000081
In the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000082
and
Figure BDA0002658011080000083
respectively an original scene in a peak-valley period;
Figure BDA0002658011080000084
and
Figure BDA0002658011080000085
respectively obtaining source load power at the nth sampling point at the sampling time t in the peak-to-valley period, wherein n is 1,2, …, k, and k is the total number of the sampling points; t istop、TlevAnd TlowRespectively, the total number of sampling instants within the peak-to-valley plateau period.
2.2) calculating a membership matrix and updating a clustering center. If V | |b+1-VbIf | is less, stopping iteration; otherwise, returning to the step 2, and continuing to iterate.
2.3) output Vtop、VlevAnd VlowClass Vtop、VlevAnd VlowIndividual cluster centers and scene occurrence probability.
3) And establishing a new energy planning model considering flexibility by taking the maximum installed capacity of the wind power and the photovoltaic of the power distribution network as a target function and comprehensively considering the normal running state and the N-1 predicted fault state of the power grid.
3.1) the objective function is as follows:
Figure BDA0002658011080000086
in the formula, Cwind.iAnd Csolar.jRespectively setting the installed capacities of the ith wind power plant and the jth photovoltaic power station; n is a radical ofwindAnd NsolarThe total number of the wind power plant and the total number of the photovoltaic power station in the power distribution network.
3.2) the constraints are respectively expressed by the formula (3) to the formula (9):
3.2.1) Power balance constraints:
Figure BDA0002658011080000087
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000091
and
Figure BDA0002658011080000092
the power values are respectively output and received by a superior power grid to a power distribution network in an s-th scene;
Figure BDA0002658011080000093
and
Figure BDA0002658011080000094
output per unit values of the ith wind power plant and the jth photovoltaic power station in the s scene are respectively;
Figure BDA0002658011080000095
and
Figure BDA0002658011080000096
the power values of the ith hydropower station, the ith thermal power plant and the ith load under the s scene are respectively; n is a radical ofwt、NtherAnd NloadRespectively the total number of hydropower stations, thermal power plants and loads in the power distribution network.
3.2.2) wind, light resource constraints:
the total capacity of wind power installation and the total capacity of photovoltaic installation in the power distribution network are not more than the constraint of available wind and light resources in a local area:
Figure BDA0002658011080000097
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000098
and
Figure BDA0002658011080000099
the maximum load-bearing installed scale of wind and light resources of the power distribution network is respectively.
3.2.3) hydroelectricity constraint:
the hydropower station has the advantages of convenience in adjustment, low cost and high flexibility, the output of the hydropower station is closely related to the power generation flow, the output of the hydropower station can be flexibly adjusted by controlling the power generation flow of the hydropower station, the adverse effect of wind and light output fluctuation on a power grid is favorably reduced, and the operation constraint of the hydropower station is as follows:
Figure BDA00026580110800000910
in the formula, eta is the total efficiency of the hydropower station;
Figure BDA00026580110800000911
and
Figure BDA00026580110800000912
respectively the water head height and the power generation flow of the ith hydropower station in the s scene;
Figure BDA00026580110800000913
and
Figure BDA00026580110800000914
the water inflow and the water abandonment of the ith hydropower station under the s scene are respectively carried out;
Figure BDA00026580110800000915
and
Figure BDA00026580110800000916
the minimum and maximum capacities of the ith hydropower station reservoir, respectively;
Figure BDA00026580110800000917
the initial capacity of a reservoir of the ith hydropower station in the s scene is respectively set;
Figure BDA00026580110800000918
and
Figure BDA00026580110800000919
the upper limit and the lower limit of the generating flow of the ith hydropower station are respectively.
3.2.4) network topology constraints:
the power distribution network is a radial connectivity network under each scene, and has no isolated island or looped network, and the related constraints are as follows:
T∈Tτ。 (6)
in the formula, T is the current operation structure of the power distribution networkτTo satisfy the radial grid structure set.
3.2.5) gateway power constraints:
in actual operation of the power distribution network, the gateway power changes constantly, and in order to suppress influence of gateway power fluctuation on a superior power grid, it is necessary to limit the gateway exchange power of a root node of the power distribution network, and relevant constraints are as follows:
Figure BDA0002658011080000101
in the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000102
and
Figure BDA0002658011080000103
respectively injecting upper and lower limits of power of the power distribution network for the superior power grid;
Figure BDA0002658011080000104
and
Figure BDA0002658011080000105
and respectively injecting upper and lower limits of power of a superior power grid into the power distribution network.
3.2.6) branch constraint:
in order to ensure safe operation of the power grid, the transmission power of the line in the N and N-1 states needs to be smaller than the allowed maximum value, and the related constraints are as follows:
normal operating condition constraint
Figure BDA0002658011080000106
In the formula (I), the compound is shown in the specification,
Figure BDA0002658011080000107
the apparent power flowing through the branch i in the s scene under the normal operation state;
Figure BDA0002658011080000108
is the transmission capacity limit of branch i;
n-1 safety constraints
Figure BDA0002658011080000109
In the formula (I), the compound is shown in the specification,
Figure BDA00026580110800001010
the apparent power flowing through branch i for the s scenario in the f envisioned fault condition.
4) Based on a typical scene and an optimization model, the flexibility of hydropower and a network frame is considered, a planning model is solved by using commercial software CPLEX, and the maximum installed capacity of new energy meeting the operation safety constraint of the power distribution network is screened out.
Example 3:
referring to fig. 3 to 6, an experiment of a new energy distribution network planning system for verifying and considering flexible resource adjustment potential mainly includes the following steps:
1) and (3) carrying out simulation analysis by taking the actual power grid of a certain area as an example to verify the effectiveness of the method provided by the invention. As shown in fig. 2, the regional power grid has 56 nodes, and includes 11 substations, wherein both the substation T1 and the substation T2 are 500kV/220kV substations; the transformer substations T3-T11 are all 220kV/110kV transformer substations, and the maximum load power is 2021 MW. The power grid comprises 1 thermal power plant, 8 installed wind power plants, 11 photovoltaic power stations and 3 hydraulic power stations, and connection points and installed capacities of the power stations are shown in table 1. S1-S6 are wind power plants and photovoltaic power plants to be planned, the dotted lines are connecting lines, switches are arranged on all branches, and the grid structure can be flexibly changed by controlling the on-off of the branch switches.
TABLE 1 installed capacities of wind farms and photovoltaic plants
Figure BDA0002658011080000111
2) And clustering the source load actual data in the typical daily peak valley period of different seasons by using a fuzzy C-means clustering method, generating typical scenes in the typical daily peak valley period of different seasons, and obtaining the probability of each typical scene.
Selecting typical days in spring, summer, autumn and winter. And dividing each typical day into three periods of peak-valley and valley-valley by combining the actual operation condition of the power distribution network, and clustering the actual load data in the peak-valley and valley-valley periods of the typical days in different seasons by adopting a fuzzy C-means clustering method. By comprehensively considering the solving efficiency and the complexity of the model, the method generates 3 types of typical scenes in a clustering mode in a typical daily peak-valley period respectively. Table 2 gives the peak-to-valley period division results for a typical day in different seasons. Taking the peak period of a typical winter day as an example, fig. 3 to 6 show typical scene clustering results in the period.
TABLE 2 typical time division in the spring, summer, fall and winter seasons
Figure BDA0002658011080000112
3) The maximum new energy access capacity of the power grid is an optimization target, and the new energy access capacity of the power distribution network is obtained by utilizing CPLEX optimization solution based on optimization scheduling of comprehensive consideration of flexible resources and a power grid new energy planning model of a system normal operation state and an N-1 predicted fault state.
Based on a test network and typical scene data, the flexibility adjustment potential of the water and the grid in the power grid is mined, the normal operation state of the system and the N-1 expected fault state are calculated, the installed capacities of the wind power and the photovoltaic power are obtained through optimization solution, and the calculation results are shown in table 3.
TABLE 3 planning results
New energy electric field S1 S2 S3 S4 S5 S6
Installed capacity (MW) 149 249 162 177 129 136

Claims (4)

1. A new energy distribution network planning system considering flexible resource adjustment potential is characterized by comprising a data acquisition module, the typical scene generation module, a new energy planning module and a database.
The data acquisition module acquires a typical grid operation day of each season in the past year, and divides the typical grid operation day into three periods of peak, flat and valley according to the load of the grid;
the data acquisition module sends three periods of peak, flat and valley of each power grid operation typical day to the typical scene generation module;
the typical scene generation module clusters power grid operation scenes in three periods of peak, flat and valley to obtain a plurality of typical scenes, and sends the typical scenes to the new energy planning module;
the new energy planning module stores a new energy planning model considering flexibility;
the new energy planning module inputs a typical scene into the new energy planning model considering the flexibility, and calculates to obtain the maximum installed capacity of new energy meeting the constraint conditions of the new energy planning model considering the flexibility;
the database stores data of the data acquisition module, the typical scene generation module and the new energy planning module.
2. The new energy distribution network planning system considering the flexible resource adjustment potential according to claim 1 or 2, wherein the step of generating the typical scenario is as follows:
1) sampling and analyzing the typical daily source load power data, and respectively establishing original scenes in each time period of a peak, a flat and a valley by taking the sampling time as a sequence, wherein the original scenes are shown as a formula (1); and setting the number of clusters, fuzzy parameters and an iteration termination threshold value, initializing a cluster center V, and making the iteration number b equal to 0.
Figure FDA0002658011070000011
In the formula (I), the compound is shown in the specification,
Figure FDA0002658011070000012
and
Figure FDA0002658011070000013
respectively an original scene in a peak-valley period;
Figure FDA0002658011070000014
and
Figure FDA0002658011070000015
respectively obtaining source load power at the nth sampling point at the sampling time t in the peak-to-valley period, wherein n is 1,2, …, k, and k is the total number of the sampling points; t istop、TlevAnd TlowRespectively, the total number of sampling instants within the peak-to-valley plateau period.
2) And calculating a membership matrix and updating a clustering center. If V | |b+1-VbIf | is less, stopping iteration; otherwise, returning to the step 2) by changing b to b +1, and continuing the iteration; vb+1Is the cluster center at b +1 iteration, VbThe cluster center of the b iteration is obtained;
3) output Vtop、VlevAnd VlowClass Vtop、VlevAnd VlowIndividual cluster centers and scene occurrence probability.
3. The new energy distribution network planning system considering flexible resource adjustment potential as claimed in claim 1, wherein the objective function maxS of the new energy planning model considering flexibility isenergyAs follows:
Figure FDA0002658011070000021
in the formula, Cwind.iAnd Csolar.jRespectively setting the installed capacities of the ith wind power plant and the jth photovoltaic power station; n is a radical ofwindAnd NsolarRespectively the total number of the wind power plant and the photovoltaic power station in the power distribution network;
constraint conditions of the new energy planning model considering the flexibility comprise power balance constraint, wind and light resource constraint, hydropower station operation constraint, network topology constraint, gateway power constraint, normal operation state constraint and N-1 safety constraint;
wherein the power balance constraint is as follows:
Figure FDA0002658011070000022
in the formula (I), the compound is shown in the specification,
Figure FDA0002658011070000023
and
Figure FDA0002658011070000024
the power values are respectively output and received by a superior power grid to a power distribution network in an s-th scene;
Figure FDA0002658011070000025
and
Figure FDA0002658011070000026
output per unit values of the ith wind power plant and the jth photovoltaic power station in the s scene are respectively;
Figure FDA0002658011070000027
and
Figure FDA0002658011070000028
the power values of the ith hydropower station, the ith thermal power plant and the ith load under the s scene are respectively; n is a radical ofwt、NtherAnd NloadRespectively the total number of hydropower stations, thermal power plants and loads in the power distribution network.
The wind and light resource constraints are as follows:
Figure FDA0002658011070000029
in the formula (I), the compound is shown in the specification,
Figure FDA00026580110700000210
and
Figure FDA00026580110700000211
the maximum bearable installed scale of wind and light resources of the power distribution network is respectively;
the hydropower station operation constraints are as follows:
Figure FDA00026580110700000212
in the formula, eta is the total efficiency of the hydropower station;
Figure FDA00026580110700000213
and
Figure FDA00026580110700000214
respectively the water head height and the power generation flow of the ith hydropower station in the s scene;
Figure FDA00026580110700000215
and
Figure FDA00026580110700000216
the water inflow and the water abandonment of the ith hydropower station under the s scene are respectively carried out;
Figure FDA00026580110700000217
and
Figure FDA00026580110700000218
are respectively the ithMinimum and maximum capacities of hydropower station reservoirs;
Figure FDA00026580110700000219
the initial capacity of a reservoir of the ith hydropower station in the s scene is respectively set;
Figure FDA0002658011070000031
and
Figure FDA0002658011070000032
the upper limit and the lower limit of the generating flow of the ith hydropower station are respectively set;
the network topology constraints are as follows:
T∈Tτ； (6)
in the formula, T is the current operation structure of the power distribution networkτTo meet the radial power grid structure set;
the gateway power constraint is as follows:
Figure FDA0002658011070000033
in the formula (I), the compound is shown in the specification,
Figure FDA0002658011070000034
and
Figure FDA0002658011070000035
respectively injecting upper and lower limits of power of the power distribution network for the superior power grid;
Figure FDA0002658011070000036
and
Figure FDA0002658011070000037
injecting upper and lower limits of power of a superior power grid into the power distribution network respectively;
the normal operating conditions are constrained as follows:
Figure FDA0002658011070000038
in the formula (I), the compound is shown in the specification,
Figure FDA0002658011070000039
the apparent power flowing through the branch i in the s scene under the normal operation state;
Figure FDA00026580110700000310
is the transmission capacity limit of branch i;
the N-1 security constraint is as follows:
Figure FDA00026580110700000311
in the formula (I), the compound is shown in the specification,
Figure FDA00026580110700000312
the apparent power flowing through branch i for the s scenario in the f envisioned fault condition.
4. The new energy distribution network planning system considering the flexible resource adjustment potential of claim 1, wherein the new energy planning module calculates the maximum installed capacity of new energy by using CPLEX.
CN202010894511.8A 2020-08-31 2020-08-31 New energy power distribution network planning system considering flexible resource adjustment potential Active CN112085362B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010894511.8A CN112085362B (en) 2020-08-31 2020-08-31 New energy power distribution network planning system considering flexible resource adjustment potential

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010894511.8A CN112085362B (en) 2020-08-31 2020-08-31 New energy power distribution network planning system considering flexible resource adjustment potential

Publications (2)

Publication Number Publication Date
CN112085362A true CN112085362A (en) 2020-12-15
CN112085362B CN112085362B (en) 2023-04-21

Family

ID=73731220

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010894511.8A Active CN112085362B (en) 2020-08-31 2020-08-31 New energy power distribution network planning system considering flexible resource adjustment potential

Country Status (1)

Country Link
CN (1) CN112085362B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2796891A1 (en) * 2011-11-24 2013-05-24 Robert F. Cruickshank, Iii Power consumer side control system, method & apparatus
WO2013174145A1 (en) * 2012-05-23 2013-11-28 国家电网公司 Large-scale wind power grid-integration reactive voltage optimization method based on improved artificial fish swarm hybrid optimization algorithm
CN104376410A (en) * 2014-11-06 2015-02-25 国家电网公司 Planning method for distributed power source in power distribution network
CN108830479A (en) * 2018-06-12 2018-11-16 清华大学 It is a kind of meter and the full cost chain of power grid master match collaborative planning method
CN109325608A (en) * 2018-06-01 2019-02-12 国网上海市电力公司 Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness
CN110729767A (en) * 2019-10-09 2020-01-24 广东电网有限责任公司阳江供电局 Water-electricity-containing regional power grid wind-solar capacity optimal configuration method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2796891A1 (en) * 2011-11-24 2013-05-24 Robert F. Cruickshank, Iii Power consumer side control system, method & apparatus
WO2013174145A1 (en) * 2012-05-23 2013-11-28 国家电网公司 Large-scale wind power grid-integration reactive voltage optimization method based on improved artificial fish swarm hybrid optimization algorithm
CN104376410A (en) * 2014-11-06 2015-02-25 国家电网公司 Planning method for distributed power source in power distribution network
CN109325608A (en) * 2018-06-01 2019-02-12 国网上海市电力公司 Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness
CN108830479A (en) * 2018-06-12 2018-11-16 清华大学 It is a kind of meter and the full cost chain of power grid master match collaborative planning method
CN110729767A (en) * 2019-10-09 2020-01-24 广东电网有限责任公司阳江供电局 Water-electricity-containing regional power grid wind-solar capacity optimal configuration method

Also Published As

Publication number Publication date
CN112085362B (en) 2023-04-21

Similar Documents

Publication Publication Date Title
Li et al. Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization
CN106786799B (en) Power stepped power generation plan optimization method for direct current connecting line
CN110224393B (en) New energy consumption assessment method based on minimum load shedding model
JP7153289B1 (en) Low-carbon CSP system collaborative optimization method and apparatus based on cluster learning
CN113489003B (en) Source network coordination planning method considering wind-light-water integrated complementary operation
Kong et al. Optimization of the hybrid solar power plants comprising photovoltaic and concentrating solar power using the butterfly algorithm
CN110676849B (en) Method for constructing islanding micro-grid group energy scheduling model
CN104578183A (en) Tie-line power transmission plan optimization method for improving low-carbon level of electric system
CN110994606A (en) Multi-energy power supply capacity configuration method based on complex adaptive system theory
CN111144655A (en) Combined optimization method for site selection, volume fixing and power distribution network frame of distributed power supply
Javadi et al. Optimal planning and operation of hybrid energy system supplemented by storage devices
CN114069687A (en) Distributed photovoltaic planning method considering reactive power regulation effect of inverter
CN116402210A (en) Multi-objective optimization method, system, equipment and medium for comprehensive energy system
Lakhoua et al. System Analysis of a Hybrid Renewable Energy System.
CN110729767A (en) Water-electricity-containing regional power grid wind-solar capacity optimal configuration method
CN109726416B (en) Scheduling decision method based on new energy cluster prediction and load flow calculation
Liu et al. Increasing wind power penetration level based on hybrid wind and photovoltaic generation
CN112085362B (en) New energy power distribution network planning system considering flexible resource adjustment potential
Le et al. Design, sizing and operation of a hybrid renewable energy system for farming
CN109615114A (en) Based on the photovoltaic power generation assessment of economic benefit method grid-connected as centralized power
Amouzad Mahdiraji et al. Optimal in smart grids considering interruptible loads and photo-voltaic sources using genetic optimization
Zhang et al. Data-Driven Distributionally Robust Optimization-Based Coordinated Dispatching for Cascaded Hydro-PV-PSH Combined System
Zeng et al. Annual renewable energy planning platform: Methodology and design
Manalu et al. Techno-economic analysis of a microgrid system to increase electricity access in rural areas
CN109390970B (en) Island microgrid distributed control method and system based on multi-Agent communication network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant