CN110880791A - Coordination optimization method for hybrid alternating current-direct current power distribution network - Google Patents

Coordination optimization method for hybrid alternating current-direct current power distribution network Download PDF

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CN110880791A
CN110880791A CN201911347841.9A CN201911347841A CN110880791A CN 110880791 A CN110880791 A CN 110880791A CN 201911347841 A CN201911347841 A CN 201911347841A CN 110880791 A CN110880791 A CN 110880791A
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power
direct current
distribution network
representing
alternating current
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康鹏
陈磊
丁凡
白昕
丁画
李云鹏
郭伟
吴昱钦
曾琳枫
张强
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State Grid Corp of China SGCC
State Grid Energy Conservation Service Co Ltd
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State Grid Energy Conservation Service 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to a hybrid alternating current and direct current power distribution network coordination optimization method based on two-stage stochastic programming, and establishes a hybrid alternating current and direct current power distribution network two-stage stochastic programming scheduling model considering wind power output uncertainty. Firstly, establishing a power flow model of an alternating current network, a direct current network and a voltage source type converter in a hybrid power distribution network; then, a simplified scene set describing the uncertain characteristic of real-time wind power output is constructed by combining scene generation and reduction technologies; and finally, establishing a hybrid alternating current-direct current power distribution network optimization model based on two-stage stochastic programming, and converting the nonlinear optimization problem into a hybrid integer second-order cone programming problem to solve. The method can be applied to the scheduling decision making of the hybrid alternating current-direct current power distribution network comprising the wind turbine generator, the converter station and the energy storage device, and is beneficial to improving the effectiveness of the scheduling scheme in the uncertain operation environment.

Description

Coordination optimization method for hybrid alternating current-direct current power distribution network
Technical Field
The invention relates to the technical field of hybrid alternating current and direct current power distribution network coordination optimization, in particular to a hybrid alternating current and direct current power distribution network coordination optimization method.
Background
At present, for the problems of increasing the running loss and reducing the running reliability of a power distribution network caused by uncertainty of the output of a distributed power supply, a hybrid power distribution network coordination optimization method which integrates various active regulation and control measures under uncertain running conditions needs to be researched. In addition, as a new trend of future intelligent power distribution network development, different types of reactive voltage equipment in a mixed alternating-current and direct-current power distribution network, an alternating-current/direct-current converter station and an energy storage system need to be further researched for effective overall coordination control, so that renewable energy consumption and flexible and reliable power supply of the power distribution network are promoted.
Disclosure of Invention
Technical problem to be solved
In order to solve the problems in the prior art, the invention provides a hybrid alternating current and direct current power distribution network coordination optimization method based on two-stage random planning.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
a hybrid alternating current-direct current power distribution network coordination optimization method based on two-stage stochastic programming is designed, and the method comprises the following steps:
step 1), establishing a power flow model of an alternating current network, a direct current network and a voltage source type converter in a hybrid power distribution network;
step 2), a simplified scene set for describing the uncertain characteristics of the real-time output of the wind power is constructed by combining a scene generation and reduction method;
step 3), combining the hybrid power distribution network power flow model and the wind power simplification scene set constructed in the steps 1) and 2), and establishing a hybrid alternating current-direct current power distribution network optimization model based on two-stage stochastic programming;
step 4), combining a linearization method and a second-order cone relaxation method, and converting the nonlinear optimization of the hybrid alternating current-direct current power distribution network based on two-stage stochastic programming in the step 3) into mixed integer second-order cone programming;
and 5) solving the scheduling model based on the mixed integer second order cone programming in the step 4) by adopting a CPLEX solver under a Matlab platform to obtain a scheduling scheme, so that the effectiveness of the scheduling decision of the mixed alternating current and direct current power distribution network under the uncertain operation condition is improved.
In the above scheme, the step 1) specifically includes the following steps:
step 1-1), establishing a power flow model of an alternating current network in a hybrid alternating current and direct current power distribution network,
Figure BDA0002333880680000021
in the formula (1), the superscript AC represents an alternating current branch set, and the subscript AC represents a node set in an alternating current network; t is a scheduling time; k (i;) denotes that branch k starts at node i; l (: i) indicates that the branch l takes the node i as the end node; r islAnd xlResistance and reactance of branch l;
Figure BDA0002333880680000022
and
Figure BDA0002333880680000023
respectively injecting active power and reactive power into the node i;
Figure BDA0002333880680000024
and
Figure BDA0002333880680000025
the line current, the transmitted active power and the transmitted reactive power of the branch k are respectively;
Figure BDA0002333880680000026
and
Figure BDA0002333880680000027
the voltages at nodes j and i, respectively;
Figure BDA0002333880680000028
and
Figure BDA0002333880680000029
respectively representing the lower limit and the upper limit of the voltage of the node i;
Figure BDA00023338806800000210
represents the maximum transmission power of the alternating current branch k;
step 1-2), establishing a power flow model of a direct current network in a hybrid alternating current and direct current power distribution network,
Figure BDA0002333880680000031
in the formula (2), the superscript DC represents a direct current branch set; subscript DC represents the set of nodes in the DC network;
Figure BDA0002333880680000032
representing the injected active power of the node i;
Figure BDA0002333880680000033
and
Figure BDA0002333880680000034
respectively representing the line current and the transmission active power of the branch k;
Figure BDA0002333880680000035
and
Figure BDA0002333880680000036
respectively representing the lower limit and the upper limit of the voltage of the node i;
Figure BDA0002333880680000037
respectively representing the maximum transmission power of the direct current branch k;
step 1-3), establishing a power flow model of a voltage source type converter in the hybrid alternating current and direct current power distribution network,
Figure BDA0002333880680000038
in the formula (3), the reaction mixture is,
Figure BDA0002333880680000039
and
Figure BDA00023338806800000310
respectively representing active power and reactive power of an alternating current side of a converter i;
Figure BDA00023338806800000311
representing the active power of the direct current side of the converter i;
Figure BDA00023338806800000312
and
Figure BDA00023338806800000313
the voltages of the AC side and the DC side of the converter i are respectively; mu represents the utilization rate of the direct current voltage; mi,tIndicating the modulation degree of the converter i.
In the above scheme, the step 2) specifically includes the following steps:
step 2-1), obtaining an original wind power scene set by combining Latin hypercube sampling and Cholesky decomposition methods:
Figure BDA00023338806800000314
in the formula (4), the first and second groups,
Figure BDA00023338806800000315
representing the predicted value of the wind power output at the moment t before the day;
Figure BDA00023338806800000316
representing the prediction error of the wind power output at the moment t; deltatThe standard deviation value represents the wind power prediction error at the moment t; g (·) represents a normal distribution; ft(. represents a random variable)
Figure BDA0002333880680000041
The cumulative distribution function of (a) is,
Figure BDA0002333880680000042
is composed of
Figure BDA0002333880680000043
The kth sample value of (2); n represents the number of samples;
the original prediction error sample matrix obtained after Cholesky decomposition processing,
Figure BDA0002333880680000044
and 2-2) combining a synchronous back-substitution reduction method to obtain a simplified scene set of wind power output. Deleting the scene k satisfying the formula (6), and adding the occurrence probability value to the scene probability nearest to the scene k,
Figure BDA0002333880680000045
in the formula (6), dk,jRepresents the Kantorovich distance between scenes k and j; pi(k)Representing the probability of occurrence of scene k;
the clipping process is repeated until a predetermined number of scenes N is obtainedsThe reduced scene set of (1) is as follows:
Figure BDA0002333880680000046
in the above scheme, the establishing of the two-phase stochastic programming scheduling model in step 3) specifically includes the following steps:
step 3-1), expressing an objective function of the hybrid alternating current and direct current power distribution network two-stage stochastic programming model as follows:
Figure BDA0002333880680000047
in the formula (8), cGRepresenting the unit power generation cost of the gas turbine unit; c. CLAnd cWThe penalty price of the unit of abandoned load and abandoned wind is represented; c. CLossExpressing unit network loss cost;
Figure BDA0002333880680000048
representing the electricity price of the upper-level power grid at the moment t; c. CRampRepresenting a penalty price of power fluctuation of a superior power grid; omegaSubRepresenting a set of substations; pisIs the occurrence probability of scene s;
step 3-2), establishing a capacitor compensation capacity constraint:
Figure BDA0002333880680000051
in the formula (9), the reaction mixture,
Figure BDA0002333880680000052
representing the number of capacitor commissioning groups;
Figure BDA0002333880680000053
represents the maximum number of commissioning groups;
Figure BDA0002333880680000054
representing the reactive capacity of a single set of capacitors;
Figure BDA0002333880680000055
represents the maximum number of operations of the capacitor during a day;
step S33: establishing energy storage system operation constraint:
Figure BDA0002333880680000056
in the formula (10), the first and second groups,
Figure BDA0002333880680000057
and
Figure BDA0002333880680000058
representing the charging and discharging state of the energy storage system i at the time t ηchAnd ηdisThe charging and discharging efficiency of the energy storage system is represented;
Figure BDA0002333880680000059
represents the maximum charge/discharge power;
Figure BDA00023338806800000510
representing an energy storage system capacity;
Figure BDA00023338806800000511
representing the electric quantity of the energy storage system at the moment t under the scene s;
Figure BDA00023338806800000512
and
Figure BDA00023338806800000513
discharging and charging power of the energy storage system at the moment t under the scene s;
Figure BDA00023338806800000514
representing the maximum state switching times of the energy storage system in one day;
and 3-4), combining the hybrid alternating current and direct current power distribution network power flow model established in the step 1), and obtaining power flow models of the alternating current power distribution network, the direct current power distribution network and the current converter in different real-time wind power output scenes s.
In the above scheme, the step 4) specifically includes the following steps:
step 4-1), introducing binary auxiliary variables
Figure BDA00023338806800000515
The absolute value constraint of the capacitor in equation (9) is linearized:
Figure BDA0002333880680000061
step 4-2), combining a second-order cone relaxation method, carrying out second-order cone conversion on the constraint containing quadratic term variables in the hybrid alternating current and direct current distribution network, and introducing the following supplementary variables of current and voltage quadratic terms:
Figure BDA0002333880680000062
carrying out second-order cone conversion on the constraint containing the voltage and current quadratic terms in the step 1):
Figure BDA0002333880680000063
step 4-3), taking the direct current distribution network as an example, establishing the following operation constraints based on two-stage random planning:
Figure BDA0002333880680000064
in the formula (14), the reaction mixture,
Figure BDA0002333880680000065
and
Figure BDA0002333880680000066
respectively representing active power transmitted by the direct current branches l and k at the moment t under a wind power scene s;
Figure BDA0002333880680000067
the active power injected by a node i in a direct-current distribution network at the moment t under a wind power scene s is represented;
Figure BDA0002333880680000068
representing a voltage square value of a node j at a moment t under a wind power scene s;
Figure BDA0002333880680000069
and the square value of the current flowing through the direct current branch k at the moment t under the wind power scene s is shown.
(III) advantageous effects
The method and the device perform coordinated optimization on the capacitor bank, the current converter and the energy storage system in the hybrid alternating current and direct current distribution network, promote the consumption of renewable energy sources in the distribution network and flexible power supply of the renewable energy sources, and establish a coordinated optimization scheduling model based on two-stage random planning, thereby being beneficial to improving the effectiveness and reliability of scheduling decisions.
Drawings
Fig. 1 is a flowchart of a hybrid ac/dc power distribution network coordination optimization method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of charging and discharging power of each energy storage system when a deterministic scheduling model and a two-stage stochastic programming scheduling model are adopted according to an embodiment of the present invention;
fig. 3 is a schematic diagram of active power and reactive power transmitted by each converter when a two-stage stochastic programming model is adopted according to an embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
As shown in fig. 1, the embodiment provides a hybrid ac/dc distribution network coordination optimization method based on two-phase stochastic programming, which includes the following steps:
step 1), establishing a power flow model of an alternating current network, a direct current network and a voltage source type converter in a hybrid power distribution network;
step 2), a simplified scene set for describing the uncertain characteristics of the real-time output of the wind power is constructed by combining a scene generation and reduction method;
step 3), combining the hybrid power distribution network power flow model and the wind power simplification scene set constructed in the steps 1) and 2), and establishing a hybrid alternating current-direct current power distribution network optimization model based on two-stage stochastic programming;
step 4), combining a linearization method and a second-order cone relaxation method, and converting the nonlinear optimization of the hybrid alternating current-direct current power distribution network based on two-stage stochastic programming in the step 3) into mixed integer second-order cone programming;
and 5) solving the scheduling model based on the mixed integer second order cone programming in the step 4) by adopting a CPLEX solver under a Matlab platform to obtain a scheduling scheme, so that the effectiveness of the scheduling decision of the mixed alternating current and direct current power distribution network under the uncertain operation condition is improved.
In the embodiment of the present invention, the step 1) specifically includes the following steps:
step 1-1), establishing a power flow model of an alternating current network in a hybrid alternating current-direct current power distribution network as follows:
Figure BDA0002333880680000081
in the formula (1), the superscript AC represents an alternating current branch set, and the subscript AC represents a node set in an alternating current network; t is a scheduling time; k (i;) denotes that branch k starts at node i; l (: i) indicates that the branch l takes the node i as the end node; r islAnd xlResistance and reactance of branch l;
Figure BDA0002333880680000082
and
Figure BDA0002333880680000083
respectively injecting active power and reactive power into the node i;
Figure BDA0002333880680000084
and
Figure BDA0002333880680000085
the line current, the transmitted active power and the transmitted reactive power of the branch k are respectively;
Figure BDA0002333880680000086
and
Figure BDA0002333880680000087
the voltages at nodes j and i, respectively;
Figure BDA0002333880680000088
and
Figure BDA0002333880680000089
respectively representing the lower limit and the upper limit of the voltage of the node i;
Figure BDA00023338806800000810
represents the maximum transmission power of the alternating current branch k;
step 1-2), establishing a power flow model of a direct current network in a hybrid alternating current-direct current power distribution network as follows:
Figure BDA00023338806800000811
in the formula (2), the superscript DC represents a direct current branch set; subscript DC represents the set of nodes in the DC network;
Figure BDA0002333880680000091
representing the injected active power of the node i;
Figure BDA0002333880680000092
and
Figure BDA0002333880680000093
respectively representing the line current and the transmission active power of the branch k;
Figure BDA0002333880680000094
and
Figure BDA0002333880680000095
respectively representing the lower limit and the upper limit of the voltage of the node i;
Figure BDA0002333880680000096
respectively representing the maximum transmission power of the direct current branch k;
step 1-3), establishing a power flow model of a voltage source type converter in a hybrid alternating current-direct current power distribution network as follows:
Figure BDA0002333880680000097
in the formula (3), the reaction mixture is,
Figure BDA0002333880680000098
and
Figure BDA0002333880680000099
respectively representing active power and reactive power of an alternating current side of a converter i;
Figure BDA00023338806800000910
representing the active power of the direct current side of the converter i;
Figure BDA00023338806800000911
and
Figure BDA00023338806800000912
the voltages of the AC side and the DC side of the converter i are respectively; mu represents the utilization rate of the direct current voltage; mi,tIndicating the modulation degree of the converter i.
In the embodiment of the present invention, the step 2) specifically includes the following steps:
step 2-1), obtaining an original wind power scene set by combining Latin hypercube sampling and Cholesky decomposition methods:
Figure BDA00023338806800000913
in the formula (4), the first and second groups,
Figure BDA00023338806800000914
representing the predicted value of the wind power output at the moment t before the day;
Figure BDA00023338806800000915
representing the prediction error of the wind power output at the moment t; deltatThe standard deviation value represents the wind power prediction error at the moment t; g (·) represents a normal distribution; ft(. represents a random variable)
Figure BDA00023338806800000916
The cumulative distribution function of (a) is,
Figure BDA00023338806800000917
is composed of
Figure BDA00023338806800000918
The kth sample value of (2); n represents the number of samples;
the Latin hypercube sampling can effectively avoid the problem of repeated sampling caused by data point concentration in the inverse transform sampling method, so that the sampling points can be discretely distributed in the whole sampling space. Cholesky decomposition method can reduce random variables at different time t
Figure BDA0002333880680000101
The correlation degree between corresponding samples, the original prediction error sample matrix obtained after Cholesky decomposition processing is as follows:
Figure BDA0002333880680000102
and 2-2) combining a synchronous back-substitution reduction method to obtain a simplified scene set of wind power output. The scene k satisfying the following condition is deleted, and the occurrence probability value thereof is added to the scene probability closest thereto.
Figure BDA0002333880680000103
In the formula (6), dk,jRepresents the Kantorovich distance between scenes k and j; pi(k)Representing the probability of occurrence of scene k;
the clipping process is repeated until a predetermined number of scenes N is obtainedsThe reduced scene set of (1) is as follows:
Figure BDA0002333880680000104
in the embodiment of the present invention, the establishing of the two-phase stochastic programming scheduling model in step 3) specifically includes the following steps:
step 3-1), expressing an objective function of the hybrid alternating current and direct current power distribution network two-stage stochastic programming model as follows:
Figure BDA0002333880680000105
in the formula (8), cGRepresenting the unit power generation cost of the gas turbine unit; c. CLAnd cWThe penalty price of the unit of abandoned load and abandoned wind is represented; c. CLossExpressing unit network loss cost;
Figure BDA0002333880680000106
representing the electricity price of the upper-level power grid at the moment t; c. CRampRepresenting a penalty price of power fluctuation of a superior power grid; omegaSubRepresenting a set of substations; pisIs the occurrence probability of scene s;
step 3-2), establishing a capacitor compensation capacity constraint:
Figure BDA0002333880680000111
in the formula (9), the reaction mixture,
Figure BDA0002333880680000112
representing the number of capacitor commissioning groups;
Figure BDA0002333880680000113
represents the maximum number of commissioning groups;
Figure BDA0002333880680000114
representing the reactive capacity of a single set of capacitors;
Figure BDA0002333880680000115
represents the maximum number of operations of the capacitor during a day;
step S33: establishing energy storage system operation constraint:
Figure BDA0002333880680000116
in the formula (10), the first and second groups,
Figure BDA0002333880680000117
and
Figure BDA0002333880680000118
representing the charging and discharging state of the energy storage system i at the time t ηchAnd ηdisThe charging and discharging efficiency of the energy storage system is represented;
Figure BDA0002333880680000119
represents the maximum charge/discharge power;
Figure BDA00023338806800001110
representing an energy storage system capacity;
Figure BDA00023338806800001111
representing the electric quantity of the energy storage system at the moment t under the scene s;
Figure BDA00023338806800001112
and
Figure BDA00023338806800001113
discharging and charging power of the energy storage system at the moment t under the scene s;
Figure BDA00023338806800001114
representing the maximum state switching times of the energy storage system in one day;
and 3-4), combining the hybrid alternating current and direct current power distribution network power flow model established in the step 1), and obtaining power flow models of the alternating current power distribution network, the direct current power distribution network and the current converter in different real-time wind power output scenes s.
In the embodiment of the present invention, the step 4) specifically includes the following steps:
step 4-1), introducing binary auxiliary variables
Figure BDA00023338806800001115
The absolute value constraint of the capacitor in equation (9) is linearized:
Figure BDA0002333880680000121
step 4-2), combining a second-order cone relaxation method, carrying out second-order cone conversion on the constraint containing quadratic term variables in the hybrid alternating current and direct current distribution network, and introducing the following supplementary variables of current and voltage quadratic terms:
Figure BDA0002333880680000122
carrying out second-order cone conversion on the constraint containing the voltage and current quadratic terms in the step 1):
Figure BDA0002333880680000123
step 4-3), taking the direct current distribution network as an example, establishing the following operation constraints based on two-stage random planning:
Figure BDA0002333880680000124
in the formula (14), the reaction mixture,
Figure BDA0002333880680000125
and
Figure BDA0002333880680000126
respectively representing active power transmitted by the direct current branches l and k at the moment t under a wind power scene s;
Figure BDA0002333880680000127
the active power injected by a node i in a direct-current distribution network at the moment t under a wind power scene s is represented;
Figure BDA0002333880680000128
representing a voltage square value of a node j at a moment t under a wind power scene s;
Figure BDA0002333880680000129
and the square value of the current flowing through the direct current branch k at the moment t under the wind power scene s is shown.
In the embodiment of the invention, the nonlinear optimization problem based on the two-stage stochastic programming is converted into the mixed integer second-order cone programming problem through the derivation, and a CPLEX solver is adopted for solving.
The hybrid alternating current and direct current distribution network scheduling model established by the embodiment is beneficial to improving the effectiveness and reliability of scheduling decisions.
In this embodiment, test example simulation is performed in an MATLAB environment, and model solution is performed by using a CPLEX solver. The modeling solution flow is shown in fig. 1.
The two-stage stochastic programming model of the embodiment of the invention takes the minimum sum of the operation costs of the pre-dispatching stage and the re-dispatching stage of the power distribution network as an objective function, and comprises the operation constraint of a direct-current power distribution network, the operation constraint of an alternating-current power distribution network, the operation constraint of a current converter, the operation constraint of a capacitor bank and the operation constraint condition of an energy storage system.
According to a specific example of the embodiment, the two-stage robust optimization method is applied to an improved IEEE-33 node hybrid ac/dc power distribution network for verification, wherein nodes 11, 13, 20, and 31 are connected to a wind turbine, node 2 is connected to a capacitor bank, and nodes 14 and 28 are connected to an energy storage system.
In this embodiment, the following four scheduling scenarios are constructed:
scenario 1: deterministically scheduling a hybrid AC/DC power distribution network without an energy storage system;
scenario 2: deterministically scheduling a hybrid alternating current-direct current power distribution network containing an energy storage system;
scenario 3: the hybrid alternating current-direct current power distribution network without the energy storage system is subjected to two-stage random planning and scheduling;
scenario 4: and (3) carrying out two-stage random planning and scheduling on the hybrid alternating current-direct current power distribution network containing the energy storage system.
The simulation results are shown in table 1:
table 1 simulation results for different scheduling scenarios
Figure BDA0002333880680000131
Analyzing the simulation results obtained under different scenarios in table 1 and fig. 2-3, it can be seen that: for the deterministic scheduling model, the operation cost of scenario 2 containing the energy storage system is reduced compared with scenario 1. For the two-stage stochastic programming model, the operation cost of each scenario 4 containing the energy storage system is reduced compared with that of scenario 3. The load abandoning cost in the collaborative optimization scheduling of the energy storage system is zero, and the power supply reliability of the power distribution network is obviously improved. The total operation cost of the deterministic scheduling model is less than that of the stochastic programming model, but the uncertainty influence of wind power output in scheduling is not taken into account, and the reliability of decision making cannot be guaranteed. The hybrid alternating current/direct current power distribution network converter station and energy storage device coordination optimization method based on two-stage stochastic programming can obtain effective scheduling decisions.
It can be seen from the above diagrams that when a two-stage stochastic programming model is adopted, the energy storage system adjusts the magnitude of the charging and discharging power of the energy storage system in the real-time scheduling stage according to the output curves of the wind power in different scenes, and the converter also adjusts the active power and the reactive power transmitted by the converter according to the change of the real-time output of the wind power, so that an effective scheduling scheme under an uncertain operation condition is obtained.
The main process of the implementation of the embodiment comprises the establishment of a two-stage hybrid alternating current and direct current power distribution network cooperative scheduling model and a conversion and solving method of the model.
According to the method, a two-stage random programming mathematical model with the power generation cost of the wind generation set under the wind power reference prediction scene and the real-time operation cost expectation of the system under the wind power simplification scene set as objective functions is established, and the hybrid AC/DC distribution network containing the wind generation set is optimally scheduled with the objective of minimum two-stage overall operation cost.
In the aspect of wind power uncertainty modeling, the simplified scene set of wind power output is obtained by combining Latin hypercube sampling, Cholesky decomposition and synchronous back substitution subtraction. In the aspect of model conversion and solution, the nonlinear optimization problem based on random programming is converted into a mixed integer second-order cone programming problem to be solved by combining a linearization method and a second-order cone relaxation technology, so that a coordinated optimization decision of the hybrid alternating current-direct current power distribution network under the wind power output simplification scene set is found.
While the present invention has been described with reference to the particular embodiments illustrated in the drawings, which are meant to be illustrative only and not limiting, it will be apparent to those of ordinary skill in the art in light of the teachings of the present invention that numerous modifications can be made without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. A hybrid alternating current-direct current distribution network coordination optimization method is characterized by comprising the following steps:
step 1), establishing a power flow model of an alternating current network, a direct current network and a voltage source type converter in a hybrid power distribution network;
step 2), a simplified scene set for describing the uncertain characteristics of the real-time output of the wind power is constructed by combining a scene generation and reduction method;
step 3), combining the hybrid power distribution network power flow model and the wind power simplification scene set constructed in the steps 1) and 2), and establishing a hybrid alternating current-direct current power distribution network optimization model based on two-stage stochastic programming;
step 4), combining a linearization method and a second-order cone relaxation method, and converting the nonlinear optimization of the hybrid alternating current-direct current power distribution network based on two-stage stochastic programming in the step 3) into mixed integer second-order cone programming;
and 5) solving the scheduling model based on the mixed integer second order cone programming in the step 4) by adopting a CPLEX solver under a Matlab platform to obtain a scheduling scheme, so that the effectiveness of the scheduling decision of the mixed alternating current and direct current power distribution network under the uncertain operation condition is improved.
2. The method according to claim 1, wherein the step 1) specifically comprises the following steps:
step 1-1), establishing a power flow model of an alternating current network in a hybrid alternating current and direct current power distribution network,
Figure FDA0002333880670000011
in the formula (1), the superscript AC represents an alternating current branch set, and the subscript AC represents a node set in an alternating current network; t is a scheduling time; k (i;) denotes that branch k starts at node i; l (: i) indicates that the branch l takes the node i as the end node; r islAnd xlResistance and reactance of branch l;
Figure FDA0002333880670000012
and
Figure FDA0002333880670000013
respectively injecting active power and reactive power into the node i;
Figure FDA0002333880670000014
and
Figure FDA0002333880670000015
the line current, the transmitted active power and the transmitted reactive power of the branch k are respectively;
Figure FDA0002333880670000021
and
Figure FDA0002333880670000022
the voltages at nodes j and i, respectively;
Figure FDA0002333880670000023
and
Figure FDA0002333880670000024
respectively representing the lower limit and the upper limit of the voltage of the node i;
Figure FDA0002333880670000025
represents the maximum transmission power of the alternating current branch k;
step 1-2), establishing a power flow model of a direct current network in a hybrid alternating current and direct current power distribution network,
Figure FDA0002333880670000026
in the formula (2), the superscript DC represents a direct current branch set; subscript DC represents the set of nodes in the DC network;
Figure FDA0002333880670000027
representing the injected active power of the node i;
Figure FDA0002333880670000028
and
Figure FDA0002333880670000029
respectively representing the line current and the transmission active power of the branch k;
Figure FDA00023338806700000210
and
Figure FDA00023338806700000211
respectively representing the lower limit and the upper limit of the voltage of the node i;
Figure FDA00023338806700000212
respectively representing the maximum transmission power of the direct current branch k;
step 1-3), establishing a power flow model of a voltage source type converter in the hybrid alternating current and direct current power distribution network,
Figure FDA00023338806700000213
in the formula (3), the reaction mixture is,
Figure FDA00023338806700000214
and
Figure FDA00023338806700000215
respectively representing active power and reactive power of an alternating current side of a converter i;
Figure FDA00023338806700000216
representing the active power of the direct current side of the converter i;
Figure FDA00023338806700000217
and
Figure FDA00023338806700000218
the voltages of the AC side and the DC side of the converter i are respectively; mu represents the utilization rate of the direct current voltage; mi,tIndicating the modulation degree of the converter i.
3. The method according to claim 1, wherein the step 2) specifically comprises the following steps:
step 2-1), obtaining an original wind power scene set by combining Latin hypercube sampling and Cholesky decomposition methods:
Figure FDA0002333880670000031
in the formula (4), the first and second groups,
Figure FDA0002333880670000032
representing the predicted value of the wind power output at the moment t before the day;
Figure FDA0002333880670000033
representing the prediction error of the wind power output at the moment t; deltatThe standard deviation value represents the wind power prediction error at the moment t; g (·) represents a normal distribution; ft(. represents a random variable)
Figure FDA0002333880670000034
Cumulative distribution function of, Δ Pt (k)Is composed of
Figure FDA0002333880670000035
The kth sample value of (2); n represents the number of samples;
the original prediction error sample matrix obtained after Cholesky decomposition processing,
Figure FDA0002333880670000036
and 2-2) combining a synchronous back-substitution reduction method to obtain a simplified scene set of wind power output. Deleting the scene k satisfying the formula (6), and adding the occurrence probability value to the scene probability nearest to the scene k,
Figure FDA0002333880670000037
in the formula (6), dk,jRepresents the Kantorovich distance between scenes k and j; pi(k)Representing the probability of occurrence of scene k;
the clipping process is repeated until a predetermined number of scenes N is obtainedsExtract of (1)The set of simple scenes is as follows:
Figure FDA0002333880670000038
4. the method according to claim 1, wherein the establishing of the two-stage stochastic programming scheduling model in step 3) specifically comprises the following steps:
step 3-1), expressing an objective function of the hybrid alternating current and direct current power distribution network two-stage stochastic programming model as follows:
Figure FDA0002333880670000039
in the formula (8), cGRepresenting the unit power generation cost of the gas turbine unit; c. CLAnd cWThe penalty price of the unit of abandoned load and abandoned wind is represented; c. CLossExpressing unit network loss cost;
Figure FDA0002333880670000041
representing the electricity price of the upper-level power grid at the moment t; c. CRampRepresenting a penalty price of power fluctuation of a superior power grid; omegaSubRepresenting a set of substations; pisIs the occurrence probability of scene s;
step 3-2), establishing a capacitor compensation capacity constraint:
Figure FDA0002333880670000042
in the formula (9), the reaction mixture,
Figure FDA0002333880670000043
representing the number of capacitor commissioning groups;
Figure FDA0002333880670000044
represents the maximum number of commissioning groups;
Figure FDA0002333880670000045
representing the reactive capacity of a single set of capacitors;
Figure FDA0002333880670000046
represents the maximum number of operations of the capacitor during a day;
step S33: establishing energy storage system operation constraint:
Figure FDA0002333880670000047
in the formula (10), the first and second groups,
Figure FDA0002333880670000048
and
Figure FDA0002333880670000049
representing the charging and discharging state of the energy storage system i at the time t ηchAnd ηdisThe charging and discharging efficiency of the energy storage system is represented;
Figure FDA00023338806700000410
represents the maximum charge/discharge power;
Figure FDA00023338806700000411
representing an energy storage system capacity;
Figure FDA00023338806700000412
representing the electric quantity of the energy storage system at the moment t under the scene s;
Figure FDA00023338806700000413
and
Figure FDA00023338806700000414
discharging and charging power of the energy storage system at the moment t under the scene s;
Figure FDA00023338806700000415
representing the maximum state switching times of the energy storage system in one day;
and 3-4), combining the hybrid alternating current and direct current power distribution network power flow model established in the step 1), and obtaining power flow models of the alternating current power distribution network, the direct current power distribution network and the current converter in different real-time wind power output scenes s.
5. The method according to claim 4, wherein the step 4) specifically comprises the following steps:
step 4-1), introducing binary auxiliary variables
Figure FDA00023338806700000416
The absolute value constraint of the capacitor in equation (9) is linearized:
Figure FDA0002333880670000051
step 4-2), combining a second-order cone relaxation method, carrying out second-order cone conversion on the constraint containing quadratic term variables in the hybrid alternating current and direct current distribution network, and introducing the following supplementary variables of current and voltage quadratic terms:
Figure FDA0002333880670000052
carrying out second-order cone conversion on the constraint containing the voltage and current quadratic terms in the step 1):
Figure FDA0002333880670000053
step 4-3), taking the direct current distribution network as an example, establishing the following operation constraints based on two-stage random planning:
Figure FDA0002333880670000054
in the formula (14), the reaction mixture,
Figure FDA0002333880670000055
and
Figure FDA0002333880670000056
respectively representing active power transmitted by the direct current branches l and k at the moment t under a wind power scene s;
Figure FDA0002333880670000057
the active power injected by a node i in a direct-current distribution network at the moment t under a wind power scene s is represented;
Figure FDA0002333880670000058
representing a voltage square value of a node j at a moment t under a wind power scene s;
Figure FDA0002333880670000059
and the square value of the current flowing through the direct current branch k at the moment t under the wind power scene s is shown.
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