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WO2018176863A1 - Procédé et dispositif d'analyse de rendement d'investissement liés à la fiabilité d'un réseau de distribution d'énergie, et support de stockage - Google Patents

Procédé et dispositif d'analyse de rendement d'investissement liés à la fiabilité d'un réseau de distribution d'énergie, et support de stockage Download PDF

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Publication number
WO2018176863A1
WO2018176863A1 PCT/CN2017/112516 CN2017112516W WO2018176863A1 WO 2018176863 A1 WO2018176863 A1 WO 2018176863A1 CN 2017112516 W CN2017112516 W CN 2017112516W WO 2018176863 A1 WO2018176863 A1 WO 2018176863A1
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WIPO (PCT)
Prior art keywords
investment
reliability
distribution network
module
benefit
Prior art date
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Ceased
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PCT/CN2017/112516
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English (en)
Chinese (zh)
Inventor
盛万兴
孟晓丽
刘科研
刁赢龙
胡丽娟
贾东梨
何开元
叶学顺
董伟杰
吕琛
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China Electric Power Research Institute Co Ltd CEPRI
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China Electric Power Research Institute Co Ltd CEPRI
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Publication of WO2018176863A1 publication Critical patent/WO2018176863A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy 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
    • 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]

Definitions

  • the invention relates to an economic benefit analysis method, in particular to a distribution network reliability investment economic benefit analysis method and device, and a computer readable storage medium.
  • the systems related to the reliability and economy of the distribution network operated by the State Grid Corporation are mainly equipment (asset) operation and maintenance lean management system (PMS2.0) and power quality online monitoring system.
  • PMS2.0 equipment operation and maintenance lean management system
  • the above two systems have unfavorable factors such as information asymmetry, variable relationship disorder, and unsynchronized development platform.
  • unfavorable factors such as information asymmetry, variable relationship disorder, and unsynchronized development platform.
  • Embodiments of the present invention provide a method and device for analyzing economic benefits of reliability of distribution network, and a computer readable storage medium.
  • Embodiments of the present invention provide a method for analyzing economic benefits of reliability of distribution network, including:
  • the calculation model of the reliability investment benefit of the distribution network is used to calculate the reliability investment benefit of the distribution network.
  • a 1 reflects the power shortage caused by unit investment
  • d is the number of days in the month
  • m i is the sum of the power shortages of the day when the power is cut off
  • C invest is the reliability investment calculated by the equipment transaction
  • a 2 reflects the power outage caused by the unit investment The number of households
  • z i is the sum of the number of households in the event of a power outage on the day.
  • the method further includes: establishing a reliability improvement prediction model of the distribution network before and after the reliability investment according to the transaction information.
  • the reliability improvement prediction model of the distribution network is obtained by using a Lagrangian multiplier method and a Carlo-Kun-Tucker condition for the least squares support vector machine.
  • the reliability improvement prediction model of the distribution network is as follows:
  • the least squares support vector is calculated as follows:
  • the Lagrangian multiplier method is used for the least squares support vector machine as follows:
  • the Carol-Kun-Tuck condition is as follows:
  • x i ⁇ R n is the sample input
  • y i ⁇ R is the sample output value
  • ⁇ is the weight vector
  • b is the paranoid amount
  • ⁇ (x) is the mapping from the low-dimensional space to the high-dimensional space
  • e i is the error
  • e ⁇ R l ⁇ 1 the error vector
  • ⁇ [ ⁇ 1 , ⁇ 2 , ... ⁇ l ] T
  • E [1, 1, ... 1] T is a l ⁇ 1 dimensional column vector
  • Y [y 1 , y 2 ,..., y l ] T
  • I is a unit matrix
  • K is a suitable kernel function
  • a 3 is the comprehensive reliability index of the distribution network reliability
  • (RS-2) i+1 is the power supply reliability index predicted by the distribution network reliability improvement prediction model
  • (RS-2) i is the investment
  • the reliability index of the previous cycle h is the total number of regional power supply households
  • C invest is the investment amount of the forecast period
  • a 2 is the number of households in the pre-arranged power outage caused by unit investment.
  • the investment cost of the device transaction is calculated as follows:
  • the base price, labor cost and equipment fee of different equipment are determined according to actual conditions.
  • the embodiment of the invention further provides a distribution network reliability investment benefit analysis device, the device comprising: a database module, a data processing module, an input module, an analysis module and an output module;
  • the database module is configured to collect and store the change information of the power supply area device
  • the data processing module is configured to calculate an investment cost of the transaction device corresponding to the transaction information, and match the investment cost with the pre-arranged power outage event to obtain an association relationship;
  • the input module is configured to transmit data processed by the data processing module to the analysis module;
  • the analysis module is configured to establish a calculation model of the reliability investment benefit of the distribution network according to the association relationship; and obtain a distribution network through the calculation model of the reliability investment benefit of the distribution network according to the data transmitted by the input module Reliability investment benefit;
  • the output module is configured to output a distribution network reliability investment benefit value.
  • a computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of any of the above methods.
  • the embodiment of the present invention is based on the problem that the investment data is not credible in the reliability economic benefit analysis of the traditional distribution network, and the calculation model of the reliability investment of the distribution network is established by the PMS2.0 system equipment transaction information, thereby solving the problem.
  • the embodiment of the invention provides a method for predicting the reliability of the distribution network in a certain period of time through the historical data when the topology of the distribution network is unknown, thereby solving the historical power outage and reliability at the macro level.
  • the investment data can be used to obtain the problem of reliability improvement of the distribution network.
  • FIG. 1 is a flow chart of a method for analyzing economic benefits of reliability investment of a distribution network according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of matching a PMS2.0 system with a power quality online monitoring system according to an embodiment of the present invention
  • FIG. 3 is a flowchart of reliability prediction of a power distribution network to a distribution network according to an embodiment of the present invention.
  • an embodiment of the present invention provides a method for analyzing the economic benefit of reliability of a distribution network, which is applied to a device (asset) operation and maintenance lean management system (PMS2.0). As shown in FIG. 1 , the method includes the following steps. :
  • Step 1 Obtain the change information of the device in the power supply area
  • the equipment (asset) operation and maintenance lean management system (PMS2.0) is a unified equipment (asset) operation and maintenance lean management system for the operation and maintenance department, covering the operation and maintenance maintenance business and production management process. Realize the life-cycle management of equipment (assets) from planning, installation, operation, decommissioning, reuse, and retirement.
  • the device (asset) change information is recorded in the PMS2.0 system, including device physical parameter information (device category, device type, voltage level), device change information (device addition method, commissioning time), and the like. According to the classification of the station equipment, the interval unit, the overhead line, the cable line, and the primary equipment in the substation, the data in the PMS2.0 system is exported in the form of excel.
  • the grid reliability data is stored in the power quality online monitoring system, in which “power outage end time”, “power outage nature”, and “number of households in power outage” are structured data.
  • the reliability data of the distribution network of a power supply area is exported in the form of excel, and the data is preprocessed.
  • the power outage data is divided into pre-arranged power outage data and fault power outage data according to the nature of power outage.
  • Step 2 Calculate the investment cost based on the transaction device corresponding to the transaction information
  • the construction network construction cost is composed of installation engineering fees, equipment purchase costs, other expenses and dynamic costs.
  • the contents and calculation formulas of each fee are shown in Table 1.
  • the base price, labor cost and equipment fee of different equipment are determined according to the actual actual value.
  • Step 3 Match the investment cost with the pre-arranged power outage event to obtain the relationship
  • the time parameter information of the device change is matched with the construction power failure time in the power quality online monitoring system, and the power outage data information of the distribution network caused by the device change construction is obtained, and the data fusion method of the two information systems is as shown in FIG. 2 Show.
  • association relationship may be referred to as an association relationship between the construction and distribution network power outage data information.
  • the equipment transaction information is linked with the distribution network reliability data to obtain the monthly variable operation of each voltage level.
  • the distribution network construction investment can be correlated with the construction power outage, so as to realize the analysis and research on the economic benefit of the distribution network reliability investment.
  • a 1 reflects the lack of power supply caused by unit investment. The larger the value of A 1 , the more power shortage is caused by unit investment.
  • d is the number of days in the month
  • m i is the sum of the power shortages of the day's power outage
  • C invest is the reliability investment calculated by the device.
  • a 2 reflects the number of households in the event of power outage caused by unit investment. The larger the value of A 2 is, the more households are in the event of power outage caused by unit investment.
  • d is the number of days in the month, and z i is the sum of the number of households in the event of a power outage on the day.
  • C invest is a reliability investment through device computing calculations.
  • Step 4 Based on the transaction information, establish a reliability improvement prediction model for the distribution network before and after the reliability investment;
  • step 4 and steps 2, 3, and 5 have no order of execution.
  • the reliability improvement prediction model of the distribution network before and after the reliability investment is established, and the reliability of the power supply before and after the investment of the reliability investment data can be obtained.
  • the pre-arranged power failure is calculated.
  • the impact of network reliability, so the reliability prediction of this part only predicts the impact of fault power outage on the reliability of the distribution network.
  • the reliability of the fault power failure can be predicted.
  • the reliability of the distribution network for the month and the forecast monthly month, the forecast of the number of severe weather days, the total monthly investment amount of the distribution network in the previous year, and the current reliability level of the predicted site are all four factors.
  • a reliability estimation based on the least squares support vector machine is proposed.
  • the sample matrix is formed by preprocessing the historical data, normalizing the data, determining the parameters, solving the objective function to obtain the regression equation, and using the regression equation to predict the reliability.
  • the core idea of the least squares support vector machine is to map the training samples to the high-dimensional feature space through a nonlinear mapping ⁇ (x), and then perform linear regression in the high-dimensional feature space.
  • the time regression function is:
  • ⁇ is the weight vector
  • b is the paranoid quantity
  • ⁇ (x) is the mapping from the low dimensional space to the high dimensional space.
  • ⁇ [ ⁇ 1 , ⁇ 2 ,... ⁇ l ] T
  • Y [y 1 , y 2 ,. .., y l ] T
  • I is the identity matrix
  • K is a suitable kernel function
  • the kernel function in the original space is used to replace the dot product operation in the high dimensional feature space.
  • the prediction model of the least squares support vector machine is:
  • ⁇ i , b can be obtained from the linear equation of the above formula
  • K(x i , x j ) represents the kernel function from the sample input space through the nonlinear mapping to the high-dimensional feature space.
  • the radial basis function (RBF) function is used as the kernel function in the least squares support vector machine model.
  • Each input sample selected by the predictive model input sample contains four characteristic indicators: the reliability and forecast monthly month of the distribution network, the forecast of severe weather days in the month, the total investment amount of the distribution network in the previous year before the forecast, and the current forecast position. Reliability level.
  • the prediction model of the least squares support vector machine can predict the reliability index of the fault blackout only in a certain month.
  • Step 5 According to the association relationship, establish a calculation model for the economic benefit of the reliability investment of the distribution network
  • a 3 is the comprehensive reliability index of the distribution network reliability
  • (RS-2) i+1 is the power supply reliability index predicted by the distribution network reliability improvement prediction model
  • (RS-2) i is the investment
  • the reliability index of the previous cycle h is the total number of regional power supply households
  • C invest is the investment amount of the forecast period
  • a 2 is the number of households in the pre-arranged power outage caused by unit investment.
  • Step 6 When the equipment in the power supply area changes, the calculation model of the reliability investment benefit of the distribution network is used to calculate the reliability investment benefit of the distribution network.
  • a 3 is greater than 0, indicating that the implementation of reliability investment can bring economic benefits, and the larger the value, the greater the reliability and economic benefit.
  • the embodiment of the present invention further provides a distribution network reliability investment benefit analysis device, the device comprising: a database module, a data processing module, an input module, an analysis module and an output module connected in sequence;
  • the database module is configured to collect and store the change information of the power supply area device
  • the data processing module is configured to calculate an investment cost of the transaction device, and match the investment cost with a pre-arranged power outage event to obtain an association relationship;
  • the input module is configured to transmit data processed by the data processing module to the analysis module;
  • the analysis module is configured to establish a calculation model of the reliability investment benefit of the distribution network according to the association relationship; and obtain a distribution network through the calculation model of the reliability investment benefit of the distribution network according to the data transmitted by the input module Reliability investment benefit;
  • the output module is configured to output a distribution network reliability investment benefit value.
  • the analysis module is further configured to establish a reliability investment according to the fault power failure information.
  • the post-distribution power grid reliability improvement prediction model predicts the reliability of the fault power outage.
  • each of the above modules may be implemented by a processor in a distribution network reliability investment benefit analysis device; specifically, the processor is configured to execute the steps of any of the above methods when the computer program is run; the computer program stores On the memory of the distribution network reliability investment benefit analysis device.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the foregoing methods are implemented.
  • the solution provided by the embodiment of the present invention acquires the transaction information of the equipment in the power supply area; calculates the investment cost according to the changed device; matches the investment cost with the pre-arranged power outage event, acquires the association relationship; and establishes the reliability according to the transaction information
  • Pre-investment distribution network reliability improvement prediction model establish a calculation model of distribution network reliability investment benefit; when the power supply area equipment changes, use the distribution network reliability investment benefit calculation model to calculate distribution network reliability investment Benefits enable the model to more accurately reflect the impact of distribution grid investments on distribution grid reliability.

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Abstract

L'invention concerne un procédé et un dispositif d'analyse de rendement d'investissement liés à la fiabilité d'un réseau de distribution d'énergie, et un support de stockage lisible par ordinateur. Le procédé comprend les étapes consistant à : acquérir des informations de changement d'appareil d'une zone de service d'énergie (étape 1); calculer, en fonction d'un changement d'appareil correspondant aux informations de changement, un coût d'investissement (étape 2); apparier le coût d'investissement à un incident de coupure de courant préalablement organisé pour obtenir une relation d'association (étape 3); établir, à partir des informations de changement, un modèle de prédiction concernant une augmentation de la fiabilité d'un réseau de distribution d'énergie pour un investissement lié à la fiabilité (étape 4); établir un modèle de calcul de rendement d'investissement pour la fiabilité d'un réseau de distribution d'énergie (étape 5); et lors d'un changement d'appareil dans la zone de service d'énergie, utiliser le modèle de calcul de rendement d'investissement pour la fiabilité d'un réseau de distribution d'énergie afin de calculer un rendement d'investissement concernant la fiabilité d'un réseau de distribution d'énergie (étape 6).
PCT/CN2017/112516 2017-04-01 2017-11-23 Procédé et dispositif d'analyse de rendement d'investissement liés à la fiabilité d'un réseau de distribution d'énergie, et support de stockage Ceased WO2018176863A1 (fr)

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CN201710214464.6 2017-04-01
CN201710214464.6A CN107123982B (zh) 2017-04-01 2017-04-01 一种基于设备异动的配电网可靠性经济效益分析方法

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