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

Abstract

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, which comprises the following steps: A) installing an intelligent gateway on a node where a target power grid is connected with a distributed power supply; B) calculating a characteristic value of the next time period, if the characteristic value is smaller than a set threshold value, only outputting active power by the wind-solar power station in the time period, and entering the step D); C) determining the output active power and reactive power of the wind and light power station; D) and according to the voltage and the frequency on the node, the intelligent gateway locally controls the power of the wind and light power station or the energy storage station. 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.

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 supply_i,i∈[1,n]Upper-mounted intelligent gateway

The intelligent gateway

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 t_j,j∈[1,n]The dispatching center is provided with a next time interval t_i+1Active power prediction value of load

And reactive power prediction

Calculating the next time period t_j+1Characteristic value

If the characteristic value

Less than a set threshold lambda_thrThen t is_j+1Wind-solar power station G in time interval_i,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

And determining wind and light power station G_i,i∈[1,l]Output active power

And reactive power

And satisfy

D) Will be of period t_j+1Equally dividing the time into N small time periods, wherein the starting time t of each small time period_{j+1|k,k∈[1,N]}Node w_iCorresponding intelligent gateway

Read node w_iVoltage and frequency on, according to node w_iVoltage and frequency on, intelligent gateway

Wind-solar power station G_iOr energy storage station E_i,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 t_j+1Internal wind and light power station G_i,i∈[1,l]Active power of

And reactive power

The distribution method comprises the following steps: C11) establishing an evaluation function

For feeder i during time period t_j+1The average active power transferred in-between,

for feeder i during time period t_j+1The average reactive power transferred in-between,

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

Minimum value wind-solar power station G_i,i∈[1,l]Reactive power of

Value, active power

For wind-solar power station G_i,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 calculated_i,i∈[1,l]Operating power factor of

Time period t_j+1Internal active power

And reactive power

Is distributed to

Always holds as a constraint of the optimization algorithm, λ'_thrTo set the threshold value, λ'_thr>λ_thr. 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 w_iConnected to wind-solar power station G_iThen, the following steps are executed: if node w_iWhen the voltage is lower than the standard value, the intelligent gateway

Controlling wind-solar power station G_iIncreasing reactive power

If node w_iIf the voltage is higher than the standard value, the intelligent gateway

Controlling wind-solar power station G_iReduction of reactive power

To output of (c).

Preferably, in step D), if the node w_iConnected thereto is an energy storage station E_iThen, the following steps are executed: if node w_iWhen the voltage is lower than the standard value, the intelligent gateway

Controlling energy storage station E_iIncrease power output or decrease charging power if node w_iIf the voltage is higher than the standard value, the intelligent gateway

Controlling energy storage station E_iIncreasing the charging power or decreasing the discharging power.

Preferably, the following steps are also performed: D11) wind and light power station G_i,i∈[1,l]Real-time force of

Deviation from predicted value

Probability of deviation of

Tau is the rate of deviation and is the rate of deviation,

σ is the deviation rate τ | t_j+1The probability of occurrence; D12) time period t_j+1Internal and real-time monitoring wind-light power station G_i,i∈[1,l]Real-time force of

Deviation rate of (τ | t)_j+1If the probability of deviation is high

Then the energy storage station E is added_i,i∈[1,m]Charging power or reducing energy storage station E_i,i∈[1,m]Otherwise, the wind-light power station G is increased according to the set amplitude_i,i∈[1,l]Reactive power of output

Until real-time output

Deviation rate of (τ | t)_j+1Corresponding deviation probability

Fall back to sigma^thrThe following.

Preferably, in step D11), the deviation probability is calculated

The method comprises the following steps: D111) counting wind-light power station G in each small period_i,i∈[1,l]Mean value of real-time forces of

r∈[1,N](ii) a D112) Will be of period t_j+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 obtained_i,i∈[1,l]Mean value of real-time forces of

u∈[j-w,j],r∈[1,N](ii) a D113) Statistics of

The maximum value and the minimum value of (c),

section of will

Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval

Of each value interval

The ratio of the number of the wind-solar power station G to w.N is used as the wind-solar power station G_i,i∈[1,l]Real-time force of

Deviation probability corresponding to falling value interval

Short-term wind-solar power station G_i,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 controlled_i,i∈[1,m]The method for increasing the charging power or increasing the discharging power comprises the following steps: D21) computing energy storage station E_i,i∈[1,m]Total increased charging power of

Rho is a set margin coefficient, rho>1; D22) establishing an evaluation function

Where z represents the number of small cycles,

for feeder i during time period t_j+1The average load of the z-th small period of time,

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 time_iReal-time force of

Mean value calculation of

A value of (d); D24) using an optimization algorithm, an evaluation function is obtained

Energy storage station E with the smallest value_i,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 supply_i,i∈[1,n]Upper-mounted intelligent gateway

Intelligent gateway

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 t_j,j∈[1,n]The dispatching center is provided with a next time interval t_i+1Active power prediction value of load

And reactive power prediction

Calculating the next time period t_j+1Characteristic value

If the characteristic value

Less than a set threshold lambda_thrThen t is_j+1Wind-solar power station G in time interval_i,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

And determining wind and light power station G_i,i∈[1,l]Output active power

And reactive power

And satisfy

As shown in FIG. 2, the period t_j+1Internal wind and light power station G_i,i∈[1,l]Active power of

And reactive power

The distribution method comprises the following steps: C11) establishing an evaluation function

For feeder i during time period t_j+1The average active power transferred in-between,

for feeder i during time period t_j+1The average reactive power transferred in-between,

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

Minimum value wind-solar power station G_i,i∈[1,l]Reactive power of

Value, active power

For wind-solar power station G_i,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 G_i,i∈[1,l]Operating power factor of

Time period t_j+1Internal active power

And reactive power

Is distributed to

Always holds as a constraint of the optimization algorithm, λ'_thrTo set the threshold value, λ'_thr>λ_thr. 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 t_j+1Equally dividing the time into N small time periods, wherein the starting time t of each small time period_{j+1|k,k∈[1,N]}Node w_iCorresponding intelligent gateway

Read node w_iVoltage and frequency on, according to node w_iVoltage and frequency on, intelligent gateway

Wind-solar power station G_iOr energy storage station E_i,i∈[1,m]And performing power control, wherein m is the number of the energy storage stations. If node w_iConnected to wind-solar power station G_iThen, the following steps are executed: if node w_iWhen the voltage is lower than the standard value, the intelligent gateway

Controlling wind-solar power station G_iIncreasing reactive power

If node w_iIf the voltage is higher than the standard value, the intelligent gateway

Controlling wind-solar power station G_iReduction of reactive power

To output of (c). If node w_iConnected thereto is an energy storage station E_iThen, the following steps are executed: if node w_iWhen the voltage is lower than the standard value, the intelligent gateway

Controlling energy storage station E_iIncrease power output or decrease charging power if node w_iIf the voltage is higher than the standard value, the intelligent gateway

Controlling energy storage station E_iIncreasing the charging power or decreasing the discharging power.

As shown in fig. 3, D11) calculating wind-solar power station G_i,i∈[1,l]In real timeOutput force

Deviation from predicted value

Probability of deviation of

Tau is the rate of deviation and is the rate of deviation,

σ is the deviation rate τ | t_j+1The probability of occurrence; D12) time period t_j+1Internal and real-time monitoring wind-light power station G_i,i∈[1,l]Real-time force of

Deviation rate of (τ | t)_j+1If the probability of deviation is high

Then the energy storage station E is added_i,i∈[1,m]Charging power or reducing energy storage station E_i,i∈[1,m]Otherwise, the wind-light power station G is increased according to the set amplitude_i,i∈[1,l]Reactive power of output

Until real-time output

Deviation rate of (τ | t)_j+1Corresponding deviation probability

Fall back to sigma^thrThe following.

In step D11), the deviation probability is calculated

The method comprises the following steps: D111) counting wind-light power station G in each small period_i,i∈[1,l]Of real time forceMean value

r∈[1,N](ii) a D112) Will be of period t_j+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 obtained_i,i∈[1,l]Mean value of real-time forces of

u∈[j-w,j],r∈[1,N](ii) a D113) Statistics of

The maximum value and the minimum value of (c),

section of will

Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval

Of each value interval

The ratio of the number of the wind-solar power station G to w.N is used as the wind-solar power station G_i,i∈[1,l]Real-time force of

Deviation probability corresponding to falling value interval

Short-term wind-solar power station G_i,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 controlled_i,i∈[1,m]The method for increasing the charging power or increasing the discharging power comprises the following steps: D21) computing energy storage station E_i,i∈[1,m]Total increased charging power of

Rho is a set margin coefficient, rho>1; D22) establishing an evaluation function

Where z represents the number of small cycles,

for feeder i during time period t_j+1The average load of the z-th small period of time,

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 time_iReal-time force of

Mean value calculation of

A value of (d); D24) obtaining an opinion using an optimization algorithmFunction of price

Energy storage station E with the smallest value_i,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. A dispatching method of a microgrid with distributed power supplies based on an intelligent gateway is provided, wherein the distributed power supplies comprise an energy storage station and a wind-light power station,

the method comprises the following steps:

A) node w for connecting target power grid with distributed power supply_i,i∈[1,n]Upper-mounted intelligent gateway

The intelligent gateway

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 t_j,j∈[1,n]The dispatching center is provided with a next time interval t_j+1Active power prediction value of load

And reactive power prediction

Calculating the next time period t_j+1Characteristic value

If the characteristic value

Less than a set threshold lambda_thrThen t is_j+1Wind-solar power station G in time interval_i,i∈[1,l]Only active power is output, l is the number of wind and light power stations,

the predicted value of the output is obtained, and the step D) is carried out, otherwise, the step C) is carried out;

C) scheduling center computation

And determining wind and light power station G_i,i∈[1,l]Output active power

And reactive power

And satisfy

D) Will be of period t_j+1Equally dividing the time into N small time periods, wherein the starting time t of each small time period_{j+1|k,k∈[1,N]}Node w_iCorresponding intelligent gateway

Read node w_iSum frequency of voltages onRate according to node w_iVoltage and frequency on, intelligent gateway

Wind-solar power station G_iOr energy storage station E_i,i∈[1,m]And performing power control, wherein m is the number of the energy storage stations.

2. The dispatching method of micro-grid with distributed power sources based on intelligent gateway as claimed in claim 1,

in step C), time period t_j+1Internal wind and light power station G_i,i∈[1,l]Active power of

And reactive power

The distribution method comprises the following steps:

C11) establishing an evaluation function

For feeder i during time period t_j+1The average active power transferred in-between,

for feeder i during time period t_j+1The average reactive power transferred in-between,

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

Minimum value wind-lightPower station G_i,i∈[1,l]Reactive power of

Value, active power

For wind-solar power station G_i,i∈[1,l]Real-time force is applied.

3. The dispatching method of micro-grid with distributed power sources based on intelligent gateway as claimed in claim 2,

in step C12), calculating the wind-solar power station G_i,i∈[1,l]Operating power factor of

Time period t_j+1Internal active power

And reactive power

Is distributed to

Always holds as a constraint of the optimization algorithm, λ'_thrTo set the threshold value, λ'_thr>λ_thr。

4. The dispatching method of micro-grid with distributed power sources based on intelligent gateway as claimed in claim 2 or 3,

in step D), if the node w_iConnected to wind-solar power station G_iThen, the following steps are executed:

if node w_iWhen the voltage is lower than the standard value, the intelligent gateway

Controlling wind-solar power station G_iIncreasing reactive power

If node w_iIf the voltage is higher than the standard value, the intelligent gateway

Controlling wind-solar power station G_iReduction of reactive power

To output of (c).

5. The dispatching method of micro-grid with distributed power sources based on intelligent gateway as claimed in claim 1, 2 or 3,

in step D), if the node w_iConnected thereto is an energy storage station E_iThen, the following steps are executed:

if node w_iWhen the voltage is lower than the standard value, the intelligent gateway

Controlling energy storage station E_iIncrease power output or decrease charging power if node w_iIf the voltage is higher than the standard value, the intelligent gateway

Controlling energy storage station E_iIncreasing the charging power or decreasing the discharging power.

6. The dispatching method of micro-grid with distributed power sources based on intelligent gateway as claimed in claim 4,

the following steps are also performed:

D11) wind, solar and electricity computingStation

Real-time force of

Deviation from predicted value

Probability of deviation of

Tau is the rate of deviation and is the rate of deviation,

σ is the deviation rate τ | t_j+1The probability of occurrence;

D12) time period t_j+1Internal and real-time monitoring wind-light power station G_i,i∈[1,l]Real-time force of

Deviation rate of (τ | t)_j+1If the probability of deviation is high

Then the energy storage station E is added_i,i∈[1,m]Charging power or reducing energy storage station E_i,i∈[1,m]Otherwise, the wind-light power station G is increased according to the set amplitude_i,i∈[1,l]Reactive power of output

Until real-time output

Deviation rate of (τ | t)_j+1Corresponding deviation probability

Fall back to sigma^thrThe following.

7. The dispatching method of micro-grid with distributed power sources based on intelligent gateway as claimed in claim 6,

in step D11), the deviation probability is calculated

The method comprises the following steps:

D111) counting wind-light power station G in each small period_i,i∈[1,l]Mean value of real-time forces of

r∈[1,N]；

D112) Will be of period t_j+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 obtained_i,i∈[1,l]Mean value of real-time forces of

u∈[j-w,j],r∈[1,N]；

D113) Statistics of

Maximum and minimum values of

Section of will

Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval

Of each value interval

The ratio of the number of (a) to w.N as the wind-lightPower station G_i,i∈[1,l]Real-time force of

Deviation probability corresponding to falling value interval

8. The dispatching method of micro-grid with distributed power sources based on intelligent gateway as claimed in claim 5,

in step D), the energy storage station E is controlled_i,i∈[1,m]The method for increasing the charging power or increasing the discharging power comprises the following steps:

D21) computing energy storage station E_i,i∈[1,m]Total increased charging power of

Rho is a set margin coefficient, rho>1；

D22) Establishing an evaluation function

Where z represents the number of small cycles,

for feeder i during time period t_j+1The average load of the z-th small period of time,

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 time_iReal-time force of

Mean value calculation of

A value of (d);

D24) using an optimization algorithm, an evaluation function is obtained

Energy storage station E with the smallest value_i,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.

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