CN102708427B - System and method for analyzing and determining influence of large-scale charging pile to community distribution system - Google Patents
System and method for analyzing and determining influence of large-scale charging pile to community distribution system Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 29
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- 230000035699 permeability Effects 0.000 claims abstract description 12
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- 238000004458 analytical method Methods 0.000 claims description 56
- 238000013480 data collection Methods 0.000 claims description 40
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Abstract
The invention discloses a system and method for analyzing and determining the influence of a large-scale charging pile to a community distribution system. The system comprises a monitoring centre for analyzing and determining the influence of the large-scale charging pile to the community distribution system and a data acquiring terminal for acquiring the data of the charging pile, wherein the data acquiring terminal communicates with the charging pile by a network. According to the invention, the community distribution system capacity model, a normal distribution model of the community power load and power time, and charging real-time data are combined, so as to compare the calculated community distribution load curve with the distribution system capacity model, so that the operation condition of the community distribution system is monitored, a coordination scheduling policy for the community distribution system operation is determined and the permeability of the community electric vehicle is assessed. Moreover, the charging behaviours of the community electric vehicle are controlled in a coordinated manner, the influence of the community electric vehicle to the distribution system and the electric energy quality is reduced, the permeability of the community electric vehicle is improved, and the community distribution system is ensured to safely and stably operate.
Description
Technical field
The invention belongs to film used for electric vehicle and ring monitoring field, particularly relate to a kind of extensive charging pile to community distribution system impact analysis decision system and method.
Background technology
For solving environmental pollution, energy shortage problem, the development of electric automobile has become a kind of trend, increasing electric automobile will enter into ordinary family, add up according to investigations, to the selection of charging modes, consumer is more ready to charge at night, and selects to charge voluntarily at residence area, therefore then becomes imperative behave for residential quarter is equipped with electric automobile charging pile.Prefectures and cities also start to promote the work that charging pile enters community, comprise built community enlarging charging pile, newly-built community and reserve charging pile installation site etc.
At the construction of residential quarters initial stage, the design of distribution system can be carried out according to the scale of Residential Area and Related service facility, select suitable transformer number of units, capacity and power supply mode.No matter whether community distribution system design considers electric automobile charging pile charging load, according to the charging behavior randomness of electric automobile and the feature of randomness, after the distribution system of extensive electric automobile charging pile access community, Hui Gei community distribution system is run and is brought uncertainty.Riding electric vehicle charging electric current is generally 10 ~ 20A, and it is even longer that the duration of charging can reach 6 hours, and it is a very large load that hundreds of electric automobile charges to community distribution system in evening peak simultaneously.If do not carry out coordination optimization to the charging behavior of electric automobile and reduce community peak of power consumption workload demand, then community distribution line and transformer load rate may be caused to raise and even to transship, and increase community distribution system network loss, the deterioration quality of power supply; Even will produce adverse influence to electric system the whole network, as the equilibrium of supply and demand, Control of Voltage, relay protection, increase electric power capacity, increases distribution system construction and operating cost.
The impact of charging electric vehicle on electric system causes extensive concern, the at present research of mainly electric automobile impact analysis that electric system the whole network is run and dispatch control method, but consider that following electric automobile quantity may be very huge, carry out charging electric vehicle scheduling controlling at electric system the whole network and have very large enforcement difficulty under current technological conditions, therefore zonal electric automobile scheduling controlling can be developing direction in the future.Residential quarters are as the important development region of riding electric vehicle charging, also be the most base layer region of electric system, a large amount of charging pile accesses the impact analysis of communities distribution system and will be not only the stable guarantee of community Electrical Safety the cooperation control of the charging behavior of community electric automobile, is also the important support to power system stability, safety, economical operation.
Through finding the literature search of prior art, China Electric Power Research Institute's kingliness duty waits people to propose the orderly charge control method of a kind of electric automobile and system (number of patent application: 201010521514.3), but this system is only applicable to electrical network to be controlled the charging of electric automobile charging station, and do not consider the diversity of region electricity consumption and the load of charging station distribution system, and this system adopts centralized dispatching pattern, when considering that charging station quantity and data persistence increase, this mode can cause the problem occurring dimension calamity.
The people such as Tsing-Hua University Hu Zechun propose a kind of coordination charging control method for electric vehicle charging station (number of patent application: 201110023668.4), the method proposes coordination control strategy to single charging station, but do not have to consider the impact on electrical network electricity consumption peak load, and this method adopts the mode directly controlling charging set charging break-make, do not consider the impact of this kind of mode on charge permeability and battery life.
CAS Electrical Engineering Research Institute is permitted extra large equality people and is proposed a kind of sequential charging control system for electric vehicles (number of patent application: 201110148662.X), but this system is applicable to charging equipment, does not propose the method for work of higher level's coordinated control system.
The people such as the Xin Jianbo of Jiangxi Electric Power research institute propose the orderly charging method of a kind of electric automobile based on multi-agent system (number of patent application: 201110277269.0), but the method is only applicable to the Monitoring and Controlling to intelligent power or power-supply unit, do not consider numerous non intelligent confession distribution and consumer, do not consider the establishment of unintelligent device model, do not possess application conditions at present yet.
Summary of the invention
Object of the present invention is exactly to solve the problem, provide a kind of extensive charging pile to community distribution system impact analysis decision system and method, it can consider the diversity of region electricity consumption and the load of charging station distribution system, the permeability of community electric automobile can be assessed, increase the serviceable life of battery, ensure the operation that community power distribution system secure is stable.
To achieve these goals, the present invention adopts following technical scheme:
A kind of extensive charging pile, to community distribution system impact analysis decision system, comprises Surveillance center, data collection station, alternating-current charging pile,
Described Surveillance center comprises preposition service unit, data storage device, analysis decision service unit, for extensive charging pile to community distribution system impact analysis decision-making;
By network mode communication between described data collection station and alternating-current charging pile;
Described preposition service unit at least comprises a server gathered for charge data, obtains the charging pile charge data that data collection station gathers;
Described data storage device comprises at least one relational database server and at least one live database server, wherein, relational database is for storing static data needed for community distribution system capacity model, community power load and the normal distribution model of electricity consumption time, community charging pile monitoring model, community distribution load historical data and other analysis decisions and model; Real-time data base is for storing community charging pile charging real time data;
Described data collection station is responsible for and the alternating-current charging pile communication in community, obtains real time data when charging, comprises charging pile charged state, charging voltage, charging current, duration of charging and the available measurement data of other charging piles; Possess the condition of protection supervisory equipment in community distribution system under, data collection station also can obtain distribution system operational factor with protection supervisory equipment communication;
Described analysis decision service unit at least comprise one for charge data monitoring, analyze, the server of decision-making, for creating model needed for analysis decision, charge data is monitored in real time, the real-time analysis of community electricity consumption situation, charging coordinated scheduling control.
Described preposition service unit and data collection station adopt IEC870-5-101, IEC60870-5-104, circulating Telecontrol Protocol, CAN protocol or other universal communication stipulations to carry out data interaction by network, RS485 bus or CAN mode.
Adopt extensive charging pile to a decision-making technique for community distribution system impact analysis decision system, comprise the following steps:
Step one, set up and store community low-voltage distribution system capacity model, community power load and the normal distribution model of electricity consumption time, community alternating-current charging pile monitoring model;
Step 2, Real-time Collection store alternating-current charging pile charge data in community;
Step 3, analysis decision server are according to charging real time data and the normal distribution model of community power load and electricity consumption time, and calculation plot distribution load also stores;
Step 4, to compare with community distribution system capacity model to generate according to distribution load curve and analyze conclusion and scheduling controlling decision-making and store; If do not need scheduling controlling, wait for that the next analysis decision cycle carries out step 3; If need dispatch, forward step 5 to;
Step 5, Surveillance center issue scheduling control commands to charging pile, control charging pile duration of charging, charging current; Wait for that the next analysis decision cycle carries out step 3.
The community low-voltage distribution system capacity model of described step one, community power load and the normal distribution model of electricity consumption time, community alternating-current charging pile monitoring model, be all saved to the relational database of data storage device;
Wherein, community low-voltage distribution system capacity model, comprises community supply line quantity; Number transformer and transformer parameter; Community distribution system mode of connection topology exhibits; Described transformer parameter comprises: rated power, rated voltage, rated current, rated capacity and other parameters that can obtain;
The normal distribution model of community power load and electricity consumption time, adopts autoregressive moving average method, generates according to community electricity consumption historical data;
Community alternating-current charging pile monitoring model, comprises community and builds charging pile quantity, the monitoring of charging pile charging real time data: charged state, charging current, charging voltage, charging warning information and the available data of other charging piles.
The Real-time Collection of described step 2 stores alternating-current charging pile charge data in community and refers to alternating-current charging pile charge data in data collection station Real-time Collection community, communication protocol according to regulation is delivered to the front-collection device of Surveillance center, front-collection device is sent to data storage device by after civilian dissection process that the data collection station received is delivered newspaper, and charging pile charge data is saved in real-time data base by data storage.
The concrete steps of described step 2 are described below:
2-1), electric vehicle alternating-current charging pile and data collection station communication, the data such as charging pile duty, charging voltage, electric current are sent to data collection station;
2-2), data collection station and preposition service unit communication, on after charging pile data centralizations all in the community of acquisition, deliver to preposition service unit;
2-3), preposition service unit judges and resolves communication packet, abandons error message, normal for parsing data are delivered to data storage device;
2-4), data storage device according to data attribute, as deviation, memory gap, judge whether to need by data stored in real-time data base, not need stored in data directly abandon.
The concrete steps of described step 3 are described below:
3-1), analysis decision service unit judges whether to arrive the timing cycle of analysis decision, if then calling model data and charging real time data from data storage device;
3-2), calculate real time charging load, superpose with the normal distribution model of community power load with the electricity consumption time, generate community distribution load;
3-3), result of calculation is stored into the relational database of database service device;
The distribution load curve of described step 4 in nearest one day, calculates gained distribution load describe to form; Distribution load curve, comprises the distribution load curve in the time, emulates distribution load curve, power load and the normal distribution curve of electricity consumption time not coordinating to charge;
Analysis conclusion comprises current area electricity condition and whether causes adverse effect to distribution network load, as the impact on the equilibrium of supply and demand, Control of Voltage, the quality of power supply, and electric automobile permeability in community under current state;
Scheduling controlling decision-making comprises the need of carrying out scheduling controlling, scheduling strategy is formed: the charging behavior coordinating community electric automobile when needing to dispatch, determine in a period of time, as following 2h, the optimum charging strategy of electric automobile in community, coordinates charging electric vehicle opportunity, size of current and time length; And emulation forms community distribution load curve and community electric automobile permeability under non-operation dispatching strategy;
Scheduling controlling decision storage is to the relational database of database service device.
The concrete steps of described step 5 are described below:
5-1), cooperation control order is issued to preposition service unit by Surveillance center;
5-2), preposition service unit is issued to data collection station after cooperation control order being reorganized;
5-3), cooperation control order is issued to and can controls maybe to need the charging pile controlled to carry out cooperation control by data collection station.Described autoregressive moving average method is, the method regards the time series of community power load as a random series, and the dependence that this group stochastic variable has embodies community power load continuity in time; Have the impact of influenced factor while of self Fluctuation, influence factor comprises community electricity consumption type, electricity consumption electricity price, weather, temperature, festivals or holidays, is designated as x
1, x
2..., x
k, by regretional analysis:
P＝β
0+β
1x
1+β
2x
2+...+β
kx
k+e
Wherein P is the observed reading (actual value) of power load, and e is error; As predicted value P
tbe subject to the impact of Self-variation, its rule can be embodied by following formula,
P
t＝β
0+β
1x
t-1+β
2x
t-2+...+β
px
t-p+e
In above-mentioned formula, β
0, β
1β
kfor model parameter, k, t, p are positive integer.
Error term has dependence at different times, is expressed from the next,
e
t＝α
0+α
1e
t-1+α
2e
t-2+...+α
qe
t-q+μ
t
Wherein, α
0, α
1α
qfor model parameter, μ
tfor average, q, t are positive integer.
Thus, arma modeling expression formula is obtained:
P
t＝β
0+β
1x
t-1+β
2x
t-2+...+β
px
t-p+α
0+α
1e
t-1+α
2e
t-2+...+α
qe
t-q+μ
t。
The present invention creates community distribution system capacity model according to the design of community distribution system; Community power load and the normal distribution model of electricity consumption time is created according to Residential Area and Related service facility situation; The monitoring model that situation creates community charging pile is built according to community charging pile; Electric automobile is obtained at charging quantity, the charging voltage of each charging pile, electric current, duration of charging and other charge data by real-time communication.According to above-mentioned condition calculation plot distribution load, generate the distribution load curve of nearest a day, utilize this curve and community distribution system capacity model comparative analysis to charge on accumulator of electric car when peak to affect community distribution system, the permeability of current area electric automobile; The need of the permeability of carrying out charging coordinated scheduling controls and coordinated scheduling controls strategy, under this strategy community electric automobile.
Beneficial effect of the present invention:
(1), native system adopts community distribution system capacity model, community power load combines with electricity consumption time normal distribution model and real time data of charging mode, more realistic application.
(2), system-computed gained community distribution load curve and the comparison of distribution system capacity model, can monitor that community distribution system operation conditions, decision-making community distribution system run coordinated scheduling strategy, assessment community electric automobile permeability.
(3), system coordination controls the charging behavior of community electric automobile, reduce community charging electric vehicle on the impact of distribution system, the quality of power supply, improve community electric automobile permeability, ensure the operation that community power distribution system secure is stable.
(4), system also provides the guarantee of basic unit for reducing the impact of charging electric vehicle on electric system the whole network on the decision-making of community charging electric vehicle behavior, and for electric system the whole network is stable, safety, economical operation provide important support.
Accompanying drawing explanation
Fig. 1 is structural representation of the present invention;
Fig. 2 is that extensive charging pile is to community distribution system impact analysis decision-making technique block diagram;
Fig. 3 is charge data collecting flowchart schematic diagram of the present invention;
Fig. 4 is analysis decision schematic flow sheet of the present invention;
Tu5Wei Mou community distribution load curve map.
Wherein, 1. analysis decision Surveillance center, 2. data storage device, 3. analysis decision service unit, 4. preposition service unit, 5. relational database server, 6. live database server, 7. data collection station, 8. alternating-current charging pile.
Embodiment
Below in conjunction with accompanying drawing and embodiment, the invention will be further described.
As shown in Figure 1, the present invention includes analysis decision Surveillance center 1, data collection station 7, alternating-current charging pile 8; By network mode communication between described data collection station 7 and alternating-current charging pile 8.
Described analysis decision Surveillance center 1 comprises preposition service unit 4, data storage device 2, analysis decision service unit 3;
Described preposition service unit 4 at least comprises a server gathered for charge data, carries out communication with data collection station 7 by network, RS485 bus or CAN mode, obtains alternating-current charging pile 8 charge data that data collection station 7 gathers.Preposition service unit 4 carries out data interaction with data collection station 7 by IEC870-5-101, IEC60870-5-104, circulating Telecontrol Protocol, CAN protocol or other universal communication stipulations.
Described data storage device 2 comprises at least one relational database server 5 and one for storing the live database server 6 of real time data.Relational database is for storing static data needed for community distribution system capacity model, community power load and the normal distribution model of electricity consumption time, community charging pile monitoring model and other analysis decisions and model; Real-time data base is for storing community charging pile charging real time data.
Described analysis decision service unit 3 at least comprises a server for charge data monitoring, analysis, decision-making.The function of analysis decision service unit 3 comprises: create model needed for analysis decision, charge data monitored in real time, the real-time analysis of community electricity consumption situation, charging scheduling controlling.
Described network is cable network or wireless network, and described wireless network comprises GPRS, CDMA, 3G or other remote-wireless communication modes.
Described data collection station 7 also claims data concentrator, be responsible for and alternating-current charging pile 8 communication in community, obtain real time data when charging, comprise charging pile charged state, charging voltage, charging current, duration of charging and the available measurement data of other alternating-current charging piles 8.Possess the condition of protection supervisory equipment in community distribution system under, data collection station 7 also can obtain distribution system operational factor with protection supervisory equipment communication.
Described analysis decision Surveillance center 1 also can be one and integratedly to store and the server of analysis decision for charge data collection, data.
As shown in Figure 2, extensive charging pile, to community distribution system impact analysis decision-making technique step, specifically describes as follows:
Step one, set up and store community low-voltage distribution system capacity model, community power load and the normal distribution model of electricity consumption time, community alternating-current charging pile monitoring model.
Step 2, Real-time Collection store alternating-current charging pile charge data in community.
Step 3, analysis decision server are according to charging real time data and the normal distribution model of community power load and electricity consumption time, and calculation plot distribution load also stores.
Step 4, to compare with community distribution system capacity model to generate according to distribution load curve and analyze conclusion and scheduling controlling decision-making and store.If do not need scheduling controlling, wait for that the next analysis decision cycle carries out step 3.Scheduling then forwards step 5 to.
Step 5, Surveillance center issue scheduling control commands to charging pile, control charging pile duration of charging, charging current.Wait for that the next analysis decision cycle carries out step 3.
As shown in Figure 3, charge data collecting flowchart of the present invention, concrete steps are described below:
The data such as charging pile duty, charging voltage, electric current are sent to data collection station 7 by step one, electric vehicle alternating-current charging pile 8 and data collection station 7 communication.
Step 2, data collection station 7 and the communication of preposition service unit 4, deliver to preposition service unit 4 on after charging pile data centralizations all in the community of acquisition.
Step 3, preposition service unit 4 judge and resolve communication packet, abandon error message, and normal for parsing data are delivered to data storage device 2.
Step 4, data storage device 2, according to data attribute, as deviation, memory gap, judge whether to need by data stored in real-time data base, not need stored in data directly abandon.
As shown in Figure 4, analysis decision flow process of the present invention, concrete steps are described below:
Step one, analysis decision service unit 3 judge whether to arrive the timing cycle of analysis decision, if then calling model data and charging real time data from data storage device 2.
Step 2, generation analysis decision result, carries out cooperation control, then return the timing cycle waiting for analysis decision next time, otherwise analysis decision service unit 3 issues coordinated scheduling order to preposition service unit 4 if does not need.
Step 3, preposition service unit 4 are issued to data collection station 7 after cooperation control order being reorganized.
Cooperation control order is issued to and can controls maybe to need the alternating-current charging pile 8 controlled to carry out cooperation control by step 4, data collection station 7.
As shown in Figure 5, the distribution load curve of certain community, comprises the distribution load curve in the time, emulates distribution load curve, power load and the normal distribution curve of electricity consumption time not coordinating to charge.
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.
Claims (3)
1. extensive charging pile is to a decision-making technique for community distribution system impact analysis decision system, it is characterized in that,
Described system comprises:
Comprise Surveillance center, data collection station, alternating-current charging pile,
Described Surveillance center comprises preposition service unit, data storage device, analysis decision service unit, for extensive charging pile to community distribution system impact analysis decision-making;
By network mode communication between described data collection station and alternating-current charging pile;
The preposition service unit of described preposition service unit at least comprises a server gathered for charge data, obtains the charging pile charge data that data collection station gathers;
Described data storage device comprises at least one relational database server and at least one live database server, wherein, relational database is for storing static data needed for community distribution system capacity model, community power load and the normal distribution model of electricity consumption time, community charging pile monitoring model, community distribution load historical data and other analysis decisions and model; Real-time data base is for storing community charging pile charging real time data;
Described data collection station is responsible for and the alternating-current charging pile communication in community, obtains real time data when charging, comprises charging pile charged state, charging voltage, charging current, duration of charging and the available measurement data of other charging piles; Possess the condition of protection supervisory equipment in community distribution system under, data collection station also can obtain distribution system operational factor with protection supervisory equipment communication;
Described analysis decision service unit at least comprise one for charge data monitoring, analyze, the server of decision-making, for creating model needed for analysis decision, charge data is monitored in real time, the real-time analysis of community electricity consumption situation, charging coordinated scheduling control;
Said method comprising the steps of:
Step one, set up and store community low-voltage distribution system capacity model, community power load and the normal distribution model of electricity consumption time, community alternating-current charging pile monitoring model; The community low-voltage distribution system capacity model of described step one, community power load and the normal distribution model of electricity consumption time, community alternating-current charging pile monitoring model, be all saved to the relational database of data storage device; Wherein, community low-voltage distribution system capacity model, comprises community supply line quantity; Number transformer and transformer parameter; Community distribution system mode of connection topology exhibits; Described transformer parameter comprises: rated power, rated voltage, rated current, rated capacity and other parameters that can obtain; The normal distribution model of community power load and electricity consumption time, adopts autoregressive moving average method, generates according to community electricity consumption historical data; Community alternating-current charging pile monitoring model, comprises community and builds charging pile quantity, the monitoring of charging pile charging real time data: the data that charged state, charging current, charging voltage, charging warning information and other charging piles provide;
Step 2, Real-time Collection store alternating-current charging pile charge data in community; The Real-time Collection of described step 2 stores alternating-current charging pile charge data in community and refers to alternating-current charging pile charge data in data collection station Real-time Collection community, communication protocol according to regulation is delivered to the front-collection device of Surveillance center, front-collection device is sent to data storage device by after civilian dissection process that the data collection station received is delivered newspaper, and charging pile charge data is saved in real-time data base by data storage;
The concrete steps of described step 2 are described below:
2-1), electric vehicle alternating-current charging pile and data collection station communication, the data such as charging pile duty, charging voltage, electric current are sent to data collection station;
2-2), data collection station and preposition service unit communication, on after charging pile data centralizations all in the community of acquisition, deliver to preposition service unit;
2-3), preposition service unit judges and resolves communication packet, abandons error message, normal for parsing data are delivered to data storage device;
2-4), data storage device according to data attribute, as deviation, memory gap, judge whether to need by data stored in real-time data base, not need stored in data directly abandon;
Step 3, analysis decision server are according to charging real time data and the normal distribution model of community power load and electricity consumption time, and calculation plot distribution load also stores;
The concrete steps of described step 3 are described below:
3-1), analysis decision service unit judges whether to arrive the timing cycle of analysis decision, if then calling model data and charging real time data from data storage device;
3-2), calculate real time charging load, superpose with the normal distribution model of community power load with the electricity consumption time, generate community distribution load;
3-3), result of calculation is stored into the relational database of database service device;
Step 4, to compare with community distribution system capacity model to generate according to distribution load curve and analyze conclusion and scheduling controlling decision-making and store; If do not need scheduling controlling, wait for that the next analysis decision cycle carries out step 3; If need dispatch, forward step 5 to; The distribution load curve of described step 4 in nearest one day, calculates gained distribution load describe to form; Distribution load curve, comprises the distribution load curve in the time, emulates distribution load curve, power load and the normal distribution curve of electricity consumption time not coordinating to charge; Analysis conclusion comprises current area electricity condition and whether causes adverse effect to distribution network load, and electric automobile permeability in community under current state; Scheduling controlling decision-making comprises the need of carrying out scheduling controlling, scheduling strategy is formed: the charging behavior coordinating community electric automobile when needing to dispatch, determine in a period of time, the optimum charging strategy of electric automobile in community, coordinate charging electric vehicle opportunity, size of current and time length; And emulation forms community distribution load curve and community electric automobile permeability under non-operation dispatching strategy; Scheduling controlling decision storage is to the relational database of database service device;
Step 5, Surveillance center issue scheduling control commands to charging pile, control charging pile duration of charging, charging current; Wait for that the next analysis decision cycle carries out step 3;
The concrete steps of described step 5 are described below:
5-1), cooperation control order is issued to preposition service unit by Surveillance center;
5-2), preposition service unit is issued to data collection station after cooperation control order being reorganized;
5-3), cooperation control order is issued to and can controls maybe to need the charging pile controlled to carry out cooperation control by data collection station.
2. decision-making technique as claimed in claim 1, it is characterized in that, described preposition service unit and data collection station adopt IEC870-5-101, IEC60870-5-104, circulating Telecontrol Protocol, CAN protocol or other universal communication stipulations to carry out data interaction by network, RS485 bus or CAN mode.
3. decision-making technique as claimed in claim 1, it is characterized in that, described autoregressive moving average method is, the method regards the time series of community power load as a random series, and the dependence that this group stochastic variable has embodies community power load continuity in time; Have the impact of influenced factor while of self Fluctuation, influence factor comprises community electricity consumption type, electricity consumption electricity price, weather, temperature, festivals or holidays, is designated as x
1, x
2..., x
k, by regretional analysis:
P＝β
0+β
1x
1+β
2x
2+...+β
kx
k+e
Wherein P is observed reading and the actual value of power load, and e is error; As predicted value P
tbe subject to the impact of Self-variation, its rule can be embodied by following formula,
P
t＝β
0+β
1x
t-1+β
2x
t-2+...+β
px
t-p+e
In above-mentioned formula, β
0, β
1β
kfor model parameter, k, t, p are positive integer;
Error term has dependence at different times, is expressed from the next,
e
t＝α
0+α
1e
t-1+α
2e
t-2+...+α
qe
t-q+μ
t
Wherein, α
0, α
1α
qfor model parameter, μ
tfor average, q, t are positive integer;
Thus, arma modeling expression formula is obtained:
P
t＝β
0+β
1x
t-1+β
2x
t-2+...+β
px
t-p+α
0+α
1e
t-1+α
2e
t-2+...+α
qe
t-q+μ
t。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210224115.XA CN102708427B (en) | 2012-06-29 | 2012-06-29 | System and method for analyzing and determining influence of large-scale charging pile to community distribution system |
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