CN109066808B - Active power distribution network operation optimization method adaptive to uncertainty of power output - Google Patents

Active power distribution network operation optimization method adaptive to uncertainty of power output Download PDF

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CN109066808B
CN109066808B CN201810979860.2A CN201810979860A CN109066808B CN 109066808 B CN109066808 B CN 109066808B CN 201810979860 A CN201810979860 A CN 201810979860A CN 109066808 B CN109066808 B CN 109066808B
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CN109066808A (en
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刘志坚
陈潇雅
边居政
王洪亮
罗凌灵
宋琪
谢静
刘亚锦
王雁红
周于尧
徐慧
王一妃
余进
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Kunming University of Science and Technology
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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
    • 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
    • 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
    • H02J3/50—Controlling the sharing of the out-of-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 relates to an active power distribution network operation optimization method adaptive to uncertainty of power output, and belongs to the technical field of active power distribution networks of power systems. The method comprises the steps of describing random uncertainty of power output in a scene mode, considering standby configuration and a standby response participation and random uncertainty elimination mechanism, constructing an active power distribution network operation optimization model adaptive to the power output uncertainty by taking the minimum operation cost of an active power distribution network as a target under the condition of meeting the technical requirement of power distribution network safe operation in a random uncertainty scene, and solving the optimized model by selecting a CONOPT solver based on a GAMS optimization platform. The invention provides an active power distribution network operation optimization model adaptive to power output uncertainty, and shows that the load voltage characteristics have positive effects on reducing the operation cost of a power distribution system, reducing the power exchange with a superior power transmission network and promoting uncertainty elimination, and the positive effects are more obvious when the load voltage response characteristic ratio is larger.

Description

Active power distribution network operation optimization method adaptive to uncertainty of power output
Technical Field
The invention relates to an active power distribution network operation optimization method adaptive to uncertainty of power output, and belongs to the technical field of active power distribution networks of power systems.
Background
The output power of clean power supplies in wind power generation, photovoltaic power generation and other forms has the characteristic of random uncertainty, and the large-scale feeding of the output power into a power distribution network brings a severe challenge to the dispatching and running of a power grid. Therefore, how to fully call flexible resources in the power distribution network and on the premise of guaranteeing safe operation of the power distribution network, the way of consuming renewable energy power generation such as wind power generation and photovoltaic power generation with random uncertainty as much as possible is a hot problem to be solved urgently in the current active power distribution network operation optimization.
Prediction is an important means for grasping the uncertainty of renewable energy power generation, and various prediction methods such as interval prediction, probability prediction and the like exist at present, but the prediction accuracy is still very low. Therefore, the deterministic optimization decision is only carried out according to the expected value of the renewable energy power generation in the power distribution network operation optimization model, so that the system is exposed to higher operation risk, and the operation optimization of the active power distribution network containing the high-permeability renewable energy power generation inevitably leads to an uncertain decision. Modeling uncertainty based on predictions is a prerequisite for reasonable uncertainty elimination for active distribution networks. The mathematical expression for uncertainty is usually in the form of fuzzy numbers, interval numbers and random numbers. The fuzzy number expression needs to use a complex membership function, the interval number expression needs to be realized by robust optimization, the whole process in the interval fluctuation range is feasible, the method is over conservative, and the random number needs to use a probability distribution function form, so that the method is convenient to be linked with the existing prediction methods such as probability prediction, interval prediction and the like. How to call active flexible resources in the power distribution network to reduce the dependence on voltage regulation and frequency modulation resources of a superior power transmission network is the key of uncertainty absorption of the active power distribution network.
Disclosure of Invention
The invention aims to solve the technical problem of providing an active power distribution network operation optimization method adaptive to uncertainty of power output, and reducing dependence on voltage regulation and frequency modulation resources of a superior power transmission network by calling active flexible resources in a power distribution network.
The technical scheme adopted by the invention is as follows: an active power distribution network operation optimization method adaptive to uncertainty of power output comprises the following steps:
step1: depicting the random uncertainty of the power output in a scene mode;
step 2: considering a mechanism of standby configuration and standby response participation in random uncertainty consumption, and aiming at minimizing the operation cost of the active power distribution network;
and step 3: under the condition that the technical requirements of safe operation of the power distribution network under the random uncertainty scene are met, an active power distribution network operation optimization model adaptive to the uncertainty of the power output is constructed;
and 4, step 4: and 4, solving the optimization model in the step 3 by using a CONOPT solver based on a GAMS optimization platform.
Specifically, the specific steps of step1 are as follows:
step1 scene production
The uncertainty of the renewable energy power generation in the whole power distribution system is the combination of random time sequences consistent with the quantity of the renewable energy, and if the quantity of the renewable power sources is r and the forward looking time range comprises t time periods, the output of the renewable power sources can be expressed as
Figure BDA0001778276980000021
Wherein
Figure BDA0001778276980000022
The output power of the r-th renewable power source in the time period t is represented, one specific implementation of a random time sequence is a scene of the renewable power source power in a forward-looking time range, and each scene in the obtained expected prediction value and the prediction deviation range is endowed with a certain probability value, such as the s-th scene
Figure BDA0001778276980000023
Its probability value is denoted as ρsEach renewable power supply correspondingly has an expected value and an upper limit and a lower limit of a fluctuation range represented by a prediction deviation in a given time period, so that random sampling in the range corresponds to a specific implementation, the combination of random sampling of all the renewable power supplies in all the time periods forms a scene, and Ns scenes are randomly generated by using the method, so that the method has the advantages of simple and convenient operation, low cost and high reliabilityThe probability of each scene is
Figure BDA0001778276980000024
Step 2: scene reduction
Let scene i mark P(i)Scene j is marked as P(j)Their occurrence probabilities are respectively denoted as ρiAnd ρjDefining the distance between two scenes as a second-order norm of vector difference between the two scenes:
Dij＝||P(i)-P(j)||2 (1)
in the formula, DijI.e. the distance between scene i and scene J, the purpose of scene reduction is to select a subset of scenes that best represents the full scene set, and the objective is to pursue equation (1) to be the minimum given the number of deleted scenes J.
Specifically, the minimum objective function of the operation cost of the active power distribution network in step2 is as follows:
Figure BDA0001778276980000025
in the formula, JGThe method comprises the steps of forming a set for all adjustable synchronous units in a power distribution system; j. the design is a squareWThe method comprises the steps of forming a set for all double-fed wind turbine generators in a power distribution system; j. the design is a squareVThe method comprises the steps of forming a set for all photovoltaic power generation systems in a power distribution system; j. the design is a squareSA collection of all power transmission elements in the power distribution system; pex,0Active power from a superordinate transmission network for an active distribution network, Cex,0Representing the marginal price of electricity of the root node; pgActive power output for the gas turbine g; rhosThe probability of the scene is represented by,
Figure BDA0001778276980000031
Figure BDA0001778276980000032
representing gas turbine stand-by power;
Figure BDA0001778276980000033
representing the reserve power of the superior power transmission network of the s scene;
Figure BDA0001778276980000034
representing the abandoned wind power of the s scene;
Figure BDA0001778276980000035
marking the light abandoning amount of the photovoltaic power generation system in the scene s; cg() Is a power generation cost characteristic function of the gas turbine g;
Figure BDA0001778276980000036
is a backup cost characteristic function of the gas turbine;
Figure BDA0001778276980000037
the cost function of the abandoned light of the photovoltaic power generation system is obtained;
Figure BDA0001778276980000038
the method comprises the steps of obtaining a characteristic function for the abandoned wind of the doubly-fed wind turbine generator;
Figure BDA0001778276980000039
the characteristic function of the standby cost of the superior transmission network; Δ τ represents the duration of the period.
Specifically, in the step 3, the electrical physical constraint and the safety technical requirement which need to be met by the operation of the power distribution system are taken as constraint conditions, and the constraint conditions are divided into equality constraint and inequality constraint, which are specifically as follows:
equation constraint
(1) Base point power balance constraint:
Figure BDA00017782769800000310
wherein, PwActive power output, P, for wind turbine dispatchvActive output, P, scheduled for a photovoltaic power generation systemdActive power demand for electrical loads, JDA set of all electrical loads in the power distribution system;
(2) node active power and reactive power balance constraint:
Figure BDA00017782769800000311
in the formula (4), Pl(s) and Ql(s) are respectively the active power and the reactive power transmitted on the power transmission element l under the scene of s, and can be respectively expressed as formula (5) and formula (6); j. the design is a squareS,iThe node i is a set formed by all power transmission elements with the node i as a first node; j. the design is a squareE,iThe node i is a set formed by all power transmission elements with the node i as a final node; j. the design is a squareNA set formed by all nodes in the power distribution system; j. the design is a squareD,iIs a set formed by all power loads on the node i; pi(s) and Qi(s) are respectively active power and reactive power injected into the node i under the scene of s, and can be respectively expressed as an expression (7) and an expression (8); pd(s) and Qd(s) respectively the active power and reactive power requirements of the power load d in the scene s;
Pl(s)＝Vi 2(s)gl-Vi(s)Vj(s)·(glcosθij(s)+blsinθij(s)) (5)
Ql(s)＝-Vi 2(s)bl+Vi(s)Vj(s)·(blcosθij(s)-glsinθij(s)) (6)
in the formula, thetaij(s) represents the phase angle difference of the voltage phasors of the node i and the node j under the scene s; vi(s) representing the voltage amplitude of the first node i node of the branch l under the scene s; vj(s) represents the voltage amplitude of the node j at the last node of branch l under scene s; glAnd blRespectively the conductance value and the susceptance value of the power transmission branch l;
Figure BDA0001778276980000041
Figure BDA0001778276980000042
wherein, JG,iThe method comprises the steps of (1) forming a set by all synchronous gas turbines on a node i; j. the design is a squareR,iA set of all renewable power sources on node i; j. the design is a squareC,iThe method comprises the following steps of (1) forming a set by all reactive compensation equipment on a node i; pg(s) and Qg(s) respectively the active power and the reactive power output by the synchronous gas turbine g under the scene of s; pr(s) and Qr(s) respectively the active power and the reactive power output by the renewable power source r under the scene of s; qc(s) is the inductive reactive power output by the reactive compensation equipment c under the scene s;
(3) the related equation constraint of the schedulable synchronous unit is as follows:
Figure BDA0001778276980000043
Figure BDA0001778276980000044
Figure BDA0001778276980000045
wherein the content of the first and second substances,
Figure BDA0001778276980000046
the method comprises the following steps of (1) synchronizing g stator reactive current of a unit under an s scene; vi(s) representing the voltage amplitude of a node i where the synchronous unit g is located in the scene s;
Figure BDA0001778276980000047
the voltage is expressed as the no-load voltage set for the excitation control of the synchronous unit g; kgRepresenting the voltage difference adjustment coefficient of the synchronous unit g; eg(s) represents the internal potential of the synchronous unit g under the scene of s; deltag(s) representing the power angle value of the synchronous unit g in the scene of s;
Figure BDA0001778276980000048
a direct-axis reactor of a synchronous unit g;
(4) load voltage characteristics:
Figure BDA0001778276980000049
Figure BDA0001778276980000051
in the formula, QdIs the reactive power demand of the electrical load;
Figure BDA0001778276980000052
and
Figure BDA0001778276980000053
respectively the active power and reactive power requirements of the power load d under the rated voltage level;
Figure BDA0001778276980000054
and
Figure BDA0001778276980000055
the constant impedance active power and the reactive power of the power load d are respectively;
Figure BDA0001778276980000056
and
Figure BDA0001778276980000057
the constant current active power and the reactive power of the power load d are respectively part;
Figure BDA0001778276980000058
and
Figure BDA0001778276980000059
respectively are a constant power active power part and a reactive power part of the power load d; v(s) is the system voltage under the s scene; v0Rated voltage of the system;
② inequality constraint
(1) The schedulable synchronous unit (small hydroelectric generating set and gas turbine) has the constraint of the upper and lower power limits of the active base points:
Figure BDA00017782769800000510
wherein, PgBase point power scheduled for the schedulable synchronous unit;
Figure BDA00017782769800000511
and
Figure BDA00017782769800000512
respectively an upper limit and a lower limit of active power allowed by the synchronous unit g;
(2) the active power output of the wind turbine generator is constrained in a base point mode:
Figure BDA00017782769800000513
wherein the content of the first and second substances,
Figure BDA00017782769800000514
the expected active power output of the wind turbine generator set;
(3) constraint of active power output of the photovoltaic power generation system in the base point mode:
Figure BDA00017782769800000515
wherein the content of the first and second substances,
Figure BDA00017782769800000516
an expected active output for the photovoltaic power generation system;
(4) backup capability range constraint provided by the upper level transmission network:
Figure BDA00017782769800000517
wherein the content of the first and second substances,
Figure BDA00017782769800000518
the maximum standby capacity is provided for the superior transmission network;
(5) and (3) limitation of the spare capacity range of the schedulable synchronous unit:
Figure BDA00017782769800000519
wherein the content of the first and second substances,
Figure BDA00017782769800000520
an upper limit value of the reserve capacity provided for the synchronous unit;
(6) and (3) output power range constraint of the schedulable synchronous unit:
Figure BDA00017782769800000521
Figure BDA00017782769800000522
Figure BDA0001778276980000061
wherein the content of the first and second substances,
Figure BDA0001778276980000062
and
Figure BDA0001778276980000063
respectively an upper limit and a lower limit of the excitation potential of the schedulable synchronous unit g; deltag(s) represents a power angle value of the schedulable synchronous unit g under the scene of s;
Figure BDA0001778276980000064
the maximum value of the g stator current of the synchronous unit;
(7) operation range constraint of doubly-fed wind turbine generator
Figure BDA0001778276980000065
Figure BDA0001778276980000066
In the formula, Pw(s) is the active power output of the wind turbine generator in the scene of s of the doubly-fed wind turbine generator w; qw(s) is the reactive power output of the wind turbine generator in the scene of s of the doubly-fed wind turbine generator w;
Figure BDA0001778276980000067
the method comprises the steps that the maximum active power which can be output by the doubly-fed wind turbine generator w under the limitation of natural conditions in an s scene is represented; vw(s) is the stator side machine end voltage of the doubly-fed wind turbine generator w under the scene of s;
Figure BDA0001778276980000068
the reactance is the stator reactance of the doubly-fed wind turbine generator w;
Figure BDA0001778276980000069
the excitation reactance is the excitation reactance of the double-fed wind turbine generator w;
Figure BDA00017782769800000610
the maximum current of the w rotor side of the doubly-fed wind turbine generator set is obtained;
(8) photovoltaic power generation system operating range constraints
Figure BDA00017782769800000611
Figure BDA00017782769800000612
Wherein: pv(s) represents the active output of the photovoltaic power generation system v in the s scene; qv(s) represents the reactive output of the photovoltaic power generation system v in the s scene;
Figure BDA00017782769800000613
the method comprises the steps that the maximum active power which can be output by a photovoltaic power generation system v under the s scene and limited by natural conditions is represented; vv(s) represents the node voltage of the grid-connected side of the photovoltaic power generation system v under the s scene;
Figure BDA00017782769800000614
representing the maximum load current of the photovoltaic power generation system v inverter;
(9) and (3) limiting the upper limit and the lower limit of the node voltage amplitude:
Figure BDA00017782769800000615
in the formula (26), Vi maxAnd Vi minRespectively representing the upper limit and the lower limit of the voltage amplitude of the node i; vi(s) is the voltage amplitude of the node i in the scene of s;
(10) the allowable thermal current range constraint of the power transmission element:
Figure BDA00017782769800000616
in the formula (27), the reaction mixture is,
Figure BDA0001778276980000071
represents the maximum current, I, of the transmission element llij(s) represents the current amplitude of the power transmission element/in s scenario, which can be expressed as:
Figure BDA0001778276980000072
in the formula (28), YlRepresents the admittance modulus value of the power transmission element l;
(11) and (3) limiting the upper and lower limits of the capacity of the reactive compensation equipment:
Figure BDA0001778276980000073
in formula (29), Qc(s) represents the reactive power compensated by the reactive power compensation equipment c in the s scenario,
Figure BDA0001778276980000074
and
Figure BDA0001778276980000075
respectively the maximum compensation reactive power and the minimum compensation reactive power of the reactive compensation equipment c under the scene s; j. the design is a squareCThe method is a set formed by all reactive compensation equipment of the power distribution system.
The invention has the beneficial effects that:
(1) depicting the random uncertainty of the power output in a scene form, including scene production and scene reduction;
(2) the method comprises the steps of considering a mechanism that standby configuration and standby response participate in the elimination of random uncertainty, taking electrical physical constraint and safety technical requirement which are required to be met by the operation of a power distribution system as constraint conditions, meeting the technical requirement of the safe operation of the power distribution network under a random uncertainty scene, fully considering the influence of voltage characteristics on a power balance mode and power flow distribution, and constructing an active power distribution network operation optimization model which is suitable for the uncertainty of power output on the basis of the random scene by aiming at the minimum operation cost of the power distribution system;
(3) the invention considers the load voltage characteristic, eliminates the influence of an uncertainty mechanism and the like, enlarges the feasible region or the optimization space of the power distribution network operation optimization model, improves the power grid operation quality to a certain extent, avoids the defects of conservatism, limitation and the like of the traditional power distribution network operation optimization decision, and realizes advanced optimization decision on the power distribution system active balance mode and the voltage support mode.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a scene reduction flow diagram;
fig. 3 is a modified IEEE33 node power distribution system electrical wiring diagram.
Detailed Description
For the purpose of illustrating the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the accompanying drawings and examples.
Example 1: as shown in fig. 1 to 3, an active power distribution network operation optimization method adapted to uncertainty of power output includes the following steps:
step1: depicting the random uncertainty of the power output in a scene mode;
step 2: considering a mechanism of standby configuration and standby response participation in random uncertainty consumption, and aiming at minimizing the operation cost of the active power distribution network;
and step 3: under the condition that the technical requirements of safe operation of the power distribution network under the random uncertainty scene are met, an active power distribution network operation optimization model adaptive to the uncertainty of the power output is constructed;
and 4, step 4: and 4, solving the optimization model in the step 3 by using a CONOPT solver based on a GAMS optimization platform.
Specifically, the specific steps of step1 are as follows:
step1 scene production
The uncertainty of the renewable energy power generation in the whole power distribution system is the combination of random time sequences consistent with the quantity of the renewable energy, and if the quantity of the renewable power sources is r and the forward looking time range comprises t time periods, the output of the renewable power sources can be expressed as
Figure BDA0001778276980000081
Wherein
Figure BDA0001778276980000082
The output power of the r-th renewable power source in the time period t is represented, one specific implementation of a random time sequence is a scene of the renewable power source power in a forward-looking time range, and each scene in the obtained expected prediction value and the prediction deviation range is endowed with a certain probability value, such as the s-th scene
Figure BDA0001778276980000083
Its probability value is denoted as ρsEach renewable power supply correspondingly has an expected value and an upper limit and a lower limit of a fluctuation range represented by a prediction deviation in a given time period, so that random sampling in the range corresponds to a specific implementation, the combination of random sampling of all the renewable power supplies in all the time periods forms a scene, Ns scenes are randomly generated by using the method, and the probability of each scene is the probability of each scene
Figure BDA0001778276980000084
Step 2: scene reduction
Let scene i mark P(i)Scene j is marked as P(j)Their occurrence probabilities are respectively denoted as ρiAnd ρjDefining the distance between two scenes as a second-order norm of vector difference between the two scenes:
Dij＝||P(i)-P(j)||2 (1)
in the formula, DijI.e. the distance between scene i and scene J, the purpose of scene reduction is to select a subset of scenes that best represents the full scene set, and the objective is to pursue equation (1) to be the minimum given the number of deleted scenes J.
The implementation of scene reduction specifically comprises the following steps:
1) initializing the iteration number k to 0, and reducing the set C of scene structureskInitializing to an empty set;
2) calculating the k iteration reduced scene P according to the traversal of the formula (4-2)k；
Figure BDA0001778276980000091
3) Updating the set of reduced scene constituents, Ck＝Ck-1∪{Pk}；
4) And updating the iteration number, wherein k is k + 1.
5) Determine if the maximum number of iterations is reached, k < α?
6) Updating the reduced scene set probability distribution, and superposing the reduced scene probability to the retained scene probability closest to the reduced scene probability;
7) a final reduced set of scenes is obtained.
The active power distribution network operation optimization model adaptive to uncertainty of power output is based on a random scene, takes the electrical physical constraint and the safety technical requirement which need to be met by the operation of a power distribution system as constraint conditions, fully considers the mechanism of response uncertainty of the power distribution system, considers the influence of voltage characteristics on a power balance mode and power flow distribution, and achieves advanced optimization decision on an active power balance mode and a voltage support mode of the power distribution system by pursuing the minimum operation cost of the power distribution system.
Further, an active power distribution network operation optimization model adaptive to uncertainty of power output is based on a random scene and aims at pursuing minimum operation cost of a power distribution system, and the minimum objective function of the active power distribution network operation cost in the step2 is as follows:
Figure BDA0001778276980000092
in the formula, JGThe method is a set formed by all adjustable synchronous units (small hydroelectric generating units and gas turbines) in a power distribution system; j. the design is a squareWThe method comprises the steps of forming a set for all double-fed wind turbine generators in a power distribution system; j. the design is a squareVThe method comprises the steps of forming a set for all photovoltaic power generation systems in a power distribution system; j. the design is a squareSA collection of all power transmission elements in the power distribution system; pex,0Active power from a superordinate transmission network for an active distribution network, Cex,0Representing the marginal price of electricity of the root node; pgActive power output for the gas turbine g; rhosThe probability of the scene is represented by,
Figure BDA0001778276980000093
Figure BDA0001778276980000094
representing gas turbine stand-by power;
Figure BDA0001778276980000095
representing the reserve power of the superior power transmission network of the s scene;
Figure BDA0001778276980000096
representing the abandoned wind power of the s scene;
Figure BDA0001778276980000097
marking the light abandoning amount of the photovoltaic power generation system in the scene s; cg() Is a power generation cost characteristic function of the gas turbine g;
Figure BDA0001778276980000098
is a backup cost characteristic function of the gas turbine;
Figure BDA0001778276980000101
the cost function of the abandoned light of the photovoltaic power generation system is obtained;
Figure BDA0001778276980000102
the method comprises the steps of obtaining a characteristic function for the abandoned wind of the doubly-fed wind turbine generator;
Figure BDA0001778276980000103
the characteristic function of the standby cost of the superior transmission network; Δ τ represents the duration of the period.
Furthermore, the optimization model fully considers the mechanism of response uncertainty of the power distribution system, considers the influence of voltage characteristics on a power balance mode and power flow distribution, and takes the electrical physical constraint and the safety technical requirement which need to be met by the operation of the power distribution system as constraint conditions to realize advanced optimization decision on the active balance mode and the voltage support mode of the power distribution system.
The equality constraint and the inequality constraint in step 3 are specifically as follows:
equation constraint
(1) Base point power balance constraint:
Figure BDA0001778276980000104
wherein, PwActive power output, P, for wind turbine dispatchvActive output, P, scheduled for a photovoltaic power generation systemdActive power demand for electrical loads, JDA set of all electrical loads in the power distribution system;
(2) node active power and reactive power balance constraints (power flow equation):
Figure BDA0001778276980000105
in the formula (4), Pl(s) and Ql(s) are respectively the active power and the reactive power transmitted on the power transmission element l under the scene of s, and can be respectively expressed as formula (5) and formula (6); j. the design is a squareS,iThe node i is a set formed by all power transmission elements with the node i as a first node; j. the design is a squareE,iThe node i is a set formed by all power transmission elements with the node i as a final node; j. the design is a squareNA set formed by all nodes in the power distribution system; j. the design is a squareD,iIs a set formed by all power loads on the node i; pi(s) and Qi(s) are respectively active power and reactive power injected into the node i under the scene of s, and can be respectively expressed as an expression (7) and an expression (8); pd(s) and Qd(s) respectively the active power and reactive power requirements of the power load d in the scene s;
Pl(s)＝Vi 2(s)gl-Vi(s)Vj(s)·(glcosθij(s)+blsinθij(s)) (5)
Ql(s)＝-Vi 2(s)bl+Vi(s)Vj(s)·(blcosθij(s)-glsinθij(s)) (6)
in the formula, thetaij(s) represents the phase angle difference of the voltage phasors of the node i and the node j under the scene s; vi(s) representing the voltage amplitude of the first node i node of the branch l under the scene s; vj(s) represents the voltage amplitude of the node j at the last node of branch l under scene s; glAnd blRespectively the conductance value and the susceptance value of the power transmission branch l;
Figure BDA0001778276980000111
Figure BDA0001778276980000112
wherein, JG,iThe method comprises the steps of (1) forming a set by all synchronous gas turbines on a node i; j. the design is a squareR,iA set of all renewable power sources on node i; j. the design is a squareC,iThe method comprises the following steps of (1) forming a set by all reactive compensation equipment on a node i; pg(s) and Qg(s) respectively the active power and the reactive power output by the synchronous gas turbine g under the scene of s; pr(s) and Qr(s) respectively the active power and the reactive power output by the renewable power source r under the scene of s; qc(s) is the inductive reactive power output by the reactive compensation equipment c under the scene s;
(3) related equation constraint of schedulable synchronous units (small hydroelectric generating units and gas turbines):
Figure BDA0001778276980000113
Figure BDA0001778276980000114
Figure BDA0001778276980000115
wherein the content of the first and second substances,
Figure BDA0001778276980000116
the method comprises the following steps of (1) synchronizing g stator reactive current of a unit under an s scene; vi(s) representing the voltage amplitude of a node i where the synchronous unit g is located in the scene s;
Figure BDA0001778276980000117
denoted as synchronous unit g excitation controlSetting a no-load voltage; kgRepresenting the voltage difference adjustment coefficient of the synchronous unit g; eg(s) represents the internal potential of the synchronous unit g under the scene of s; deltag(s) representing the power angle value of the synchronous unit g in the scene of s;
Figure BDA0001778276980000118
a direct-axis reactor of a synchronous unit g;
(4) load voltage characteristics:
Figure BDA0001778276980000119
Figure BDA00017782769800001110
in the formula, QdIs the reactive power demand of the electrical load;
Figure BDA00017782769800001111
and
Figure BDA00017782769800001112
respectively the active power and reactive power requirements of the power load d under the rated voltage level;
Figure BDA00017782769800001113
and
Figure BDA00017782769800001114
the constant impedance active power and the reactive power of the power load d are respectively;
Figure BDA0001778276980000121
and
Figure BDA0001778276980000122
the constant current active power and the reactive power of the power load d are respectively part;
Figure BDA0001778276980000123
and
Figure BDA0001778276980000124
respectively are a constant power active power part and a reactive power part of the power load d; v(s) is the system voltage under the s scene; v0Rated voltage of the system;
② inequality constraint
(1) The schedulable synchronous unit (small hydroelectric generating set and gas turbine) has the constraint of the upper and lower power limits of the active base points:
Figure BDA0001778276980000125
wherein, PgBase point power scheduled for the schedulable synchronous unit;
Figure BDA0001778276980000126
and
Figure BDA0001778276980000127
respectively an upper limit and a lower limit of active power allowed by the synchronous unit g;
(2) the active power output of the wind turbine generator is constrained in a base point mode:
Figure BDA0001778276980000128
wherein the content of the first and second substances,
Figure BDA0001778276980000129
the expected active power output of the wind turbine generator set;
(3) constraint of active power output of the photovoltaic power generation system in the base point mode:
Figure BDA00017782769800001210
wherein the content of the first and second substances,
Figure BDA00017782769800001211
an expected active output for the photovoltaic power generation system;
(4) backup capability range constraint provided by the upper level transmission network:
Figure BDA00017782769800001212
wherein the content of the first and second substances,
Figure BDA00017782769800001213
the maximum standby capacity is provided for the superior transmission network;
(5) and (3) limitation of the spare capacity range of the schedulable synchronous unit:
Figure BDA00017782769800001214
wherein the content of the first and second substances,
Figure BDA00017782769800001215
an upper limit value of the reserve capacity provided for the synchronous unit;
(6) and (3) output power range constraint of the schedulable synchronous unit:
Figure BDA00017782769800001216
Figure BDA00017782769800001217
Figure BDA00017782769800001218
wherein the content of the first and second substances,
Figure BDA00017782769800001219
and
Figure BDA00017782769800001220
are respectively in schedulable synchronismThe upper and lower limits of the excitation potential of the unit g; deltag(s) represents a power angle value of the schedulable synchronous unit g under the scene of s;
Figure BDA00017782769800001221
the maximum value of the g stator current of the synchronous unit;
(7) operation range constraint of doubly-fed wind turbine generator
Figure BDA0001778276980000131
Figure BDA0001778276980000132
In the formula, Pw(s) is the active power output of the wind turbine generator in the scene of s of the doubly-fed wind turbine generator w; qw(s) is the reactive power output of the wind turbine generator in the scene of s of the doubly-fed wind turbine generator w;
Figure BDA0001778276980000133
the method comprises the steps that the maximum active power which can be output by the doubly-fed wind turbine generator w under the limitation of natural conditions in an s scene is represented; vw(s) is the stator side machine end voltage of the doubly-fed wind turbine generator w under the scene of s;
Figure BDA0001778276980000134
the reactance is the stator reactance of the doubly-fed wind turbine generator w;
Figure BDA0001778276980000135
the excitation reactance is the excitation reactance of the double-fed wind turbine generator w;
Figure BDA0001778276980000136
the maximum current of the w rotor side of the doubly-fed wind turbine generator set is obtained;
(8) photovoltaic power generation system operating range constraints
Figure BDA0001778276980000137
Figure BDA0001778276980000138
Wherein: pv(s) represents the active output of the photovoltaic power generation system v in the s scene; qv(s) represents the reactive output of the photovoltaic power generation system v in the s scene;
Figure BDA0001778276980000139
the method comprises the steps that the maximum active power which can be output by a photovoltaic power generation system v under the s scene and limited by natural conditions is represented; vv(s) represents the node voltage of the grid-connected side of the photovoltaic power generation system v under the s scene;
Figure BDA00017782769800001310
representing the maximum load current of the photovoltaic power generation system v inverter;
(9) and (3) limiting the upper limit and the lower limit of the node voltage amplitude:
Figure BDA00017782769800001311
in the formula (26), Vi maxAnd Vi minRespectively representing the upper limit and the lower limit of the voltage amplitude of the node i; vi(s) is the voltage amplitude of the node i in the scene of s;
(10) the allowable thermal current range constraint of the power transmission element:
Figure BDA00017782769800001312
in the formula (27), the reaction mixture is,
Figure BDA00017782769800001313
represents the maximum current, I, of the transmission element ll,ij(s) represents the current amplitude of the power transmission element/in s scenario, which can be expressed as:
Figure BDA00017782769800001314
in the formula (28), YlRepresents the admittance modulus value of the power transmission element l;
(11) and (3) limiting the upper and lower limits of the capacity of the reactive compensation equipment:
Figure BDA0001778276980000141
in formula (29), Qc(s) represents the reactive power compensated by the reactive power compensation equipment c in the s scenario,
Figure BDA0001778276980000142
and
Figure BDA0001778276980000143
respectively the maximum compensation reactive power and the minimum compensation reactive power of the reactive compensation equipment c under the scene s; j. the design is a squareCThe method is a set formed by all reactive compensation equipment of the power distribution system.
The present invention will be further described with reference to the following specific embodiments.
The invention utilizes an IEEE33 node power distribution system as a basis, distributed renewable energy sources such as wind power, photovoltaic and the like are added for power generation, and synchronous form power sources such as small hydropower, a micro gas turbine and the like are added to verify the effectiveness of the invention. The modified IEEE33 node distribution system electrical wiring diagram is shown in fig. 3, and in an example simulation analysis, the reference power of the distribution system is selected to be 10MVA, the reference voltage is selected to be 12.66kV, the deviation range of the node voltage is assumed to be +/-5% of the rated voltage, and the optimization period is selected to be 15 min. Through simulation calculation, the operation optimization results of the synchronous generator set, the wind turbine generator set and the photovoltaic power generation system are respectively shown in tables 1 to 3, and the target function values, the exchange power from the superior transmission network and the reserve capacity value are shown in a table 4.
TABLE 1 optimization results of synchronous unit operation
Figure BDA0001778276980000144
TABLE 2 double-fed wind turbine generator system operation optimization results
Figure BDA0001778276980000145
TABLE 3 photovoltaic power generation system operation optimization results
Figure BDA0001778276980000146
Table 4 comparison results of operation optimization considering and not considering voltage characteristics
Figure BDA0001778276980000147
Compared with the random optimization method for the power distribution network without the method, the method has the advantages that the total cost of the optimized operation decided by the method for optimizing the power distribution network operation is low, the exchange power with a superior transmission network is low, and the expected values of the total wind abandon and the light abandon are reduced, so that the economic benefit of considering the response uncertainty of the voltage regulation characteristic participation is demonstrated.
The invention provides an active power distribution network operation optimization model adaptive to power output uncertainty, which shows that the load voltage characteristic has positive influence on reducing the operation cost of a power distribution system, reducing the power exchange with a superior power transmission network and promoting uncertainty elimination, and the positive influence is more obvious when the load voltage response characteristic ratio is larger, so that the feasible region range is expanded from the aspect of mathematical optimization, and the operation benefit of the power distribution system is favorably improved.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit and scope of the present invention.

Claims (2)

1. An active power distribution network operation optimization method adaptive to uncertainty of power output is characterized by comprising the following steps: the method comprises the following steps:
step1: depicting the random uncertainty of the power output in a scene mode;
step 2: considering a mechanism of standby configuration and standby response participation in random uncertainty consumption, and aiming at minimizing the operation cost of the active power distribution network;
and step 3: under the condition of meeting the technical requirement of safe operation of the power distribution network in a random uncertainty scene, constructing an active power distribution network operation optimization model adaptive to uncertainty of power output;
and 4, step 4: solving the optimization model in the step 3 by using a CONOPT solver based on a GAMS optimization platform;
the minimum objective function of the operation cost of the active power distribution network in the step2 is as follows:
Figure FDA0002983989680000011
in the formula, JGThe method comprises the steps of forming a set for all adjustable synchronous units in a power distribution system; j. the design is a squareWThe method comprises the steps of forming a set for all double-fed wind turbine generators in a power distribution system; j. the design is a squareVThe method comprises the steps of forming a set for all photovoltaic power generation systems in a power distribution system; j. the design is a squareSA collection of all power transmission elements in the power distribution system; pex,0Active power from a superordinate transmission network for an active distribution network, Cex,0Representing the marginal price of electricity of the root node; pgActive power output for the gas turbine g; rhosThe probability of the scene is represented by,
Figure FDA0002983989680000012
Figure FDA0002983989680000013
representing gas turbine stand-by power;
Figure FDA0002983989680000014
representing the reserve power of the superior power transmission network of the s scene;
Figure FDA0002983989680000015
representing the abandoned wind power of the s scene;
Figure FDA0002983989680000016
marking the light abandoning amount of the photovoltaic power generation system in the scene s; cg() Is a power generation cost characteristic function of the gas turbine g;
Figure FDA0002983989680000017
is a backup cost characteristic function of the gas turbine;
Figure FDA0002983989680000018
the cost function of the abandoned light of the photovoltaic power generation system is obtained;
Figure FDA0002983989680000019
the method comprises the steps of obtaining a characteristic function for the abandoned wind of the doubly-fed wind turbine generator;
Figure FDA00029839896800000110
the characteristic function of the standby cost of the superior transmission network; Δ τ represents the duration of the period;
and 3, taking the electrical physical constraint and the safety technical requirement which are required to be met by the operation of the power distribution system as constraint conditions, wherein the constraint conditions are divided into equality constraint and inequality constraint, and the method comprises the following specific steps:
equation constraint
(1) Base point power balance constraint:
Figure FDA00029839896800000111
wherein, PwActive power output, P, for wind turbine dispatchvActive output, P, scheduled for a photovoltaic power generation systemdActive power demand for electrical loads, JDA set of all electrical loads in the power distribution system;
(2) node active power and reactive power balance constraint:
Figure FDA0002983989680000021
in the formula (4), Pl(s) and Ql(s) are respectively the active power and the reactive power transmitted on the power transmission element l under the scene of s, and can be respectively expressed as formula (5) and formula (6); j. the design is a squareS,iThe node i is a set formed by all power transmission elements with the node i as a first node; j. the design is a squareE,iThe node i is a set formed by all power transmission elements with the node i as a final node; j. the design is a squareNA set formed by all nodes in the power distribution system; j. the design is a squareD,iIs a set formed by all power loads on the node i; pi(s) and Qi(s) are respectively active power and reactive power injected into the node i under the scene of s, and can be respectively expressed as an expression (7) and an expression (8); pd(s) and Qd(s) respectively the active power and reactive power requirements of the power load d in the scene s;
Pl(s)＝Vi 2(s)gl-Vi(s)Vj(s)·(glcosθij(s)+blsinθij(s)) (5)
Ql(s)＝-Vi 2(s)bl+Vi(s)Vj(s)·(blcosθij(s)-glsinθij(s)) (6)
in the formula, thetaij(s) represents the phase angle difference of the voltage phasors of the node i and the node j under the scene s; vi(s) representing the voltage amplitude of the first node i node of the branch l under the scene s; vj(s) represents the voltage amplitude of the node j at the last node of branch l under scene s; glAnd blRespectively the conductance value and the susceptance value of the power transmission branch l;
Figure FDA0002983989680000022
Figure FDA0002983989680000023
wherein, JG,iThe method comprises the steps of (1) forming a set by all synchronous gas turbines on a node i; j. the design is a squareR,iA set of all renewable power sources on node i; j. the design is a squareC,iThe method comprises the following steps of (1) forming a set by all reactive compensation equipment on a node i; pg(s) and Qg(s) respectively the active power and the reactive power output by the synchronous gas turbine g under the scene of s; pr(s) and Qr(s) respectively the active power and the reactive power output by the renewable power source r under the scene of s; qc(s) is the inductive reactive power output by the reactive compensation equipment c under the scene s;
(3) the related equation constraint of the schedulable synchronous unit is as follows:
Figure FDA0002983989680000031
Figure FDA0002983989680000032
Figure FDA0002983989680000033
wherein the content of the first and second substances,
Figure FDA0002983989680000034
the method comprises the following steps of (1) synchronizing g stator reactive current of a unit under an s scene; vi(s) representing the voltage amplitude of a node i where the synchronous unit g is located in the scene s;
Figure FDA0002983989680000035
the voltage is expressed as the no-load voltage set for the excitation control of the synchronous unit g; kgRepresenting the voltage difference adjustment coefficient of the synchronous unit g; eg(s) represents the internal potential of the synchronous unit g under the scene of s; deltag(s) representing the power angle value of the synchronous unit g in the scene of s;
Figure FDA0002983989680000036
a direct-axis reactor of a synchronous unit g;
(4) load voltage characteristics:
Figure FDA0002983989680000037
Figure FDA0002983989680000038
in the formula, QdIs the reactive power demand of the electrical load;
Figure FDA0002983989680000039
and
Figure FDA00029839896800000310
respectively the active power and reactive power requirements of the power load d under the rated voltage level;
Figure FDA00029839896800000311
and
Figure FDA00029839896800000312
the constant impedance active power and the reactive power of the power load d are respectively;
Figure FDA00029839896800000313
and
Figure FDA00029839896800000314
the constant current active power and the reactive power of the power load d are respectively part;
Figure FDA00029839896800000315
and
Figure FDA00029839896800000316
respectively are a constant power active power part and a reactive power part of the power load d; v(s) is the system voltage under the s scene; v0Rated voltage of the system;
② inequality constraint
(1) The method comprises the following steps of (1) power upper and lower limit constraint of active base points of a schedulable synchronous unit:
Figure FDA00029839896800000317
wherein, PgBase point power scheduled for the schedulable synchronous unit;
Figure FDA00029839896800000318
and
Figure FDA00029839896800000319
respectively an upper limit and a lower limit of active power allowed by the synchronous unit g;
(2) the active power output of the wind turbine generator is constrained in a base point mode:
Figure FDA00029839896800000320
wherein the content of the first and second substances,
Figure FDA00029839896800000321
the expected active power output of the wind turbine generator set;
(3) constraint of active power output of the photovoltaic power generation system in the base point mode:
Figure FDA0002983989680000041
wherein the content of the first and second substances,
Figure FDA0002983989680000042
is expected to be provided for a photovoltaic power generation systemOutput power;
(4) backup capability range constraint provided by the upper level transmission network:
Figure FDA0002983989680000043
wherein the content of the first and second substances,
Figure FDA0002983989680000044
the maximum standby capacity is provided for the superior transmission network;
(5) and (3) limitation of the spare capacity range of the schedulable synchronous unit:
Figure FDA0002983989680000045
wherein the content of the first and second substances,
Figure FDA0002983989680000046
an upper limit value of the reserve capacity provided for the synchronous unit;
(6) and (3) output power range constraint of the schedulable synchronous unit:
Figure FDA0002983989680000047
Figure FDA0002983989680000048
Figure FDA0002983989680000049
wherein the content of the first and second substances,
Figure FDA00029839896800000410
and
Figure FDA00029839896800000411
respectively an upper limit and a lower limit of the excitation potential of the schedulable synchronous unit g; deltag(s) represents a power angle value of the schedulable synchronous unit g under the scene of s;
Figure FDA00029839896800000412
the maximum value of the g stator current of the synchronous unit;
(7) operation range constraint of doubly-fed wind turbine generator
Figure FDA00029839896800000413
Figure FDA00029839896800000414
In the formula, Pw(s) is the active power output of the wind turbine generator in the scene of s of the doubly-fed wind turbine generator w; qw(s) is the reactive power output of the wind turbine generator in the scene of s of the doubly-fed wind turbine generator w;
Figure FDA00029839896800000415
the method comprises the steps that the maximum active power which can be output by the doubly-fed wind turbine generator w under the limitation of natural conditions in an s scene is represented; vw(s) is the stator side machine end voltage of the doubly-fed wind turbine generator w under the scene of s;
Figure FDA00029839896800000416
the reactance is the stator reactance of the doubly-fed wind turbine generator w;
Figure FDA00029839896800000417
the excitation reactance is the excitation reactance of the double-fed wind turbine generator w;
Figure FDA00029839896800000418
the maximum current of the w rotor side of the doubly-fed wind turbine generator set is obtained;
(8) photovoltaic power generation system operating range constraints
Figure FDA00029839896800000419
Figure FDA0002983989680000051
Wherein: pv(s) represents the active output of the photovoltaic power generation system v in the s scene; qv(s) represents the reactive output of the photovoltaic power generation system v in the s scene;
Figure FDA0002983989680000052
the method comprises the steps that the maximum active power which can be output by a photovoltaic power generation system v under the s scene and limited by natural conditions is represented; vv(s) represents the node voltage of the grid-connected side of the photovoltaic power generation system v under the s scene;
Figure FDA0002983989680000053
representing the maximum load current of the photovoltaic power generation system v inverter;
(9) and (3) limiting the upper limit and the lower limit of the node voltage amplitude:
Figure FDA0002983989680000054
in the formula (26), Vi maxAnd Vi minRespectively representing the upper limit and the lower limit of the voltage amplitude of the node i; vi(s) is the voltage amplitude of the node i in the scene of s;
(10) the allowable thermal current range constraint of the power transmission element:
Figure FDA0002983989680000055
in the formula (27), the reaction mixture is,
Figure FDA0002983989680000056
represents the maximum current, I, of the transmission element ll,ij(s) represents the current amplitude of the power transmission element/in s scenario, which can be expressed as:
Figure FDA0002983989680000057
in the formula (28), YlRepresents the admittance modulus value of the power transmission element l;
(11) and (3) limiting the upper and lower limits of the capacity of the reactive compensation equipment:
Figure FDA0002983989680000058
in formula (29), Qc(s) represents the reactive power compensated by the reactive power compensation equipment c in the s scenario,
Figure FDA0002983989680000059
and
Figure FDA00029839896800000510
respectively the maximum compensation reactive power and the minimum compensation reactive power of the reactive compensation equipment c under the scene s; j. the design is a squareCThe method is a set formed by all reactive compensation equipment of the power distribution system.
2. The active power distribution network operation optimization method adapting to uncertainty of power output according to claim 1, characterized in that: the specific steps of step1 are as follows:
step1 scene production
The uncertainty of the renewable energy power generation in the whole power distribution system is the combination of random time sequences consistent with the quantity of the renewable energy, and if the quantity of the renewable power sources is r and the forward looking time range comprises t time periods, the output of the renewable power sources can be expressed as
Figure FDA00029839896800000511
Wherein
Figure FDA00029839896800000512
The output power of the r-th renewable power source in the time period t is represented, one specific implementation of a random time sequence is a scene of the renewable power source power in a forward-looking time range, and each scene in the obtained expected prediction value and the prediction deviation range is endowed with a certain probability value, such as the s-th scene
Figure FDA0002983989680000061
Its probability value is denoted as ρsEach renewable power supply correspondingly has an expected value and an upper limit and a lower limit of a fluctuation range represented by a prediction deviation in a given time period, so that random sampling in the range corresponds to a specific implementation, the combination of random sampling of all the renewable power supplies in all the time periods forms a scene, Ns scenes are randomly generated by using the method, and the probability of each scene is the probability of each scene
Figure FDA0002983989680000062
Step 2: scene reduction
Let scene i mark P(i)Scene j is marked as P(j)Their occurrence probabilities are respectively denoted as ρiAnd ρjDefining the distance between two scenes as a second-order norm of vector difference between the two scenes:
Dij＝||P(i)-P(j)||2 (1)
in the formula, DijI.e. the distance between scene i and scene J, the purpose of scene reduction is to select a subset of scenes that best represents the full scene set, and the objective is to pursue equation (1) to be the minimum given the number of deleted scenes J.
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