CN108039739B - Dynamic random economic dispatching method for active power distribution network - Google Patents

Dynamic random economic dispatching method for active power distribution network Download PDF

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CN108039739B
CN108039739B CN201711205188.3A CN201711205188A CN108039739B CN 108039739 B CN108039739 B CN 108039739B CN 201711205188 A CN201711205188 A CN 201711205188A CN 108039739 B CN108039739 B CN 108039739B
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CN108039739A (en
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吴素农
吴文传
杨为群
张伯明
熊宁
于金镒
栗子豪
孙宏斌
李迎军
徐俊杰
李伟伟
翟亚军
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State Grid Jiangxi Electric Power Co
Tsinghua University
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
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State Grid Jiangxi Electric Power Co
Tsinghua University
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention provides a dynamic random economic dispatching method for an active power distribution network, and belongs to the field of dispatching and controlling of power systems. Firstly, establishing an active power distribution network dynamic random economic dispatching model consisting of a target function and constraint conditions; then, converting constraint conditions of the model, collecting a prediction error value set of active loads of all nodes in the power distribution network in each period and a distributed power source power prediction error value set, respectively constructing a probability distribution set of corresponding uncertain quantities according to statistical information, constructing opportunity constraint comprising rotating standby constraint, and converting the opportunity constraint into deterministic linear constraint by utilizing convex relaxation; and finally, solving the model by using a convex planning algorithm to obtain the optimal economic dispatching power plan of each generator set. The method considers power randomness, can be used for solving the problem of active power distribution network economic dispatching with uncertain power prediction, and has the advantages of robustness, reliability and high application value.

Description

Dynamic random economic dispatching method for active power distribution network
Technical Field
The invention belongs to the technical field of power system scheduling and control, and particularly relates to a dynamic random economic scheduling method for an active power distribution network.
Background
In order to solve the technical problems caused by large-scale access of distributed power sources mainly comprising photovoltaic power and wind power in an active power distribution network and reduce the overall operation power generation cost of the active power distribution network, dynamic economic dispatching needs to be carried out on the active power distribution network, and an optimal power scheme of a generator set is worked out so as to achieve the aim of efficient economic operation in the active power distribution network.
Because the distributed power supply power, including active power and reactive power of an all-day distributed power supply, has obvious volatility and intermittency under the influence of weather and environmental factors, the existing prediction technology cannot accurately predict the future power of the distributed power supply; similarly, the existing prediction technology cannot accurately predict the node load in the power distribution network. Therefore, the prediction error of the distributed power supply power and load introduces strong uncertainty to the dynamic economic scheduling problem in the active power distribution network.
However, the existing deterministic power distribution network dynamic random economic dispatching method does not consider the existence of the uncertainty, and only adopts the predicted values of the power and the load of the distributed power supply in the dispatching problem modeling process. On the other hand, the traditional opportunity constraint-based dynamic random economic dispatching method for the power distribution network faces two problems in practical application: (1) an accurate random variable probability density function is required, which is most difficult to obtain in reality; (2) the random optimization model established by the method is basically based on a sampling scene method, and the calculation amount is overlarge.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a dynamic random economic dispatching method for an active power distribution network. The method considers the power randomness when solving the dynamic random economic dispatching problem of the active power distribution network, can be used for solving the economic dispatching problem of the active power distribution network with uncertain power prediction, and has a dispatching strategy with robustness and reliability and higher application value.
The invention provides a dynamic random economic dispatching method for an active power distribution network, which is characterized by comprising the following steps of:
1) establishing a dynamic random economic dispatching model of the active power distribution network, wherein the model consists of a target function and constraint conditions; the method comprises the following specific steps:
1-1) establishing an objective function of a model; the expression is shown in formula (1):
Figure GDA0002562561940000021
wherein,
Figure GDA0002562561940000022
the generated power of the unit j in the time period t is obtained; ΨGThe method comprises the steps of collecting all generator sets in an active power distribution network; a. thei,2、Ai,1、Ai,0Respectively are consumption characteristic coefficients of the generator set j; is the total number of time periods of the scheduling cycle;
1-2) determining constraint conditions of the model; the method comprises the following specific steps:
1-2-1) power balance constraint of the active power distribution network, as shown in formula (2):
Figure GDA0002562561940000023
wherein，
Figure GDA0002562561940000024
The node i has real load power during the time period t,
Figure GDA0002562561940000025
for node i, the actual power of the distributed power supply in the period t, PLoss,tThe grid loss value of the active power distribution network in the t period, psinThe method comprises the steps of collecting all nodes in an active power distribution network;
1-2-2) network loss constraint of the active power distribution network, as shown in formula (3):
Figure GDA0002562561940000026
wherein, wLossThe power generation network loss coefficient;
1-2-3) generator set power constraint, as shown in formula (4):
Figure GDA0002562561940000027
wherein,
Figure GDA0002562561940000028
respectively the lower power limit and the upper power limit of the generator set j;
1-2-4) the ramp rate constraint of the generator set, as shown in formula (5):
Figure GDA0002562561940000029
wherein,
Figure GDA00025625619400000210
respectively a lower climbing limit value and an upper climbing limit value of the generator set j;
1-2-5) upper and lower rotation standby constraints of the active power distribution network, which are respectively shown as a formula (6) and a formula (7):
Figure GDA00025625619400000211
Figure GDA00025625619400000212
wherein,
Figure GDA00025625619400000213
respectively meeting the upper standby requirement and the lower standby requirement of the active power distribution network in the time period t;
2) converting the constraint conditions of the step 1); the method comprises the following specific steps:
2-1) constructing opportunity constraints according to constraint conditions (6) and (7), as shown in formulas (8) and (9), respectively:
Figure GDA00025625619400000214
Figure GDA0002562561940000031
where Pr () is the probability of an event occurring and ξ is the probability of a given inequality constraint (8) and (9) being destroyed;
2-2) collecting the prediction error value set of the active load of all nodes in the power distribution network in each period of time and recording the prediction error value set as
Figure GDA0002562561940000032
Collecting the distributed power source power prediction error values of all nodes in the power distribution network in each period and recording the error values as a set
Figure GDA0002562561940000033
Wherein
Figure GDA0002562561940000034
The error is predicted for the active load of node i during the time period t,
Figure GDA0002562561940000035
predicting an error for the distributed power supply power of the node i in the t period;
are respectively paired
Figure GDA0002562561940000036
And solving corresponding error per unit parameters as shown in the formulas (10) and (11):
Figure GDA0002562561940000037
Figure GDA0002562561940000038
wherein max (| |) is the maximum value of the absolute values of the elements in the solution set;
Figure GDA0002562561940000039
for the per unit parameter of the active load error of the node i in the time period t,
Figure GDA00025625619400000310
a distributed power source power error per unit parameter is obtained for the node i in the time period t;
setting up
Figure GDA00025625619400000311
The prediction error is unified for the active load of the node i in the time period t,
Figure GDA00025625619400000312
the prediction error is unified for the distributed power source power per unit in the period t of the node i,
Figure GDA00025625619400000313
respectively is
Figure GDA00025625619400000314
Is defined as [ -1,1 [)]A set consisting of any mutually independent distributions with the mean value of 0;
2-3) representing the real power of the active load and the real power of the distributed power supply in the power distribution network into forms shown in the formulas (12) and (13), respectively:
Figure GDA00025625619400000315
Figure GDA00025625619400000316
Figure GDA00025625619400000317
wherein,
Figure GDA00025625619400000318
predicting power for the node i active load during the period t,
Figure GDA00025625619400000323
predicting power for the distributed power supply of the node i in the t period;
2-4) according to formula (2), (3), (10), (11), (12), (13)
Figure GDA00025625619400000319
Expressed as shown in formula (14):
Figure GDA00025625619400000320
2-5) substituting equation (14) into constraint conditional equations (8) and (9), according to the chance constrained convex relaxation transformation method, equations (8) and (9) are transformed into equations (15) and (16), respectively:
Figure GDA00025625619400000321
Figure GDA00025625619400000322
wherein the coefficients
Figure GDA0002562561940000041
The definition is shown in formula (17):
Figure GDA0002562561940000042
3) solving the model;
solving the model by applying a convex programming algorithm according to the target function formula (1) and the constraint conditional formulas (2), (3), (4), (5), (15), (16) and (17); to obtain finally
Figure GDA0002562561940000043
Namely, the generated power of the optimal economic dispatch of each generator set in t time period and in each time period
Figure GDA0002562561940000044
And the generated power is collected to be optimally and economically scheduled in each time period of the whole day for each generator set.
The invention has the characteristics and beneficial effects that:
the invention provides a dynamic random economic dispatching algorithm for an active power distribution network, which is characterized in that a probability distribution set of uncertain quantity is constructed according to known statistical information, opportunity constraints including rotation standby constraints are constructed, and convex relaxation is utilized to convert the opportunity constraints into deterministic linear constraints, so that the dynamic random economic dispatching problem of the active power distribution network is effectively solved. The method meets the actual situation of production, the obtained scheduling strategy has robustness and reliability, the calculation efficiency is high under the condition of considering the running randomness, and the method has higher application value.
Detailed Description
The dynamic random economic dispatching method for the active power distribution network, which is provided by the invention, is further described in detail below by combining specific embodiments.
The invention provides a dynamic random economic dispatching method for an active power distribution network, which comprises the following steps:
1) establishing a dynamic random economic dispatching model of the active power distribution network, wherein the model consists of a target function and constraint conditions; the method comprises the following specific steps:
1-1) establishing an objective function of a model; the expression is shown in formula (1):
Figure GDA0002562561940000045
wherein,
Figure GDA0002562561940000046
the generated power of the unit j in the time period t is obtained; ΨGThe method comprises the steps of collecting all generator sets in an active power distribution network; a. thei,2、Ai,1、Ai,0The consumption characteristic coefficients of the generator set j (which can be coal-fired, gas, water and electricity and the like) are respectively (the coefficients are given by a distribution network dispatching center); the total number of time periods for the scheduling cycle is typically taken to be 96;
1-2) determining constraint conditions of the model; the method comprises the following specific steps:
1-2-1) power balance constraint of the active power distribution network, as shown in formula (2):
Figure GDA0002562561940000051
wherein,
Figure GDA0002562561940000052
the node i has real load power during the time period t,
Figure GDA0002562561940000053
for node i, the actual power of the distributed power supply in the period t, PLoss,tThe grid loss value of the active power distribution network in the t period, psinThe method comprises the steps of collecting all nodes in an active power distribution network;
1-2-2) network loss constraint of the active power distribution network, as shown in formula (3):
Figure GDA0002562561940000054
wherein, wLossThe power generation network loss coefficient (the conventional value is 0.01-0.05);
1-2-3) generator set power constraint, as shown in formula (4):
Figure GDA0002562561940000055
wherein,
Figure GDA00025625619400000517
respectively the lower power limit and the upper power limit of the generator set j;
1-2-4) the ramp rate constraint of the generator set, as shown in formula (5):
Figure GDA0002562561940000056
wherein,
Figure GDA0002562561940000057
respectively a lower climbing limit value and an upper climbing limit value of the generator set j;
1-2-5) upper and lower rotation standby constraints of the active power distribution network, which are respectively shown as a formula (6) and a formula (7):
Figure GDA0002562561940000058
Figure GDA0002562561940000059
wherein,
Figure GDA00025625619400000510
respectively the upper and lower standby requirements of the active power distribution network in the time period t, and generally taking 5% of the total load of the system;
2) converting the constraint conditions of the step 1); the method comprises the following specific steps:
2-1) constructing opportunity constraints according to constraint conditions (6) and (7), as shown in formulas (8) and (9), respectively:
Figure GDA00025625619400000511
Figure GDA00025625619400000512
wherein, Pr () is the probability of occurrence of an event, ξ is the probability of destruction of the given inequality constraints (8) and (9), and the value range is [0,1], in this example the value is 0.1;
2-2) collecting the prediction error value set of the active load of all nodes in the power distribution network in each period of time and recording the prediction error value set as
Figure GDA00025625619400000513
Collecting the distributed power source power prediction error values of all nodes in the power distribution network in each period and recording the error values as a set
Figure GDA00025625619400000514
Wherein
Figure GDA00025625619400000515
The error is predicted for the active load of node i during the time period t,
Figure GDA00025625619400000516
predicting an error for the distributed power supply power of the node i in the t period; error data of each time period (one time period every 15 minutes, total 96 time periods in the whole day) of the whole day are collected, and the data amount of each time period is provided according to the data of the forecasting institution, and the more the data is, the better the data is. The prediction error is specifically the difference between the measured actual value and the predicted value (actual power minus the corresponding predicted power);
are respectively paired
Figure GDA0002562561940000061
And solving corresponding error per unit parameters as shown in the formulas (10) and (11):
Figure GDA0002562561940000062
Figure GDA0002562561940000063
wherein max (| |) is the maximum value of the absolute values of the elements in the solution set;
Figure GDA0002562561940000064
for the per unit parameter of the active load error of the node i in the time period t,
Figure GDA0002562561940000065
a distributed power source power error per unit parameter is obtained for the node i in the time period t;
setting up
Figure GDA0002562561940000066
The prediction error is unified for the active load of the node i in the time period t,
Figure GDA0002562561940000067
the prediction error is unified for the distributed power source power per unit in the period t of the node i,
Figure GDA0002562561940000068
respectively is
Figure GDA0002562561940000069
Is defined as [ -1,1 [)]A set consisting of any mutually independent distributions with the mean value of 0;
2-3) representing the real power of the active load and the real power of the distributed power supply in the power distribution network into forms shown in the formulas (12) and (13), respectively:
Figure GDA00025625619400000610
Figure GDA00025625619400000611
Figure GDA00025625619400000612
wherein,
Figure GDA00025625619400000613
predicting power for the node i active load during the period t,
Figure GDA00025625619400000614
predicting power for a distributed power supply of a node i in a time period t, wherein the two predicted powers are given by a special prediction mechanism;
Figure GDA00025625619400000615
for the uncertainty variable (per unit prediction error) of the active load of the node i in the time period t,
Figure GDA00025625619400000616
the per unit parameter of the active load error of the node i in the time period t (more than or equal to 0),
Figure GDA00025625619400000617
for the distributed power source power uncertainty variable (per unit prediction error) of node i during time t,
Figure GDA00025625619400000618
and (4) a distributed power source power error per unit parameter (greater than or equal to 0) for the node i in the time period t. In the present invention, all parameters are considered to be per unit and have a unit of 1.
2-4) according to formula (2), (3), (10), (11), (12), (13)
Figure GDA00025625619400000619
Expressed as shown in formula (14):
Figure GDA00025625619400000620
2-5) substituting equation (14) into constraint conditional equations (8) and (9), according to the chance constrained convex relaxation transformation method, equations (8) and (9) are transformed into equations (15) and (16), respectively:
Figure GDA00025625619400000621
Figure GDA0002562561940000071
wherein the coefficients
Figure GDA0002562561940000072
The definition is shown in formula (17):
Figure GDA0002562561940000073
3) solving the model;
solving the model by applying a convex programming algorithm according to the target function formula (1) and the constraint conditional formulas (2), (3), (4), (5), (15), (16) and (17); to obtain finally
Figure GDA0002562561940000074
Namely the generated power of the optimal economic dispatch of each generator set in the t time period. At each time interval
Figure GDA0002562561940000075
And the generated power is collected to be economically scheduled for each time interval of each generator set all day.

Claims (1)

1. A dynamic random economic dispatching method for an active power distribution network is characterized by comprising the following steps:
1) establishing a dynamic random economic dispatching model of the active power distribution network, wherein the model consists of a target function and constraint conditions; the method comprises the following specific steps:
1-1) establishing an objective function of a model; the expression is shown in formula (1):
Figure FDA0002562561930000011
wherein,
Figure FDA0002562561930000012
the generated power of the unit j in the time period t is obtained; ΨGThe method comprises the steps of collecting all generator sets in an active power distribution network; a. thei,2、Ai,1、Ai,0Respectively are consumption characteristic coefficients of the generator set j;is the total number of time periods of the scheduling cycle;
1-2) determining constraint conditions of the model; the method comprises the following specific steps:
1-2-1) power balance constraint of the active power distribution network, as shown in formula (2):
Figure FDA0002562561930000013
wherein,
Figure FDA0002562561930000014
the node i has real load power during the time period t,
Figure FDA0002562561930000015
for node i, the actual power of the distributed power supply in the period t, PLoss,tThe grid loss value of the active power distribution network in the t period, psinThe method comprises the steps of collecting all nodes in an active power distribution network;
1-2-2) network loss constraint of the active power distribution network, as shown in formula (3):
Figure FDA0002562561930000016
wherein, wLossThe power generation network loss coefficient;
1-2-3) generator set power constraint, as shown in formula (4):
Figure FDA0002562561930000017
wherein,
Figure FDA0002562561930000018
respectively the lower power limit and the upper power limit of the generator set j;
1-2-4) the ramp rate constraint of the generator set, as shown in formula (5):
Figure FDA0002562561930000019
wherein,
Figure FDA00025625619300000110
respectively a lower climbing limit value and an upper climbing limit value of the generator set j;
1-2-5) upper and lower rotation standby constraints of the active power distribution network, which are respectively shown as a formula (6) and a formula (7):
Figure FDA00025625619300000111
Figure FDA00025625619300000112
wherein,
Figure FDA00025625619300000113
respectively meeting the upper standby requirement and the lower standby requirement of the active power distribution network in the time period t;
2) converting the constraint conditions of the step 1); the method comprises the following specific steps:
2-1) constructing opportunity constraints according to constraint conditions (6) and (7), as shown in formulas (8) and (9), respectively:
Figure FDA0002562561930000021
Figure FDA0002562561930000022
wherein, Pr () is the probability of the event occurrence, and ξ is the probability of the destruction of inequality constraints (8) and (9);
2-2) collecting the prediction error value set of the active load of all nodes in the power distribution network in each period of time and recording the prediction error value set as
Figure FDA0002562561930000023
Collecting distributed power source power prediction errors of all nodes in power distribution network in each time periodThe value set is recorded as
Figure FDA0002562561930000024
Wherein
Figure FDA0002562561930000025
The error is predicted for the active load of node i during the time period t,
Figure FDA0002562561930000026
predicting an error for the distributed power supply power of the node i in the t period;
are respectively paired
Figure FDA0002562561930000027
And solving corresponding error per unit parameters as shown in the formulas (10) and (11):
Figure FDA0002562561930000028
Figure FDA0002562561930000029
wherein max (| |) is the maximum value of the absolute values of the elements in the solution set;
Figure FDA00025625619300000210
for the per unit parameter of the active load error of the node i in the time period t,
Figure FDA00025625619300000211
a distributed power source power error per unit parameter is obtained for the node i in the time period t;
setting up
Figure FDA00025625619300000212
The prediction error is unified for the active load of the node i in the time period t,
Figure FDA00025625619300000213
the prediction error is unified for the distributed power source power per unit in the period t of the node i,
Figure FDA00025625619300000214
respectively is
Figure FDA00025625619300000215
Is defined as [ -1,1 [)]A set consisting of any mutually independent distributions with the mean value of 0;
2-3) representing the real power of the active load and the real power of the distributed power supply in the power distribution network into forms shown in the formulas (12) and (13), respectively:
Figure FDA00025625619300000216
Figure FDA00025625619300000217
Figure FDA00025625619300000218
wherein,
Figure FDA00025625619300000219
predicting power for the node i active load during the period t,
Figure FDA00025625619300000220
predicting power for the distributed power supply of the node i in the t period;
2-4) according to formula (2), (3), (10), (11), (12), (13)
Figure FDA00025625619300000221
Expressed as shown in formula (14):
Figure FDA00025625619300000222
2-5) substituting equation (14) into constraint conditional equations (8) and (9), according to the chance constrained convex relaxation transformation method, equations (8) and (9) are transformed into equations (15) and (16), respectively:
Figure FDA0002562561930000031
Figure FDA0002562561930000032
wherein the coefficients
Figure FDA0002562561930000033
The definition is shown in formula (17):
Figure FDA0002562561930000034
3) solving the model;
solving the model by applying a convex programming algorithm according to the target function formula (1) and the constraint conditional formulas (2), (3), (4), (5), (15), (16) and (17); to obtain finally
Figure FDA0002562561930000035
Namely, the generated power of the optimal economic dispatch of each generator set in t time period and in each time period
Figure FDA0002562561930000036
And the generated power is collected to be optimally and economically scheduled in each time period of the whole day for each generator set.
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