CN108334990B - Reactive power compensation site selection and capacity optimization method and system for large power grid - Google Patents

Reactive power compensation site selection and capacity optimization method and system for large power grid Download PDF

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CN108334990B
CN108334990B CN201810145430.0A CN201810145430A CN108334990B CN 108334990 B CN108334990 B CN 108334990B CN 201810145430 A CN201810145430 A CN 201810145430A CN 108334990 B CN108334990 B CN 108334990B
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reactive
power
node
annual
determining
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CN108334990A (en
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熊浩清
易俊
张毅明
安军
戴飞
陈军
孙冉
张建立
姚峰
樊东峰
张锋
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
State Grid Henan Electric Power Co Ltd
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    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/82—Energy audits or management systems therefor
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a reactive power compensation site selection and capacity optimization method and system for a large power grid, which comprises the following steps: calculating an actually measured reactive power analysis index of the power grid by utilizing annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS), and determining whether to start annual reactive power optimization overall planning or not; determining starting annual reactive power optimization overall planning, carrying out statistics at the moment of lowest voltage margin, determining the typical operation mode of weakest power grid voltage, and carrying out network load flow calculation to determine a simulation database; determining reactive compensation site selection by using a reactive balance analysis method; calculating the initially selected reactive compensation capacity through voltage reactive sensitivity analysis, correcting the initially selected reactive compensation capacity according to the dynamic reactive reserve of the generator set corresponding to the node, and determining the corrected reactive compensation capacity; and determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the corrected reactive compensation capacity and annual power grid monitoring data counted by the wide area monitoring system WAMS.

Description

Reactive power compensation site selection and capacity optimization method and system for large power grid
Technical Field
The invention relates to the technical field of reactive power compensation automatic site selection of a large power grid, in particular to a reactive power compensation site selection and capacity optimization method and system for the large power grid.
Background
In 1961, the french scholars j.carpentier originally proposed an Optimal Power Flow (OPF) model of an electric Power system based on strict mathematical foundation, which is a prototype of modern reactive Power optimization, although no special research field for reactive Power optimization was formed. In 1968, by utilizing the physical weak coupling of a power transmission network, a scholars decouple the active power and the reactive power, the reactive optimal power flow is provided at the earliest, and the independent research on the reactive optimization problem is started. Later, the reactive power optimization problem is more and more emphasized by the academic and engineering circles.
In 1981, Thompson et al used an integer branch-and-bound method to perform substation optimization planning. Then, experts adopt a sensitivity analysis method based on Newton-Raphson load flow calculation to perform reactive power optimization calculation. Various optimization methods in operational research are almost researched, tried and applied to reactive power optimization calculation. Among the more classical algorithms are:
gradient-like algorithms, newton's method, quadratic programming method and linear programming method. The occurrence of artificial intelligence in the 90 s has made a great leap in reactive power optimization. Most of the existing reactive power planning methods use artificial intelligence algorithms, including modern heuristic search algorithms, expert systems, artificial neuron networks and the like. Especially, the modern heuristic algorithm obtains a great deal of research results in the application of the reactive power optimization problem of the power system, and the modern heuristic algorithm has the advantage that the robustness provides a reliable solution for the reactive power optimization problem. In recent years, experts and scholars have been dedicated to improving and perfecting algorithms, so that the artificial intelligence algorithms are more suitable for solving the problems after improvement, and currently, genetic algorithms, tabu search algorithms, particle swarm optimization and the like are commonly used.
With the rapid development of a scheduling control technology support System, especially the gradual optimization arrangement of Wide Area Measurement System (WAMS) integration and vector Measurement unit (Phase Measurement unit, PMU) provides an effective comprehensive technical means for the safe and stable analysis of reactive voltage. The existing reactive power planning optimization usually takes the minimum active network loss or comprehensive cost of a system as an objective function, meets the constraint of safety conditions through a given voltage range, determines a compensation place and compensation capacity, cannot realize automatic site selection, increases the workload of reactive power compensation manual calculation, and simultaneously has certain limitation on the efficiency and effect of reactive power planning due to the deviation of a simulation calculation tool model and an actual load mechanism.
Disclosure of Invention
The invention provides a large power grid reactive power compensation site selection and capacity optimization method and system, and aims to solve the problem of how to perform optimization calculation on the large power grid reactive power compensation site selection and capacity.
In order to solve the above problem, according to an aspect of the present invention, there is provided a large grid reactive power compensation addressing and capacity optimization method, the method including:
calculating an actual measurement power grid reactive power analysis index by utilizing annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS), and determining whether to start annual reactive power optimization overall planning or not according to the actual measurement power grid reactive power analysis index;
determining starting annual reactive power optimization overall planning, counting the annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, determining a typical operation mode of weakest power grid voltage, and performing network load flow calculation to determine a simulation database;
determining reactive compensation site selection by using a reactive balance analysis method;
calculating the initially selected reactive compensation capacity through voltage reactive sensitivity analysis, correcting the initially selected reactive compensation capacity according to the dynamic reactive reserve of the generator set corresponding to the node, and determining the corrected reactive compensation capacity;
and determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the corrected reactive compensation capacity and annual power grid monitoring data counted by the wide area monitoring system WAMS.
Preferably, the calculation formula of the actually measured grid reactive power analysis index is as follows:
Figure BDA0001578692690000021
wherein S is an actually measured reactive power analysis index of the power grid; n is the number of nodes in the electric network; | AvgVi,kL is the voltage vector average value of the ith node recorded by the k month wide area monitoring system WAMS; min | VkI is the per unit value of the lowest voltage vector of the monthly node of the power grid recorded by the k-th month wide area monitoring system WAMS; and lambda is a preset coefficient and is set according to experience.
Preferably, the determining whether to start an annual reactive power optimization overall plan according to the measured grid reactive power analysis index includes:
comparing the actual measurement power grid reactive power analysis index with a preset judgment threshold value, and starting annual reactive power optimization overall planning if the actual measurement power grid reactive power analysis index is smaller than the preset judgment threshold value; otherwise, the annual reactive power optimization overall planning is not started, wherein the preset judgment threshold is set according to experience.
Preferably, the counting is performed at the time of lowest voltage margin according to the annual power grid monitoring data counted by the wide area monitoring system WAMS, a typical operation mode of weakest power grid voltage is determined, and a simulation database is determined by performing network load flow calculation, and the method includes:
counting annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, calculating a typical operation mode index according to a typical operation mode index calculation formula, and selecting a typical operation mode corresponding to the minimum typical operation mode index as the typical operation mode with the weakest power grid voltage, wherein the typical operation mode index calculation formula is as follows:
St＝(∑|Vi,t|)/N+λt·Min(|Vi,t|) (2)
wherein St is a typical operation mode index; t is the standard point moment; n is the number of nodes in the electric network; vi,tRepresenting the magnitude of the voltage phase at the ith node at the time of the tth moment in the year; λ t is an adjustment coefficient, and is set according to experience;
and (5) performing network load flow calculation by using a power system analysis integration program PSASP to determine a simulation database.
Preferably, the determining reactive compensation addressing by using a reactive balance analysis method includes:
determining an automatic site selection index of each node in the electric network by using a reactive power balance analysis method;
and determining reactive compensation site selection of the nodes according to the automatic site selection indexes of each node.
Preferably, the determining an automatic site selection index of each node in the electric network by using a reactive balance analysis method includes:
MAi＝a·Mi+b·Ti (3)
Mivoltage fluctuation bus number ratio + lambdaiAmplitude average fluctuation level (4)
Figure BDA0001578692690000041
Ti′＝1/cosθij (6)
Ti＝Ti′/T max (7)
The node I is a node I, and a and b are weight coefficients respectively, wherein MAi is an automatic address selection index of the node I, and a + b is 1; mi is a voltage abnormal fluctuation level coefficient, namely a first index; the voltage fluctuation bus number ratio is the bus fluctuation number point of which the voltage fluctuation number exceeds a threshold value alpha divided by the total bus number detected by a PMU system of the vector measurement unit in the region; alpha is a numerical value given according to Chinese national standards and enterprise standards; lambda [ alpha ]iIs a preset modulation value; the amplitude average fluctuation level formula is the average level of the voltage abnormal fluctuation amplitude 1 in the jth area;
Figure BDA0001578692690000042
to the amplitude of the fluctuation, NJThe number of fluctuations in the jth sub-region; the lower power factor cos theta with Ti' as node iijThe reciprocal of (a); a second index of Ti; tmax is the maximum of a plurality of Ti', and node i is the lower power factor cos θijAnd the transformer is smaller than the corresponding preset lower power factor threshold value.
Preferably, the determining the reactive compensation capacity of the candidate node according to the reactive compensation site selection, the corrected reactive compensation capacity and the annual grid monitoring data counted by the wide area monitoring system WAMS includes:
and performing reactive power planning optimization simulation calculation by using a power system analysis integration program PSASP according to the reactive power compensation site selection, the corrected reactive power compensation capacity and the annual power grid monitoring data counted by the WAMS, and determining the reactive power compensation capacity of the alternative node.
According to another aspect of the invention, a large power grid reactive compensation site selection and capacity optimization system is provided, which comprises:
the annual reactive power optimization overall planning starting determining unit is used for calculating an actual measurement power grid reactive power analysis index by utilizing annual power grid monitoring data counted by a wide area monitoring system WAMS and determining whether to start the annual reactive power optimization overall planning or not according to the actual measurement power grid reactive power analysis index;
the system comprises a database establishing unit, a simulation database and a data processing unit, wherein the database establishing unit is used for determining starting annual reactive power optimization overall planning, counting the annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, determining the typical operation mode of weakest power grid voltage, and performing network load flow calculation to determine the simulation database;
the reactive compensation site selection determining unit is used for determining a reactive compensation site selection by using a reactive balance analysis method;
the corrected reactive compensation capacity determining unit is used for calculating the initially selected reactive compensation capacity through voltage reactive sensitivity analysis, correcting the initially selected reactive compensation capacity according to the dynamic reactive storage amount of the generator set corresponding to the node, and determining the corrected reactive compensation capacity;
and the reactive compensation capacity determining unit of the alternative node is used for determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the corrected reactive compensation capacity and annual power grid monitoring data counted by the wide area monitoring system WAMS.
Preferably, the following formula is used to calculate the reactive power analysis index of the measured power grid:
Figure BDA0001578692690000051
wherein S is an actually measured reactive power analysis index of the power grid; n is the number of nodes in the electric network; | AvgVi,kL is the voltage vector average value of the ith node recorded by the k month wide area monitoring system WAMS; min | VkI is the per unit value of the lowest voltage vector of the monthly node of the power grid recorded by the k-th month wide area monitoring system WAMS; and lambda is a preset coefficient and is set according to experience.
Preferably, the annual reactive power optimization overall planning starting determining unit, which determines whether to start the annual reactive power optimization overall planning according to the measured grid reactive power analysis indicator, includes:
comparing the actual measurement power grid reactive power analysis index with a preset judgment threshold value, and starting annual reactive power optimization overall planning if the actual measurement power grid reactive power analysis index is smaller than the preset judgment threshold value; otherwise, the annual reactive power optimization overall planning is not started, wherein the preset judgment threshold is set according to experience.
Preferably, the database establishing unit performs statistics at the time of lowest voltage margin according to annual power grid monitoring data counted by the wide area monitoring system WAMS, determines a typical operation mode of weakest power grid voltage, and performs network load flow calculation to determine the simulation database, and includes:
counting annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, calculating a typical operation mode index according to a typical operation mode index calculation formula, and selecting a typical operation mode corresponding to the minimum typical operation mode index as the typical operation mode with the weakest power grid voltage, wherein the typical operation mode index calculation formula is as follows:
St＝(∑|Vi,t|)/N+λt·Min(|Vi,t|) (2)
wherein St is a typical operation mode index; t is the standard point moment; n is the number of nodes in the electric network; vi,tRepresenting the magnitude of the voltage phase at the ith node at the time of the tth moment in the year; λ t is an adjustment coefficient, and is set according to experience;
and (5) performing network load flow calculation by using a power system analysis integration program PSASP to determine a simulation database.
Preferably, the reactive compensation site selection determining unit, which determines the reactive compensation site selection by using a reactive balance analysis method, includes:
determining an automatic site selection index of each node in the electric network by using a reactive power balance analysis method;
and determining reactive compensation site selection of the nodes according to the automatic site selection indexes of each node.
Preferably, the determining an automatic site selection index of each node in the electric network by using a reactive balance analysis method includes:
MAi＝a·Mi+b·Ti (3)
Mivoltage fluctuation bus number ratio + lambdaiAmplitude average fluctuation level (4)
Figure BDA0001578692690000061
Ti′＝1/cosθij (6)
Ti＝Ti′/T max (7)
The node I is a node I, and a and b are weight coefficients respectively, wherein MAi is an automatic address selection index of the node I, and a + b is 1; mi is a voltage abnormal fluctuation level coefficient, namely a first index; the voltage fluctuation bus number ratio is the bus fluctuation number point of which the voltage fluctuation number exceeds a threshold value alpha divided by the total bus number detected by a PMU system of the vector measurement unit in the region; alpha is a numerical value given according to Chinese national standards and enterprise standards; lambda [ alpha ]iIs a preset modulation value; the amplitude average fluctuation level formula is the average level of the voltage abnormal fluctuation amplitude 1 in the jth area;
Figure BDA0001578692690000062
to the amplitude of the fluctuation, NJThe number of fluctuations in the jth sub-region; the lower power factor cos theta with Ti' as node iijThe reciprocal of (a); a second index of Ti; tmax is the maximum of a plurality of Ti', and node i is the lower power factor cos θijAnd the transformer is smaller than the corresponding preset lower power factor threshold value.
Preferably, the determining unit of the reactive compensation capacity of the candidate node determines the reactive compensation capacity of the candidate node according to the reactive compensation site selection, the modified reactive compensation capacity and the annual grid monitoring data counted by the wide area monitoring system WAMS, and includes:
and performing reactive power planning optimization simulation calculation by using a power system analysis integration program PSASP according to the reactive power compensation site selection, the corrected reactive power compensation capacity and the annual power grid monitoring data counted by the WAMS, and determining the reactive power compensation capacity of the alternative node.
The invention provides a reactive power compensation addressing and capacity optimization method and system for a large power grid, which are characterized in that statistics is carried out at the moment of lowest voltage margin according to annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS), a typical operation mode of weakest power grid voltage is determined, and a simulation database is determined through network load flow calculation; determining the automatic site selection of reactive compensation by using a normalization processing method and different weight coefficients; calculating the initially selected reactive compensation capacity through voltage reactive sensitivity analysis, correcting and determining the corrected reactive compensation capacity; and determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the corrected reactive compensation capacity and annual power grid monitoring data counted by the wide area monitoring system WAMS. The invention realizes the automatic site selection of the reactive compensation, reduces the workload of the manual calculation of the reactive compensation, improves the efficiency and the effect of the reactive planning and improves the working efficiency of the reactive planning personnel.
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A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
fig. 1 is a flow chart of a large grid reactive compensation siting and capacity optimization method 100 according to an embodiment of the present invention; and
fig. 2 is a schematic structural diagram of a large power grid reactive power compensation site selection and capacity optimization system 20 according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Fig. 1 is a flowchart of a large power grid reactive compensation site selection and capacity optimization method 100 according to an embodiment of the present invention. As shown in fig. 1, according to the reactive power compensation addressing and capacity optimization method 100 for a large power grid provided by the embodiment of the present invention, statistics is performed at the time of lowest voltage margin according to annual power grid monitoring data counted by a wide area monitoring system WAMS, a typical operation mode of weakest power grid voltage is determined, and a simulation database is determined by performing network load flow calculation; determining the automatic site selection of reactive compensation by using a normalization processing method and different weight coefficients; calculating the initially selected reactive compensation capacity through voltage reactive sensitivity analysis, correcting and determining the corrected reactive compensation capacity; and determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the corrected reactive compensation capacity and the annual power grid monitoring data counted by the wide area monitoring system WAMS, so that the automatic site selection of the reactive compensation is realized, the workload of manual reactive compensation calculation is reduced, the efficiency and the effect of reactive planning are improved, and the working efficiency of reactive planning personnel is improved. The large power grid reactive power compensation site selection and capacity optimization method 100 provided by the embodiment of the invention starts from step 101, calculates the measured power grid reactive power analysis index by using the annual power grid monitoring data counted by the wide area monitoring system WAMS in step 101, and determines whether to start the annual reactive power optimization overall planning according to the measured power grid reactive power analysis index.
Preferably, the calculation formula of the actually measured grid reactive power analysis index is as follows:
Figure BDA0001578692690000081
wherein S is an actually measured reactive power analysis index of the power grid; n is the number of nodes in the electric network; | AvgVi,kL is the voltage vector average value of the ith node recorded by the k month wide area monitoring system WAMS; min | VkI is the per unit value of the lowest voltage vector of the monthly node of the power grid recorded by the k-th month wide area monitoring system WAMS; and lambda is a preset coefficient and is set according to experience.
Preferably, the determining whether to start an annual reactive power optimization overall plan according to the measured grid reactive power analysis index includes:
comparing the actual measurement power grid reactive power analysis index with a preset judgment threshold value, and starting annual reactive power optimization overall planning if the actual measurement power grid reactive power analysis index is smaller than the preset judgment threshold value; otherwise, the annual reactive power optimization overall planning is not started, wherein the preset judgment threshold is set according to experience.
Preferably, in step 102, an annual reactive power optimization overall plan is determined, statistics is performed at the moment of lowest voltage margin according to annual power grid monitoring data counted by the wide area monitoring system WAMS, a typical operation mode of weakest power grid voltage is determined, and a simulation database is determined through network load flow calculation.
Preferably, the counting is performed at the time of lowest voltage margin according to the annual power grid monitoring data counted by the wide area monitoring system WAMS, a typical operation mode of weakest power grid voltage is determined, and a simulation database is determined by performing network load flow calculation, and the method includes:
counting annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, calculating a typical operation mode index according to a typical operation mode index calculation formula, and selecting a typical operation mode corresponding to the minimum typical operation mode index as the typical operation mode with the weakest power grid voltage, wherein the typical operation mode index calculation formula is as follows:
St＝(∑|Vi,t|)/N+λt·Min(|Vi,t|) (2)
wherein St is a typical operation mode index; t is the standard point moment; n is the number of nodes in the electric network; vi,tRepresenting the magnitude of the voltage phase at the ith node at the time of the tth moment in the year; λ t is an adjustment coefficient, and is set according to experience;
and (5) performing network load flow calculation by using a power system analysis integration program PSASP to determine a simulation database.
In the implementation mode of the invention, according to annual power grid monitoring data of a WAMS system, a typical operation mode of weak power grid voltage is determined by counting at the time of lowest voltage margin, and relevant power grid parameters including relevant loads, generators, lines and transformers are input according to a PSASP7.1-PF power flow format to perform network power flow calculation to obtain a simulation database. The method specifically comprises the following steps:
step 1: determining a typical operation mode with weakest power grid voltage by adopting a WAMS statistical result, wherein the typical operation mode index calculation formula is as follows:
St＝(∑|Vi，t|)/N+λ·Min(|Vi，t|) (2)
selecting the minimum StAs a typical mode of operation for the weakest mains voltage. t is the standard point moment; n is the number of nodes in the electrical network, Vi,tRepresenting the magnitude of the voltage at the ith node at the time t of the year. Lambda is an adjustment coefficient and is set manually.
Step 2: and inputting related load, generator, line and transformer parameters according to the PSASP7.1-PF load flow format to calculate the network load flow. The expression formula of the power flow equation in the power flow calculation is as follows:
Figure BDA0001578692690000101
wherein, P is a micro-increment column vector with a node injected with power and has n-1 elements; delta Q is a node injection reactive micro-increment column vector with nPQAn element; delta theta is a node voltage phase angle change column vector and has n-1 elements; Δ U is the column vector of the node voltage amplitude variation, with nPQAn element;
Figure BDA0001578692690000102
is a system jacobian matrix under polar coordinates, abbreviated as J,
Figure BDA0001578692690000103
the formula (1) is a mathematical model for analyzing the static stability of the system and is also a correction equation of a power flow equation; in conventional static voltage stability analysis, it is generally assumed that the generator and load nodes are active, i.e. Δ P is 0, then
Figure BDA0001578692690000104
Wherein the content of the first and second substances,
Figure BDA0001578692690000105
i.e. a jacobian matrix that is a simplification of the system.
Preferably, reactive compensation siting is determined in step 103 using a reactive balance analysis method.
Preferably, the determining reactive compensation addressing by using a reactive balance analysis method includes: determining an automatic site selection index of each node in the electric network by using a reactive power balance analysis method; and determining reactive compensation site selection of the nodes according to the automatic site selection indexes of each node.
Preferably, the determining an automatic site selection index of each node in the electric network by using a reactive balance analysis method includes:
MAi＝a·Mi+b·Ti (3)
Mivoltage fluctuation bus number ratio + lambdaiAmplitude average fluctuation level (4)
Figure BDA0001578692690000106
Ti′＝1/cosθij (6)
Ti＝Ti′/T max (7)
The node I is a node I, and a and b are weight coefficients respectively, wherein MAi is an automatic address selection index of the node I, and a + b is 1; mi is a voltage abnormal fluctuation level coefficient, namely a first index; the voltage fluctuation bus number ratio is the bus fluctuation number point of which the voltage fluctuation number exceeds a threshold value alpha divided by the total bus number detected by a PMU system of the vector measurement unit in the region; alpha is a numerical value given according to Chinese national standards and enterprise standards; lambda [ alpha ]iIs a preset modulation value; the amplitude average fluctuation level formula is the average level of the voltage abnormal fluctuation amplitude 1 in the jth area;
Figure BDA0001578692690000114
to the amplitude of the fluctuation, NJThe number of fluctuations in the jth sub-region; the lower power factor cos theta with Ti' as node iijThe reciprocal of (a); a second index of Ti; tmax is the maximum of a plurality of Ti', and node i is the lower power factor cos θijAnd the transformer is smaller than the corresponding preset lower power factor threshold value.
In the embodiment of the invention, the reactive compensation site selection is determined by using a reactive balance analysis method, which specifically comprises the following steps:
step 1: determining a first index voltage abnormal fluctuation level coefficient M by a voltage jitter analysis method in combination with the statistical data of the WAMSi. Wherein, the voltage fluctuation waveform of the annual abnormal fluctuation information of the statistical data of the WAMS is combined and counted, and the node voltage abnormal fluctuation level coefficient M is calculatedi。
MiVoltage fluctuation bus number ratio + lambdaiAmplitude average fluctuation level (4)
Figure BDA0001578692690000111
In the embodiment of the invention, the voltage fluctuation information of 500 kV and 220 kV areas is extracted, and the area voltage abnormal fluctuation level coefficient M1Voltage fluctuation bus number ratio + lambda1The average fluctuation level of the amplitude value, the voltage fluctuation bus number ratio is the bus fluctuation number point of which the voltage fluctuation number exceeds a threshold value alpha divided by the total number of all buses detected by a PMU system in the region, wherein alpha is a value given according to Chinese national standards and enterprise standards, and lambda is a value given according to the Chinese national standards and the enterprise standards1A modulation value set artificially; formula for average fluctuation level of amplitude
Figure BDA0001578692690000112
Indicating the average level of the amplitude of the abnormal fluctuation of the voltage in the j-th area, wherein,
Figure BDA0001578692690000113
to the amplitude of the fluctuation, NJIs the number of undulations in the j sub-region.
Step 2: and determining a second index Ti by a reactive balance analysis method in combination with the statistical data of the WAMS. When a second index is determined, firstly, a requirement is provided for load side compensation according to a basic principle of reactive compensation, when the load side reactive compensation meets an assessment requirement, reactive compensation capacity is enhanced on a main network side, and dynamic reactive compensation equipment is compensated to improve the dynamic voltage support capability of a power grid; when the above measures cannot solve the problem, the transformer tap is adjusted; the method comprises the steps of calculating the lower power factor of the transformer, analyzing and counting to find out the lower power factor cos thetaijThe transformer does not meet the condition that the preset bet power factor threshold is 0.95, and the time when cos theta ij is less than 0.95 in historical WAMS annual statistical data exceeds 2000 hours, wherein the low-voltage side buses of the three-coil transformer and the two-coil transformer are selected as reactive compensation alternative addresses; finally, the bet power factor cos θ of the node i is calculated according to the formulas (6) and (7)ijAnd performing normalization processing on the Ti' to obtain a second index Ti. The lower the power factor is, the more reactive compensation equipment needs to be added, so the reciprocal of the power factor is taken as an index of site selection. If the node i is a non-optional node, Ti is 0.
Step 3: and (3) determining the automatic address selection index MAi of the node i by using a formula (3), wherein if the node i is a non-selectable node, various corresponding indexes of the node i are set to be zero.
Step 4: and determining reactive compensation site selection according to the automatic site selection index MAi of the node i.
Preferably, in step 104, the initially selected reactive compensation capacity is calculated through voltage reactive sensitivity analysis, and the initially selected reactive compensation capacity is corrected according to the dynamic reactive storage amount of the generator set corresponding to the node, so as to determine the corrected reactive compensation capacity.
In the embodiment of the invention, the voltage reactive sensitivity method is adopted to determine the reactive compensation single-group capacity, and the corresponding delta Q is determined by the delta U in the formula (8), wherein the delta Q is the maximum value of the corresponding allowed single-group capacity, namely
Figure BDA0001578692690000121
Wherein, diag (J)R) Take matrix JRDiagonal elements of (a).
Preferably, the reactive compensation capacity of the candidate node is determined in step 105 according to the reactive compensation site selection, the corrected reactive compensation capacity and the annual grid monitoring data counted by the wide area monitoring system WAMS.
Preferably, the determining the reactive compensation capacity of the candidate node according to the reactive compensation site selection, the corrected reactive compensation capacity and the annual grid monitoring data counted by the wide area monitoring system WAMS includes: and performing reactive power planning optimization simulation calculation by using a power system analysis integration program PSASP according to the reactive power compensation site selection, the corrected reactive power compensation capacity and the annual power grid monitoring data counted by the WAMS, and determining the reactive power compensation capacity of the alternative node.
In the invention, after the reactive compensation scheme of the alternative node is determined, the capacity in the reactive compensation scheme of the alternative node is finely adjusted according to N-1 fault scanning, faults with larger influence on voltage stability in annual faults are comprehensively analyzed, the determined reactive compensation scheme of the alternative node is verified, whether the voltage stability is improved or not is judged, and if the voltage does not meet the voltage specified by stable calculation, the reactive compensation capacity is added according to manual experience until the determined reactive compensation scheme of the alternative node meets the voltage specified by stable calculation.
Fig. 2 is a schematic structural diagram of a large power grid reactive power compensation site selection and capacity optimization system 200 according to an embodiment of the present invention. As shown in fig. 2, the large grid reactive power compensation addressing and capacity optimizing system 200 provided by the embodiment of the present invention includes: the system comprises an annual reactive power optimization overall planning starting determination unit 201, a database establishing unit 202, a reactive power compensation addressing determination unit 203, a modified reactive power compensation capacity determination unit 204 and a reactive power compensation capacity determination unit 205 of an alternative node. Preferably, in the annual reactive power optimization overall planning start determining unit 201, an actual measurement power grid reactive power analysis index is calculated by using the annual power grid monitoring data counted by the wide area monitoring system WAMS, and whether to start the annual reactive power optimization overall planning is determined according to the actual measurement power grid reactive power analysis index.
Preferably, the following formula is used to calculate the reactive power analysis index of the measured power grid:
Figure BDA0001578692690000131
wherein S is an actually measured reactive power analysis index of the power grid; n is the number of nodes in the electric network; | AvgVi,kL is the voltage vector average value of the ith node recorded by the k month wide area monitoring system WAMS; min | VkI is the per unit value of the lowest voltage vector of the monthly node of the power grid recorded by the k-th month wide area monitoring system WAMS; and lambda is a preset coefficient and is set according to experience.
Preferably, the annual reactive power optimization overall planning starting determining unit, which determines whether to start the annual reactive power optimization overall planning according to the measured grid reactive power analysis indicator, includes: comparing the actual measurement power grid reactive power analysis index with a preset judgment threshold value, and starting annual reactive power optimization overall planning if the actual measurement power grid reactive power analysis index is smaller than the preset judgment threshold value; otherwise, the annual reactive power optimization overall planning is not started, wherein the preset judgment threshold is set according to experience.
Preferably, in the database establishing unit 202, an annual reactive power optimization overall plan is determined to be started, statistics is performed at the time of lowest voltage margin according to annual power grid monitoring data counted by the wide area monitoring system WAMS, a typical operation mode of weakest power grid voltage is determined, and a simulation database is determined through network load flow calculation.
Preferably, the database establishing unit performs statistics at the time of lowest voltage margin according to annual power grid monitoring data counted by the wide area monitoring system WAMS, determines a typical operation mode of weakest power grid voltage, and performs network load flow calculation to determine the simulation database, and includes:
counting annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, calculating a typical operation mode index according to a typical operation mode index calculation formula, and selecting a typical operation mode corresponding to the minimum typical operation mode index as the typical operation mode with the weakest power grid voltage, wherein the typical operation mode index calculation formula is as follows:
St＝(∑|Vi,t|)/N+λt·Min(|Vi,t|) (2)
wherein St is a typical operation mode index; t is the standard point moment; n is the number of nodes in the electric network; vi,tRepresenting the magnitude of the voltage phase at the ith node at the time of the tth moment in the year; λ t is an adjustment coefficient, and is set according to experience;
and (5) performing network load flow calculation by using a power system analysis integration program PSASP to determine a simulation database.
Preferably, in the reactive compensation site selection determining unit 203, a reactive compensation site selection is determined by using a reactive balance analysis method.
Preferably, the reactive compensation site selection determining unit, which determines the reactive compensation site selection by using a reactive balance analysis method, includes: determining an automatic site selection index of each node in the electric network by using a reactive power balance analysis method; and determining reactive compensation site selection of the nodes according to the automatic site selection indexes of each node.
Preferably, the determining an automatic site selection index of each node in the electric network by using a reactive balance analysis method includes:
MAi＝a·Mi+b·Ti (3)
Mivoltage fluctuation bus number ratio + lambdaiAmplitude average fluctuation level (4)
Figure BDA0001578692690000151
Ti′＝1/cosθij (6)
Ti＝Ti′/T max (7)
Wherein MAi is an automatic address selection index of the node iA and b are weight coefficients respectively, and a + b is 1; mi is a voltage abnormal fluctuation level coefficient, namely a first index; the voltage fluctuation bus number ratio is the bus fluctuation number point of which the voltage fluctuation number exceeds a threshold value alpha divided by the total bus number detected by a PMU system of the vector measurement unit in the region; alpha is a numerical value given according to Chinese national standards and enterprise standards; lambda [ alpha ]iIs a preset modulation value; the amplitude average fluctuation level formula is the average level of the voltage abnormal fluctuation amplitude 1 in the jth area;
Figure BDA0001578692690000152
to the amplitude of the fluctuation, NJThe number of fluctuations in the jth sub-region; the lower power factor cos theta with Ti' as node iijThe reciprocal of (a); a second index of Ti; tmax is the maximum of a plurality of Ti', and node i is the lower power factor cos θijAnd the transformer is smaller than the corresponding preset lower power factor threshold value.
Preferably, in the modified reactive compensation capacity determining unit 204, a primarily selected reactive compensation capacity is calculated through voltage reactive sensitivity analysis, and the primarily selected reactive compensation capacity is modified according to the dynamic reactive storage amount of the generator set corresponding to the node, so as to determine a modified reactive compensation capacity.
Preferably, in the reactive compensation capacity determination unit 205 of the candidate node, the reactive compensation capacity of the candidate node is determined according to the reactive compensation site selection, the corrected reactive compensation capacity, and the annual grid monitoring data counted by the wide area monitoring system WAMS.
Preferably, the determining unit of the reactive compensation capacity of the candidate node determines the reactive compensation capacity of the candidate node according to the reactive compensation site selection, the modified reactive compensation capacity and the annual grid monitoring data counted by the wide area monitoring system WAMS, and includes: and performing reactive power planning optimization simulation calculation by using a power system analysis integration program PSASP according to the reactive power compensation site selection, the corrected reactive power compensation capacity and the annual power grid monitoring data counted by the WAMS, and determining the reactive power compensation capacity of the alternative node.
The large power grid reactive power compensation site selection and capacity optimization system 200 of the embodiment of the present invention corresponds to the large power grid reactive power compensation site selection and capacity optimization method 100 of another embodiment of the present invention, and details thereof are not repeated herein.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

Claims (12)

1. A large power grid reactive compensation site selection and capacity optimization method is characterized by comprising the following steps:
calculating an actual measurement power grid reactive power analysis index by utilizing annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS), and determining whether to start annual reactive power optimization overall planning or not according to the actual measurement power grid reactive power analysis index;
determining starting annual reactive power optimization overall planning, counting the annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, determining a typical operation mode of weakest power grid voltage, and performing network load flow calculation to determine a simulation database;
determining reactive compensation site selection by using a reactive balance analysis method;
calculating the initially selected reactive compensation capacity through voltage reactive sensitivity analysis, correcting the initially selected reactive compensation capacity according to the dynamic reactive reserve of the generator set corresponding to the node, and determining the corrected reactive compensation capacity;
determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the corrected reactive compensation capacity and annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS);
the calculation formula of the actually measured power grid reactive power analysis index is as follows:
Figure FDA0002926856020000011
wherein S is an actually measured reactive power analysis index of the power grid; n is the number of nodes in the electric network; | AvgVi,kL is the voltage vector average value of the ith node recorded by the k month wide area monitoring system WAMS; min | VkI is the per unit value of the lowest voltage vector of the monthly node of the power grid recorded by the k-th month wide area monitoring system WAMS; and lambda is a preset coefficient and is set according to experience.
2. The method of claim 1, wherein determining whether to initiate an annual reactive power optimization population plan based on the measured grid reactive power analysis metrics comprises:
comparing the actual measurement power grid reactive power analysis index with a preset judgment threshold value, and starting annual reactive power optimization overall planning if the actual measurement power grid reactive power analysis index is smaller than the preset judgment threshold value; otherwise, the annual reactive power optimization overall planning is not started, wherein the preset judgment threshold is set according to experience.
3. The method as claimed in claim 1, wherein the step of counting annual power grid monitoring data counted by the wide area monitoring system WAMS at the moment of lowest voltage margin, determining a typical operation mode of weakest power grid voltage, and performing network load flow calculation to determine the simulation database comprises:
counting annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, calculating a typical operation mode index according to a typical operation mode index calculation formula, and selecting a typical operation mode corresponding to the minimum typical operation mode index as the typical operation mode with the weakest power grid voltage, wherein the typical operation mode index calculation formula is as follows:
St＝(∑|Vi,t|)/N+λt·Min(|Vi,t|) (2)
wherein St is a typical operation mode index; t is the standard point moment; n is the number of nodes in the electric network; vi,tRepresenting the magnitude of the voltage phase at the ith node at the time of the tth moment in the year; λ t is an adjustment coefficient, and is set according to experience;
and (5) performing network load flow calculation by using a power system analysis integration program PSASP to determine a simulation database.
4. The method of claim 1, wherein determining reactive compensation siting using a reactive balance analysis method comprises:
determining an automatic site selection index of each node in the electric network by using a reactive power balance analysis method;
and determining reactive compensation site selection of the nodes according to the automatic site selection indexes of each node.
5. The method of claim 4, wherein determining an automatic site selection indicator for each node in the electrical network using a reactive balance analysis method comprises:
MAi＝a·Mi+b·Ti (3)
Mivoltage fluctuation bus number ratio + lambdaiAmplitude average fluctuation level (4)
Figure FDA0002926856020000021
Ti′＝1/cosθij (6)
Ti＝Ti′/Tmax (7)
The node I is a node I, and a and b are weight coefficients respectively, wherein MAi is an automatic address selection index of the node I, and a + b is 1; mi is a voltage abnormal fluctuation level coefficient, namely a first index; the voltage fluctuation bus number ratio is the bus fluctuation number point of which the voltage fluctuation number exceeds a threshold value alpha divided by the areaThe total number of buses detected by a PMU system of a vector measurement unit; alpha is a numerical value given according to Chinese national standards and enterprise standards; lambda [ alpha ]iIs a preset modulation value; the amplitude average fluctuation level formula is the average level of the voltage abnormal fluctuation amplitude 1 in the jth area;
Figure FDA0002926856020000031
to the amplitude of the fluctuation, NJThe number of fluctuations in the jth sub-region; the lower power factor cos theta with Ti' as node iijThe reciprocal of (a); a second index of Ti; tmax is the maximum of a plurality of Ti', and node i is the lower power factor cos θijAnd the transformer is smaller than the corresponding preset lower power factor threshold value.
6. The method of claim 1, wherein determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the modified reactive compensation capacity and annual grid monitoring data counted by a Wide Area Monitoring System (WAMS) comprises:
and performing reactive power planning optimization simulation calculation by using a power system analysis integration program PSASP according to the reactive power compensation site selection, the corrected reactive power compensation capacity and the annual power grid monitoring data counted by the WAMS, and determining the reactive power compensation capacity of the alternative node.
7. A large power grid reactive compensation site selection and capacity optimization system is characterized by comprising:
the annual reactive power optimization overall planning starting determining unit is used for calculating an actual measurement power grid reactive power analysis index by utilizing annual power grid monitoring data counted by a wide area monitoring system WAMS and determining whether to start the annual reactive power optimization overall planning or not according to the actual measurement power grid reactive power analysis index;
the system comprises a database establishing unit, a simulation database and a data processing unit, wherein the database establishing unit is used for determining starting annual reactive power optimization overall planning, counting the annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, determining the typical operation mode of weakest power grid voltage, and performing network load flow calculation to determine the simulation database;
the reactive compensation site selection determining unit is used for determining a reactive compensation site selection by using a reactive balance analysis method;
the corrected reactive compensation capacity determining unit is used for calculating the initially selected reactive compensation capacity through voltage reactive sensitivity analysis, correcting the initially selected reactive compensation capacity according to the dynamic reactive storage amount of the generator set corresponding to the node, and determining the corrected reactive compensation capacity;
the reactive compensation capacity determining unit of the alternative node is used for determining the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the corrected reactive compensation capacity and annual power grid monitoring data counted by the wide area monitoring system WAMS;
the method comprises the following steps of calculating a reactive power analysis index of an actual measurement power grid by using the following formula:
Figure FDA0002926856020000041
wherein S is an actually measured reactive power analysis index of the power grid; n is the number of nodes in the electric network; | AvgVi,kL is the voltage vector average value of the ith node recorded by the k month wide area monitoring system WAMS; min | VkI is the per unit value of the lowest voltage vector of the monthly node of the power grid recorded by the k-th month wide area monitoring system WAMS; and lambda is a preset coefficient and is set according to experience.
8. The system of claim 7, wherein the annual reactive power optimization overall plan start determining unit determining whether to start an annual reactive power optimization overall plan according to the measured grid reactive power analysis indicator comprises:
comparing the actual measurement power grid reactive power analysis index with a preset judgment threshold value, and starting annual reactive power optimization overall planning if the actual measurement power grid reactive power analysis index is smaller than the preset judgment threshold value; otherwise, the annual reactive power optimization overall planning is not started, wherein the preset judgment threshold is set according to experience.
9. The system of claim 7, wherein the database establishing unit performs statistics at the time of lowest voltage margin according to annual grid monitoring data counted by the wide area monitoring system WAMS, determines a typical operation mode of weakest grid voltage, and performs network load flow calculation to determine the simulation database, and includes:
counting annual power grid monitoring data counted by a Wide Area Monitoring System (WAMS) at the moment of lowest voltage margin, calculating a typical operation mode index according to a typical operation mode index calculation formula, and selecting a typical operation mode corresponding to the minimum typical operation mode index as the typical operation mode with the weakest power grid voltage, wherein the typical operation mode index calculation formula is as follows:
St＝(∑|Vi,t|)/N+λt·Min(|Vi,t|) (2)
wherein St is a typical operation mode index; t is the standard point moment; n is the number of nodes in the electric network; vi,tRepresenting the magnitude of the voltage phase at the ith node at the time of the tth moment in the year; λ t is an adjustment coefficient, and is set according to experience;
and (5) performing network load flow calculation by using a power system analysis integration program PSASP to determine a simulation database.
10. The system of claim 7, wherein the reactive compensation siting determination unit determines reactive compensation siting using a reactive balance analysis method, comprising:
determining an automatic site selection index of each node in the electric network by using a reactive power balance analysis method;
and determining reactive compensation site selection of the nodes according to the automatic site selection indexes of each node.
11. The system of claim 10, wherein determining an automatic site selection indicator for each node in the electrical network using a reactive balance analysis method comprises:
MAi＝a·Mi+b·Ti (3)
Mivoltage fluctuation bus number ratio + lambdaiAmplitude average fluctuation level (4)
Figure FDA0002926856020000051
Ti′＝1/cosθij (6)
Ti＝Ti′/Tmax (7)
The node I is a node I, and a and b are weight coefficients respectively, wherein MAi is an automatic address selection index of the node I, and a + b is 1; mi is a voltage abnormal fluctuation level coefficient, namely a first index; the voltage fluctuation bus number ratio is the bus fluctuation number point of which the voltage fluctuation number exceeds a threshold value alpha divided by the total bus number detected by a PMU system of the vector measurement unit in the region; alpha is a numerical value given according to Chinese national standards and enterprise standards; lambda [ alpha ]iIs a preset modulation value; the amplitude average fluctuation level formula is the average level of the voltage abnormal fluctuation amplitude 1 in the jth area;
Figure FDA0002926856020000052
to the amplitude of the fluctuation, NJThe number of fluctuations in the jth sub-region; the lower power factor cos theta with Ti' as node iijThe reciprocal of (a); a second index of Ti; tmax is the maximum of a plurality of Ti', and node i is the lower power factor cos θijAnd the transformer is smaller than the corresponding preset lower power factor threshold value.
12. The system of claim 7, wherein the reactive compensation capacity determination unit of the alternative node determines the reactive compensation capacity of the alternative node according to the reactive compensation site selection, the modified reactive compensation capacity and the annual grid monitoring data counted by the Wide Area Monitoring System (WAMS), and comprises:
and performing reactive power planning optimization simulation calculation by using a power system analysis integration program PSASP according to the reactive power compensation site selection, the corrected reactive power compensation capacity and the annual power grid monitoring data counted by the WAMS, and determining the reactive power compensation capacity of the alternative node.
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