CN112350378B - Micro-grid robust optimization method for coping with environment and load demand changes - Google Patents

Micro-grid robust optimization method for coping with environment and load demand changes Download PDF

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CN112350378B
CN112350378B CN202011101239.XA CN202011101239A CN112350378B CN 112350378 B CN112350378 B CN 112350378B CN 202011101239 A CN202011101239 A CN 202011101239A CN 112350378 B CN112350378 B CN 112350378B
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王智良
苏昌奇
杨珺
张化光
刘鑫蕊
孙秋野
王迎春
杨东升
黄博南
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Abstract

The invention provides a micro-grid robust optimization method for meeting environment and load demand changes, and relates to the technical field of micro-grid operation optimization. The microgrid robust optimization model established by the optimization method provided by the invention solves the randomness problem of the requirements of renewable energy sources such as wind energy, solar energy and the like and loads by utilizing robust optimization, can adjust the robustness level of the microgrid, completes microgrid robust optimization modeling by establishing the tidal current power balance of the microgrid, the voltage and current of the microgrid, the traditional distributed power supply and the charge-discharge constraint conditions of the energy storage device, outputs an optimization result, completes microgrid robust optimization corresponding to the change of environment and load requirements, and ensures that the running cost of the microgrid is lowest.

Description

Microgrid robust optimization method for coping with environment and load demand changes
Technical Field
The invention relates to the technical field of microgrid operation optimization, in particular to a microgrid robust optimization method for meeting environment and load demand changes.
Background
Energy shortage, environmental pollution and climate change are important factors for restricting the sustainable development of economy and society in the world at present, and energy and environmental problems become important strategic problems with high concern at home and abroad. Meanwhile, clean energy sources such as wind energy and solar energy are rich in total amount, low-carbon, environment-friendly and renewable, have huge development potential, and are rapidly developed.
The micro-grid is an autonomous system which is composed of a distributed power supply, an energy storage device, a load and a corresponding control device and can realize self-control, protection and management. The micro-grid has flexible operation characteristics, can be operated in a grid-connected mode with the power grid, and can also be operated in an isolated island mode when the power grid breaks down or is separated from the main grid when necessary. The micro-grid is an excellent method for integrating the distributed power supply into the grid for operation, the adverse effect of the distributed power supply on the large-scale dispersed grid-connected operation can be obviously reduced, the utilization rate of renewable energy sources is improved, the integrated operation of the distributed power supply and a load is realized, and the pollution emission of a micro-grid system is reduced.
However, renewable energy sources such as wind energy and solar energy and load requirements have randomness and volatility, and are difficult problems to be solved in optimization operation of the micro-grid, and if the randomness influence of the factors cannot be properly treated, stable and reliable operation of the micro-grid cannot be guaranteed. In addition, in order to ensure stable and reliable operation of the microgrid, the microgrid is required to have robustness, and the traditional microgrid operation robustness level is fixed, so that the robustness level of the microgrid cannot be adjusted according to actual requirements, the situation that the operation cost is increased due to pursuit of a higher robustness level of the microgrid is caused, and the microgrid cannot be operated more economically.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a micro-grid robust optimization method for meeting the environment and load demand changes. The micro-grid robust optimization model established by the optimization method provided by the invention solves the problem of randomness of renewable energy sources such as wind energy, solar energy and the like and load requirements by using robust optimization, and can adjust the robust level of the micro-grid so as to minimize the running cost of the micro-grid.
The technical scheme adopted by the invention is as follows:
a micro-grid robust optimization method for coping with environment and load demand changes comprises the following steps:
step 1: establishing a basic grid structure model according to the microgrid information;
the microgrid information comprises node information, branch information, output information of a renewable distributed power supply, output information of a traditional distributed power supply, energy storage device information and load demand information; wherein the nodes comprise individual nodes in a microgrid; the branches comprise each branch in the microgrid; the renewable distributed power supply comprises a photovoltaic power generation power supply and a wind power generation power supply in a microgrid; the traditional distributed power source is a gas turbine; the energy storage device comprises an energy storage device in a microgrid; the load demand comprises a load demand in a microgrid;
the basic grid structure model comprises a node connection state matrix, an inter-node information matrix, a renewable distributed power supply output matrix, a traditional distributed power supply output matrix, an energy storage device information matrix and a load demand matrix; wherein the elements in the node connection state matrix only contain 0 or 1, 0 represents that the connection state between the nodes is disconnected, and 1 represents that the connection state between the nodes is connected; the inter-node information matrix is an inter-node line impedance matrix; the renewable distributed power output matrix comprises a photovoltaic power generation output matrix and a wind power generation output matrix; the traditional distributed power output matrix is a gas turbine output matrix; the energy storage device information matrix comprises a position information matrix of the energy storage device; the load demand matrix comprises a load demand value matrix;
step 2: carrying out robust equivalent representation on the output and load requirements of the renewable distributed power supply;
step 2.1: establishing a robust equivalent representation of the output of the renewable distributed power source, including a photovoltaic power generation output robust equivalent representation and a wind power generation output robust equivalent representation:
the photovoltaic power generation output robust equivalent characterization is as follows:
Figure RE-GDA0002856458560000021
wherein the content of the first and second substances,
Figure RE-GDA0002856458560000022
for robust equivalent characterization of photovoltaic power generation output,
Figure RE-GDA0002856458560000023
is the inverse function of the cumulative distribution function of the photovoltaic power supply of node p over time period t,
Figure RE-GDA0002856458560000024
as a function of the cumulative distribution of the photovoltaic power generation output,
Figure RE-GDA0002856458560000025
E PV in order to expect the photovoltaic power generation output,
Figure RE-GDA0002856458560000026
obeying Beta distribution for the probability density function of the photovoltaic generator set on the node p in the time period t, namely
Figure RE-GDA0002856458560000027
α p,t 、β p,t Two parameters of Beta distribution are respectively, zeta is a robust adjustment parameter, omega PV Collecting all nodes configured with photovoltaic power generation power supplies;
the robust equivalent representation of the wind power generation output is shown as the following formula:
Figure RE-GDA0002856458560000028
wherein, the first and the second end of the pipe are connected with each other,
Figure RE-GDA0002856458560000029
for a robust equivalent representation of the wind power generation output,
Figure RE-GDA00028564585600000210
wind power supply of node w in time periodthe inverse of the cumulative distribution function within t,
Figure RE-GDA00028564585600000211
is a cumulative distribution function of the wind power generation output,
Figure RE-GDA00028564585600000212
E WT,t for the expectation of the wind power generation output,
Figure RE-GDA00028564585600000213
obeying Weibull distribution for the probability density function of the wind generating set on the node omega in the time period t, namely
Figure RE-GDA00028564585600000214
κ ω,t 、λ ω,t Two parameters, omega, of the Weibull distribution, respectively WT And collecting all nodes configured with the wind power generation power supply.
Step 2.2: establishing a robust equivalent representation of the load demand, as shown in the following equation:
Figure RE-GDA00028564585600000215
wherein the content of the first and second substances,
Figure RE-GDA00028564585600000216
respectively representing the robust equivalent characteristics of the active power and the reactive power of the load demand in the time period t of the node i,
Figure RE-GDA00028564585600000217
as an inverse function of the cumulative distribution function of the load demand of node i over time period t,
Figure RE-GDA00028564585600000218
respectively the cumulative distribution functions of the active power and the reactive power of the load demand in the time period t of the node i,
Figure RE-GDA0002856458560000031
E P,t in order to be loaded with the expectation of the active demand,
Figure RE-GDA0002856458560000032
E Q,t in order to be expected for the reactive demand of the load,
Figure RE-GDA0002856458560000033
obey bivariate normal distribution function, i.e. probability density function of active power and reactive power of load demand in time period t of node i
Figure RE-GDA0002856458560000034
Wherein
Figure RE-GDA0002856458560000035
Respectively the average value and the standard deviation of the active demand of the node i in the time period t,
Figure RE-GDA0002856458560000036
respectively the mean value and the standard deviation rho of the reactive demand of the node i in the time period t i,t For the point i, the correlation coefficient between the active demand and the reactive demand in the time period t, Ω D Collecting all nodes configured with loads;
step 2.3: calculating a robust adjustment parameter zeta in the photovoltaic power generation output robust equivalent representation, the wind power generation output robust equivalent representation and the load demand robust equivalent representation, wherein the robust adjustment parameter zeta is shown as the following formula:
ζ＝A*γ
wherein gamma is a robustness adjustment parameter of different probability density functions and has a value of 0<γ<1, parameter A has
Figure RE-GDA0002856458560000037
Wherein
Figure RE-GDA0002856458560000038
And step 3: establishing an objective function of the microgrid robust optimization model, which is shown as the following formula:
Figure RE-GDA0002856458560000039
wherein, Delta t For the length of the time segment t,
Figure RE-GDA00028564585600000310
for the unit cost of the input power from the main network for the time period t,
Figure RE-GDA00028564585600000311
the power input from the main network for the time period t for the node i,
Figure RE-GDA00028564585600000312
for the unit cost of conventional distributed power generation at node g,
Figure RE-GDA00028564585600000313
the active power of the conventional distributed power supply for the node g during the time period t,
Figure RE-GDA00028564585600000314
for the cost per unit of discharge of the energy storage device at node b,
Figure RE-GDA00028564585600000315
is the discharge power of the energy storage device at node b,
Figure RE-GDA00028564585600000316
for a unit cost of charging the energy storage device at node b,
Figure RE-GDA00028564585600000317
for the charging power of the energy storage device at node b,
Figure RE-GDA00028564585600000318
is the unit offloading cost at node i, Ψ i,t For a binary variable associated with load shedding by node i during time period t, Ψ when load shedding is required i,t When not required, 1On-duty Ψ i,t ＝0，Ω T For a set of time periods comprising all time periods t, Ω S 、Ω DG 、Ω ESS And omega are respectively a node set comprising a common connection point PCC, a node set configured with a traditional distributed power supply, a node set configured with an energy storage device and a node set comprised in a microgrid.
And 4, step 4: establishing the constraint conditions of micro-grid tidal current power balance, micro-grid voltage and current, a traditional distributed power supply and an energy storage device charge-discharge;
the micro-grid power flow balance constraint condition is shown as the following formula:
Figure RE-GDA00028564585600000319
Figure RE-GDA0002856458560000041
wherein, P ki,t 、P ij,t Respectively, the active power flow, I, of the line ki and the line ij in the time period t ij,t For the current value of line ij in time period t, Ω l Is a collection of lines, R, contained in a microgrid ij Is the equivalent resistance of the line ij,
Figure RE-GDA0002856458560000042
for active power input from the main network at node i during the t time period, omega PV For a set of nodes comprising a photovoltaic power supply, Ω WT For a set of nodes comprising a wind power source, Q ki,t ，Q ij,t Respectively, the reactive power flow, X, of the line ki and the line ij in the time period t ij Is the equivalent reactance of the line ij,
Figure RE-GDA0002856458560000043
for reactive power input from the main network at node i during the time period t,
Figure RE-GDA0002856458560000044
is the reactive power of the conventional distributed power supply at node g during time period t.
The microgrid voltage and current constraint condition is shown as the following formula:
Figure RE-GDA0002856458560000045
Figure RE-GDA0002856458560000046
Figure RE-GDA0002856458560000047
Figure RE-GDA0002856458560000048
Figure RE-GDA0002856458560000049
wherein, V j,t For the voltage value of node j during time period t,V、
Figure RE-GDA00028564585600000410
respectively a minimum voltage amplitude and a maximum voltage amplitude,
Figure RE-GDA00028564585600000411
for the maximum current amplitude of the line ij,
Figure RE-GDA00028564585600000412
maximum apparent power input for the main network at node i;
the traditional distributed power supply constraint condition is shown as follows:
Figure RE-GDA00028564585600000413
Figure RE-GDA00028564585600000414
Figure RE-GDA00028564585600000415
Figure RE-GDA00028564585600000416
F g,t ≥F g
Figure RE-GDA00028564585600000417
wherein, the first and the second end of the pipe are connected with each other,
Figure RE-GDA00028564585600000418
respectively the active power and the reactive power generated by the traditional distributed power supply in the time period t by the node g,
Figure RE-GDA00028564585600000419
is the maximum value of the power generated by the traditional distributed power supply in the time period t for the node g, pi g,t II for a binary variable of the node g associated with the conventional distributed power supply during the time period t, when the conventional distributed power supply is switched on g,t 1, and when the conventional distributed power supply is not turned on, Π g,t ＝0，pf g For the power factor limitation of a conventional distributed power supply at node g,
Figure RE-GDA0002856458560000051
respectively a descending limit, a climbing limit and F of the traditional distributed power supply at the node g g,t The diesel generator residual fuel at the node g in the time period t,
Figure RE-GDA0002856458560000052
fuel efficiency, FC, for diesel-electric generator set at node g g Fuel capacity of diesel-electric set at node g, H g Is the heating value of the diesel generator unit fuel at node g,F g the minimum fuel of the diesel generating set at the node g.
The charge and discharge constraint condition of the energy storage device is shown as the following formula:
Figure RE-GDA0002856458560000053
Figure RE-GDA0002856458560000054
Figure RE-GDA0002856458560000055
Figure RE-GDA0002856458560000056
Figure RE-GDA0002856458560000057
Λ b,t +Φ b,t ≤1
Figure RE-GDA0002856458560000058
wherein, SOC b,t Is the state of charge, ξ, of the energy storage device of node b during time period t b Is the self-discharge rate, EC, of the energy storage device at node b b Is the energy capacity of the energy storage device at node b,
Figure RE-GDA0002856458560000059
for the efficiency of the energy storage device discharge at node b,
Figure RE-GDA00028564585600000510
for the charging efficiency, phi, of the energy storage device at node b b,t For the node b, a binary variable related to the discharge operation of the energy storage device in the time period t, when discharging phi b,t 1, when charged b,t ＝0，
Figure RE-GDA00028564585600000511
Respectively the minimum value and the maximum value of the discharge power of the energy storage device at the node b, and the lambda b,t For the binary variable of the node b in the time period t, the charging time lambda is related to the charging operation of the energy storage device b,t Discharge time ═ 1, Λ b,t ＝0，
Figure RE-GDA00028564585600000512
Respectively as the minimum value and the maximum value of the charging power of the energy storage device at the node b,SOC b 、
Figure RE-GDA00028564585600000513
respectively the minimum and maximum charge states of the energy storage system at the node b,
Figure RE-GDA00028564585600000514
the minimum state of charge for node b within the period tau;
and 5: and (4) completing the robust optimization modeling of the micro-grid, outputting an optimization result, and completing the robust optimization of the micro-grid corresponding to the change of the environment and the load demand.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
the invention provides a microgrid robust optimization method for meeting the environment and load demand changes, and the established microgrid robust optimization model can give consideration to both the robustness and the economy of a microgrid and balance the economic optimization and the robust optimization of the microgrid. Compared with the micro-grid robust optimization, the optimization model provided by the invention can better ensure the economy of the micro-grid; compared with the micro-grid economic optimization, the optimization model provided by the invention can ensure the robustness of the micro-grid.
Drawings
FIG. 1 is a flow chart of a microgrid robust optimization method of the present invention;
fig. 2 is a diagram of a 14-node microgrid structure in the embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the invention refers to the accompanying drawings.
A microgrid robust optimization method for coping with environment and load demand changes is shown in figure 1 and comprises the following steps:
step 1: establishing a basic grid structure model according to the microgrid information;
the microgrid information comprises node information, branch information, output information of a renewable distributed power supply, output information of a traditional distributed power supply, energy storage device information and load demand information; wherein the nodes comprise individual nodes in a microgrid; the branches comprise each branch in the microgrid; the renewable distributed power supply comprises a photovoltaic power generation power supply and a wind power generation power supply in a microgrid; the traditional distributed power source is a gas turbine; the energy storage device comprises an energy storage device in a microgrid; the load demand comprises a load demand in a microgrid;
the basic grid structure model comprises a node connection state matrix, an inter-node information matrix, a renewable distributed power supply output matrix, a traditional distributed power supply output matrix, an energy storage device information matrix and a load demand matrix; wherein the elements in the node connection state matrix only contain 0 or 1, 0 represents that the connection state between the nodes is disconnected, and 1 represents that the connection state between the nodes is connected; the inter-node information matrix is an inter-node line impedance matrix; the renewable distributed power output matrix comprises a photovoltaic power generation output matrix and a wind power generation output matrix; the traditional distributed power output matrix is a gas turbine output matrix; the energy storage device information matrix comprises a position information matrix of the energy storage device; the load demand matrix comprises a load demand value matrix;
in the present embodiment, a 14-node microgrid system is selected, as shown in fig. 2, wherein ESS is an energy storage device, PV is a photovoltaic generator set, WT is a wind turbine generator set, and DE is a small diesel generator set. The impedance of the lines between the nodes of the microgrid is shown in table 1.
TABLE 1
Figure RE-GDA0002856458560000061
Figure RE-GDA0002856458560000071
Step 2: carrying out robust equivalent representation on the output and load requirements of the renewable distributed power supply;
step 2.1: establishing a robust equivalent representation of the output of the renewable distributed power source, including a photovoltaic power generation output robust equivalent representation and a wind power generation output robust equivalent representation:
the photovoltaic power generation output robust equivalent characterization is as follows:
Figure RE-GDA0002856458560000072
wherein the content of the first and second substances,
Figure RE-GDA0002856458560000073
for robust equivalent characterization of photovoltaic power generation output,
Figure RE-GDA0002856458560000074
is the inverse of the cumulative distribution function of the photovoltaic power supply at node p over time period t,
Figure RE-GDA0002856458560000075
as a function of the cumulative distribution of the photovoltaic power generation output,
Figure RE-GDA0002856458560000076
E PV calculating the photovoltaic power generation output according to the photovoltaic power generation output,
Figure RE-GDA0002856458560000077
obeying Beta distribution for the probability density function of the photovoltaic generator set on the node p in the time period t, namely
Figure RE-GDA0002856458560000078
α p,t 、β p,t Two parameters of Beta distribution are respectively, zeta is a robust adjustment parameter omega PV Collecting all nodes configured with photovoltaic power generation power supplies;
the robust equivalent representation of the wind power generation output is shown as the following formula:
Figure RE-GDA0002856458560000079
wherein, the first and the second end of the pipe are connected with each other,
Figure RE-GDA00028564585600000710
for a robust equivalent representation of the wind power generation output,
Figure RE-GDA00028564585600000711
is the inverse function of the cumulative distribution function of the wind power supply of the node w in the time period t,
Figure RE-GDA00028564585600000712
is a cumulative distribution function of the wind power generation output,
Figure RE-GDA00028564585600000713
E WT,t and calculating the expected wind power generation output according to the wind power generation output,
Figure RE-GDA00028564585600000714
obeying Weibull distribution for the probability density function of the wind generating set on the node omega in the time period t, namely
Figure RE-GDA00028564585600000715
κ ω,t 、λ ω,t Two parameters, omega, of the Weibull distribution, respectively WT For all that isAnd configuring a node set of the wind power generation power supply.
Step 2.2: establishing a robust equivalent representation of the load demand as shown in the following equation:
Figure RE-GDA00028564585600000716
wherein the content of the first and second substances,
Figure RE-GDA00028564585600000717
respectively representing the robust equivalent characteristics of the active power and the reactive power of the load demand in the time period t of the node i,
Figure RE-GDA00028564585600000718
as an inverse function of the cumulative distribution function of the load demand of node i over time period t,
Figure RE-GDA00028564585600000719
respectively the cumulative distribution functions of the active power and the reactive power of the load demand in the time period t of the node i,
Figure RE-GDA00028564585600000720
E P,t in order to be loaded with the expectation of the active demand,
Figure RE-GDA00028564585600000721
E Q,t for the expectation of reactive demand of the load, E P,t And E Q,t The calculation is carried out according to the load value,
Figure RE-GDA00028564585600000722
obey bivariate normal distribution function, i.e. probability density function of active power and reactive power of load demand in time period t of node i
Figure RE-GDA0002856458560000081
Wherein
Figure RE-GDA0002856458560000082
Respectively the average value and the standard deviation of the active demand of the node i in the time period t,
Figure RE-GDA0002856458560000083
respectively the mean value and the standard deviation rho of the reactive demand of the node i in the time period t i,t Is the correlation coefficient of the active demand and the reactive demand of the point i in the time period t, omega D Collecting all nodes configured with loads;
step 2.3: calculating a robust adjustment parameter zeta in the photovoltaic power generation output robust equivalent representation, the wind power generation output robust equivalent representation and the load demand robust equivalent representation, wherein the robust adjustment parameter zeta is shown as the following formula:
ζ＝A*γ
wherein gamma is a robustness adjustment parameter of different probability density functions and has a value of 0<γ<1, parameter A has
Figure RE-GDA0002856458560000084
Wherein
Figure RE-GDA0002856458560000085
And step 3: establishing an objective function of the microgrid robust optimization model, which is shown as the following formula:
Figure RE-GDA0002856458560000086
wherein, Delta t For the length of the time segment t,
Figure RE-GDA0002856458560000087
for a unit cost of power input from the main network for a time period t,
Figure RE-GDA0002856458560000088
the power input from the main network for the time period t for the node i,
Figure RE-GDA0002856458560000089
for the unit cost of conventional distributed power generation at node g,
Figure RE-GDA00028564585600000810
the active power of the conventional distributed power supply for the node g during the time period t,
Figure RE-GDA00028564585600000811
for the cost per unit of discharge of the energy storage device at node b,
Figure RE-GDA00028564585600000812
is the discharge power of the energy storage device at node b,
Figure RE-GDA00028564585600000813
for a unit cost of charging the energy storage device at node b,
Figure RE-GDA00028564585600000814
for the charging power of the energy storage device at node b,
Figure RE-GDA00028564585600000815
is the unit offloading cost at node i, Ψ i,t For a binary variable associated with load shedding by node i during time period t, Ψ when load shedding is required i,t 1, when no load shedding is required Ψ i,t ＝0，Ω T For a set of time periods comprising all time periods t, Ω S 、Ω DG 、Ω ESS And omega are respectively a node set comprising a common connection point PCC, a node set configured with a traditional distributed power supply, a node set configured with an energy storage device and a node set comprised in a microgrid.
And 4, step 4: establishing constraint conditions of tidal current power balance of a micro-grid, voltage and current of the micro-grid, a traditional distributed power supply and charging and discharging of an energy storage device;
the constraint condition of the micro-grid power flow balance is shown as the following formula:
Figure RE-GDA00028564585600000816
Figure RE-GDA00028564585600000817
wherein, P ki,t 、P ij,t Respectively the active power flow, I, of line ki and line ij in time period t ij,t For the current value of line ij in time period t, Ω l Is a collection of lines, R, contained in a microgrid ij Is the equivalent resistance of the line ij,
Figure RE-GDA0002856458560000091
for active power input from the main network at node i during the t period, Ω PV For a set of nodes comprising a photovoltaic power supply, Ω WT For a set of nodes comprising a wind-power source, Q ki,t ，Q ij,t Respectively, the reactive power flow, X, of the line ki and the line ij in the time period t ij Is the equivalent reactance of the line ij,
Figure RE-GDA0002856458560000092
for reactive power input from the main network at node i during the time period t,
Figure RE-GDA0002856458560000093
is the reactive power of the conventional distributed power supply at node g during time period t.
The microgrid voltage and current constraint condition is shown as the following formula:
Figure RE-GDA0002856458560000094
Figure RE-GDA0002856458560000095
Figure RE-GDA0002856458560000096
Figure RE-GDA0002856458560000097
Figure RE-GDA0002856458560000098
wherein, V j,t For the voltage value of node j during time period t,V、
Figure RE-GDA0002856458560000099
respectively a minimum voltage amplitude and a maximum voltage amplitude,
Figure RE-GDA00028564585600000910
for the maximum current amplitude of the line ij,
Figure RE-GDA00028564585600000911
maximum apparent power input for the main network at node i;
the traditional distributed power supply constraint condition is shown as follows:
Figure RE-GDA00028564585600000912
Figure RE-GDA00028564585600000913
Figure RE-GDA00028564585600000914
Figure RE-GDA00028564585600000915
F g,t ≥F g
Figure RE-GDA00028564585600000916
wherein, the first and the second end of the pipe are connected with each other,
Figure RE-GDA00028564585600000917
respectively the active power and the reactive power generated by the traditional distributed power supply in the time period t by the node g,
Figure RE-GDA00028564585600000918
is the maximum value of the power generated by the traditional distributed power supply in the time period t of the node g, pi g,t II is a binary variable of the node g related to the traditional distributed power supply in the time period t when the traditional distributed power supply is switched on g,t 1, and when the conventional distributed power supply is not turned on, Π g,t ＝0，pf g For the power factor limitation of a conventional distributed power supply at node g,
Figure RE-GDA00028564585600000919
respectively a traditional distributed power supply descending limit, a climbing limit and F at a node g g,t Is the residual fuel (%) of the diesel generator at the node g in the time period t,
Figure RE-GDA0002856458560000101
fuel efficiency, FC, for diesel-electric generator set at node g g Fuel capacity of diesel-electric set at node g, H g Is the heating value of the diesel generator unit fuel at node g,F g the minimum fuel of the diesel generating set at the node g.
The charge and discharge constraint condition of the energy storage device is as follows:
Figure RE-GDA0002856458560000102
Figure RE-GDA0002856458560000103
Figure RE-GDA0002856458560000104
Figure RE-GDA0002856458560000105
Figure RE-GDA0002856458560000106
Λ b,t +Φ b,t ≤1
Figure RE-GDA0002856458560000107
therein, SOC b,t Is the state of charge, ξ, of the energy storage device of node b during time period t b Is the self-discharge rate, EC, of the energy storage device at node b b Is the energy capacity of the energy storage device at node b,
Figure RE-GDA0002856458560000108
for the efficiency of the energy storage device discharge at node b,
Figure RE-GDA0002856458560000109
charging efficiency, phi, for the energy storage device at node b b,t For the binary variable of the node b in the time period t, phi during discharge, associated with the discharge operation of the energy storage device b,t 1, at charging time Φ b,t ＝0，
Figure RE-GDA00028564585600001010
Respectively as the minimum value and the maximum value of the discharge power of the energy storage device at the node b, and Λ b,t For the binary variable of the node b in the time period t, the charging time lambda is related to the charging operation of the energy storage device b,t Discharge time ═ 1, Λ b,t ＝0，
Figure RE-GDA00028564585600001011
Charging power for energy storage device at node b respectivelyThe minimum value and the maximum value are calculated,SOC b 、
Figure RE-GDA00028564585600001012
respectively the minimum and maximum charge states of the energy storage system at the node b,
Figure RE-GDA00028564585600001013
the minimum state of charge for node b within the period tau;
in the present embodiment, the parameters of the conventional distributed power source, i.e., the diesel generator, are shown in table 2, and the parameters of the energy storage device are shown in table 3.
TABLE 2
Figure RE-GDA00028564585600001014
TABLE 3
Figure RE-GDA0002856458560000111
And 5: and completing the robust optimization modeling of the microgrid, outputting an optimization result and completing the robust optimization of the microgrid corresponding to the change of the environment and the load demand.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.

Claims (1)

1. A micro-grid robust optimization method for dealing with environment and load demand changes is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a basic grid structure model according to the microgrid information;
in the step 1, the microgrid information comprises node information, branch information, output information of a renewable distributed power supply, output information of a traditional distributed power supply, energy storage device information and load demand information; wherein the nodes comprise individual nodes in a microgrid; the branches comprise each branch in the microgrid; the renewable distributed power supply comprises a photovoltaic power generation power supply and a wind power generation power supply in a microgrid; the traditional distributed power source is a gas turbine; the energy storage device comprises an energy storage device in a microgrid; the load demand comprises a load demand in a microgrid;
the basic grid structure model comprises a node connection state matrix, an inter-node information matrix, a renewable distributed power supply output matrix, a traditional distributed power supply output matrix, an energy storage device information matrix and a load demand matrix; wherein the elements in the node connection state matrix only contain 0 or 1, 0 represents that the connection state between the nodes is disconnection, and 1 represents that the connection state between the nodes is connection; the inter-node information matrix is an inter-node line impedance matrix; the renewable distributed power output matrix comprises a photovoltaic power generation output matrix and a wind power generation output matrix; the traditional distributed power output matrix is a gas turbine output matrix; the energy storage device information matrix comprises a position information matrix of the energy storage device; the load demand matrix comprises a load demand value matrix;
and 2, step: carrying out robust equivalent representation on the output and load requirements of the renewable distributed power supply;
step 2.1: establishing a robust equivalent representation of the output of the renewable distributed power source, including a photovoltaic power generation output robust equivalent representation and a wind power generation output robust equivalent representation:
the photovoltaic power generation output robust equivalent characterization is as follows:
Figure FDA0003736206530000011
wherein the content of the first and second substances,
Figure FDA0003736206530000012
for robust equivalent characterization of photovoltaic power generation output,
Figure FDA0003736206530000013
is the inverse of the cumulative distribution function of the photovoltaic power supply at node p over time period t,
Figure FDA0003736206530000014
as a function of the cumulative distribution of the photovoltaic power generation output,
Figure FDA0003736206530000015
E PV in order to expect the photovoltaic power generation output,
Figure FDA0003736206530000016
obeying Beta distribution for the probability density function of the photovoltaic generator set on the node p in the time period t, namely
Figure FDA0003736206530000017
α p,t 、β p,t Two parameters of Beta distribution are respectively, zeta is a robust adjustment parameter, omega PV Collecting all nodes configured with photovoltaic power generation power supplies;
the robust equivalent representation of the wind power generation output is shown as the following formula:
Figure FDA0003736206530000018
wherein the content of the first and second substances,
Figure FDA0003736206530000019
for a robust equivalent representation of the wind power generation output,
Figure FDA00037362065300000110
is the inverse function of the cumulative distribution function of the wind power supply of the node w in the time period t,
Figure FDA00037362065300000111
is a cumulative distribution function of the wind power generation output,
Figure FDA00037362065300000112
E WT t is the expectation of wind power generation output,
Figure FDA00037362065300000113
obeying Weibull distribution for the probability density function of the wind generating set on the node omega in the time period t, namely
Figure FDA0003736206530000021
κ ω,t 、λ ω,t Two parameters, Ω, of the Weibull distribution, respectively WT The method comprises the steps that a node set of all the configured wind power generation power supplies is provided;
step 2.2: establishing a robust equivalent representation of the load demand as shown in the following equation:
Figure FDA0003736206530000022
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003736206530000023
respectively representing the robust equivalent characteristics of the active power and the reactive power of the load demand in a time period t of a node i,
Figure FDA0003736206530000024
as an inverse function of the cumulative distribution function of the load demand of node i over time period t,
Figure FDA0003736206530000025
respectively the cumulative distribution function of the active power and the reactive power of the load demand in the time period t of the node i,
Figure FDA0003736206530000026
E P,t in order to be loaded with the expectation of the active demand,
Figure FDA0003736206530000027
E Q,t in order to be expected for the reactive demand of the load,
Figure FDA0003736206530000028
obey bivariate normal distribution function, i.e. probability density function of active power and reactive power of load demand in time period t of node i
Figure FDA0003736206530000029
Figure FDA00037362065300000210
Wherein
Figure FDA00037362065300000211
Figure FDA00037362065300000212
Respectively the average value and the standard deviation of the active demand of the node i in the time period t,
Figure FDA00037362065300000213
respectively the mean value and the standard deviation rho of the reactive demand of the node i in the time period t i,t For the point i, the correlation coefficient between the active demand and the reactive demand in the time period t, Ω D Collecting all nodes configured with loads;
step 2.3: calculating a robust adjustment parameter zeta in the photovoltaic power generation output robust equivalent representation, the wind power generation output robust equivalent representation and the load demand robust equivalent representation, wherein the robust adjustment parameter zeta is shown as the following formula:
ζ＝A*Υ
wherein upsilon is a robustness adjustment parameter with different probability density functions and has 0<Υ<1, parameter A has
Figure FDA00037362065300000214
Wherein
Figure FDA00037362065300000215
And step 3: establishing an objective function of a microgrid robust optimization model;
the objective function in step 3 is shown as follows:
Figure FDA00037362065300000216
wherein, Delta t For the length of the time segment t,
Figure FDA00037362065300000217
for a unit cost of power input from the main network for a time period t,
Figure FDA00037362065300000218
the power input from the main network for the time period t for the node i,
Figure FDA00037362065300000219
for the unit cost of conventional distributed power generation at node g,
Figure FDA00037362065300000220
the active power of the conventional distributed power supply for the node g during the time period t,
Figure FDA00037362065300000221
for the cost per unit of discharge of the energy storage device at node b,
Figure FDA00037362065300000222
is the discharge power of the energy storage device at node b,
Figure FDA00037362065300000223
is a section ofThe unit charge cost of the energy storage device at point b,
Figure FDA00037362065300000224
the charging power for the energy storage device at node b,
Figure FDA00037362065300000225
is the unit offloading cost at node i, Ψ i,t For a binary variable associated with offloading of node i during time period t, Ψ when offloading is required i,t 1, when no load shedding is required Ψ i,t ＝0，Ω T For a set of time periods comprising all time periods t, Ω S 、Ω DG 、Ω ESS Omega is a node set comprising a common connection point PCC, all node sets configured with a traditional distributed power supply, all node sets configured with an energy storage device and all node sets contained in a microgrid respectively;
and 4, step 4: establishing constraint conditions of tidal current power balance of a micro-grid, voltage and current of the micro-grid, a traditional distributed power supply and charging and discharging of an energy storage device;
and 4, the micro-grid power flow balance constraint condition in the step 4 is shown as the following formula:
Figure FDA0003736206530000031
Figure FDA0003736206530000032
wherein, P ki,t 、P ij,t Respectively, the active power flow, I, of the line ki and the line ij in the time period t ij,t For the current value of line ij in time period t, Ω l Is a collection of lines, R, contained in a microgrid ij Is the equivalent resistance of the line ij,
Figure FDA0003736206530000033
for the slave main network at node i in the t time periodInput active power, Ω PV For a set of nodes comprising a photovoltaic power supply, Ω WT For a set of nodes comprising a wind-power source, Q ki,t ，Q ij,t Respectively, the reactive power flow, X, of the line ki, the line ij in the time period t ij Is the equivalent reactance of the line ij,
Figure FDA0003736206530000034
for reactive power input from the main network at node i during the time period t,
Figure FDA0003736206530000035
is the reactive power of the conventional distributed power supply at node g during time period t;
the microgrid voltage and current constraint condition is shown as the following formula:
Figure FDA0003736206530000036
Figure FDA0003736206530000037
Figure FDA0003736206530000038
Figure FDA0003736206530000039
Figure FDA00037362065300000310
wherein, V j,t Is the voltage value of the node j in the time period t, V,
Figure FDA00037362065300000311
Respectively a minimum voltage amplitude and a maximum voltage amplitude,
Figure FDA00037362065300000312
for the maximum current amplitude of the line ij,
Figure FDA00037362065300000313
maximum apparent power input for the main network at node i;
the traditional distributed power supply constraint condition is shown as follows:
Figure FDA00037362065300000314
Figure FDA00037362065300000315
Figure FDA00037362065300000316
Figure FDA0003736206530000041
F g,t ≥F g
Figure FDA0003736206530000042
wherein the content of the first and second substances,
Figure FDA0003736206530000043
respectively the active power and the reactive power generated by the traditional distributed power supply in the time period t by the node g,
Figure FDA0003736206530000044
is the maximum value of the power generated by the traditional distributed power supply in the time period t for the node g, pi g,t II is a binary variable of the node g related to the traditional distributed power supply in the time period t when the traditional distributed power supply is switched on g,t 1, and pi when the conventional distributed power supply is not put in g,t ＝0，pf g For the power factor limitation of a conventional distributed power supply at node g,
Figure FDA0003736206530000045
respectively a traditional distributed power supply descending limit, a climbing limit and F at a node g g,t The diesel generator residual fuel at the node g in the time period t,
Figure FDA0003736206530000046
fuel efficiency, FC, for diesel-electric generator set at node g g Fuel capacity of diesel-electric set at node g, H g Is the heating value of the diesel generator unit fuel at node g,F g the minimum fuel of the diesel generator set at the node g;
the charge and discharge constraint condition of the energy storage device is shown as the following formula:
Figure FDA0003736206530000047
Figure FDA0003736206530000048
Figure FDA0003736206530000049
Figure FDA00037362065300000410
Figure FDA00037362065300000411
Λ b,t +Φ b,t ≤1
Figure FDA00037362065300000412
therein, SOC b,t State of charge, ξ, of the energy storage device for node b over time period t b Is the self-discharge rate, EC, of the energy storage device at node b b Is the energy capacity of the energy storage device at node b,
Figure FDA00037362065300000413
for the efficiency of the energy storage device discharge at node b,
Figure FDA00037362065300000414
for the charging efficiency, phi, of the energy storage device at node b b,t For the node b, a binary variable related to the discharge operation of the energy storage device in the time period t, when discharging phi b,t 1, when charged b,t ＝0，
Figure FDA00037362065300000415
Respectively as the minimum value and the maximum value of the discharge power of the energy storage device at the node b, and Λ b,t For the binary variable of the node b in the time period t, the charging time lambda is related to the charging operation of the energy storage device b,t Discharge time ═ 1, Λ b,t ＝0，
Figure FDA00037362065300000416
Respectively representing the minimum value and the maximum value of the charging power of the energy storage device at the node b,SOC b 、
Figure FDA00037362065300000417
respectively the minimum and maximum charge states of the energy storage system at the node b,
Figure FDA00037362065300000418
the minimum state of charge for node b within the period tau;
and 5: and (4) completing the robust optimization modeling of the micro-grid, outputting an optimization result, and completing the robust optimization of the micro-grid corresponding to the change of the environment and the load demand.
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