CN114400667A - Safety risk assessment method for AC/DC hybrid power distribution network - Google Patents

Safety risk assessment method for AC/DC hybrid power distribution network Download PDF

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CN114400667A
CN114400667A CN202111345233.1A CN202111345233A CN114400667A CN 114400667 A CN114400667 A CN 114400667A CN 202111345233 A CN202111345233 A CN 202111345233A CN 114400667 A CN114400667 A CN 114400667A
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power
time
risk
node
distribution network
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董杰
赵建军
李洋
余中枢
丁晨
刘欢
刘佳林
孔乾坤
边竞
王振浩
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State Grid Jibei Power Co ltd Smart Distribution Network Center
Northeast Electric Power University
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State Grid Jibei Power Co ltd Smart Distribution Network Center
Northeast Dianli University
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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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • 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
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution 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
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • 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
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging

Abstract

A safety risk assessment method for an alternating current-direct current hybrid power distribution network belongs to the technical field of power systems. The invention aims to effectively reduce the running risk of an alternating current-direct current hybrid power distribution network under the condition of simultaneously considering factors such as safety, economy and the like, and relates to a safety risk evaluation method of the alternating current-direct current hybrid power distribution network by considering new energy and demand response load access. The method comprises the following steps: constructing a wind-solar output and EV time sequence probability distribution model, and replacing the subjectively set initial charge states of multiple types of EVs with daily driving mileage; based on a complex network theory, time sequence safety indexes such as voltage out-of-limit risks, branch power overload operation risks and the like are provided, and economic risk and power grid efficiency risk indexes are established according to economic operation of a power distribution network. The method effectively distributes the power distribution network, is safe and economical, effectively reduces the operation risk of the power distribution network under the condition of simultaneously considering factors such as safety, economy and the like, and has positive guiding significance for planning the EV capacity in a certain area.

Description

Safety risk assessment method for AC/DC hybrid power distribution network
Technical Field
The invention belongs to the technical field of power systems.
Background
In recent years, with the rapid increase of new energy and Electric Vehicle (EV) access capacity, the structure, the trend, and the operation mode of the conventional power distribution network have been greatly changed. On the one hand, randomness and uncertainty of DG (Distributed Generation) output can cause adverse effects such as line overload, power quality reduction and system loss increase. On the other hand, the random charging behavior of the EV brings new risk challenges to the safe and economically stable operation of the power system, and therefore, the operation risk after the DG and the EV are connected to the power grid is a problem to be solved urgently by the power system.
For risk assessment of a DG and an EV which are simultaneously accessed into a power distribution network, a traditional risk assessment analysis method mainly inherits a reliability assessment method and can be generally divided into an analytic method and a simulation method. However, most of the indexes are determined by expert evaluation or semi-quantitative analysis, and the indexes are easily influenced by subjective factors, and the actual size of the risk indexes is not completely considered. The relevance influence and the data dimension among evaluation indexes are effectively reduced by applying a dimensionality reduction objective weight evaluation method, so that the obtained evaluation is more credible, the defect of subjective empowerment is avoided, and the information value contained in the risk index can be fully reflected.
On the other hand, uncertainty factors in the distribution network are increasing in the large environment of mass access of such new energy, the explosive development of EVs, and the reform of the electric power market. These uncertainties have themselves adversely affected the operation of the distribution network and, in addition, when the number and capacity of such source-containing load points connected to the distribution network reaches a certain level, new changes to the overall distribution network system may result. The traditional single-power radiation type network is transformed into a multi-power combined power supply bidirectional tidal current network, which brings certain risks to the operation and control of a power distribution network.
Disclosure of Invention
The invention aims to effectively reduce the running risk of an alternating current-direct current hybrid power distribution network under the condition of simultaneously considering factors such as safety, economy and the like, and relates to a safety risk evaluation method of the alternating current-direct current hybrid power distribution network by considering new energy and demand response load access.
The method comprises the following steps:
s1, constructing a wind-solar output and EV time sequence probability distribution model, and replacing the subjectively set initial charge states of multiple types of EVs with daily driving mileage; the factors influencing different EV charging loads can be generally summarized into three aspects of electric vehicle time characteristics, space characteristics and battery characteristics
1) Time characteristic: setting the charging and discharging behaviors of each EV only once every day, and the time f for the EV to be connected into the power grids(x) Time f of departure from EV gridx(x) Obey normal distribution
Figure RE-GDA0003552800000000011
Figure RE-GDA0003552800000000021
Wherein mux、μsAnd σx、σsRespectively an expected value and a standard deviation of the normal distribution function of the early peak and the late peak;
2) spatial characteristics: road cross nodes are represented by an integer sequence 1, 2, …, m, wherein m is the number of nodes, roads running from the node i to the node j are represented by directed edges (i, j), owners of different vehicles can correspond to different trip purposes, urban areas are divided into four types, namely a residential area, a working area, a commercial area and a comprehensive area, the longitude and latitude coordinates of the known global positioning system of the first node and the last node of the road are located, and the linear distance between 2 nodes is calculated by a formula (7)
Figure RE-GDA0003552800000000022
The change in temperature, D, affects the air conditioning rate D of the EV, in addition to the depletion of the batteryon；
The urban road is divided into four levels, namely a expressway, a main road, a secondary road and a branch road, the roads of each level have different speeds v under different traffic road conditions, and the energy consumption corresponding to the roads is as follows:
Figure RE-GDA0003552800000000023
corresponding to different temperatures, the opening rate D of the air conditioneronExpressed as:
Don＝q1d3+q2d2+q3d+b1 (9)
wherein q is1、q2、q3And b1Is a fitting parameter; ratio D of unit distance energy consumption when the air conditioner is turned on to unit distance energy consumption when the air conditioner is turned offrateExpressed as:
Drate＝q4(d+b2)2+b3 (10)
wherein q is4、b2And b3To fit the parameters, and to sum up, the energy consumption per distance of the EV is expressed as:
Figure RE-GDA0003552800000000024
g is the set of all roads, the shortest path L is represented as:
Figure RE-GDA0003552800000000025
3) battery characteristics: the charging place is selected to be a unit parking lot and a residential area parking lot at night, if the unit parking lot is charged, the charging time is usually not more than 3 hours, if the unit parking lot is charged, the charging time can be prolonged to the whole night, so a conventional charging mode is selected, which charging mode is mainly influenced by time and place, the operation time and routes of buses are relatively centralized, centralized charging can be performed, charging is not arranged in the daytime operation peak period, quick charging is performed in the noon shift lunch time period, and conventional charging is performed after work at night, as the rest time of a taxi is limited, the time sensitivity is very high, and the electric quantity needs to be timely supplemented, the electric taxi selects the quick charging mode, a housekeeper is used as a daily public affair trip of the government, and the driving characteristic is similar to that of a private car without considering long-distance trip;
s2, based on the complex network theory, providing time sequence safety indexes such as voltage out-of-limit risk and branch power overload operation risk, and establishing economic risk and power grid high efficiency risk indexes according to economic operation of the power distribution network
Short-term safety risk indexes: the short-term security risk assessment model of the power grid in combination with the vulnerability of the network structure and the risk theory comprehensively considers the node importance degree, the betweenness and the proportion of the conventional load connected with the node, and the branch importance degree is measured by the line degree and the betweenness:
ρv,i＝α1Dv,j+α2Bv,j+α3NPj (13)
ρl,l＝β1Dl,k+β2Bl,k (14)
where ρ isv,i、ρl,lRespectively the node importance of the node i and the branch importance of the line l; dv,j、Bv,jRespectively is node degree and betweenness; n is a radical ofPjInjecting power for the node; dl,jAnd Bl,jRespectively, the number of lines and the number of medians; alpha is alpha1、α2、α3Weight coefficients of node degree, node betweenness and node injection power respectively, and has alpha1+α2+α3＝1；β1、β2Weight coefficients of line degree and line number, respectively, and has beta1+β2＝1；
The node voltage out-of-limit operation risk indexes are as follows:
Figure RE-GDA0003552800000000031
Figure RE-GDA0003552800000000032
wherein R isv,i(t) is a voltage out-of-limit operation risk index value of the node i at the time t; n isv,i(t) is the number of voltage states of node i at time t; p (S)v,j) Probability of being the jth voltage state; sv,j(t) the severity of voltage loss at node i at jth voltage state at time t; v and Vmax、VminThe voltage qualified range and the per unit values of the upper limit and the lower limit of the voltage qualified range are respectively;
the line power out-of-limit risk indexes are as follows:
Figure RE-GDA0003552800000000041
Figure RE-GDA0003552800000000042
wherein R isl,l(t) is the power out-of-limit risk index value of the line l at the time t; n isl,l(t) is the number of flow states of the line l at the time t; p (S)l,k) Probability of the kth tidal state; p (S)l,k) The severity of the voltage loss of the kth power flow state of the line l at the time t; l islThe ratio of the actual active power of the line l to the rated active power is obtained;
RSRIthe short-term comprehensive safety risk coefficient of the operation of the characterization system,
Figure RE-GDA0003552800000000043
characterizing voltage risks caused by node voltage violations of the distribution grid system and distribution uncertainties thereof,
Figure RE-GDA0003552800000000044
representing the power flow risk caused by the out-of-limit of branch power and the distribution uncertainty of the branch power of the power distribution network system, and then:
Figure RE-GDA0003552800000000045
wherein, γ1、γ2Is a security risk weight coefficient and has a1+γ2＝1；
Second, economic risk index: the economic risk index ERI of the DG and EV charging loads accessed to the power distribution network consists of a line loss risk ELLR and an operation loss risk EPLR
CERI(t)＝CELLR(t)-CEPLR(t) (20)
CELLR(t)＝Cprice(t)Ploss(t) (21)
Figure RE-GDA0003552800000000046
Figure RE-GDA0003552800000000047
Figure RE-GDA0003552800000000048
Figure RE-GDA0003552800000000049
Wherein, CERI(t) is the ERI value of the power distribution network at the moment t; cELLR(t)、CEPLR(t) the ELLR and EPLR index values of the power distribution network at the time t respectively; cprice(t) the electricity price of the power distribution network at the moment t; ploss(t) is the network loss power of the power distribution network at the moment t; cenv(t) subsidy benefits given by the government for the power distribution network at the moment t;
Figure RE-GDA00035528000000000410
the operation and maintenance cost and the electricity selling income of the ith' station DG at the moment t are respectively; n is the number of DGs;
Figure RE-GDA00035528000000000411
the time-varying electricity price of the unit power of the ith' station DG at the moment t;
Figure RE-GDA00035528000000000412
the active output power of the ith' station DG at the moment t; n is the number of DGs; mu.si′The maintenance cost of the unit power of the ith' station DG;
Figure RE-GDA0003552800000000051
the active output power of the ith' type DG of the node m at the time t; pWDG(t)、PWODG(t) power acquired by the front and rear power distribution networks from the large power grid at the moment of time t by DG access respectively; mjThe j-th type pollution gas emission coefficient is the unit generated power of the power distribution network; cjThe treatment cost of the j-th type pollution gas is high; m' is dirtyNumber of classes of dye gas. S1, constructing a wind-solar output and EV time sequence probability distribution model, and replacing the subjectively set initial charge states of multiple types of EVs with daily driving mileage; the factors influencing different EV charging loads can be generally summarized into three aspects of electric vehicle time characteristics, space characteristics and battery characteristics
1) Time characteristic: setting the charging and discharging behaviors of each EV only once every day, and the time f for the EV to be connected into the power grids(x) Time f of departure from EV gridx(x) Obey normal distribution
Figure RE-GDA0003552800000000052
Figure RE-GDA0003552800000000053
Wherein mux、μsAnd σx、σsRespectively an expected value and a standard deviation of the normal distribution function of the early peak and the late peak;
2) spatial characteristics: road cross nodes are represented by an integer sequence 1, 2, …, m, wherein m is the number of nodes, roads running from the node i to the node j are represented by directed edges (i, j), owners of different vehicles can correspond to different trip purposes, urban areas are divided into four types, namely a residential area, a working area, a commercial area and a comprehensive area, the longitude and latitude coordinates of the known global positioning system of the first node and the last node of the road are located, and the linear distance between 2 nodes is calculated by a formula (7)
Figure RE-GDA0003552800000000054
The change in temperature, D, affects the air conditioning rate D of the EV, in addition to the depletion of the batteryon；
The urban road is divided into four levels, namely a expressway, a main road, a secondary road and a branch road, the roads of each level have different speeds v under different traffic road conditions, and the energy consumption corresponding to the roads is as follows:
Figure RE-GDA0003552800000000055
corresponding to different temperatures, the opening rate D of the air conditioneronExpressed as:
Don＝q1d3+q2d2+q3d+b1 (9)
wherein q is1、q2、q3And b1Is a fitting parameter; ratio D of unit distance energy consumption when the air conditioner is turned on to unit distance energy consumption when the air conditioner is turned offrateExpressed as:
Drate＝q4(d+b2)2+b3 (10)
wherein q is4、b2And b3To fit the parameters, and to sum up, the energy consumption per distance of the EV is expressed as:
Figure RE-GDA0003552800000000061
g is the set of all roads, the shortest path L is represented as:
Figure RE-GDA0003552800000000062
3) battery characteristics: the charging place is selected to be a unit parking lot and a residential area parking lot at night, if the unit parking lot is charged, the charging time is usually not more than 3 hours, if the unit parking lot is charged, the charging time can be prolonged to the whole night, so a conventional charging mode is selected, which charging mode is mainly influenced by time and place, the operation time and routes of buses are relatively centralized, centralized charging can be performed, charging is not arranged in the daytime operation peak period, quick charging is performed in the noon shift lunch time period, and conventional charging is performed after work at night, as the rest time of a taxi is limited, the time sensitivity is very high, and the electric quantity needs to be timely supplemented, the electric taxi selects the quick charging mode, a housekeeper is used as a daily public affair trip of the government, and the driving characteristic is similar to that of a private car without considering long-distance trip;
s2, based on the complex network theory, providing time sequence safety indexes such as voltage out-of-limit risk and branch power overload operation risk, and establishing economic risk and power grid high efficiency risk indexes according to economic operation of the power distribution network
Short-term safety risk indexes: the short-term security risk assessment model of the power grid in combination with the vulnerability of the network structure and the risk theory comprehensively considers the node importance degree, the betweenness and the proportion of the conventional load connected with the node, and the branch importance degree is measured by the line degree and the betweenness:
ρv,i＝α1Dv,j+α2Bv,j+α3NPj (13)
ρl,l＝β1Dl,k+β2Bl,k (14)
where ρ isv,i、ρl,lRespectively the node importance of the node i and the branch importance of the line l; dv,j、Bv,jRespectively is node degree and betweenness; n is a radical ofPjInjecting power for the node; dl,jAnd Bl,jRespectively, the number of lines and the number of medians; alpha is alpha1、α2、α3Weight coefficients of node degree, node betweenness and node injection power respectively, and has alpha1+α2+α3＝1；β1、β2Weight coefficients of line degree and line number, respectively, and has beta1+β2＝1；
The node voltage out-of-limit operation risk indexes are as follows:
Figure RE-GDA0003552800000000071
Figure RE-GDA0003552800000000072
wherein R isv,i(t) is a voltage out-of-limit operation risk index value of the node i at the time t; n isv,i(t) is the number of voltage states of node i at time t; p (S)v,j) Probability of being the jth voltage state; sv,j(t) the severity of voltage loss at node i at jth voltage state at time t; v and Vmax、VminThe voltage qualified range and the per unit values of the upper limit and the lower limit of the voltage qualified range are respectively;
the line power out-of-limit risk indexes are as follows:
Figure RE-GDA0003552800000000073
Figure RE-GDA0003552800000000074
wherein R isl,l(t) is the power out-of-limit risk index value of the line l at the time t; n isl,l(t) is the number of flow states of the line l at the time t; p (S)l,k) Probability of the kth tidal state; p (S)l,k) The severity of the voltage loss of the kth power flow state of the line l at the time t; l islThe ratio of the actual active power of the line l to the rated active power is obtained;
RSRIthe short-term comprehensive safety risk coefficient of the operation of the characterization system,
Figure RE-GDA0003552800000000075
characterizing voltage risks caused by node voltage violations of the distribution grid system and distribution uncertainties thereof,
Figure RE-GDA0003552800000000076
representing the power flow risk caused by the out-of-limit of branch power and the distribution uncertainty of the branch power of the power distribution network system, and then:
Figure RE-GDA0003552800000000077
wherein, γ1、γ2Is a security risk weight coefficient and has a1+γ2＝1；
Second, economic risk index: the economic risk index ERI of the DG and EV charging loads accessed to the power distribution network consists of a line loss risk ELLR and an operation loss risk EPLR
CERI(t)＝CELLR(t)-CEPLR(t) (20)
CELLR(t)＝Cprice(t)Ploss(t) (21)
Figure RE-GDA0003552800000000078
Figure RE-GDA0003552800000000081
Figure RE-GDA0003552800000000082
Figure RE-GDA0003552800000000083
Wherein, CERI(t) is the ERI value of the power distribution network at the moment t; cELLR(t)、CEPLR(t) the ELLR and EPLR index values of the power distribution network at the time t respectively; cprice(t) the electricity price of the power distribution network at the moment t; ploss(t) is the network loss power of the power distribution network at the moment t; cenv(t) subsidy benefits given by the government for the power distribution network at the moment t;
Figure RE-GDA0003552800000000084
the operation and maintenance cost and the electricity selling income of the ith' station DG at the moment t are respectively; n is the number of DGs;
Figure RE-GDA0003552800000000085
the time-varying electricity price of the unit power of the ith' station DG at the moment t;
Figure RE-GDA0003552800000000086
the active output power of the ith' station DG at the moment t; n is the number of DGs; mu.si′The maintenance cost of the unit power of the ith' station DG;
Figure RE-GDA0003552800000000087
the active output power of the ith' type DG of the node m at the time t; pWDG(t)、PWODG(t) power acquired by the front and rear power distribution networks from the large power grid at the moment of time t by DG access respectively; mjThe j-th type pollution gas emission coefficient is the unit generated power of the power distribution network; cjThe treatment cost of the j-th type pollution gas is high; m' is the number of types of the polluted gas.
The method effectively distributes the power distribution network, is safe and economical, effectively reduces the operation risk of the power distribution network under the condition of simultaneously considering factors such as safety, economy and the like, and has positive guiding significance for planning the EV capacity in a certain area.
Drawings
Fig. 1 is a diagram of an improved IEEE33 node system;
FIG. 2 shows photovoltaic power distribution parameters α and β and wind velocity distribution parameter cwAnd k iswA drawing;
FIG. 3 is a wind-light time interval force diagram;
FIG. 4 is a diagram of EV-type charging power expectations;
FIG. 5 is a graph of deterministic evaluation and proposed short-term security risk indicator results;
FIG. 6 is an AC distribution network node voltage out-of-limit operating risk graph;
FIG. 7 is a graph of the risk of out-of-limit operation of the branch power flow of the AC distribution network;
FIG. 8 is a distribution diagram of the cross-limit risk of branch load flow of the AC distribution network;
FIG. 9 is a diagram of DC distribution network branch power flow out-of-limit operation risk;
FIG. 10 is a DC distribution network branch load flow out-of-limit risk time distribution diagram;
FIG. 11 is a graph of ERI results for an AC distribution network;
fig. 12 is a graph of ERI results for a dc distribution network.
Detailed Description
The method effectively reduces the operation risk of the power distribution network under the condition of simultaneously considering factors such as safety, economy and the like, and has positive guiding significance for planning the EV capacity in a certain area. Firstly, constructing a wind-solar output and EV time sequence probability distribution model, and replacing the initial charge states of various types of EV subjectively set by daily driving mileage; secondly, based on a complex network theory, time sequence safety indexes such as voltage out-of-limit risks, branch power overload operation risks and the like are provided, and economic risks and power grid high-efficiency risk indexes are established according to economic operation of the power distribution network.
The wind-solar output and EV time sequence probability distribution model constructed by the invention is as follows:
firstly, a wind power model: the wind power output is mainly determined by the wind speed, wherein the statistical characteristic of the wind speed follows the double-parameter Weibull distribution. Therefore, the active power P of the fanwThe distribution function expression of (a) is:
Figure RE-GDA0003552800000000091
wherein, PrRated output power of the fan; v is the wind speed; v. ofco、vci、vcrRespectively cut-out, cut-in and rated wind speed; k is a radical ofw、 cwRespectively, scale and shape parameters.
A photovoltaic power generation model: the solar illumination intensity is different due to different geographic environments and positions, the Beta distribution can be used for representing the solar illumination intensity distribution in one day based on a large amount of measurement data, and then the probability density function of the active power output of the photovoltaic power generation is as follows:
Figure RE-GDA0003552800000000092
Figure RE-GDA0003552800000000093
wherein Γ (·) is a Gamma function; alpha and Beta are 2 parameters for representing the shape of the Beta distribution function; psolar、Psolar,maxActual output and maximum output of the photovoltaic array are respectively obtained; r is the solar irradiance; eta and A are the electric energy conversion efficiency and the total area of the photovoltaic array respectively.
③ the conventional load model: the normal load at any moment adopts normal distribution to reflect the randomness and uncertainty thereof, and the active power P of the normal loadLDAnd active power QLDThe probability density function of (a) is:
Figure RE-GDA0003552800000000094
wherein, muLP,t、μLQ,tRespectively the expected values of the active power and the reactive power of the conventional load at the time t; lambda [ alpha ]LP,t、λLQ,tThe coefficient of variation of the active power and the reactive power of the conventional load at the moment t are respectively.
Fourthly, the electric automobile space-time characteristic and charging mode model: electric vehicles considered include four types, private cars, business cars, taxis and buses. The factors affecting different EV charging loads can be generalized into 3 aspects of electric vehicle temporal characteristics, spatial characteristics, and battery characteristics.
1) Time characteristic: the charging time of the EV has strong randomness, the travel time of a single EV is difficult to study, but the travel time characteristic of the EV cluster conforms to normal distribution, and the charging and discharging behaviors of each EV are set to be carried out only once every day, so that the time f when the EV is connected into a power grids(x) Time f of departure from EV gridx(x) Obey a normal distribution.
Figure RE-GDA0003552800000000101
Figure RE-GDA0003552800000000102
Wherein mux、μsAnd σx、σsThe expected value and standard deviation, mu, of the normal distribution function of the early peak and the late peak, respectivelys＝17.5，σs＝3.3，μx＝10，σx＝3.1。
The time cost of the travel of the vehicle owner is considered in the ordered charging and discharging strategy model, the cost is mainly influenced by the time sensitivity degree of the vehicle owner, different electric vehicle types can correspond to different time sensitivity coefficients, and the time sensitivity coefficients of the four vehicle types are shown in table 1.
TABLE 1
Figure RE-GDA0003552800000000103
Because the rest time of the taxi is very limited and basically follows the principle of being close to in the selection of the charging pile, the time sensitivity of the taxi is 0.4. The charging time of the bus is influenced by the shift arrangement, and the bus is generally charged in a centralized manner at night and at rest time, so that the time sensitivity of the bus is 0.2. The time sensitivity of private cars and public cars is influenced by actual conditions, the randomness is strong, and the distribution is shown in table 1.
2) Spatial characteristics: the traffic network can be abstracted into a network graph formed by line segments and points, and the traffic network is modeled by adopting a graph theory method. The road intersection nodes are represented by an integer sequence 1, 2, …, m (m is the number of nodes), and the road traveling from node i to node j is represented by a directed edge (i, j). Car owners of different car types can correspond to different travel purposes, the urban area is divided into 4 types, namely a residential area, a working area, a commercial area and a comprehensive area, and the travel purposes corresponding to each car type are shown in table 2.
TABLE 2
Figure RE-GDA0003552800000000111
Given the Global Positioning System (GPS) positioning longitude and latitude coordinates of the first node and the last node of the road, the linear distance between 2 nodes can be calculated by the formula (7), the actual road is considered to have certain tortuosity and gradient, and the tortuosity coefficient is multiplied by 1.15 during simulation.
Figure RE-GDA0003552800000000112
The road condition and the weather temperature are two key factors for determining unit energy consumption, the EV can correspond to different unit distance energy consumption when driving on roads with different road conditions, and the change of the temperature D can not only influence the loss of the battery, but also influence the air conditioning rate D of the EVon。
The urban road is divided into 4 grades, namely a expressway, a main road, a secondary road and a branch road, the roads of each grade have different speeds v under different traffic road conditions, and the corresponding energy consumption is as follows:
Figure RE-GDA0003552800000000113
corresponding to different temperatures, the opening rate D of the air conditioneronCan be expressed as:
Don＝q1d3+q2d2+q3d+b1 (9)
wherein q is1、q2、q3And b1Are fitting parameters.
Ratio D of unit distance energy consumption when the air conditioner is turned on to unit distance energy consumption when the air conditioner is turned offrateCan be expressed as:
Drate＝q4(d+b2)2+b3 (10)
wherein q is4、b2And b3Are fitting parameters.
In summary, the energy consumption per unit distance of the EV can be expressed as:
Figure RE-GDA0003552800000000121
and (3) solving the shortest path from the starting point to the end point by adopting a Dijkstra algorithm, wherein G is the set of all roads, and the shortest path L can be expressed as:
Figure RE-GDA0003552800000000122
3) battery characteristics: due to the trip characteristic of the private car, the charging place can be selected to be in a unit parking lot and a residential area parking lot at night, if the private car is charged in the unit parking lot, the charging time length is usually not more than 3h, therefore, the quick charging mode is selected, and if the private car is charged in the residential area parking lot, the charging time length can be continued all night, so that the conventional charging mode is selected, and which charging mode is mainly influenced by time and place. The bus has the advantages that the operation time and the route of the bus are relatively centralized, centralized charging can be carried out, charging is not arranged in the daytime operation peak period, quick charging is carried out in the lunch change lunch time period at noon, and conventional charging is carried out after work at night. The electric taxi selects a quick charging mode because the taxi has limited rest time and high time sensitivity and needs to supplement electric quantity in time. The business car is mainly used as a daily business trip of a government organization, and the running characteristic of the business car is similar to that of a private car without considering long-distance trip. The battery characteristics and charging methods for different vehicle types are shown in table 3.
TABLE 3
Figure RE-GDA0003552800000000123
The short-term safety risk index and the economic risk index of the invention are as follows:
short-term safety risk indexes:
the grid is a complex system, the nodes of which are not isolated connections, but rather a whole of constraints and influences on each other, wherein the vulnerability of each element is not only related to the structural position of the element in the grid, but also related to the influence of the element on the nodes of other elements when the grid is in operation. Therefore, when the risk of accessing the DG and the EV into the power grid is evaluated, the influence of various factors needs to be comprehensively considered, so the invention provides a power grid short-term safety risk evaluation model combining network structure vulnerability and a risk theory. The node importance comprehensively considers the node degree, the betweenness and the proportion of the conventional load connected with the node, the branch importance is measured by the line degree and the betweenness, and the calculation formulas are respectively as follows:
ρv,i＝α1Dv,j+α2Bv,j+α3NPj (13)
ρl,l＝β1Dl,k+β2Bl,k (14)
where ρ isv,i、ρl,lRespectively the node importance of the node i and the branch importance of the line l; dv,j、Bv,jRespectively is node degree and betweenness; n is a radical ofPjInjecting power for the node; dl,jAnd Bl,jRespectively, the number of lines and the number of medians; alpha is alpha1、α2、α3Weight coefficients of node degree, node betweenness and node injection power respectively, and has alpha1+α2+α3＝1；β1、β2Weight coefficients of line degree and line number, respectively, and has beta1+β 21. The invention adopts an AHP method to determine the magnitude of each weight coefficient.
The EV charging load brings short-term safety risk to a power grid, and the influence indexes comprise node voltage out-of-limit risk indexes and line power out-of-limit risk indexes.
The calculation formula of the node voltage out-of-limit operation risk index is as follows:
Figure RE-GDA0003552800000000131
Figure RE-GDA0003552800000000132
wherein R isv,i(t) is a voltage out-of-limit operation risk index value of the node i at the time t;nv,i(t) is the number of voltage states of node i at time t; p (S)v,j) Probability of being the jth voltage state; sv,j(t) the severity of voltage loss at node i at jth voltage state at time t; v and Vmax、VminThe voltage qualified range and the per unit values of the upper limit and the lower limit of the voltage qualified range are respectively.
The calculation formula of the line power out-of-limit risk index is as follows:
Figure RE-GDA0003552800000000133
Figure RE-GDA0003552800000000134
wherein R isl,l(t) is the power out-of-limit risk index value of the line l at the time t; n isl,l(t) is the number of flow states of the line l at the time t; p (S)l,k) Probability of the kth tidal state; p (S)l,k) The severity of the voltage loss of the kth power flow state of the line l at the time t; l islIs the ratio of the actual active power to the rated active power of the line l.
RSRIThe short-term comprehensive safety risk coefficient of the operation of the characterization system,
Figure RE-GDA0003552800000000135
characterizing voltage risks caused by node voltage violations of the distribution grid system and distribution uncertainties thereof,
Figure RE-GDA0003552800000000136
representing the power flow risk caused by the out-of-limit of branch power and the distribution uncertainty of the branch power of the power distribution network system, and then:
Figure RE-GDA0003552800000000137
wherein, γ1、γ2Is a security risk weight coefficient and has a1+γ2＝1。
Second, economic risk index:
the Economic Risk index ERI (Economic Risk indicator) of the DG and EV charging loads accessed to the power distribution network consists of two parts of Line Loss Risk ELLR (Economic Line-Loss Risk) and business benefit Risk EPLR (Economic operational Risk or Loss Risk), and the calculation formula is as follows:
CERI(t)＝CELLR(t)-CEPLR(t) (20)
CELLR(t)＝Cprice(t)Ploss(t) (21)
Figure RE-GDA0003552800000000141
Figure RE-GDA0003552800000000142
Figure RE-GDA0003552800000000143
Figure RE-GDA0003552800000000144
wherein, CERI(t) is the ERI value of the power distribution network at the moment t; cELLR(t)、CEPLR(t) the ELLR and EPLR index values of the power distribution network at the time t respectively; cprice(t) the electricity price of the power distribution network at the moment t; ploss(t) is the network loss power of the power distribution network at the moment t; cenv(t) subsidy benefits given by the government for the power distribution network at the moment t;
Figure RE-GDA0003552800000000145
the operation and maintenance cost and the electricity selling income of the ith' station DG at the moment t are respectively; n is the number of DGs;
Figure RE-GDA0003552800000000146
the time-varying electricity price of the unit power of the ith' station DG at the moment t;
Figure RE-GDA0003552800000000147
the active output power of the ith' station DG at the moment t; n is the number of DGs; mu.si′The maintenance cost of the unit power of the ith' station DG;
Figure RE-GDA0003552800000000148
the active output power of the ith' type DG of the node m at the time t; pWDG(t)、PWODG(t) power acquired by the front and rear power distribution networks from the large power grid at the moment of time t by DG access respectively; mjThe j-th type pollution gas emission coefficient is the unit generated power of the power distribution network; cjThe treatment cost of the j-th type pollution gas is high; m' is the number of types of the polluted gas.
The IEEE33 node is modified into an AC/DC hybrid power distribution network, and the topological structure is shown in figure 1 and is used for verifying the effect of a risk assessment system. The alternating current system is a 10kV network, the reference voltage is 12.66kV, the three-phase power reference value is 10MV & A, the node 1 is a balance node, and the voltage is set to be 1.05 p.u.. The DC system reference voltage is 10kV, and the reference capacity is 10MV & A. The wind power equivalent is connected into an alternating current node 18, the EV charging load of 13MW is equivalently connected into an alternating current node 27, the photovoltaic equivalent is connected into a direct current node 10, and the sum of the expected peak values of the conventional loads is 3.715 MW. Relevant parameters for EV are shown in tables 4-7. The simulation parameter and distribution parameter variation curves of DG are respectively shown in Table 8 and FIG. 2, and the DG output curve is shown in FIG. 3. The power factors of the load and the power supply are both 0.95. The operating and maintaining costs of wind power and photovoltaic power generation are both 55 yuan. Accuracy k of MCS methodeSet to 0.05% in order to make the maximum variance coefficients less than keThe number of simulations was set to 4000.
TABLE 4
Figure RE-GDA0003552800000000151
TABLE 5
Figure RE-GDA0003552800000000152
TABLE 6
Figure RE-GDA0003552800000000153
TABLE 7
Figure RE-GDA0003552800000000154
TABLE 8
Figure RE-GDA0003552800000000161
(1) EV charging load analysis
Expected charging power values for 4 EV types are obtained based on the MCS method, as shown in fig. 4. As can be seen from fig. 4, for the private car, it takes a fast charge mode with a large constant current for the time periods 09: 00-12: 00, 14: 00-17: 00 to charge, resulting in a double peak load state; in the time periods of 00: 00-07: 00 and 19: 00-24: 00, although a conventional charging mode is adopted, a load peak is caused by access of a large number of private cars, wherein the charging load and the conventional load of the private cars in the time period of 19: 00-24: 00 reach peak values, the operation risk of a power grid is aggravated, and in the time period of 00: 00-07: 00, the electric quantity of a battery of most private cars is close to saturation, so that the charging load of the private cars is reduced. For a bus, it takes a regular charging mode for a period of 19: 00-24: 00, creating a single peak load, which to some extent exacerbates the total load for that period. For a bus, the bus is charged in a quick charging mode in a time period of 13: 00-16: 00, so that the load reaches the peak value in the day; the conventional charging mode is adopted in the time periods of 00: 00-01: 00 and 23: 00-24: 00, so that the load climbs at night, impact is brought to the operation risk of the power distribution network, but the charging mode is complementary to the charging load of other types of EVs, and the load peak-valley difference is reduced to a certain extent. For taxis, a fast charging mode with a large constant current is adopted in a period of 03: 00-05:00, and the taxi occupies the dominant position of EV charging load; the fast charging mode is also adopted in the 12: 00-14: 00 time period, which increases the peak value of the charging load in the daytime, but reduces the fluctuation of the load to a certain extent. In summary, the charging load fluctuates dramatically during the day, so it is necessary to analyze the grid operating risk.
(2) Safety and economic risk index analysis
In order to investigate the rationality and necessity of the short-term safety risk indicator, the present invention first compares the certainty assessment with the short-term safety risk indicator proposed by the present invention, i.e., equivalent access to 13MW of EV charge load at ac node 27, and estimates the ac voltage at nodes 1-18 over a period of 20: 00-21: 00, as shown in fig. 5 (ac voltage is per unit). Randomness of DG output power, EV charging power, and regular load is ignored in making deterministic evaluations. And calculating the alternating-current node voltage by adopting the average equivalent access node power. As can be seen from the left diagram of fig. 5, when deterministic evaluation is adopted, the nodes 9-18 are nodes whose voltage exceeds 0.93p.u., in other words, only 10 nodes have voltage violations under deterministic evaluation; however, as shown in the right diagram of fig. 5, according to the risk indicator provided by the present invention, the nodes 4-18 all have the voltage threshold risk, i.e., most of the nodes have the voltage threshold probability. It can be seen that the evaluation result does not reflect the actual operating state, since the deterministic evaluation ignores the "probability" and uncertainty.
Because the short-term safety risks of the DG and the EV in the power distribution network have difference under different time sequences, the AC node voltage out-of-limit risk index result at each time is obtained by considering the time sequence on the basis of fig. 5, as shown in fig. 6. As can be seen in fig. 6, the voltage violations in the spatial dimension are mainly concentrated at the nodes 5-18, 25-33 and exhibit a gradual trend up, which is caused by the nodes 18, 33 being at the end of the power distribution network system and being at a short electrical distance from the DG or EV. The time sequence change also has great influence on the power quality of the nodes 5 to 18 and the nodes 25 to 33, and the time dimension shows that the alternating current distribution network nodes are in a ratio of 06: 00-19: voltage out-of-limit occurs during the 00 time period because the EV charging load is small and the DG output power is too large at this time; in addition, a large number of taxis are charged quickly in the period of 01: 00-05:00, so that certain voltage out-of-limit occurs on nodes. The direct current distribution network node voltage is constant, and the node voltage out-of-limit condition is avoided, which shows that the direct current distribution network node is not influenced by time sequence change and is relatively stable.
Fig. 7-10 show the results of the ac and dc line power out-of-limit risk indicators. As can be seen from fig. 7, the line power out-of-limit risk is mainly concentrated at the head end of the ac distribution network during the period from 20:00 to 21: 00. It can be seen from fig. 8 that the line power off-limit risk is greatest for lines 1-2 during the 21:00 period, which is also the peak of the EV charging load and normal load overlap, resulting in the line power off-limit risk being greatest. As can be seen from fig. 9 and 10, at 06: 00-19: the reason why the branch load current of the direct current power distribution network exceeds the limit in the 00 time period and the risk value is larger than that of other time periods is that the EV charging load of the alternating current power distribution network is smaller and the DG output power is overlarge at the moment.
The results of the one-day ERI of the ac and dc power distribution networks are shown in fig. 11 and 12 through the time-series economic risk assessment. As can be seen from FIG. 11, CERI is positive for periods 05:00 and 20:00-22:00, and the maximum occurs between periods 21: 00-22: 00. The period of maximum risk is 20:00-22:00, where the economic and safety risk values are high because the normal load and EV charging load add up to the peak load. Meanwhile, the operating state of the distribution network can be classified into the following 2 types: during periods 06: 00-19: 00, 23:00-03:00, the value of RSRI is greater than 0, and the value of CERI is less than 0, indicating that the operating state is economical but unsafe, measures should be taken to reduce RSRI, for example reducing the output power of DG or increasing the charging power of EV station; and secondly, in the time periods of 04:00-05:00 and 20:00-22:00, the values of RSRI and CERI are positive, which shows that the running state of the power distribution network is neither safe nor economic due to huge load demand and DG output fluctuation, and measures are taken to improve the running quality of the actual power distribution network. As can be seen from fig. 12, the RSRI of the dc distribution network at each time interval is almost 0, while the CERI is less than 0, and the distribution network is safe and economical.

Claims (1)

1. A safety risk assessment method for an alternating current-direct current hybrid power distribution network is characterized by comprising the following steps: the method comprises the following steps:
s1, constructing a wind-solar output and EV time sequence probability distribution model, and replacing the subjectively set initial charge states of multiple types of EVs with daily driving mileage; the factors influencing different EV charging loads can be generally summarized into three aspects of electric vehicle time characteristics, space characteristics and battery characteristics
1) Time characteristic: setting the charging and discharging behaviors of each EV only once every day, and the time f for the EV to be connected into the power grids(x) Time f of departure from EV gridx(x) Obey normal distribution
Figure FDA0003353763960000011
Figure FDA0003353763960000012
Wherein mux、μsAnd σx、σsRespectively an expected value and a standard deviation of the normal distribution function of the early peak and the late peak;
2) spatial characteristics: road cross nodes are represented by an integer sequence 1, 2, …, m, wherein m is the number of nodes, roads running from the node i to the node j are represented by directed edges (i, j), owners of different vehicles can correspond to different trip purposes, urban areas are divided into four types, namely a residential area, a working area, a commercial area and a comprehensive area, the longitude and latitude coordinates of the known global positioning system of the first node and the last node of the road are located, and the linear distance between 2 nodes is calculated by a formula (7)
Figure FDA0003353763960000013
The change in temperature, D, affects the air conditioning rate D of the EV, in addition to the depletion of the batteryon；
The urban road is divided into four levels, namely a expressway, a main road, a secondary road and a branch road, the roads of each level have different speeds v under different traffic road conditions, and the energy consumption corresponding to the roads is as follows:
Figure FDA0003353763960000014
corresponding to different temperatures, the opening rate D of the air conditioneronExpressed as:
Don＝q1d3+q2d2+q3d+b1 (9)
wherein q is1、q2、q3And b1Is a fitting parameter; ratio D of unit distance energy consumption when the air conditioner is turned on to unit distance energy consumption when the air conditioner is turned offrateExpressed as:
Drate＝q4(d+b2)2+b3 (10)
wherein q is4、b2And b3To fit the parameters, and to sum up, the energy consumption per distance of the EV is expressed as:
Figure FDA0003353763960000021
g is the set of all roads, the shortest path L is represented as:
Figure FDA0003353763960000022
3) battery characteristics: the charging place is selected to be a unit parking lot and a residential area parking lot at night, if the unit parking lot is charged, the charging time is usually not more than 3 hours, if the unit parking lot is charged, the charging time can be prolonged to the whole night, so a conventional charging mode is selected, which charging mode is mainly influenced by time and place, the operation time and routes of buses are relatively centralized, centralized charging can be performed, charging is not arranged in the daytime operation peak period, quick charging is performed in the noon shift lunch time period, and conventional charging is performed after work at night, as the rest time of a taxi is limited, the time sensitivity is very high, and the electric quantity needs to be timely supplemented, the electric taxi selects the quick charging mode, a housekeeper is used as a daily public affair trip of the government, and the driving characteristic is similar to that of a private car without considering long-distance trip;
s2, based on the complex network theory, providing time sequence safety indexes such as voltage out-of-limit risk and branch power overload operation risk, and establishing economic risk and power grid high efficiency risk indexes according to economic operation of the power distribution network
Short-term safety risk indexes: the short-term security risk assessment model of the power grid in combination with the vulnerability of the network structure and the risk theory comprehensively considers the node importance degree, the betweenness and the proportion of the conventional load connected with the node, and the branch importance degree is measured by the line degree and the betweenness:
ρv,i＝α1Dv,j+α2Bv,j+α3NPj (13)
ρl,l＝β1Dl,k+β2Bl,k (14)
where ρ isv,i、ρl,lRespectively the node importance of the node i and the branch importance of the line l; dv,j、Bv,jRespectively is node degree and betweenness; n is a radical ofPjInjecting power for the node; dl,jAnd Bl,jRespectively, the number of lines and the number of medians; alpha is alpha1、α2、α3Weight coefficients of node degree, node betweenness and node injection power respectively, and has alpha1+α2+α3＝1；β1、β2Weight coefficients of line degree and line number, respectively, and has beta1+β2＝1；
The node voltage out-of-limit operation risk indexes are as follows:
Figure FDA0003353763960000031
Figure FDA0003353763960000032
wherein R isv,i(t) is a voltage out-of-limit operation risk index value of the node i at the time t; n isv,i(t) is the number of voltage states of node i at time t; p (S)v,j) Probability of being the jth voltage state; sv,j(t) the severity of voltage loss at node i at jth voltage state at time t; v and Vmax、VminThe voltage qualified range and the per unit values of the upper limit and the lower limit of the voltage qualified range are respectively;
the line power out-of-limit risk indexes are as follows:
Figure FDA0003353763960000033
Figure FDA0003353763960000034
wherein R isl,l(t) is the power out-of-limit risk index value of the line l at the time t; n isl,l(t) is the number of flow states of the line l at the time t; p (S)l,k) Probability of the kth tidal state; p (S)l,k) The severity of the voltage loss of the kth power flow state of the line l at the time t; l islThe ratio of the actual active power of the line l to the rated active power is obtained;
RSRIthe short-term comprehensive safety risk coefficient of the operation of the characterization system,
Figure FDA0003353763960000035
characterizing voltage risks caused by node voltage violations of the distribution grid system and distribution uncertainties thereof,
Figure FDA0003353763960000036
representing the power flow risk caused by the out-of-limit of branch power and the distribution uncertainty of the branch power of the power distribution network system, and then:
Figure FDA0003353763960000037
wherein, γ1、γ2Is a security risk weight coefficient and has a1+γ2＝1；
Second, economic risk index: the economic risk index ERI of the DG and EV charging loads accessed to the power distribution network consists of a line loss risk ELLR and an operation loss risk EPLR
CERI(t)＝CELLR(t)-CEPLR(t) (20)
CELLR(t)＝Cprice(t)Ploss(t) (21)
Figure FDA0003353763960000038
Figure FDA0003353763960000039
Figure FDA0003353763960000041
Figure FDA0003353763960000042
Wherein, CERI(t) is the ERI value of the power distribution network at the moment t; cELLR(t)、CEPLR(t) the ELLR and EPLR index values of the power distribution network at the time t respectively; cprice(t) the electricity price of the power distribution network at the moment t; ploss(t) is the network loss power of the power distribution network at the moment t; cenv(t) subsidy benefits given by the government for the power distribution network at the moment t;
Figure FDA0003353763960000043
the operation and maintenance cost and the electricity selling income of the ith' station DG at the moment t are respectively; n is the number of DGs;
Figure FDA0003353763960000044
the time-varying electricity price of the unit power of the ith' station DG at the moment t;
Figure FDA0003353763960000045
the active output power of the ith' station DG at the moment t; n is the number of DGs; mu.si′The maintenance cost of the unit power of the ith' station DG;
Figure FDA0003353763960000046
the active output power of the ith' type DG of the node m at the time t; pWDG(t)、PWODG(t) power acquired by the front and rear power distribution networks from the large power grid at the moment of time t by DG access respectively; mjThe j-th type pollution gas emission coefficient is the unit generated power of the power distribution network; cjThe treatment cost of the j-th type pollution gas is high; m' is the number of types of the polluted gas.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115313529A (en) * 2022-08-05 2022-11-08 国网安徽省电力有限公司经济技术研究院 Electric energy quality assessment method considering spatial characteristics and alternating current-direct current two-side coupling effect
CN116822436A (en) * 2023-06-30 2023-09-29 四川大学 Oscillation risk sensitivity analysis method for direct-current transmission end alternating-current system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115313529A (en) * 2022-08-05 2022-11-08 国网安徽省电力有限公司经济技术研究院 Electric energy quality assessment method considering spatial characteristics and alternating current-direct current two-side coupling effect
CN115313529B (en) * 2022-08-05 2023-09-12 国网安徽省电力有限公司经济技术研究院 Electric energy quality assessment method considering spatial characteristics and coupling effect of alternating current and direct current
CN116822436A (en) * 2023-06-30 2023-09-29 四川大学 Oscillation risk sensitivity analysis method for direct-current transmission end alternating-current system
CN116822436B (en) * 2023-06-30 2024-02-27 四川大学 Oscillation risk sensitivity analysis method for direct-current transmission end alternating-current system

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