CN111404195B - Intelligent gateway-based scheduling method for microgrid with distributed power supply - Google Patents

Intelligent gateway-based scheduling method for microgrid with distributed power supply Download PDF

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CN111404195B
CN111404195B CN202010112419.1A CN202010112419A CN111404195B CN 111404195 B CN111404195 B CN 111404195B CN 202010112419 A CN202010112419 A CN 202010112419A CN 111404195 B CN111404195 B CN 111404195B
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power
wind
station
intelligent gateway
node
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CN111404195A (en
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张盛
徐勇明
史建勋
郁云忠
李飞伟
徐晶
李运钱
张冲标
张帆
陈鼎
唐锦江
郑伟军
钱伟杰
程振龙
毕江林
吴晗
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Jiashan Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Jiashan Hengxing Electric Power Construction Co Ltd
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Jiashan Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Jiashan Hengxing Electric Power Construction Co Ltd
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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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

本发明涉及微电网技术领域，具体涉及一种基于智能网关的含分布式电源微电网的调度方法，包括以下步骤：A）在目标电网与分布式电源连接的节点上安装智能网关；B）计算下一时段特征值，若特征值小于设定阈值，则时段内风光电站仅输出有功功率，并进入步骤D）；C）确定风光电站输出有功功率和无功功率；D）根据节点上的电压和频率，智能网关就地对风光电站或储能站进行功率控制。本发明的实质性效果是：适合具有高渗透率的可再生能源的微电网的调度，安装扩展方便，适合分布式电源的接入；降低传统能源为适应可再生能源接入而新增的成本，提高微电网运行的可靠性和安全性，提高微电网的整体能源利用效率。

Figure 202010112419

The invention relates to the technical field of microgrids, in particular to a scheduling method for a microgrid with distributed power sources based on an intelligent gateway, comprising the following steps: A) installing an intelligent gateway on a node connecting a target power grid and a distributed power source; B) calculating The eigenvalue of the next period, if the eigenvalue is less than the set threshold, the wind and solar power station will only output active power during the period, and enter step D); C) Determine the output active power and reactive power of the wind and solar power station; D) According to the voltage on the node and frequency, the intelligent gateway controls the power of the wind and solar power station or the energy storage station on the spot. The substantial effects of the present invention are: it is suitable for the scheduling of the microgrid with high penetration rate of renewable energy, easy to install and expand, and suitable for the access of distributed power sources; it reduces the added cost of traditional energy for adapting to the access of renewable energy. , improve the reliability and safety of microgrid operation, and improve the overall energy utilization efficiency of microgrid.

Figure 202010112419

Description

Intelligent gateway-based scheduling method for microgrid with distributed power supply
Technical Field
The invention relates to the technical field of micro-grids, in particular to a dispatching method of a micro-grid containing a distributed power supply based on an intelligent gateway.
Background
The traditional energy structure causes severe ecological environmental pressure, forces people to review the problem of energy supply structure, and has unprecedented strong willingness to use and develop pollution-free distributed renewable green energy. Renewable energy is being developed and utilized as one of the effective means to solve global energy and environmental problems. In recent years, with the rapid construction of wind energy and light energy power farms, a large number of distributed power sources are put into production in a grid-connected mode. The permeability of renewable energy sources in a distribution network is rapidly increased, the problems of backward flow of feeder lines and local overvoltage occur, and the power supply safety is influenced. In order to deal with the challenge of the high-density distributed power supply to the future power grid development, a more effective distributed power supply information access and local control technology needs to be explored and researched. And the comprehensive utilization efficiency of the distributed renewable energy sources and the safety of the power distribution network are improved through local intelligent control.
For example, chinese patent CN108494022A, published 2018, 9, 4, a method for controlling precise scheduling based on a distributed power supply in a microgrid, embeds a peer-to-peer frequency control method in a conventional economic scheduling method, so that a power balance condition is satisfied under the condition of output fluctuation or load fluctuation of the distributed power supply, and achieves precise scheduling of the distributed power supply in the microgrid. In this control method, the active and reactive power demanded by the load is balanced with the active and reactive power generated by the generator; compared with a periodic economic dispatching method in a traditional centralized power system, the method can ensure that the micro-grid can realize accurate control of economic dispatching under different operation scenes. However, the mode that the active power and the reactive power required by the load are balanced with the active power and the reactive power generated by the generator is adopted, when a large number of renewable energy power farms exist in the microgrid, the power factor of the traditional generator can be caused to operate in an unreasonable range, the efficiency of the traditional generator is reduced, and even the normal work of the traditional generator is influenced.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the technical problem that the energy utilization efficiency of the existing microgrid with the distributed power supply is low is solved. The dispatching method of the micro-grid with the distributed power supply based on the intelligent gateway is capable of improving energy utilization efficiency.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a method for scheduling a microgrid with a distributed power supply based on an intelligent gateway, wherein the distributed power supply comprises an energy storage station and a wind-solar power station, comprises the following steps: A) node w for connecting target power grid with distributed power supplyi,i∈[1,n]Upper-mounted intelligent gateway
Figure BDA0002390483600000021
The intelligent gateway
Figure BDA0002390483600000022
The system comprises a communication module connected with a dispatching center, a monitoring module for monitoring the voltage and current of a node, a phase monitoring module for monitoring the phase of the node, a frequency monitoring module for monitoring the frequency of the node and a control module connected with a distributed power supply; B) dividing a day into n periods tj,j∈[1,n]The dispatching center is provided with a next time interval ti+1Active power prediction value of load
Figure BDA0002390483600000023
And reactive power prediction
Figure BDA0002390483600000024
Calculating the next time period tj+1Characteristic value
Figure BDA0002390483600000025
If the characteristic value
Figure BDA0002390483600000026
Less than a set threshold lambdathrThen t isj+1Wind-solar power station G in time intervali,i∈[1,l]Only outputting active power, wherein l is the number of wind-solar power stations, and entering the step D), otherwise, entering the step C); C) scheduling center computation
Figure BDA0002390483600000027
And determining wind and light power station Gi,i∈[1,l]Output active power
Figure BDA0002390483600000028
And reactive power
Figure BDA0002390483600000029
And satisfy
Figure BDA00023904836000000210
D) Will be of period tj+1Equally dividing the time into N small time periods, wherein the starting time t of each small time periodj+1|k,k∈[1,N]Node wiCorresponding intelligent gateway
Figure BDA00023904836000000211
Read node wiVoltage and frequency on, according to node wiVoltage and frequency on, intelligent gateway
Figure BDA00023904836000000212
Wind-solar power station GiOr energy storage station Ei,i∈[1,m]And performing power control, wherein m is the number of the energy storage stations. The access scheduling task can be completed only by acquiring the corresponding state data of the nodes through the monitoring module, the phase monitoring module and the frequency monitoring module, the installation and the expansion are convenient, and the method is suitable for the access of the distributed power supply. When the proportion of the reactive power in the distribution network is large, the distributed power supply is dispatched to output partial reactive power, although partial benefits can be reduced, the power factor of the transformer can be effectively improved, the stability of the microgrid is improved, the cost of newly adding the traditional energy source for adapting to the access of the renewable energy source is obviously reduced, and the distributed power supply dispatching method is suitable for dispatching the microgrid with the renewable energy source with high permeability.
Preferably, in step C), the time period tj+1Internal wind and light power station Gi,i∈[1,l]Active power of
Figure BDA00023904836000000213
And reactive power
Figure BDA00023904836000000214
The distribution method comprises the following steps: C11) establishing an evaluation function
Figure BDA00023904836000000215
Figure BDA00023904836000000216
For feeder i during time period tj+1The average active power transferred in-between,
Figure BDA00023904836000000217
for feeder i during time period tj+1The average reactive power transferred in-between,
Figure BDA00023904836000000218
h is the number of feeders, i is the upper limit of the load of the feeder i; C12) using an optimization algorithm, an evaluation function is obtained
Figure BDA00023904836000000219
Minimum value wind-solar power station Gi,i∈[1,l]Reactive power of
Figure BDA00023904836000000220
Value, active power
Figure BDA00023904836000000221
Figure BDA00023904836000000222
For wind-solar power station Gi,i∈[1,l]Real-time force is applied. The power flow of the feeder line is optimized, and the overall transmission capacity of the feeder line is improved.
Preferably, in step C12), the wind-solar power station G is calculatedi,i∈[1,l]Operating power factor of
Figure BDA00023904836000000223
Figure BDA00023904836000000224
Time period tj+1Internal active power
Figure BDA00023904836000000225
And reactive power
Figure BDA00023904836000000226
Is distributed to
Figure BDA00023904836000000227
Figure BDA00023904836000000228
Always holds as a constraint of the optimization algorithm, λ'thrTo set the threshold value, λ'thr>λthr. The working state of the wind and light power station is ensured to be at a better level, and excessive renewable energy waste is avoided.
Preferably, in step D), if the node wiConnected to wind-solar power station GiThen, the following steps are executed: if node wiWhen the voltage is lower than the standard value, the intelligent gateway
Figure BDA0002390483600000031
Controlling wind-solar power station GiIncreasing reactive power
Figure BDA0002390483600000032
If node wiIf the voltage is higher than the standard value, the intelligent gateway
Figure BDA0002390483600000033
Controlling wind-solar power station GiReduction of reactive power
Figure BDA0002390483600000034
To output of (c).
Preferably, in step D), if the node wiConnected thereto is an energy storage station EiThen, the following steps are executed: if node wiWhen the voltage is lower than the standard value, the intelligent gateway
Figure BDA0002390483600000035
Controlling energy storage station EiIncrease power output or decrease charging power if node wiIf the voltage is higher than the standard value, the intelligent gateway
Figure BDA0002390483600000036
Controlling energy storage station EiIncreasing the charging power or decreasing the discharging power.
Preferably, the following steps are also performed: D11) wind and light power station Gi,i∈[1,l]Real-time force of
Figure BDA0002390483600000037
Deviation from predicted value
Figure BDA0002390483600000038
Probability of deviation of
Figure BDA0002390483600000039
Tau is the rate of deviation and is the rate of deviation,
Figure BDA00023904836000000310
Figure BDA00023904836000000311
σ is the deviation rate τ | tj+1The probability of occurrence; D12) time period tj+1Internal and real-time monitoring wind-light power station Gi,i∈[1,l]Real-time force of
Figure BDA00023904836000000312
Deviation rate of (τ | t)j+1If the probability of deviation is high
Figure BDA00023904836000000313
Then the energy storage station E is addedi,i∈[1,m]Charging power or reducing energy storage station Ei,i∈[1,m]Otherwise, the wind-light power station G is increased according to the set amplitudei,i∈[1,l]Reactive power of output
Figure BDA00023904836000000314
Until real-time output
Figure BDA00023904836000000315
Deviation rate of (τ | t)j+1Corresponding deviation probability
Figure BDA00023904836000000316
Fall back to sigmathrThe following.
Preferably, in step D11), the deviation probability is calculated
Figure BDA00023904836000000317
The method comprises the following steps: D111) counting wind-light power station G in each small periodi,i∈[1,l]Mean value of real-time forces of
Figure BDA00023904836000000318
r∈[1,N](ii) a D112) Will be of period tj+1The former w time periods execute the step C11), and the wind-solar power station G in each small period in each time period is obtainedi,i∈[1,l]Mean value of real-time forces of
Figure BDA00023904836000000319
u∈[j-w,j],r∈[1,N](ii) a D113) Statistics of
Figure BDA00023904836000000320
The maximum value and the minimum value of (c),
Figure BDA00023904836000000321
section of will
Figure BDA00023904836000000322
Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval
Figure BDA00023904836000000323
Of each value interval
Figure BDA00023904836000000324
The ratio of the number of the wind-solar power station G to w.N is used as the wind-solar power station Gi,i∈[1,l]Real-time force of
Figure BDA00023904836000000325
Deviation probability corresponding to falling value interval
Figure BDA00023904836000000326
Short-term wind-solar power station Gi,i∈[1,l]The real-time output distribution probability has certain stability, when the real-time output is at a level with lower occurrence probability, the dispatching of the distribution network does not need to be adjusted by a large margin, and only the energy storage station needs to be used for balancing real-time output fluctuation in a short time.
Preferably, in step D), the energy storage station E is controlledi,i∈[1,m]The method for increasing the charging power or increasing the discharging power comprises the following steps: D21) computing energy storage station Ei,i∈[1,m]Total increased charging power of
Figure BDA00023904836000000327
Figure BDA0002390483600000041
Rho is a set margin coefficient, rho>1; D22) establishing an evaluation function
Figure BDA0002390483600000042
Figure BDA0002390483600000043
Where z represents the number of small cycles,
Figure BDA0002390483600000044
for feeder i during time period tj+1The average load of the z-th small period of time,
Figure BDA0002390483600000045
h is the number of feeders, i is the upper limit of the load of the feeder i; D23) at a set time before the beginning of the z-th small period, the wind-light power station G within the (z-1) th small period to the current timeiReal-time force of
Figure BDA0002390483600000046
Mean value calculation of
Figure BDA0002390483600000047
A value of (d); D24) using an optimization algorithm, an evaluation function is obtained
Figure BDA0002390483600000048
Energy storage station E with the smallest valuei,i∈[1,m]The charging power is increased in real time, and the process returns to step D23) at a predetermined timing before the start of the next small cycle.
The substantial effects of the invention are as follows: the method is suitable for dispatching the microgrid of renewable energy sources with high permeability, is convenient to install and expand, and is suitable for accessing a distributed power supply; the cost of newly adding traditional energy sources for adapting to the access of renewable energy sources is reduced, the reliability and the safety of the operation of the micro-grid are improved, and the overall energy utilization efficiency of the micro-grid is improved.
Drawings
Fig. 1 is a flowchart of a scheduling method of a microgrid according to an embodiment.
FIG. 2 is a flow chart of a wind-solar power station power distribution method according to an embodiment.
Fig. 3 is a flowchart illustrating an intelligent gateway local power control method according to an embodiment.
Fig. 4 is a schematic structural diagram of an intelligent gateway according to an embodiment.
Wherein: 100. the device comprises a communication module 200, a monitoring module 300, a control module 400, a phase monitoring module 500 and a frequency monitoring module.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
The first embodiment is as follows:
a method for scheduling a microgrid with distributed power supplies based on an intelligent gateway, wherein the distributed power supplies comprise energy storage stations and wind and light power stations, as shown in figure 1, the method comprises the following steps:
A) node w for connecting target power grid with distributed power supplyi,i∈[1,n]Upper-mounted intelligent gateway
Figure BDA0002390483600000049
Intelligent gateway
Figure BDA00023904836000000410
The system comprises a communication module 100 connected with a dispatching center, a monitoring module 200 for monitoring node voltage and current, a phase monitoring module 400 for monitoring node phase, a frequency monitoring module 500 for monitoring node frequency and a control module 300 connected with a distributed power supply.
B) Dividing a day into n periods tj,j∈[1,n]The dispatching center is provided with a next time interval ti+1Active power prediction value of load
Figure BDA0002390483600000051
And reactive power prediction
Figure BDA0002390483600000052
Calculating the next time period tj+1Characteristic value
Figure BDA0002390483600000053
Figure BDA0002390483600000054
If the characteristic value
Figure BDA0002390483600000055
Less than a set threshold lambdathrThen t isj+1Wind-solar power station G in time intervali,i∈[1,l]And only outputting active power, wherein l is the number of the wind-solar power stations, and entering the step D), and otherwise, entering the step C).
C) Scheduling center computation
Figure BDA0002390483600000056
And determining wind and light power station Gi,i∈[1,l]Output active power
Figure BDA0002390483600000057
And reactive power
Figure BDA0002390483600000058
And satisfy
Figure BDA0002390483600000059
As shown in FIG. 2, the period tj+1Internal wind and light power station Gi,i∈[1,l]Active power of
Figure BDA00023904836000000510
And reactive power
Figure BDA00023904836000000511
The distribution method comprises the following steps: C11) establishing an evaluation function
Figure BDA00023904836000000512
Figure BDA00023904836000000513
For feeder i during time period tj+1The average active power transferred in-between,
Figure BDA00023904836000000514
for feeder i during time period tj+1The average reactive power transferred in-between,
Figure BDA00023904836000000515
h is the number of feeders, i is the upper limit of the load of the feeder i; C12) using an optimization algorithm, an evaluation function is obtained
Figure BDA00023904836000000516
Minimum value wind-solar power station Gi,i∈[1,l]Reactive power of
Figure BDA00023904836000000517
Value, active power
Figure BDA00023904836000000518
Figure BDA00023904836000000519
For wind-solar power station Gi,i∈[1,l]Real-time force is applied. The power flow of the feeder line is optimized, and the overall transmission capacity of the feeder line is improved. In step C12), calculating the wind-solar power station Gi,i∈[1,l]Operating power factor of
Figure BDA00023904836000000520
Time period tj+1Internal active power
Figure BDA00023904836000000521
And reactive power
Figure BDA00023904836000000522
Is distributed to
Figure BDA00023904836000000523
Always holds as a constraint of the optimization algorithm, λ'thrTo set the threshold value, λ'thr>λthr. The working state of the wind and light power station is ensured to be at a better level, and excessive renewable energy waste is avoided.
D) Will be of period tj+1Equally dividing the time into N small time periods, wherein the starting time t of each small time periodj+1|k,k∈[1,N]Node wiCorresponding intelligent gateway
Figure BDA00023904836000000524
Read node wiVoltage and frequency on, according to node wiVoltage and frequency on, intelligent gateway
Figure BDA00023904836000000525
Wind-solar power station GiOr energy storage station Ei,i∈[1,m]And performing power control, wherein m is the number of the energy storage stations. If node wiConnected to wind-solar power station GiThen, the following steps are executed: if node wiWhen the voltage is lower than the standard value, the intelligent gateway
Figure BDA00023904836000000526
Controlling wind-solar power station GiIncreasing reactive power
Figure BDA00023904836000000527
If node wiIf the voltage is higher than the standard value, the intelligent gateway
Figure BDA00023904836000000528
Controlling wind-solar power station GiReduction of reactive power
Figure BDA00023904836000000529
To output of (c). If node wiConnected thereto is an energy storage station EiThen, the following steps are executed: if node wiWhen the voltage is lower than the standard value, the intelligent gateway
Figure BDA00023904836000000530
Controlling energy storage station EiIncrease power output or decrease charging power if node wiIf the voltage is higher than the standard value, the intelligent gateway
Figure BDA00023904836000000531
Controlling energy storage station EiIncreasing the charging power or decreasing the discharging power.
As shown in fig. 3, D11) calculating wind-solar power station Gi,i∈[1,l]In real timeOutput force
Figure BDA0002390483600000061
Deviation from predicted value
Figure BDA0002390483600000062
Probability of deviation of
Figure BDA0002390483600000063
Tau is the rate of deviation and is the rate of deviation,
Figure BDA0002390483600000064
σ is the deviation rate τ | tj+1The probability of occurrence; D12) time period tj+1Internal and real-time monitoring wind-light power station Gi,i∈[1,l]Real-time force of
Figure BDA0002390483600000065
Deviation rate of (τ | t)j+1If the probability of deviation is high
Figure BDA0002390483600000066
Then the energy storage station E is addedi,i∈[1,m]Charging power or reducing energy storage station Ei,i∈[1,m]Otherwise, the wind-light power station G is increased according to the set amplitudei,i∈[1,l]Reactive power of output
Figure BDA0002390483600000067
Until real-time output
Figure BDA0002390483600000068
Deviation rate of (τ | t)j+1Corresponding deviation probability
Figure BDA0002390483600000069
Fall back to sigmathrThe following.
In step D11), the deviation probability is calculated
Figure BDA00023904836000000610
The method comprises the following steps: D111) counting wind-light power station G in each small periodi,i∈[1,l]Of real time forceMean value
Figure BDA00023904836000000611
r∈[1,N](ii) a D112) Will be of period tj+1The former w time periods execute the step C11), and the wind-solar power station G in each small period in each time period is obtainedi,i∈[1,l]Mean value of real-time forces of
Figure BDA00023904836000000612
u∈[j-w,j],r∈[1,N](ii) a D113) Statistics of
Figure BDA00023904836000000613
The maximum value and the minimum value of (c),
Figure BDA00023904836000000614
section of will
Figure BDA00023904836000000615
Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval
Figure BDA00023904836000000616
Of each value interval
Figure BDA00023904836000000617
The ratio of the number of the wind-solar power station G to w.N is used as the wind-solar power station Gi,i∈[1,l]Real-time force of
Figure BDA00023904836000000618
Deviation probability corresponding to falling value interval
Figure BDA00023904836000000619
Short-term wind-solar power station Gi,i∈[1,l]The real-time output distribution probability has certain stability, when the real-time output is at a level with lower occurrence probability, the dispatching of the distribution network does not need to be adjusted by a large margin, and only the energy storage station needs to be used for balancing real-time output fluctuation in a short time.
In this embodiment, the access scheduling task can be completed only by acquiring the corresponding state data of the node through the monitoring module 200, the phase monitoring module 400 and the frequency monitoring module 500, and the installation and expansion are convenient, so that the method is suitable for the access of the distributed power supply. When the proportion of the reactive power in the distribution network is large, the distributed power supply is dispatched to output partial reactive power, although partial benefits can be reduced, the power factor of the transformer can be effectively improved, the stability of the microgrid is improved, the cost of newly adding the traditional energy source for adapting to the access of the renewable energy source is obviously reduced, and the distributed power supply dispatching method is suitable for dispatching the microgrid with the renewable energy source with high permeability.
Example two:
in this embodiment, a further improvement is made on the basis of the first embodiment, in this embodiment, in the step D), the energy storage station E is controlledi,i∈[1,m]The method for increasing the charging power or increasing the discharging power comprises the following steps: D21) computing energy storage station Ei,i∈[1,m]Total increased charging power of
Figure BDA00023904836000000620
Rho is a set margin coefficient, rho>1; D22) establishing an evaluation function
Figure BDA00023904836000000621
Where z represents the number of small cycles,
Figure BDA0002390483600000071
for feeder i during time period tj+1The average load of the z-th small period of time,
Figure BDA0002390483600000072
h is the number of feeders, i is the upper limit of the load of the feeder i; D23) at a set time before the beginning of the z-th small period, the wind-light power station G within the (z-1) th small period to the current timeiReal-time force of
Figure BDA0002390483600000073
Mean value calculation of
Figure BDA0002390483600000074
A value of (d); D24) obtaining an opinion using an optimization algorithmFunction of price
Figure BDA0002390483600000075
Energy storage station E with the smallest valuei,i∈[1,m]The charging power is increased in real time, and the process returns to step D23) at a predetermined timing before the start of the next small cycle. When the optimization algorithm runs, the transformer load limitation, the feeder flow limitation and the distributed power output upper limit are taken as limiting conditions, which are known in the art and are not described herein again. Compared with the first embodiment, the embodiment provides the small-period local power control through the intelligent gateway, and the operation safety and the comprehensive energy utilization efficiency of the micro-grid are further improved.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.

Claims (8)

1.一种基于智能网关的含分布式电源微电网的调度方法，所述分布式电源包括储能站和风光电站，其特征在于，1. a dispatch method based on an intelligent gateway containing a distributed power source microgrid, the distributed power source comprises an energy storage station and a wind and solar power station, and is characterized in that, 包括以下步骤：Include the following steps: A)在目标电网与分布式电源连接的节点wi,i∈[1,n]上安装智能网关
Figure FDA00031537316300000120
所述智能网关
Figure FDA00031537316300000121
具有与调度中心连接的通信模块、监测节点电压电流的监测模块、监测节点相位的相位监测模块、监测节点频率的频率监测模块以及与分布式电源连接的控制模块；A) Install a smart gateway on the node w i,i∈[1,n] where the target grid is connected to the distributed power source
Figure FDA00031537316300000120
the intelligent gateway
Figure FDA00031537316300000121
It has a communication module connected with the dispatch center, a monitoring module for monitoring the voltage and current of the node, a phase monitoring module for monitoring the phase of the node, a frequency monitoring module for monitoring the frequency of the node, and a control module for connecting with the distributed power source; B)将一日划分为n个时段tj,j∈[1,n]，调度中心由下一时段tj+1负载的有功功率预测值
Figure FDA0003153731630000011
和无功功率预测值
Figure FDA0003153731630000012
计算下一时段tj+1特征值
Figure FDA0003153731630000013
若特征值
Figure FDA0003153731630000014
小于设定阈值λthr，则tj+1时段内风光电站Gi,i∈[1,l]仅输出有功功率，l为风光电站的数量，
Figure FDA0003153731630000015
为出力预测值，并进入步骤D)，反之，进入步骤C)；B) Divide a day into n time periods t j,j∈[1,n] , and the dispatch center will predict the value of the active power of the load in the next time period t j+1
Figure FDA0003153731630000011
and predicted reactive power
Figure FDA0003153731630000012
Calculate the eigenvalues of the next time period t j+1
Figure FDA0003153731630000013
If the eigenvalue
Figure FDA0003153731630000014
is less than the set threshold λ thr , then the wind and solar power plants Gi ,i∈[1,l] only output active power in the period of t j+1 , where l is the number of wind and solar power plants,
Figure FDA0003153731630000015
For the predicted value of output, and enter step D), otherwise, enter step C); C)调度中心计算
Figure FDA0003153731630000016
并确定风光电站Gi,i∈[1,l]输出有功功率
Figure FDA0003153731630000017
和无功功率
Figure FDA0003153731630000018
且满足
Figure FDA0003153731630000019
C) Dispatch center calculation
Figure FDA0003153731630000016
And determine the output active power of wind and solar power station G i,i∈[1,l]
Figure FDA0003153731630000017
and reactive power
Figure FDA0003153731630000018
and satisfy
Figure FDA0003153731630000019
 D)将时段tj+1等分为N个小时间段，每个小时间段起始时刻tj+1|k,k∈[1,N]，节点wi对应的智能网关
Figure FDA00031537316300000122
读取节点wi上的电压和频率，根据节点wi上的电压和频率，智能网关
Figure FDA00031537316300000123
就地对风光电站Gi或储能站Ei,i∈[1,m]进行功率控制，m为储能站的数量。D) Divide the time period t j+1 into N small time periods equally, the starting time of each small time period t j+1|k,k∈[1,N] , the intelligent gateway corresponding to the node w i
Figure FDA00031537316300000122
Read the voltage and frequency on the node wi , according to the voltage and frequency on the node wi , the smart gateway
Figure FDA00031537316300000123
Power control is performed on the wind and solar power station G i or the energy storage station E i,i∈[1,m] , where m is the number of energy storage stations. 2.根据权利要求1所述的一种基于智能网关的含分布式电源微电网的调度方法，其特征在于，2. a kind of scheduling method based on intelligent gateway containing distributed power supply microgrid according to claim 1, is characterized in that, 步骤C)中，时段tj+1内风光电站Gi,i∈[1,l]的有功功率
Figure FDA00031537316300000110
和无功功率
Figure FDA00031537316300000111
的分配方法包括：In step C), the active power of the wind and solar power station G i,i∈[1,l] in the period t j+1
Figure FDA00031537316300000110
and reactive power
Figure FDA00031537316300000111
The allocation methods include: C11)建立评价函数
Figure FDA00031537316300000112
Figure FDA00031537316300000113
为馈线i在时段tj+1内传递的平均有功功率，
Figure FDA00031537316300000114
为馈线i在时段tj+1内传递的平均无功功率，
Figure FDA00031537316300000115
为馈线i的负载上限,h是馈线数量；C11) Establish an evaluation function
Figure FDA00031537316300000112
Figure FDA00031537316300000113
is the average active power delivered by feeder i during time period t j+1 ,
Figure FDA00031537316300000114
is the average reactive power delivered by feeder i in time period t j+1 ,
Figure FDA00031537316300000115
is the upper limit of the load of feeder i, h is the number of feeders; C12)使用优化算法，获得使评价函数
Figure FDA00031537316300000116
值最小的风光电站Gi,i∈[1,l]的无功功率
Figure FDA00031537316300000117
值，有功功率
Figure FDA00031537316300000118
Figure FDA00031537316300000119
为风光电站Gi,i∈[1,l]的实时出力。C12) Use the optimization algorithm to obtain the evaluation function
Figure FDA00031537316300000116
The reactive power of the wind and solar power station G i,i∈[1,l] with the smallest value
Figure FDA00031537316300000117
value, active power
Figure FDA00031537316300000118
Figure FDA00031537316300000119
is the real-time output of the wind and solar power station Gi ,i∈[1,l] . 3.根据权利要求2所述的一种基于智能网关的含分布式电源微电网的调度方法，其特征在于，3. a kind of scheduling method based on intelligent gateway containing distributed power supply microgrid according to claim 2, is characterized in that, 步骤C12)中，计算风光电站Gi,i∈[1,l]的工作功率因数
Figure FDA0003153731630000021
时段tj+1内有功功率
Figure FDA0003153731630000022
和无功功率
Figure FDA0003153731630000023
的分配使
Figure FDA0003153731630000024
总是成立作为优化算法的限制条件，λ′thr为设定阈值，λ′thr>λthr。In step C12), calculate the working power factor of the wind and solar power station G i,i∈[1,l]
Figure FDA0003153731630000021
Active power in period t j+1
Figure FDA0003153731630000022
and reactive power
Figure FDA0003153731630000023
allocation of
Figure FDA0003153731630000024
It is always established as a constraint condition of the optimization algorithm, λ′ thr is the set threshold, λ′ thr >λ thr . 4.根据权利要求2或3所述的一种基于智能网关的含分布式电源微电网的调度方法，其特征在于，4. a kind of scheduling method containing distributed power supply microgrid based on intelligent gateway according to claim 2 or 3, is characterized in that, 步骤D)中，若节点wi上连接的是风光电站Gi，则执行以下步骤：In step D), if the node wi is connected to the wind and solar power station G i , the following steps are performed: 若节点wi电压低于标准值，则智能网关
Figure FDA0003153731630000025
控制风光电站Gi增加无功功率
Figure FDA0003153731630000026
的输出，若节点wi电压高于标准值，则智能网关
Figure FDA0003153731630000027
控制风光电站Gi减少无功功率
Figure FDA0003153731630000028
的输出。If the node wi voltage is lower than the standard value, the intelligent gateway
Figure FDA0003153731630000025
Control wind and solar power station Gi to increase reactive power
Figure FDA0003153731630000026
output, if the node wi voltage is higher than the standard value, the intelligent gateway
Figure FDA0003153731630000027
Control wind and solar power station Gi to reduce reactive power
Figure FDA0003153731630000028
Output. 5.根据权利要求1或2或3所述的一种基于智能网关的含分布式电源微电网的调度方法，其特征在于，5. The scheduling method for a microgrid containing distributed power sources based on an intelligent gateway according to claim 1 or 2 or 3, characterized in that, 步骤D)中，若节点wi上连接的是储能站Ei，则执行以下步骤：In step D), if the energy storage station E i is connected to the node wi , the following steps are performed: 若节点wi电压低于标准值，则智能网关
Figure FDA0003153731630000029
控制储能站Ei增加功率输出或降低充电功率，若节点wi电压高于标准值，则智能网关
Figure FDA00031537316300000210
控制储能站Ei增加充电功率或降低放电功率。If the node wi voltage is lower than the standard value, the intelligent gateway
Figure FDA0003153731630000029
Control the energy storage station E i to increase the power output or reduce the charging power, if the node wi voltage is higher than the standard value, the intelligent gateway
Figure FDA00031537316300000210
Control the energy storage station E i to increase the charging power or decrease the discharging power. 6.根据权利要求4所述的一种基于智能网关的含分布式电源微电网的调度方法，其特征在于，6. a kind of scheduling method based on intelligent gateway containing distributed power microgrid according to claim 4, is characterized in that, 还执行以下步骤：Also perform the following steps: D11)计算风光电站
Figure FDA00031537316300000211
的实时出力
Figure FDA00031537316300000213
偏离出力预测值
Figure FDA00031537316300000214
的偏离概率
Figure FDA00031537316300000212
τ为偏离率，
Figure FDA00031537316300000215
σ为偏离率τ|tj+1出现的概率；D11) Calculate wind and solar power station
Figure FDA00031537316300000211
real-time output
Figure FDA00031537316300000213
Deviation from output forecast
Figure FDA00031537316300000214
deviation probability of
Figure FDA00031537316300000212
τ is the deviation rate,
Figure FDA00031537316300000215
σ is the probability of occurrence of deviation rate τ|t j+1 ; D12)时段tj+1内，实时监控风光电站Gi,i∈[1,l]的实时出力
Figure FDA00031537316300000216
的偏离率τ|tj+1，若偏离概率
Figure FDA00031537316300000217
则增加储能站Ei,i∈[1,m]的充电功率或减小储能站Ei,i∈[1,m]的放电功率，反之，则按设定幅度增大风光电站Gi,i∈[1,l]输出的无功功率
Figure FDA00031537316300000218
直到实时出力
Figure FDA00031537316300000219
的偏离率τ|tj+1对应的偏离概率
Figure FDA00031537316300000220
回落至σthr以下。D12) During the period t j+1 , real-time monitoring of the real-time output of the wind and solar power station G i,i∈[1,l]
Figure FDA00031537316300000216
The deviation rate τ|t j+1 of , if the deviation probability
Figure FDA00031537316300000217
Then increase the charging power of the energy storage station E i ,i∈[1,m] or decrease the discharge power of the energy storage station E i,i∈[1,m] , otherwise, increase the wind and solar power station G by the set range i,i∈[1,l] output reactive power
Figure FDA00031537316300000218
until real-time output
Figure FDA00031537316300000219
The deviation rate τ|t j+1 corresponds to the deviation probability of
Figure FDA00031537316300000220
falls back below σ thr . 7.根据权利要求6所述的一种基于智能网关的含分布式电源微电网的调度方法，其特征在于，7. a kind of scheduling method based on intelligent gateway containing distributed power microgrid according to claim 6, is characterized in that, 步骤D11)中，计算偏离概率
Figure FDA0003153731630000031
的方法包括：In step D11), the deviation probability is calculated
Figure FDA0003153731630000031
methods include: D111)统计每个小周期内风光电站Gi,i∈[1,l]的实时出力的均值
Figure FDA0003153731630000032
r∈[1,N]；D111) Count the mean value of real-time output of wind and solar power plants Gi ,i∈[1,l] in each small period
Figure FDA0003153731630000032
r∈[1,N]; D112)将时段tj+1前的w个时段执行步骤C11)，获得每个时段内的每个小周期内风光电站Gi,i∈[1,l]的实时出力的均值
Figure FDA0003153731630000033
u∈[j-w,j],r∈[1,N]；D112) Perform step C11) for the w periods before the period t j+1 to obtain the mean value of the real-time output of the wind and solar power plants Gi ,i∈[1,l] in each small period in each period
Figure FDA0003153731630000033
u∈[jw,j],r∈[1,N]; D113)统计
Figure FDA0003153731630000034
的最大值和最小值
Figure FDA0003153731630000035
将区间
Figure FDA0003153731630000036
等分为若干个取值区间，分别统计落入每个区间内的
Figure FDA0003153731630000037
的数量，将每个取值区间内的
Figure FDA0003153731630000038
的数量与w·N的比值，作为风光电站Gi,i∈[1,l]的实时出力
Figure FDA0003153731630000039
落入的取值区间对应的偏离概率
Figure FDA00031537316300000317
D113) Statistics
Figure FDA0003153731630000034
maximum and minimum
Figure FDA0003153731630000035
the interval
Figure FDA0003153731630000036
Divide into several value intervals, and count the values that fall within each interval.
Figure FDA0003153731630000037
the number of
Figure FDA0003153731630000038
The ratio of the number to w·N, as the real-time output of the wind and solar power station
Figure FDA0003153731630000039
The deviation probability corresponding to the falling value interval
Figure FDA00031537316300000317
 8.根据权利要求5所述的一种基于智能网关的含分布式电源微电网的调度方法，其特征在于，8. The scheduling method for a microgrid with distributed power sources based on an intelligent gateway according to claim 5, characterized in that, 步骤D)中，控制储能站Ei,i∈[1,m]增加充电功率或增加放电功率的方法包括：In step D), the method for controlling the energy storage station E i,i∈[1,m] to increase the charging power or increase the discharging power includes: D21)计算储能站Ei,i∈[1,m]的总增加充电功率D21) Calculate the total added charging power of the energy storage station E i,i∈[1,m]
Figure FDA00031537316300000313
ρ为设定裕度系数，ρ>1；
Figure FDA00031537316300000313
ρ is the set margin coefficient, ρ>1; D22)建立评价函数
Figure FDA00031537316300000310
其中z表示小周期序数，
Figure FDA00031537316300000312
为馈线i在时段tj+1的第z个小周期的平均负载，
Figure FDA00031537316300000311
为馈线i的负载上限,h是馈线数量；D22) Establish an evaluation function
Figure FDA00031537316300000310
where z represents the small period ordinal,
Figure FDA00031537316300000312
is the average load of feeder i in the zth small period of time period t j+1 ,
Figure FDA00031537316300000311
is the upper limit of the load of feeder i, h is the number of feeders; D23)在第z个小周期开始前的设定时刻，以第(z-1)个小周期至当前时刻内的风光电站Gi的实时出力
Figure FDA00031537316300000314
的均值计算
Figure FDA00031537316300000315
的值；D23) At the set time before the start of the zth small cycle, the real-time output of the wind and solar power station Gi within the (z-1) th small cycle to the current time
Figure FDA00031537316300000314
mean calculation of
Figure FDA00031537316300000315
the value of; D24)使用优化算法，获得使评价函数
Figure FDA00031537316300000316
值最小的储能站Ei,i∈[1,m]的实时增加充电功率，在下一个小周期开始前的设定时刻返回步骤D23)执行。D24) Use the optimization algorithm to obtain the evaluation function
Figure FDA00031537316300000316
The energy storage station E i,i∈[1,m] with the smallest value increases the charging power in real time, and returns to step D23) at the set time before the start of the next small cycle.
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