CN112994063B - Power distribution network optimized operation method based on energy storage ordered charging and intelligent soft switch control model - Google Patents

Power distribution network optimized operation method based on energy storage ordered charging and intelligent soft switch control model Download PDF

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CN112994063B
CN112994063B CN202110476833.5A CN202110476833A CN112994063B CN 112994063 B CN112994063 B CN 112994063B CN 202110476833 A CN202110476833 A CN 202110476833A CN 112994063 B CN112994063 B CN 112994063B
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CN112994063A (en
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周念成
田雨禾
王健
廖建权
王强钢
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Chongqing 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • 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
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • 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
    • Y02T90/12—Electric charging stations

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Abstract

The invention discloses a power distribution network optimized operation method based on energy storage ordered charging and an intelligent soft switch control model. And aiming at the change of a certain load curve, classifying the time-of-use electricity price by using a membership function, taking the electric automobile as energy storage, and providing a constraint condition for orderly charging the energy storage. Establishing a two-stage optimization model of the power distribution network based on rolling optimization scheduling: firstly, determining a charging plan corresponding to each user by taking the minimum charging cost of the user as an optimization target; on the premise of the first-stage optimization model, the second-stage optimization target with the minimum power loss of the power distribution network is achieved by controlling the power of the intelligent soft switch. The invention solves the technical problems of orderly charging of stored energy and intelligent soft switch control in a comprehensive optimization power system, researches the coordination and coordination problems among different technologies, and improves the voltage distribution condition of a power distribution network.

Description

Power distribution network optimized operation method based on energy storage ordered charging and intelligent soft switch control model
Technical Field
The invention relates to the fields of energy storage sequential charging technology, intelligent soft switch power control technology and power distribution network optimized operation.
Background
Electric vehicles are increasingly receiving attention from various countries as a clean and efficient vehicle. However, with the large-scale access of future electric vehicles to the distribution system, the phenomenon of excessive power consumption of the power grid load can be caused in certain specific time periods, and in addition, the charging behavior of the electric vehicles has randomness and uncertainty, centralized charging even causes insufficient reactive power of the power system in the peak period of load, and the stability of the power distribution network is influenced. Especially, in the off-duty peak period, the situation is more obvious when the owner of the electric automobile is charged in a centralized way.
Therefore, the charging load of the electric vehicle needs to be controlled. On one hand, by controlling the electric automobile in the charging station, the electric automobile can realize the bidirectional real-time information communication of 'automobile-pile-network' through the interaction of the charging infrastructure and the power distribution network, thereby forming the real-time scheduling of the electric automobile; on the other hand, as a novel power electronic device, the intelligent soft switch effectively improves the influence of centralized charging on the power distribution network in a manner of transferring tide or compensating reactive power. Under the environment of large-scale electric automobile grid connection, the voltage quality condition of a power distribution network is considered, the energy storage ordered charging technology and the intelligent soft switching power control technology are researched, and the method has important significance for safe and stable operation of a power grid.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the power distribution network optimized operation method based on the energy storage ordered charging and intelligent soft switch control model, solves the technical problem of how to comprehensively optimize the energy storage ordered charging and the intelligent soft switch control in the power system, and considers the coordination and coordination problems among different technologies so as to improve the voltage distribution condition of the power distribution network.
In order to solve the technical problem, the invention adopts the following scheme:
a power distribution network optimized operation method based on energy storage ordered charging and intelligent soft switch control is characterized by comprising the following steps:
1) When time-of-use electricity price classification is carried out, corresponding classification is carried out by adopting a membership function, the probabilities of all points on the load curve at all time periods are calculated by the semi-trapezoidal membership functions which are larger and smaller, and the expression is as follows;
Figure GDA0003845515070000021
Figure GDA0003845515070000022
in the formula, a and b respectively represent the values of a valley point and a peak point of a load per unit curve of the power distribution network;
2) Determining three time periods of time-of-use electricity price according to the calculation result of the load curve, wherein the three time periods comprise a peak electricity price time period, a flat electricity price time period and a valley electricity price time period;
3) Establishing an electric vehicle ordered charging strategy based on time-of-use electricity price classification of the membership function, wherein basic constraint conditions of ordered charging need to meet basic charging requirements of electric vehicle users;
the charging is only carried out within the staying time of the user, when the user leaves the electric vehicle charging station, the electric quantity of the battery meets the driving requirement of the user, meanwhile, the charging cost of the electric vehicle user is required to be met, and the expression is as follows;
Figure GDA0003845515070000023
Figure GDA0003845515070000024
Figure GDA0003845515070000025
emin≤ei,x,t≤emax；
Figure GDA0003845515070000026
ti,x,c≤ti,x,d-ti,x,a；
Figure GDA0003845515070000027
in the formula, η represents the charging efficiency of the electric vehicle; s isCIndicating the rated capacity of the charging pile; n is a radical of hydrogenEVRepresenting the number of electric vehicles of the node i;
Figure GDA0003845515070000028
represents a period of tActive power of the charging pile side of the electric automobile x in the node i;
Figure GDA0003845515070000029
the active power of the x battery side of the electric automobile in the node i in the t period is represented;
Figure GDA00038455150700000210
representing the total active power of the electric vehicle at a node i in a period t;
Figure GDA00038455150700000211
representing the active power of the x battery side of the electric automobile in the node i in the k period; e.g. of the typei,x,tRepresenting the state of charge of the electric vehicle x in the node i in the period t, considering the requirements of a user, and simultaneously ensuring the use safety of a battery, eminAnd emaxSet to 0.2 and 0.95, respectively; t is ti,x,cRepresenting the residence time of the electric vehicle x in the t period node i; t is ti,x,aAnd ti,x,dRespectively representing the arrival time and the expected departure time of the electric vehicle x in the t period node i; e.g. of the typei,x,aAnd ei,x,dRespectively representing the initial and expected states of charge of the electric vehicle x in the node i in the t period; Δ t represents a time interval; s isEThe rated capacity of the electric automobile is represented;
the optimization operation model of the power distribution network needs to take the benefits of both the user side and the power grid side into consideration;
on the user side, guiding the user to participate in the ordered charging strategy based on a mechanism of time-of-use electricity price so as to reduce the charging cost;
and on the power grid side, the loss of the power distribution network is reduced based on reactive compensation and branch power transfer of the intelligent soft switch, so that the economy of the power distribution network is improved.
Further, the power distribution network optimization operation model is optimized in two stages, the optimization goal of the first stage is that the charging cost of a user is minimum, and the expression is as follows;
Figure GDA0003845515070000031
in the formula, ctAnd represents the time of use electricity price.
On the premise of ensuring the benefits of electric automobile users, the network loss of the power distribution network is reduced by utilizing the reactive compensation and branch power transfer capability of the intelligent soft switch. Therefore, the optimization goal of the second stage is that the power distribution network loss is minimum, the power distribution network loss mainly comprises branch active loss and intelligent soft switch active loss, and the expression is as follows;
Figure GDA0003845515070000032
in the formula, E represents all branch sets in the network; n is a radical ofSOPRepresenting a set of nodes comprising intelligent soft switches;
Figure GDA0003845515070000033
representing the active loss of the branch ij in the period t;
Figure GDA0003845515070000034
and
Figure GDA0003845515070000035
respectively representing the active loss of a voltage source type converter i and a voltage source type converter j in a period t;
further, the power distribution network optimization operation model adopts rolling scheduling, and when a new control period comes, a first stage and a second stage are circulated until the end of one day.
Compared with the prior art, the invention has the following beneficial effects:
1. the method adopts the ordered charging method, avoids the problems of increasing the maximum load peak value of the distribution network due to centralized charging, preventing the node voltage from decreasing, preventing the network loss from increasing and the like, and leads the electric automobile to be charged in the low-load period of the distribution network by the ordered charging, thereby reducing the peak value of a load curve.
2. The method adopts a strategy of time-of-use electricity price, and firstly, the charging plan of the electric automobile is determined by taking the minimum charging cost of a user as an optimization target. Then, the benefit maximization of electric automobile users is ensured, and meanwhile, the second stage takes the minimum power distribution network loss as an optimization target, and the increase of the economic benefit of the power distribution network is achieved.
3. According to the invention, the intelligent soft switch power control is adopted, and after the intelligent soft switch is connected into the system, the active power of the branch can be transferred, the reactive compensation is carried out on the power distribution network, the node voltage of the power distribution network is improved, and the loss of the power distribution network is reduced.
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Fig. 1 is a flow chart for optimizing operation of a power distribution network in consideration of energy storage sequential charging and intelligent soft switching control in the present embodiment.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Referring to fig. 1, the invention provides a power distribution network optimal operation method based on energy storage ordered charging and intelligent soft switching control, which is characterized by comprising the following steps:
1) When time-of-use electricity price classification is carried out, corresponding classification is carried out by adopting a membership function, the probabilities of all points on the load curve of each time period are calculated by adopting the membership functions of larger half trapezoids and smaller half trapezoids, and the expression is as follows;
Figure GDA0003845515070000041
Figure GDA0003845515070000042
in the formula, a and b respectively represent values of a valley point and a peak point of a load per unit curve of the power distribution network;
2) Determining three time periods of time-of-use electricity price according to the calculation result of the load curve, wherein the three time periods comprise a peak electricity price time period, a flat electricity price time period and a valley electricity price time period;
3) Establishing an electric automobile ordered charging strategy based on the time-of-use electricity price classification of the membership function, wherein basic constraint conditions of ordered charging need to meet basic charging requirements of electric automobile users;
the charging is carried out only within the stay time of the user, when the user leaves the electric vehicle charging station, the electric quantity of the battery meets the driving requirement of the user, meanwhile, the lowest charging cost of the electric vehicle user needs to be met, and the expression is as follows;
Figure GDA0003845515070000051
Figure GDA0003845515070000052
Figure GDA0003845515070000053
emin≤ei,x,t≤emax；
Figure GDA0003845515070000054
ti,x,c≤ti,x,d-ti,x,a；
Figure GDA0003845515070000055
in the formula, η represents the charging efficiency of the electric vehicle; sCIndicating the rated capacity of the charging pile; n is a radical ofEVRepresenting the number of electric vehicles of the node i;
Figure GDA0003845515070000056
the active power of the x charging pile side of the electric automobile in the node i in the t period is represented;
Figure GDA0003845515070000057
representing the active power of the x battery side of the electric automobile in the node i in the t period;
Figure GDA0003845515070000058
electric automobile assembly for representing t-period node iActive power;
Figure GDA0003845515070000059
the active power of the x battery side of the electric automobile in the node i in the k time period is represented; e.g. of the typei,x,tRepresenting the state of charge of the electric vehicle x in the node i in the period t, considering the requirements of a user, and simultaneously ensuring the use safety of a battery, eminAnd emaxSet to 0.2 and 0.95, respectively; t is ti,x,cRepresenting the stay time of the electric vehicle x in the node i in the period t; t is ti,x,aAnd ti,x,dRespectively representing the arrival time and the expected departure time of the electric vehicle x in the t period node i; e.g. of the typei,x,aAnd ei,x,dRespectively representing the initial state of charge and the expected state of charge of the electric vehicle x in the node i in the t period; Δ t represents a time interval; sERepresenting the rated capacity of the electric automobile;
the optimization operation model of the power distribution network needs to take the benefits of both a user side and a power grid side into consideration;
on the user side, guiding the user to participate in the ordered charging strategy based on a mechanism of time-of-use electricity price so as to reduce the charging cost;
and on the side of the power grid, the loss of the power distribution network is reduced based on reactive compensation and branch power transfer of the intelligent soft switch so as to improve the economy of the power distribution network.
Further, the power distribution network optimization operation model needs to be optimized in two stages, the optimization goal in the first stage is that the charging cost of a user is minimum, and the expression is as follows;
Figure GDA00038455150700000510
in the formula, ctRepresenting the time of use electricity price.
Furthermore, on the premise of ensuring the benefits of electric automobile users, the network loss of the power distribution network is reduced by utilizing the reactive power compensation and branch power transfer capability of the intelligent soft switch. Therefore, the optimization goal of the second stage is that the power distribution network loss is minimum, the power distribution network loss mainly comprises branch active loss and intelligent soft switch active loss, and the expression is as follows;
Figure GDA0003845515070000061
in the formula, E represents all branch sets in the network; n is a radical ofSOPRepresenting a set of nodes comprising intelligent soft switches;
Figure GDA0003845515070000062
representing the active loss of the branch ij in the period t;
Figure GDA0003845515070000063
and
Figure GDA0003845515070000064
respectively representing the active loss of a voltage source type converter i and a voltage source type converter j in a period t;
furthermore, the power distribution network optimization operation model adopts rolling scheduling, and when a new control period is reached, one stage and two stages are circulated until one day is finished.
The invention utilizes the membership function to classify the time-of-use electricity price aiming at the change of a certain load curve, takes the electric automobile as energy storage, and provides a constraint condition for orderly charging the energy storage. Establishing a two-stage optimization model of the power distribution network based on rolling optimization scheduling: and determining a charging plan corresponding to each user by taking the minimum charging cost of the user as an optimization target. On the premise of the first-stage optimization model, the second-stage optimization target with the minimum power loss of the power distribution network is achieved by controlling the power of the intelligent soft switch. The invention solves the technical problems of orderly charging of stored energy and intelligent soft switch control in a comprehensive optimization power system, researches the coordination and coordination problems among different technologies, and improves the voltage distribution condition of a power distribution network. By adopting the ordered charging method, the problem that the peak value of the maximum load of the power distribution network is increased by centralized charging is avoided, the problems of node voltage reduction, network loss increase and the like are prevented, and the ordered charging enables the electric automobile to be charged in the low-load period of the power distribution network, so that the peak value of a load curve is reduced. Due to the fact that the strategy of time-of-use electricity price is adopted, the charging plan of the electric automobile is determined by taking the minimum charging cost of the user as an optimization target, the benefit maximization of the user of the electric automobile is guaranteed, meanwhile, the second stage takes the minimum loss of the power distribution network as the optimization target, and the increase of the economic benefit of the power distribution network is achieved. And moreover, by adopting intelligent soft switch power control, after the intelligent soft switch is connected into the system, the active power of the branch can be transferred, reactive compensation is carried out on the power distribution network, the node voltage of the power distribution network is improved, and the loss of the power distribution network is reduced.
Finally it is noted that while the present invention has been described with reference to preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.

Claims (3)

1. A power distribution network optimal operation method based on energy storage ordered charging and intelligent soft switch control is characterized by comprising the following steps:
1) When time-of-use electricity price classification is carried out, corresponding classification is carried out by adopting a membership function, the probabilities of all points on the load curve at all time periods are calculated by the semi-trapezoidal membership functions which are larger and smaller, and the expression is as follows;
Figure FDA0003825633210000011
Figure FDA0003825633210000012
in the formula, a and b respectively represent values of a valley point and a peak point of a load per unit curve of the power distribution network;
2) Determining three time periods of time-of-use electricity price according to the calculation result of the load curve, wherein the three time periods comprise a peak electricity price time period, a flat electricity price time period and a valley electricity price time period;
3) Establishing an electric vehicle ordered charging strategy based on time-of-use electricity price classification of the membership function, wherein basic constraint conditions of ordered charging need to meet basic charging requirements of electric vehicle users;
the charging is carried out only within the stay time of the user, when the user leaves the electric vehicle charging station, the electric quantity of the battery meets the driving requirement of the user, meanwhile, the lowest charging cost of the electric vehicle user needs to be met, and the expression is as follows;
Figure FDA0003825633210000013
Figure FDA0003825633210000014
Figure FDA0003825633210000015
emin≤ei,x,t≤emax；
Figure FDA0003825633210000016
ti,x,c≤ti,x,d-ti,x,a；
Figure FDA0003825633210000021
in the formula, η represents the charging efficiency of the electric vehicle; sCThe rated capacity of the charging pile is represented; n is a radical of hydrogenEVRepresenting the number of electric vehicles of the node i;
Figure FDA0003825633210000022
the active power of the x charging pile side of the electric automobile in the node i in the t period is represented;
Figure FDA0003825633210000023
showing that the x battery side of the electric automobile exists in the node i in the t periodWork power;
Figure FDA0003825633210000024
representing the total active power of the electric vehicle at a node i in a period t;
Figure FDA0003825633210000025
representing the active power of the x battery side of the electric automobile in the node i in the k period; e.g. of a cylinderi,x,tRepresenting the state of charge of the electric vehicle x in the node i in the period t, considering the requirements of users, and simultaneously ensuring the use safety of the battery, eminAnd emaxSet to 0.2 and 0.95, respectively; t is ti,x,cRepresenting the stay time of the electric vehicle x in the node i in the period t; t is ti,x,aAnd ti,x,dRespectively representing the arrival time and the expected departure time of the electric vehicle x in the node i in the t period; e.g. of the typei,x,aAnd ei,x,dRespectively representing the initial state of charge and the expected state of charge of the electric vehicle x in the node i in the t period; Δ t represents a time interval; sEThe rated capacity of the electric automobile is represented;
the optimization operation model of the power distribution network needs to take the benefits of both the user side and the power grid side into consideration;
on the user side, guiding the user to participate in the ordered charging strategy based on a mechanism of time-of-use electricity price so as to reduce the charging cost;
and on the side of the power grid, the loss of the power distribution network is reduced based on reactive compensation and branch power transfer of the intelligent soft switch so as to improve the economy of the power distribution network.
2. The optimal operation method of the power distribution network based on the energy storage ordered charging and the intelligent soft switching control is characterized in that the optimal operation model of the power distribution network is optimized in two stages, the optimization goal of the first stage is that the charging cost of a user is minimum, and the expression is as follows;
Figure FDA0003825633210000026
in the formula, ctRepresenting the time of use electricity price;
the optimization goal of the second stage is that the power distribution network loss is minimum, the power distribution network loss mainly comprises branch active loss and intelligent soft switch active loss, and the expression is as follows;
Figure FDA0003825633210000027
in the formula, E represents all branch sets in the network; n is a radical of hydrogenSOPRepresenting a set of nodes comprising intelligent soft switches;
Figure FDA0003825633210000031
representing the active loss of the branch ij in the period t;
Figure FDA0003825633210000032
and
Figure FDA0003825633210000033
and respectively representing the active loss of the voltage source type converter i and the voltage source type converter j in the period t.
3. The method for optimizing the operation of the power distribution network based on the energy storage orderly charging and the intelligent soft switching control according to claim 1, wherein the power distribution network optimization operation model adopts rolling scheduling, and one phase and two phases are circulated every time a new control period comes, until the end of a day.
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