CN111985686A - Power distribution network distribution robust optimization scheduling method based on probability prediction - Google Patents

Power distribution network distribution robust optimization scheduling method based on probability prediction Download PDF

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CN111985686A
CN111985686A CN202010679281.3A CN202010679281A CN111985686A CN 111985686 A CN111985686 A CN 111985686A CN 202010679281 A CN202010679281 A CN 202010679281A CN 111985686 A CN111985686 A CN 111985686A
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周亦洲
卫志农
孙国强
臧海祥
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Abstract

The invention discloses a power distribution network distribution robust optimization scheduling method based on probability prediction, which comprises the following steps: adopting a probability distribution interval to construct a fuzzy set based on probability prediction; according to the constructed fuzzy set, constructing a distribution network scheduling model based on distribution robust optimization; applying an affine strategy and a strong dual theory to convert the established power distribution network scheduling model into a mixed integer linear programming problem; and solving the mixed integer linear programming problem to obtain the optimal decision of the distribution robust optimized scheduling of the power distribution network. The invention adopts the fuzzy set based on probability prediction, and can accurately depict the probability distribution of uncertain parameters, thereby improving the authenticity and the effectiveness of the solution. The method can convert the distributed robust optimization model into an easily-solved mixed integer linear programming problem, improves the calculation efficiency and reduces the solving difficulty.

Description

Power distribution network distribution robust optimization scheduling method based on probability prediction
Technical Field
The invention belongs to the field of power system scheduling, and particularly relates to a distributed robust optimization method.
Background
With the rapid development of society, the increasing problems of environmental pollution and energy shortage, non-renewable energy sources such as fossil energy and the like are not enough to support the sustainable development of human society. According to the International Energy Outlook published by the Energy information administration in 2019, the worldwide power demand will be greatly increased in 2020 and 2050, and the power supply will be changed significantly: 1) the increment of the renewable energy power generation is in the first place, and the renewable energy power generation replaces coal-fired power generation to become the primary power generation energy in 2025 years; 2) the gas turbine is rapidly developed due to the advantages of short construction period, quick response, cleanness, environmental protection and the like.
The randomness and intermittency of the output of the renewable energy source bring great challenges to the safe and stable operation of the power system. Distribution robust optimization is used as a new uncertain optimization method, the problems that random planning is over-dependent on accurate probability distribution and the calculated amount is too large can be solved, the problem that the traditional robust optimization is over conservative is effectively avoided, and the method is gradually applied to the field of power system optimization in recent years. However, the current distribution robust optimization method mostly adopts the statistical rules of uncertain parameters (such as first-order and second-order moment information) to construct a fuzzy set, and the depiction of the uncertain parameters is rough, so that the obtained optimization result deviates from the actual situation. In addition, most of the existing solutions of the distributed robust optimization model are converted into semi-definite planning or second-order cone planning problems, so that the solutions are difficult and the calculation efficiency is low.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a distribution network distribution robust optimization scheduling method based on probability prediction.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a distribution network distribution robust optimization scheduling method based on probability prediction comprises the following steps:
(1) adopting a probability distribution interval to construct a fuzzy set based on probability prediction;
(2) constructing a distribution network scheduling model based on distribution robust optimization according to the fuzzy set constructed in the step (1);
(3) applying an affine strategy and a strong dual theory to convert the power distribution network scheduling model established in the step (2) into a mixed integer linear programming problem;
(4) and (4) solving the mixed integer linear programming problem in the step (3) to obtain the optimal decision of the distribution robust optimal scheduling of the power distribution network.
Further, the specific process of step (1) is as follows:
(101) obtaining K continuous renewable energy output prediction intervals and interval probabilities thereof by adopting a probability prediction method, wherein K is the number of the intervals;
(102) according to the obtained prediction interval and interval probability, constructing a fuzzy set F based on probability prediction:
Figure BDA0002585185670000021
Figure BDA0002585185670000022
in the above formula, wtThe renewable energy output is obtained in the time period t; p is wtA probability distribution of (a); r is wtAll possible output conditions; p (R) is wtAll possible probability distributions; wkIs wtAn indeterminate set of kth intervals; p is a radical ofkIs wtProbability of kth interval; epIndicating an expected value;μ tand
Figure BDA0002585185670000023
is wtLower and upper limits of expected values;w k,tand
Figure BDA0002585185670000024
are respectively wtLower and upper limits of the k-th interval.
Further, the specific process of step (2) is as follows:
(201) constructing an objective function of a distribution network scheduling model based on distribution robust optimization:
Figure BDA0002585185670000025
in the above formula, the first and second carbon atoms are,
Figure BDA0002585185670000031
respectively, the start, stop, fixing and unit power generation costs of the gas turbine e; boolean variable ue,t、ve,t、xe,tRespectively indicating whether the gas turbine e is started, stopped and operated in the time period t, if so, setting 1, and otherwise, setting 0;
Figure BDA0002585185670000032
output power of gas turbine e for time period t; sup denotes supremum;
(202) constructing constraint conditions of a distribution network scheduling model based on distribution robust optimization:
a) gas turbine restraint:
xe,t-xe,t-1＝ue,t-ve,t
Figure BDA0002585185670000033
Figure BDA0002585185670000034
Figure BDA0002585185670000035
Figure BDA0002585185670000036
Figure BDA0002585185670000037
Figure BDA0002585185670000038
in the above formula, the Boolean variable xe,τIndicating whether the gas turbine e is started in the period tau, if so, setting 1, otherwise, setting 0;
Figure BDA0002585185670000039
Figure BDA00025851856700000310
minimum startup and shutdown times of the gas turbine e, respectively;
Figure BDA00025851856700000315
minimum and maximum output power of the gas turbine e, respectively;
Figure BDA00025851856700000312
the maximum upward and downward climbing rates of the gas turbine e are respectively;
Figure BDA00025851856700000313
the output power of the gas turbine e for a period t-1; boolean variable xe,t-1Indicating whether the gas turbine e works in the period of t-1, if so, setting 1, otherwise, setting 0;
b) power flow constraint of the power distribution network:
Figure BDA00025851856700000314
Figure BDA0002585185670000041
Vj,t＝Vi,t-(Pij,trij+Qij,txij)/V0
in the above formula, the first and second carbon atoms are,
Figure BDA0002585185670000042
respectively, the active power output power and the reactive power output power of a node j in a period t, wherein,
Figure BDA0002585185670000043
the output power and the renewable energy output of the gas turbine comprising the node j are included; pij,t、Qij,tRespectively the active power and the reactive power of the branch circuits i-j in the t period;
Figure BDA0002585185670000044
all branch sets with head end node j; pjl,t、Qjl,tRespectively the active power and the reactive power of the branch j-l in the time period t;
Figure BDA0002585185670000045
respectively the active load and the reactive load of a node j in the period t; vi,t、Vj,tThe voltage amplitudes of the nodes i and j in the period t are respectively; r isij、xijRespectively the resistance and reactance of the branch circuits i-j; v0Is a voltage reference value.
Further, the specific process of step (3) is as follows:
(301) applying an affine strategy and a strong dual theory to convert a target function of a power distribution network scheduling model:
applying an affine policy, will
Figure BDA0002585185670000046
Is denoted by wtLinear affine function of (1):
Figure BDA0002585185670000047
in the above formula, the first and second carbon atoms are,
Figure BDA0002585185670000048
is a linear coefficient;
according to the definition of the fuzzy set F, the supremum problem sup in the objective function is expressed as follows:
Figure BDA0002585185670000049
Figure BDA00025851856700000410
Figure BDA00025851856700000411
Figure BDA00025851856700000412
f(wt)≥0
in the above formula, f (w)t) Is wtA measure of probability of; df (w)t) Is f (w)t) Differentiation of (1); alpha is alphak，βt，γtIs a dual variable;
applying strong dual theory, the model is converted into:
Figure BDA0002585185670000051
according to the uncertain set WkIs defined as
Figure BDA0002585185670000052
Expressed as:
Figure BDA0002585185670000053
in the above formula, the first and second carbon atoms are,k,tandk,tis a dual variable;
and (3) applying the strong dual theory again to convert the model into:
Figure BDA0002585185670000054
(302) applying an affine strategy and a strong dual theory to convert the constraint conditions of the power distribution network scheduling model:
here under the constraint of
Figure BDA0002585185670000055
For example, according to the affine strategy, it is expressed as:
Figure BDA0002585185670000056
according to the uncertain set WkThe above formula is expressed as:
Figure BDA0002585185670000061
in the above formula, the first and second carbon atoms are,e,k,tande,k,tis a dual variable;
applying strong dual theory, the model is converted into:
Figure BDA0002585185670000062
for other constraints, the conversion process is basically consistent and is not repeated.
Adopt the beneficial effect that above-mentioned technical scheme brought:
the invention adopts the probability interval to construct the fuzzy set, and can more accurately depict the probability distribution of uncertain parameters, thereby leading the optimization result to be more in line with the practice. In addition, the power distribution network scheduling model based on distribution robust optimization is converted into the mixed integer linear programming problem through an affine strategy and a strong dual theory, and compared with the mixed integer semi-definite programming or second-order cone programming problem, the mixed integer linear programming problem has lower solving difficulty and higher solving efficiency.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a schematic diagram of an IEEE33 node power distribution network test system.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The invention designs a distribution network distribution robust optimization scheduling method based on probability prediction, which comprises the following specific steps as shown in figure 1:
step 1: adopting a probability distribution interval to construct a fuzzy set based on probability prediction;
step 2: constructing a distribution network scheduling model based on distribution robust optimization according to the fuzzy set constructed in the step 1;
and step 3: applying an affine strategy and a strong dual theory to convert the power distribution network scheduling model established in the step 2 into a mixed integer linear programming problem;
and 4, step 4: and (4) solving the mixed integer linear programming problem in the step (3) to obtain the optimal decision of the distribution robust optimal scheduling of the power distribution network.
An IEEE33 node power distribution network test system is taken as an example, and a schematic diagram is shown in FIG. 2. Renewable energy generator sets that are considered include wind turbines and photovoltaic sets. The gas turbine GT1, the gas turbine GT2, the wind turbine WT and the photovoltaic generator PV are respectively connected to the nodes 17, 32, 24 and 21. The gas turbine parameters are shown in table 1. The model was solved using GAMS software, and the results were as follows.
TABLE 1 gas turbine parameters
Figure BDA0002585185670000071
The objective function values (costs) of the probability prediction-based distributed robust optimization method and the conventional statistical rule-based distributed robust optimization method (mixed integer second-order cone programming problem) are shown in table 2. It can be seen that the method of the present invention has a lower cost, because the method can more accurately depict the probability distribution of the renewable energy output, thereby effectively reducing the conservation of the solution and improving the effectiveness of the solution.
TABLE 2 comparison of objective function values for different methods
Figure BDA0002585185670000072
The calculated times for both methods are shown in table 3. It can be seen that the calculation time of the method of the present invention is only half of that of the conventional method, because the solving speed of the mixed integer linear programming problem obtained by the method of the present invention is much higher than that of the mixed integer second-order cone programming problem obtained by the conventional method. The method of the invention can improve the calculation efficiency.
TABLE 3 comparison of calculation times by different methods
Figure BDA0002585185670000081
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.

Claims (4)

1. A distribution network distribution robust optimization scheduling method based on probability prediction is characterized by comprising the following steps:
(1) adopting a probability distribution interval to construct a fuzzy set based on probability prediction;
(2) constructing a distribution network scheduling model based on distribution robust optimization according to the fuzzy set constructed in the step (1);
(3) applying an affine strategy and a strong dual theory to convert the power distribution network scheduling model established in the step (2) into a mixed integer linear programming problem;
(4) and (4) solving the mixed integer linear programming problem in the step (3) to obtain the optimal decision of the distribution robust optimal scheduling of the power distribution network.
2. The distribution network distribution robust optimization scheduling method based on probability prediction as claimed in claim 1 is characterized in that the specific process of step (1) is as follows:
(101) obtaining K continuous renewable energy output prediction intervals and interval probabilities thereof by adopting a probability prediction method, wherein K is the number of the intervals;
(102) according to the obtained prediction interval and interval probability, constructing a fuzzy set F based on probability prediction:
Figure FDA0002585185660000011
Figure FDA0002585185660000012
in the above formula, wtThe renewable energy output is obtained in the time period t; p is wtA probability distribution of (a); r is wtAll possible output conditions; p (R) is wtAll possible probability distributions; wkIs wtAn indeterminate set of kth intervals; p is a radical ofkIs wtProbability of kth interval; epIndicating an expected value;μ tand
Figure FDA0002585185660000013
is wtLower and upper limits of expected values;w k,tand
Figure FDA0002585185660000014
are respectively wtLower and upper limits of the k-th interval.
3. The distribution network distribution robust optimization scheduling method based on probability prediction as claimed in claim 2, wherein the specific process of step (2) is as follows:
(201) constructing an objective function of a distribution network scheduling model based on distribution robust optimization:
Figure FDA0002585185660000021
in the above formula, the first and second carbon atoms are,
Figure FDA0002585185660000022
respectively, the start, stop, fixing and unit power generation costs of the gas turbine e; boolean variable ue,t、ve,t、xe,tRespectively indicating whether the gas turbine e is started, stopped and operated in the time period t, if so, setting 1, and otherwise, setting 0;
Figure FDA0002585185660000023
output power of gas turbine e for time period t; sup denotes supremum;
(202) constructing constraint conditions of a distribution network scheduling model based on distribution robust optimization:
a) gas turbine restraint:
xe,t-xe,t-1＝ue,t-ve,t
Figure FDA0002585185660000024
Figure FDA0002585185660000025
Figure FDA0002585185660000026
Figure FDA0002585185660000027
Figure FDA0002585185660000028
Figure FDA0002585185660000029
in the above formula, the Boolean variable xe,τIndicating whether the gas turbine e is started in the period tau, if so, setting 1, otherwise, setting 0;
Figure FDA00025851856600000210
Figure FDA00025851856600000211
minimum startup and shutdown times of the gas turbine e, respectively;
Figure FDA00025851856600000212
minimum and maximum output power of the gas turbine e, respectively;
Figure FDA00025851856600000213
the maximum upward and downward climbing rates of the gas turbine e are respectively;
Figure FDA00025851856600000214
the output power of the gas turbine e for a period t-1; boolean variable xe,t-1Indicating whether the gas turbine e works in the period of t-1, if so, setting 1, otherwise, setting 0;
b) power flow constraint of the power distribution network:
Figure FDA0002585185660000031
Figure FDA0002585185660000032
Vj,t＝Vi,t-(Pij,trij+Qij,txij)/V0
in the above formula, the first and second carbon atoms are,
Figure FDA0002585185660000033
respectively, the active power output power and the reactive power output power of a node j in a period t, wherein,
Figure FDA0002585185660000034
the output power and the renewable energy output of the gas turbine comprising the node j are included; pij,t、Qij,tRespectively the active power and the reactive power of the branch circuits i-j in the t period;
Figure FDA0002585185660000035
all branch sets with head end node j; pjl,t、Qjl,tRespectively the active power and the reactive power of the branch j-l in the time period t;
Figure FDA0002585185660000036
respectively the active load and the reactive load of a node j in the period t; vi,t、Vj,tThe voltage amplitudes of the nodes i and j in the period t are respectively; r isij、xijRespectively the resistance and reactance of the branch circuits i-j; v0Is a voltage reference value.
4. The distribution network distribution robust optimization scheduling method based on probability prediction as claimed in claim 3, wherein the specific process of step (3) is as follows:
(301) applying an affine strategy and a strong dual theory to convert a target function of a power distribution network scheduling model: applying an affine policy, will
Figure FDA0002585185660000037
Is denoted by wtLinear affine function of (1):
Figure FDA0002585185660000038
in the above formula, the first and second carbon atoms are,
Figure FDA0002585185660000039
is a linear coefficient;
according to the definition of the fuzzy set F, the supremum problem sup in the objective function is expressed as follows:
Figure FDA00025851856600000310
Figure FDA00025851856600000311
Figure FDA0002585185660000041
Figure FDA0002585185660000042
f(wt)≥0
in the above formula, f (w)t) Is wtA measure of probability of; df (w)t) Is f (w)t) Differentiation of (1); alpha is alphak，βt，γtIs a dual variable;
applying strong dual theory, the model is converted into:
Figure FDA0002585185660000043
βt≤0,γt≥0
Figure FDA0002585185660000044
according to the uncertain set WkIs defined as
Figure FDA0002585185660000045
Expressed as:
Figure FDA0002585185660000046
wt≥w k,t:k,t
Figure FDA0002585185660000047
in the above formula, the first and second carbon atoms are,k,tandk,tis a dual variable;
and (3) applying the strong dual theory again to convert the model into:
Figure FDA0002585185660000048
Figure FDA0002585185660000049
k,t≤0,k,t≥0
(302) applying an affine strategy and a strong dual theory to convert the constraint conditions of the power distribution network scheduling model: for constraint conditions
Figure FDA00025851856600000410
According to the affine strategy, it is expressed as:
Figure FDA0002585185660000051
according to the uncertain set WkThe above formula is expressed as:
Figure FDA0002585185660000052
wt≥w k,t:e,k,t
Figure FDA0002585185660000053
in the above formula, the first and second carbon atoms are,e,k,tande,k,tis a dual variable;
applying strong dual theory, the model is converted into:
Figure FDA0002585185660000054
Figure FDA0002585185660000055
e,k,t≤0,e,k,t≥0。
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561732A (en) * 2020-12-14 2021-03-26 南方电网科学研究院有限责任公司 Method and device for optimizing active power scheduling of power system and readable storage medium
CN114781866A (en) * 2022-04-21 2022-07-22 河海大学 Comprehensive energy system robust intraday rolling scheduling method based on data driving
CN114896768A (en) * 2022-04-21 2022-08-12 河海大学 Virtual power plant distribution robustness optimization method based on new energy quantile regression
CN117688793A (en) * 2024-02-04 2024-03-12 中国地质大学（武汉） Combined modeling and solving method and equipment for distributed robust unit and storage equipment
CN118297439B (en) * 2024-06-04 2024-07-30 国网经济技术研究院有限公司 Multi-stage transmission and storage collaborative distribution robust planning method and system for power system

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120185106A1 (en) * 2011-01-14 2012-07-19 International Business Machines Corporation Integration of demand response and renewable resources for power generation management
US20140052408A1 (en) * 2012-08-17 2014-02-20 International Business Machines Corporation Data-driven distributionally robust optimization
CN104809327A (en) * 2014-09-02 2015-07-29 长沙理工大学 Uncertain distribution robust optimization method of new energy-containing power dispatching moment
CN108388973A (en) * 2018-01-11 2018-08-10 河海大学 A kind of virtual plant ADAPTIVE ROBUST method for optimizing scheduling
CN108629449A (en) * 2018-04-26 2018-10-09 东南大学 A kind of distribution robust formula Optimization Scheduling for alternating current-direct current mixing microgrid
CN110739687A (en) * 2019-10-24 2020-01-31 福州大学 electric power system distribution robust scheduling method considering wind power high-order uncertainty

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120185106A1 (en) * 2011-01-14 2012-07-19 International Business Machines Corporation Integration of demand response and renewable resources for power generation management
US20140052408A1 (en) * 2012-08-17 2014-02-20 International Business Machines Corporation Data-driven distributionally robust optimization
CN104809327A (en) * 2014-09-02 2015-07-29 长沙理工大学 Uncertain distribution robust optimization method of new energy-containing power dispatching moment
CN108388973A (en) * 2018-01-11 2018-08-10 河海大学 A kind of virtual plant ADAPTIVE ROBUST method for optimizing scheduling
CN108629449A (en) * 2018-04-26 2018-10-09 东南大学 A kind of distribution robust formula Optimization Scheduling for alternating current-direct current mixing microgrid
CN110739687A (en) * 2019-10-24 2020-01-31 福州大学 electric power system distribution robust scheduling method considering wind power high-order uncertainty

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
孙国强 等: "含电动汽车的虚拟电厂鲁棒随机优化调度", 《电力***自动化》 *
臧海洋 等: "计及安全约束的虚拟电厂两层优化调度", 《电力自动化设备》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561732A (en) * 2020-12-14 2021-03-26 南方电网科学研究院有限责任公司 Method and device for optimizing active power scheduling of power system and readable storage medium
CN114781866A (en) * 2022-04-21 2022-07-22 河海大学 Comprehensive energy system robust intraday rolling scheduling method based on data driving
CN114896768A (en) * 2022-04-21 2022-08-12 河海大学 Virtual power plant distribution robustness optimization method based on new energy quantile regression
CN114781866B (en) * 2022-04-21 2023-10-31 河海大学 Robust intra-day rolling scheduling method for comprehensive energy system based on data driving
CN114896768B (en) * 2022-04-21 2024-03-01 河海大学 Virtual power plant distribution robust optimization method based on new energy quantile regression
CN117688793A (en) * 2024-02-04 2024-03-12 中国地质大学（武汉） Combined modeling and solving method and equipment for distributed robust unit and storage equipment
CN117688793B (en) * 2024-02-04 2024-05-10 中国地质大学（武汉） Combined modeling and solving method and equipment for distributed robust unit and storage equipment
CN118297439B (en) * 2024-06-04 2024-07-30 国网经济技术研究院有限公司 Multi-stage transmission and storage collaborative distribution robust planning method and system for power system

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