CN116780638A - Snowflake power distribution network operation optimization method and device with soft switch and distributed energy storage - Google Patents

Snowflake power distribution network operation optimization method and device with soft switch and distributed energy storage Download PDF

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CN116780638A
CN116780638A CN202311058853.6A CN202311058853A CN116780638A CN 116780638 A CN116780638 A CN 116780638A CN 202311058853 A CN202311058853 A CN 202311058853A CN 116780638 A CN116780638 A CN 116780638A
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node
snowflake
power
distribution network
power distribution
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王哲
段佳莉
罗凤章
王斌
姚宗强
张章
吴璇
张雪菲
葛楠
王海波
张梁
王楠
赵长伟
刘畅
施晓辰
张思涵
李�浩
王冬阳
范中林
马瑞
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Chengnan Power Supply Co of State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Chengnan Power Supply Co of State Grid Tianjin Electric Power Co Ltd
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Publication of CN116780638A publication Critical patent/CN116780638A/en
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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
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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
    • H02J3/04—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
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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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
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Abstract

The invention provides a snowflake power distribution network operation optimization method and device containing soft switches and distributed energy storage, which are suitable for the technical field of power distribution network regulation and control. The method comprises the following steps: the method comprises the steps of establishing a snowflake power grid operation optimization model with soft switches and distributed energy storage, wherein the soft switches and the distributed energy storage are used for reducing network loss and improving voltage level as comprehensive objective functions, introducing active and reactive power which comprises energy storage charge-discharge power and SOP injection at each moment as decision variables, and taking system power flow constraint, node power balance constraint, operation voltage and branch current constraint, SOP operation constraint, energy storage operation constraint and the like as constraint conditions to fully cope with uncertainty of distributed power supply output and load demand, improving voltage fluctuation level, reducing network loss, introducing a second order cone model to reduce solving difficulty and improving solving speed of a power distribution network optimization scheduling model.

Description

Snowflake power distribution network operation optimization method and device with soft switch and distributed energy storage
Technical Field
The invention belongs to the technical field of operation planning of power distribution systems, and particularly relates to a snowflake power distribution network operation optimization method and device containing soft switches and distributed energy storage.
Background
At present, the construction process of a novel power system taking new energy as a main body is continuously accelerated, the permeability of a distributed power supply in a power distribution system is rapidly improved, the random fluctuation of a source load side is enhanced, and a series of problems such as bidirectional tide, voltage out-of-limit, network blocking and network loss increase are more serious. Traditional distribution systems have limited regulation and control modes, lack of flexibility, and cannot effectively address challenges brought by high-permeability distributed power supplies. Soft Open Point (SOP) and distributed energy storage (distributed energy storage system, DESS) can respectively realize flexible adjustment of tide in two dimensions of space and time, can effectively relieve power space-time fluctuation caused by power load fluctuation of a power distribution network, reduce network loss of the power distribution network and improve voltage fluctuation level, further improve operation stability of the power distribution network, and promote distributed photovoltaic absorption capacity.
As the permeability of distributed power sources in a distribution network continues to increase, operational control problems become more and more complex. However, the research of considering the combined optimization operation of soft switching and distributed energy storage is less aiming at the power distribution network of a snowflake power distribution network structure at present.
Disclosure of Invention
The invention provides a snowflake power distribution network operation optimization method and device with soft switches and distributed energy storage, which can fully cope with uncertainty of output and load demands of a distributed power supply, improve voltage fluctuation level, reduce network loss, introduce a second order cone model to reduce solving difficulty and improve solving speed of an optimal scheduling model of the power distribution network.
Aiming at the problems, the invention adopts the following technical scheme:
in a first aspect, a snowflake power distribution network operation optimization method with soft switches and distributed energy storage is provided. The method comprises the following steps:
basic data are acquired, wherein the basic data comprise components, component structures, equipment parameters and economic parameters of the snowflake power distribution network;
establishing a snowflake power distribution network operation optimization model containing soft switches and distributed energy storage, wherein the snowflake power distribution network operation optimization model aims at reducing network loss of the snowflake power distribution network and improving voltage fluctuation level;
determining constraint conditions of a snowflake power distribution network operation optimization model, wherein the constraint conditions comprise a system power flow constraint condition, an operation voltage and current constraint condition, a node power balance constraint condition, a soft switch operation constraint condition and an energy storage operation constraint condition;
solving a snowflake power distribution network operation optimization model based on a CPLEX solver;
and outputting a solving result, wherein the solving result is used for formulating a day-ahead scheduling strategy for minimizing network loss and voltage deviation of the snowflake power distribution network, and the day-ahead scheduling strategy comprises interaction power with a commercial power network, the lowest and highest node voltage distribution condition of each period of the snowflake power distribution network and an objective function value.
Optionally, the objective function is:
(1)
(2)
(3)
wherein ,nthe total node number of the snowflake distribution network is;for the collection of all branches in a snowflake distribution network,Tis the total number of time periods; />Is a branch circuitijAt the position oftCurrent amplitude at time; />Is a branch circuitijResistance of (2); />Is a nodeiAt the position oftThe voltage amplitude at the moment; />Weight coefficient for network loss of snowflake distribution network, < ->Is a weight coefficient of voltage deviation of the snowflake distribution network,+/>=1；/>network loss of the snowflake distribution network; />Voltage deviation of the snowflake distribution network; />Is the minimum of the objective function.
Optionally, at any time, for any node in the snowflake distribution networkjThe constraint conditions of the system tide are as follows:
(4)
(5)
(6)
wherein ,expressed in terms of nodesjHead-end node set of branches being end nodes, < +.>Expressed in terms of nodesjEnd node set of branches being head end node, < -> and />Respectively nodesiFlow direction nodejActive power and reactive power, +.> and />Respectively branch circuitsijResistance and reactance of (a); />Is a branch circuitijIs set, the current amplitude of (a); />Is a nodeiVoltage amplitude of>Is a nodejActive power, < >>Is a nodejFlow direction nodekActive power, < >>Is a nodejReactive power of>Is a nodejFlow direction nodekReactive power of>Is a nodejVoltage amplitude of>To be at a nodejA kth end node in the set of end nodes that is a leg of the head end node.
Optionally, the node power balancing constraint condition satisfies:
(7)
wherein , and />Respectively nodesjAt the position oftNet injection values of active and reactive power at time;is a nodejUpper access distributed photovoltaic celltActive power at time; /> and />Is a nodejLoad on accesstActive power and reactive power at the moment; /> and />Is a nodejSoft switch ontActive power and reactive power transmitted at a moment; /> and />Is a nodejUpper access distributed energy storage intDischarge power and charge power at a moment.
Optionally, the operating voltage current constraint condition satisfies:
(8)
wherein , and />The upper voltage limit and the lower voltage limit are allowed by the node i respectively; />Maximum current allowed for branch ij.
Optionally, the energy storage operation constraint condition satisfies:
(12)
wherein ,，/>，/>representing nodes respectivelyiAt the position oftElectric quantity, charging power and discharging power of ESS at moment, < -> and />Charging efficiency and discharging efficiency of the ESS respectively; />Is a nodeiThe rated power of the stored energy is calculated,is a nodeiRated capacity of stored energy>Is thattState of charge of ESS at time ∈ ->Is thattDischarge state of ESS at moment, charge>1>1, 0 in idle state, +.> and />Respectively istThe time ESS state of charge is the highest.
Optionally, the soft switch operation constraint satisfies:
(13)
(14)
wherein , and />Respectively nodesiSum nodejSoft switch attActive power injected at a moment; /> and />Respectively nodesiSum nodejSoft switch attReactive power injected at any time;is a nodei、jThe build capacity of the soft switch in between.
Optionally, solving the snowflake power distribution network operation optimization model based on the CPLEX solver comprises:
converting equation (14) to the following second order cone constraint:
(15)
and (5) solving a snowflake power distribution network operation optimization model based on the formula (15).
In a second aspect, a snowflake power distribution network operation optimization device containing a soft switch and distributed energy storage is provided. The device comprises:
basic data are acquired, wherein the basic data comprise components, component structures, equipment parameters and economic parameters of the snowflake power distribution network;
establishing a snowflake power distribution network operation optimization model containing soft switches and distributed energy storage, wherein the snowflake power distribution network operation optimization model aims at reducing network loss of the snowflake power distribution network and improving voltage fluctuation level;
determining constraint conditions of a snowflake power distribution network operation optimization model, wherein the constraint conditions comprise a system power flow constraint condition, an operation voltage and current constraint condition, a node power balance constraint condition, a soft switch operation constraint condition and an energy storage operation constraint condition;
solving a snowflake power distribution network operation optimization model based on a CPLEX solver;
and outputting a solving result, wherein the solving result is used for formulating a day-ahead scheduling strategy for minimizing network loss and voltage deviation of the snowflake power distribution network, and the day-ahead scheduling strategy comprises interaction power with a commercial power network, the lowest and highest node voltage distribution condition of each period of the snowflake power distribution network and an objective function value.
Optionally, the objective function is:
(1)
(2)
(3)
wherein ,nthe total node number of the snowflake distribution network is;for the collection of all branches in a snowflake distribution network,Tis the total number of time periods; />Is a branch circuitijAt the position oftCurrent amplitude at time; />Is a branch circuitijResistance of (2); />Is a nodeiAt the position oftThe voltage amplitude at the moment; />Weight coefficient for network loss of snowflake distribution network, < ->Weight coefficient for voltage deviation of snowflake distribution network, < ->+/>=1；/>Network loss of the snowflake distribution network; />Voltage deviation of the snowflake distribution network; />Is the minimum of the objective function.
Optionally, at any time, for any node in the snowflake distribution networkjThe constraint conditions of the system tide are as follows:
(4)
(5)
(6)
wherein ,expressed in terms of nodesjHead-end node set of branches being end nodes, < +.>Expressed in terms of nodesjEnd node set of branches being head end node, < -> and />Respectively nodesiFlow direction nodejActive power and reactive power, +.> and />Respectively branch circuitsijResistance and reactance of (a); />Is a branch circuitijIs set, the current amplitude of (a); />Is a nodeiVoltage amplitude of>Is a nodejActive power, < >>Is a nodejFlow direction nodekActive power, < >>Is a nodejReactive power of>Is a nodejFlow direction nodekReactive power of>Is a nodejVoltage amplitude of>To be at a nodejIn a set of end nodes which are branches of a head-end nodeThe kth end node.
Optionally, the node power balancing constraint condition satisfies:
(7)
wherein , and />Respectively nodesjAt the position oftNet injection values of active and reactive power at time;is a nodejUpper access distributed photovoltaic celltActive power at time; /> and />Is a nodejLoad on accesstActive power and reactive power at the moment; /> and />Is a nodejSoft switch ontActive power and reactive power transmitted at a moment; /> and />Is a nodejUpper access distributed energy storage intDischarge power and charge power at a moment.
Optionally, the operating voltage current constraint condition satisfies:
(8)
wherein , and />The upper voltage limit and the lower voltage limit are allowed by the node i respectively; />Maximum current allowed for branch ij.
Optionally, the energy storage operation constraint condition satisfies:
(12)
wherein ,，/>，/>representing nodes respectivelyiAt the position oftElectric quantity, charging power and discharging power of ESS at moment, < -> and />Charging efficiency and discharging efficiency of the ESS respectively; />Is a nodeiThe rated power of the stored energy is calculated,is a nodeiRated capacity of stored energy>Is thattCharging of an ESS at timeElectric state (I)>Is thattDischarge state of ESS at moment, charge>1>1, 0 in idle state, +.> and />Respectively istThe time ESS state of charge is the highest.
Optionally, the soft switch operation constraint satisfies:
(13)
(14)
wherein , and />Respectively nodesiSum nodejSoft switch attActive power injected at a moment; /> and />Respectively nodesiSum nodejSoft switch attReactive power injected at any time;is a nodei、jSoft opening betweenThe capacity of the gateway is built.
Optionally, the solving module is further configured to convert the formula (14) into a second order cone constraint type, and solve the snowflake power distribution network operation optimization model based on the formula (15):
(15)。
in a third aspect, another snowflake power distribution network operation optimizing device containing a soft switch and distributed energy storage is provided, including: the processor may be configured to perform the steps of,
the processor is coupled with the memory;
the processor is used for reading and executing a program or instructions stored in the memory, so that the device executes the snowflake power distribution network operation optimization method containing the soft switch and the distributed energy storage.
In a fourth aspect, a computer readable storage medium is provided, in which a program or an instruction is stored, and when the program or the instruction is read and executed by a computer, the computer is caused to execute the snowflake power distribution network operation optimization method including the soft switch and the distributed energy storage according to the first aspect.
Based on the snowflake power distribution network operation optimization method and device with the soft switch and the distributed energy storage, which are provided by the invention, a snowflake power grid operation optimization model with the soft switch and the distributed energy storage, which takes the reduction of system network loss and the improvement of voltage level as comprehensive objective functions, is established, the active and reactive power which comprises the energy storage charging and discharging power at each moment and SOP injection are introduced as decision variables, and the constraint conditions of system power flow constraint, node power balance constraint, operation voltage and branch current constraint, SOP operation constraint, energy storage operation constraint and the like are taken as constraint conditions, so that the problems of network loss, voltage overrun and the like caused by the rapid development of the current distributed power supply are solved.
Furthermore, the snowflake power grid operation optimization model containing the soft switch and the distributed energy storage is a mixed integer nonlinear programming problem in mathematics, the model can be converted into a convex programming model constrained by a second order cone, and mature mathematical software such as Gurobi, CPLEX and the like is adopted for direct solving, so that solving difficulty is reduced, and solving efficiency is improved.
Compared with the prior art, the invention has the following beneficial effects: the snowflake power grid operation optimization method and device provided by the invention can fully cope with uncertainty of output and load demands of a distributed power supply, and play roles in improving voltage fluctuation level of a snowflake power distribution network, reducing network loss of the system and the like. Furthermore, a Distflow second-order cone model can be adopted to model the regional power system, and a mathematical programming method is adopted to solve the model, so that the solving difficulty is reduced, and the solving speed of the regional power distribution system day-ahead optimal scheduling model is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a distribution network with a hand-in-hand structure formed by two branches in a snowflake distribution network according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a typical solar load and photovoltaic output provided by an embodiment of the present invention;
fig. 4 is a schematic diagram of interaction power between a substation a and a utility network according to an embodiment of the present invention;
fig. 5 is a schematic diagram of interaction power between a substation B and a utility network according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of maximum and minimum voltages for scenario 1 and scenario 2 provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of maximum and minimum voltages for scenario 2 and scenario 3 provided by an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of another snow flake distribution network operation optimization device with soft switch and distributed energy storage according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of another snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
First, with reference to fig. 1, a snowflake power distribution network including a soft switch and distributed energy storage according to an embodiment of the present invention will be described in detail.
Fig. 1 is a schematic diagram of a distribution network with a hand-in-hand structure formed by two branches in a snowflake distribution network according to an embodiment of the present invention. As shown in fig. 1, considering symmetry, a soft switch may be used instead of a tie switch to connect the two branches.
The embodiment of the invention provides a snowflake power distribution network operation optimization method with soft switch and distributed energy storage, which is provided by the embodiment of the invention, with reference to fig. 2-7.
Fig. 2 is a schematic flow chart of a snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to an embodiment of the present invention. As shown in fig. 2, the method includes:
s201, basic data is acquired.
The basic data comprise components, a component structure, equipment parameters and economical efficiency parameters of the snowflake power distribution network.
For specific examples of active and reactive power of node loads, maximum and minimum values allowed by node voltages, and branch impedance values in snowflake distribution networks, please refer to tables 1 and 2 below; see table 3 for detailed parameters of photovoltaic, soft switching, distributed energy storage devices.
Fig. 3 is a schematic diagram of a typical solar load, photovoltaic output provided by an embodiment of the present invention. As shown in the figure, a load curve is simulated by using a load prediction method at intervals of 1 hour, the photovoltaic output curve processing mode is similar, and the current limit value of each branch is 500A; the reference voltage of the distribution network is set to be 12.66kV, and the reference power is set to be 10MVA.
TABLE 1
Node numbering Active load (kW) Reactive load (kVar) Maximum allowable voltage (p.u.) Voltage allowed minimum (p.u.)
1 0 0 1.1 0.9
2 100 60 1.1 0.9
3 90 40 1.1 0.9
4 120 80 1.1 0.9
5 60 30 1.1 0.9
6 60 20 1.1 0.9
7 200 100 1.1 0.9
8 200 100 1.1 0.9
9 60 20 1.1 0.9
10 60 20 1.1 0.9
11 45 30 1.1 0.9
12 60 35 1.1 0.9
13 60 35 1.1 0.9
14 120 80 1.1 0.9
15 60 10 1.1 0.9
16 60 20 1.1 0.9
17 60 20 1.1 0.9
18 90 40 1.1 0.9
19 0 0 1.1 0.9
20 100 60 1.1 0.9
21 90 40 1.1 0.9
22 120 80 1.1 0.9
23 60 30 1.1 0.9
24 60 20 1.1 0.9
25 200 100 1.1 0.9
26 200 100 1.1 0.9
27 60 20 1.1 0.9
28 60 20 1.1 0.9
29 45 30 1.1 0.9
30 60 35 1.1 0.9
31 60 35 1.1 0.9
32 120 80 1.1 0.9
TABLE 2
Head node End node Resistor (omega) Reactance (omega)
1 2 0.0922 0.0470
2 3 0.4930 0.2511
3 4 0.3660 0.1864
4 5 0.3811 0.1941
5 6 0.8190 0.7070
6 7 0.1872 0.6188
7 8 0.7114 0.2351
8 9 1.0300 0.7400
9 10 1.0440 0.7400
10 11 0.1966 0.0650
11 12 0.3744 0.1238
12 13 1.4680 1.1550
13 14 0.5416 0.7129
14 15 0.5910 0.5260
15 16 0.7463 0.5450
16 17 1.2890 1.7210
17 18 0.7320 0.5740
19 20 0.0922 0.0470
20 21 0.4930 0.2511
21 22 0.3660 0.1864
22 23 0.3811 0.1941
23 24 0.8190 0.7070
24 25 0.1872 0.6188
25 26 0.7114 0.2351
26 27 1.0300 0.7400
27 28 1.0440 0.7400
28 29 0.1966 0.0650
29 30 0.3744 0.1238
30 31 1.4680 1.1550
31 32 0.5416 0.7129
TABLE 3 Table 3
S202, establishing a snowflake power distribution network operation optimization model containing soft switches and distributed energy storage, wherein the snowflake power distribution network operation optimization model aims at reducing network loss of the snowflake power distribution network and improving voltage fluctuation level.
Optionally, the objective function is:
(1)
(2)
(3)
wherein ,nthe total node number of the snowflake distribution network is;for the collection of all branches in a snowflake distribution network,Tis the total number of time periods; />Is a branch circuitijAt the position oftCurrent amplitude at time; />Is a branch circuitijResistance of (2); />Is a nodeiAt the position oftThe voltage amplitude at the moment; />Weight coefficient for network loss of snowflake distribution network, < ->Weight coefficient for voltage deviation of snowflake distribution network, < ->+/>=1；/>Network loss of the snowflake distribution network; />Voltage deviation of the snowflake distribution network; />Is the minimum of the objective function. For example, in the embodiment of the present invention, the weight coefficient ω 1 、ω 2 0.8333 and 0.167, respectively.
S203, determining constraint conditions of a snowflake power distribution network operation optimization model.
The constraint conditions comprise a system power flow constraint condition, an operation voltage and current constraint condition, a node power balance constraint condition, a soft switch operation constraint condition and an energy storage operation constraint condition.
Optionally, at any time, for any node in the snowflake distribution networkjThe constraint conditions of the system tide are as follows:
(4)
(5)/>
(6)
wherein ,expressed in terms of nodesjHead-end node set of branches being end nodes, < +.>Expressed in terms of nodesjEnd node set of branches being head end node, < -> and />Respectively nodesiFlow direction nodejActive power and reactive power, +.> and />Respectively branch circuitsijResistance and reactance of (a); />Is a branch circuitijIs set, the current amplitude of (a); />Is a nodeiVoltage amplitude of>Is a nodejActive power, < >>Is a nodejFlow direction nodekActive power, < >>Is a nodejReactive power of>Is a nodejFlow direction nodekReactive power of>Is a nodejVoltage amplitude of>To be at a nodejA kth end node in the set of end nodes that is a leg of the head end node.
Optionally, the node power balancing constraint condition satisfies:
(7)
wherein , and />Respectively nodesjAt the position oftNet injection values of active and reactive power at time;is a nodejUpper access distributed photovoltaic celltActive power at time; /> and />Is a nodejLoad on accesstActive power and reactive power at the moment; /> and />Is a nodejSoft switch ontActive power and reactive power transmitted at a moment; /> and />Is a nodejUpper access distributed energy storage intDischarge power and charge power at a moment.
Optionally, the operating voltage current constraint condition satisfies:
(8)
wherein , and />The upper voltage limit and the lower voltage limit are allowed by the node i respectively; />Maximum current allowed for branch ij.
When the objective function is satisfiedUnder the conditions of strict increasing function and the like, the following modifications can be made to the formula (6):
（9）
order the，/>Relaxing the branch apparent power quadratic constraint shown in the formula (5) into a conical constraint:
（10）
the constraints of node voltage and branch current can be expressed as:
（11）
optionally, the energy storage operation constraint condition satisfies:
(12)
wherein ,，/>，/>representing nodes respectivelyiAt the position oftElectric quantity, charging power and discharging power of ESS at moment, < -> and />Respectively, the charging efficiency and the discharging efficiency of the ESS are 90% in the embodiment; />Is a nodeiRated power of energy storage, ">Is a nodeiRated capacity of stored energy>Is thattState of charge of ESS at time ∈ ->Is thattDischarge state of ESS at moment, charge>1>1, 0 in idle state, +.> and />Respectively istThe time ESS state of charge is the highest.
Optionally, the soft switch operation constraint satisfies:
(13)
(14)
wherein , and />Respectively nodesiSum nodejSoft switch attActive power injected at a moment; /> and />Respectively nodesiSum nodejSoft switch attReactive power injected at any time;is a nodei、jThe build capacity of the soft switch in between.
S204, solving a snowflake power distribution network operation optimization model based on a CPLEX solver;
optionally, solving the snowflake power distribution network operation optimization model based on the CPLEX solver comprises:
converting equation (14) to the following second order cone constraint:
(15)/>
and (5) solving a snowflake power distribution network operation optimization model based on the formula (15).
Specifically, an optimization model can be run for the snowflake distribution network based on the combination of the soft switch and the distributed energy storage system, programming is carried out on a MATLAB 2022a platform through a YALMIP optimization tool kit, and an IBM ILOG CPLEX algorithm package is called for solving.
For the embodiment, the following three scenes may be selected for comparison analysis, where scene 1 (case 1) is not accessed to SOP and ESS; scenario 2 (case 2) is access-only ESS; scenario 3 (case 3) is to access ESS and SOP.
S205, outputting a solving result.
The solving result comprises the results of system network loss and voltage deviation under different conditions, interaction power of two substations and a commercial power network, daily operation strategies of SOP and ESS and the like, and the solving result can be used for formulating a daily scheduling strategy which enables the network loss and the voltage deviation of the snowflake power distribution network to be minimum, wherein the daily scheduling strategy comprises interaction power with the commercial power network, the lowest and highest node voltage distribution condition of each period of the snowflake power distribution network and an objective function value.
The network loss and voltage bias under the above three scenarios are shown in table 4.
TABLE 4 Table 4
Context System network loss (kW h) Voltage deviation (p.u.)
case1 748 22.97
case2 568 20.11
case3 390 10.41
As can be seen from table 4, the network loss and voltage deviation of the scenarios 1, 2, and 3 gradually decrease, which means that the simultaneous access of the SOP and ESS is more conducive to reducing the network loss and voltage deviation, thereby improving the operation efficiency and stability of the power distribution network.
The interaction power of two substations (a substation A and a substation B) and a commercial network in different scenes is shown in fig. 4 and 5. Compared with the scene 1, in the scene 2, as the distributed energy storage is connected to the node 12, peak clipping and valley filling can be realized, so that the return power of the transformer station A is effectively reduced; on the contrary, as no energy storage is connected into the feeder line B of the transformer substation, the interaction power with the commercial power network is not changed.
Further, compared with the scene 2, on the basis of being configured with SOP and distributed energy storage, the scene 3 is configured with SOP soft switches between the nodes 18 and 32, the two feeder lines can realize power interaction, photovoltaic output is large when the ratio is 10:00-14:00, electric energy generated by distributed photovoltaic can be consumed through the feeder line of the transformer substation A or the feeder line of the transformer substation B, so that the transformer substation A does not need to send power back to a commercial network, namely, all consumption is realized, and the purchase power of the transformer substation B to the commercial network is reduced to a certain extent.
The lowest and highest node voltage distribution conditions of the whole power distribution network in each period under different scenes are shown in fig. 6 and 7. Compared with the scene 1, the scene 2 realizes peak clipping and valley filling by optimizing the operation of the ESS and by comprehensively planning the output condition of the distributed photovoltaic in each period and the power consumption requirement of the load, thereby being beneficial to reducing network loss; the node voltage change range is obviously reduced, the power supply quality of the whole power distribution network is effectively improved, and the problems of low voltage and node voltage rise after the distributed photovoltaic is connected with the power distribution network can be effectively relieved when the load is heavy, so that the acceptance of the power distribution network to the distributed photovoltaic is further improved. Compared with the scene 2, the SOP in the scene 3 can relieve the unbalanced condition of the feeder load, reduce network loss, effectively relieve the deviation between the voltage and the standard value and improve the voltage level curve.
Based on the snowflake power distribution network operation optimization method with soft switches and distributed energy storage, which is provided by the invention, a snowflake power distribution network operation optimization model with soft switches and distributed energy storage, which takes the reduction of system network loss and the improvement of voltage level as comprehensive objective functions, is established, the active and reactive power which comprises energy storage charging and discharging power and SOP injection at each moment is introduced as decision variables, and the system power flow constraint, node power balance constraint, operation voltage and branch current constraint, SOP operation constraint, energy storage operation constraint and the like are taken as constraint conditions, so that the problems of network loss, voltage overrun and the like caused by the rapid development of the current distributed power supply are solved.
Furthermore, the snowflake power grid operation optimization model containing the soft switch and the distributed energy storage is a mixed integer nonlinear programming problem in mathematics, the model can be converted into a convex programming model constrained by a second order cone, and mature mathematical software such as Gurobi, CPLEX and the like is adopted for direct solving, so that solving difficulty is reduced, and solving efficiency is improved.
Compared with the prior art, the snowflake power distribution network operation optimization method with the soft switch and the distributed energy storage has the following beneficial effects:
the snowflake power distribution network operation optimization method with the soft switch and the distributed energy storage can fully cope with uncertainty of output and load demands of a distributed power supply, and plays roles in improving voltage fluctuation level of the snowflake power distribution network, reducing network loss of a system and the like. Further, a Distflow second order cone model is adopted to model a regional power system, and a mathematical programming method is adopted to solve the model, so that the solving difficulty is reduced, and the solving speed of a regional power distribution system day-ahead optimal scheduling model is improved.
The snowflake power distribution network operation optimization method with soft switch and distributed energy storage provided by the embodiment of the invention is described in detail above with reference to fig. 2-7, and the snowflake power distribution network operation optimization device with soft switch and distributed energy storage provided by the embodiment of the invention is described below with reference to fig. 8 and 9.
Fig. 8 is a schematic structural diagram of a snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to an embodiment of the present invention. The device can execute the snowflake power distribution network operation optimization method containing the soft switch and the distributed energy storage.
As shown in fig. 8, the apparatus 800 includes: an acquisition module 801, a building module 802, a determination module 803, and a solving module 804; wherein,
the acquisition module 801 is configured to acquire basic data, where the basic data includes components, a component structure, equipment parameters, and economic parameters of the snowflake power distribution network;
the building module 802 is configured to build a snowflake power distribution network operation optimization model including soft switches and distributed energy storage, where the snowflake power distribution network operation optimization model is an objective function with the objective of reducing network loss and improving voltage fluctuation level of the snowflake power distribution network;
the determining module 803 is configured to determine constraint conditions of the snowflake power distribution network operation optimization model, where the constraint conditions include a system power flow constraint condition, an operation voltage current constraint condition, a node power balance constraint condition, a soft switch operation constraint condition, and an energy storage operation constraint condition;
the solving module 804 is configured to solve the snowflake power distribution network operation optimization model based on the CPLEX solver;
the solving module 804 is further configured to output a solving result, where the solving result is used to formulate a day-ahead scheduling policy that minimizes network loss and voltage deviation of the snowflake power distribution network, and the day-ahead scheduling policy includes interaction power with the electric network, minimum and maximum node voltage distribution conditions of each period of the snowflake power distribution network, and objective function values.
Optionally, the objective function is:
(1)
(2)/>
(3)
wherein ,nthe total node number of the snowflake distribution network is;for the collection of all branches in a snowflake distribution network,Tis the total number of time periods; />Is a branch circuitijAt the position oftCurrent amplitude at time; />Is a branch circuitijResistance of (2); />Is a nodeiAt the position oftThe voltage amplitude at the moment; />Weight coefficient for network loss of snowflake distribution network, < ->Weight coefficient for voltage deviation of snowflake distribution network, < ->+/>=1；/>Network loss of the snowflake distribution network; />Voltage deviation of the snowflake distribution network; />Is the minimum of the objective function.
Optionally, at any time, toAny node in snowflake distribution networkjThe constraint conditions of the system tide are as follows:
(4)
(5)
(6)
wherein ,expressed in terms of nodesjHead-end node set of branches being end nodes, < +.>Expressed in terms of nodesjEnd node set of branches being head end node, < -> and />Respectively nodesiFlow direction nodejActive power and reactive power, +.> and />Respectively branch circuitsijResistance and reactance of (a); />Is a branch circuitijIs set, the current amplitude of (a); />Is a nodeiVoltage amplitude of>Is a nodejActive power, < >>Is a nodejFlow direction nodekActive power, < >>Is a nodejReactive power of>Is a nodejFlow direction nodekReactive power of>Is a nodejVoltage amplitude of>To be at a nodejA kth end node in the set of end nodes that is a leg of the head end node.
Optionally, the node power balancing constraint condition satisfies:
(7)
wherein , and />Respectively nodesjAt the position oftNet injection values of active and reactive power at time;is a nodejUpper access distributed photovoltaic celltActive power at time; /> and />Is a nodejLoad on accesstActive power and reactive power at the moment; /> and />Is a nodejSoft switch ontActive power and reactive power transmitted at a moment; /> and />Is a nodejUpper access distributed energy storage intDischarge power and charge power at a moment.
Optionally, the operating voltage current constraint condition satisfies:
(8)
wherein , and />The upper voltage limit and the lower voltage limit are allowed by the node i respectively; />Maximum current allowed for branch ij.
Optionally, the energy storage operation constraint condition satisfies:
(12)
wherein ,，/>，/>representing nodes respectivelyiAt the position oftElectric quantity, charging power and discharging power of ESS at moment, < -> and />Charging efficiency and discharging efficiency of the ESS respectively; />Is a nodeiThe rated power of the stored energy is calculated,is a nodeiRated capacity of stored energy>Is thattState of charge of ESS at time ∈ ->Is thattDischarge state of ESS at moment, charge>1>1, 0 in idle state, +.> and />Respectively istThe time ESS state of charge is the highest.
Optionally, the soft switch operation constraint satisfies:
(13)
(14)
wherein , and />Respectively nodesiSum nodejSoft switch attActive power injected at a moment; /> and />Respectively nodesiSum nodejSoft switch attReactive power injected at any time;is a nodei、jThe build capacity of the soft switch in between.
Optionally, the solution module 804 is further configured to convert the formula (14) into the following second order cone constraint, and solve the snowflake power distribution network operation optimization model based on the formula (15):
(15)。
fig. 9 is a schematic structural diagram of still another operation optimizing device for a snowflake power distribution network including a soft switch and distributed energy storage according to an embodiment of the present invention.
As shown in fig. 9, the apparatus 900 includes: a processor 901, the processor 901 coupled to the memory 902;
the processor 901 is configured to read and execute a program or an instruction stored in the memory 902, so that the device 900 executes the snowflake power distribution network operation optimization method including soft switching and distributed energy storage according to the above method embodiment.
Optionally, the apparatus 900 may further comprise a transceiver 903 for the apparatus 900 to communicate with other apparatuses.
For convenience of description, fig. 8 and fig. 9 only show main components of the snowflake power distribution network operation optimizing device including the soft switch and the distributed energy storage. In practical applications, the snow flake power distribution network operation optimization device with soft switch and distributed energy storage may further include components or assemblies not shown in the figure.
The embodiment of the invention also provides a computer readable storage medium which stores a program or instructions, and when the program or instructions are read and executed by a computer, the computer is enabled to execute the operation optimization method of the snowflake power distribution network containing the soft switch and the distributed energy storage.
Although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (18)

1. A snowflake power distribution network operation optimization method containing soft switch and distributed energy storage is characterized by comprising the following steps:
basic data are acquired, wherein the basic data comprise components, component structures, equipment parameters and economic parameters of the snowflake power distribution network;
establishing a snowflake power distribution network operation optimization model containing soft switches and distributed energy storage, wherein the snowflake power distribution network operation optimization model aims at reducing network loss of the snowflake power distribution network and improving voltage fluctuation level;
determining constraint conditions of the snowflake power distribution network operation optimization model, wherein the constraint conditions comprise system power flow constraint conditions, operation voltage and current constraint conditions, node power balance constraint conditions, soft switch operation constraint conditions and energy storage operation constraint conditions;
solving a snowflake power distribution network operation optimization model based on a CPLEX solver;
and outputting a solving result, wherein the solving result is used for formulating a day-ahead scheduling strategy for minimizing network loss and voltage deviation of the snowflake power distribution network, and the day-ahead scheduling strategy comprises interaction power with a commercial power network, the lowest and highest node voltage distribution condition of each period of the snowflake power distribution network and an objective function value.
2. The snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to claim 1, wherein the objective function is:
(1)
(2)
(3)
wherein ,nthe total node number of the snowflake distribution network is set;for the collection of all branches in the snowflake distribution network,Tis the total number of time periods; />Is a branch circuitijAt the position oftCurrent amplitude at time; />Is a branch circuitijResistance of (2); />Is a nodeiAt the position oftThe voltage amplitude at the moment; />Weight coefficient for network loss of the snowflake distribution network, < >>Weight coefficient for the voltage deviation of the snowflake distribution network, +.>+/>=1；/>Network loss of the snowflake power distribution network; />Voltage deviation of the snowflake power distribution network; />Is the minimum of the objective function.
3. The method for optimizing operation of a snowflake power distribution network with soft switching and distributed energy storage according to claim 1, wherein for any node in the snowflake power distribution network at any timejThe system tide constraint conditions meet the following conditions:
(4)
(5)
(6)
wherein ,expressed in terms of nodesjHead-end node set of branches being end nodes, < +.>Expressed in terms of nodesjEnd node set of branches being head end node, < -> and />Respectively nodesiFlow direction nodejActive power and reactive power, +.> and />Respectively branch circuitsijResistance and reactance of (a); />Is a branch circuitijIs set, the current amplitude of (a); />Is a nodeiVoltage amplitude of>Is a nodejActive power, < >>Is a nodejFlow direction nodekActive power, < >>Is a nodejReactive power of>Is a nodejFlow direction nodekReactive power of>Is a nodejVoltage amplitude of>To be at a nodejA kth end node in the set of end nodes that is a leg of the head end node.
4. The snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to claim 1, wherein the node power balance constraint condition is satisfied:
(7)
wherein , and />Respectively nodesjAt the position oftNet injection values of active and reactive power at time; />Is a nodejUpper access distributed photovoltaic celltActive power at time; /> and />Is a nodejLoad on accesstActive power and reactive power at the moment; /> and />Is a nodejSoft switch ontActive power and reactive power transmitted at a moment; /> and />Is a nodejUpper access distributed energy storage intDischarge power and charge power at a moment.
5. The snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to claim 1, wherein the operation voltage and current constraint conditions are as follows:
(8)
wherein , and />The upper voltage limit and the lower voltage limit are allowed by the node i respectively; />Maximum current allowed for branch ij.
6. The snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to claim 1, wherein the energy storage operation constraint condition is satisfied:
(12)
wherein ,，/>，/>representing nodes respectivelyiAt the position oftElectric quantity, charging power and discharging power of ESS at moment, < -> and />Charging efficiency and discharging efficiency of the ESS respectively; />Is a nodeiRated power of energy storage, ">Is a nodeiRated capacity of stored energy>Is thattState of charge of ESS at time ∈ ->Is thattDischarge state of ESS at moment, charge>1>1, 0 in idle state, +.> and />Respectively istThe time ESS state of charge is the highest.
7. The snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to claim 1, wherein the soft switch operation constraint condition is satisfied:
(13)
(14)
wherein , and />Respectively nodesiSum nodejSoft switch attActive power injected at a moment; and />Respectively nodesiSum nodejSoft switch attReactive power injected at any time; />Is a nodei、jThe build capacity of the soft switch in between.
8. The snowflake power distribution network operation optimization method with soft switch and distributed energy storage according to any one of claims 1-7, wherein the solving the snowflake power distribution network operation optimization model based on the CPLEX solver comprises:
converting equation (14) to the following second order cone constraint:
(15)
and solving the snowflake power distribution network operation optimization model based on the formula (15).
9. A snowflake power distribution network operation optimizing device that contains soft switch and distributed energy storage, characterized in that, the device includes: the system comprises an acquisition module, an establishment module, a determination module and a solving module; wherein,
the acquisition module is used for acquiring basic data, wherein the basic data comprises components, a composition structure, equipment parameters and economical efficiency parameters of the snowflake power distribution network;
the building module is used for building a snowflake power distribution network operation optimization model containing soft switches and distributed energy storage, and the snowflake power distribution network operation optimization model aims at reducing network loss of the snowflake power distribution network and improving an objective function of voltage fluctuation level;
the determining module is used for determining constraint conditions of the snowflake power distribution network operation optimization model, wherein the constraint conditions comprise a system power flow constraint condition, an operation voltage current constraint condition, a node power balance constraint condition, a soft switch operation constraint condition and an energy storage operation constraint condition;
the solving module is used for solving the snowflake power distribution network operation optimization model based on the CPLEX solver;
the solving module is further used for outputting a solving result, and the solving result is used for formulating a day-ahead scheduling strategy for minimizing network loss and voltage deviation of the snowflake power distribution network, wherein the day-ahead scheduling strategy comprises interaction power with a commercial power network, the lowest and highest node voltage distribution conditions of each period of the snowflake power distribution network and an objective function value.
10. The snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to claim 9, wherein the objective function is:
(1)
(2)
(3)
wherein ,nthe total node number of the snowflake distribution network is set;for the collection of all branches in the snowflake distribution network,Tis the total number of time periods; />Is a branch circuitijAt the position oftCurrent amplitude at time; />Is a branch circuitijResistance of (2); />Is a nodeiAt the position oftThe voltage amplitude at the moment; />Weight coefficient for network loss of the snowflake distribution network, < >>Weight coefficient for the voltage deviation of the snowflake distribution network, +.>+/>=1；/>Network loss of the snowflake power distribution network; />Voltage deviation of the snowflake power distribution network; />Is the minimum of the objective function.
11. The snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to claim 9, wherein for any node in the snowflake power distribution network at any timejThe system tide constraint conditions meet the following conditions:
(4)
(5)
(6)
wherein ,expressed in terms of nodesjHead-end node set of branches being end nodes, < +.>Expressed in terms of nodesjEnd node set of branches being head end node, < -> and />Respectively nodesiFlow direction nodejActive power and reactive power, +.> and />Respectively branch circuitsijResistance and reactance of (a); />Is a branch circuitijIs set, the current amplitude of (a); />Is a nodeiVoltage amplitude of>Is a nodejActive power, < >>Is a nodejFlow direction nodekActive power, < >>Is a nodejReactive power of>Is a nodejFlow direction nodekReactive power of>Is a nodejVoltage amplitude of>To be at a nodejA kth end node in the set of end nodes that is a leg of the head end node.
12. The snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to claim 9, wherein the node power balance constraint condition is satisfied:
(7)
wherein , and />Respectively nodesjAt the position oftNet injection values of active and reactive power at time; />Is a nodejUpper access distributed photovoltaic celltActive power at time; /> and />Is a nodejLoad on accesstActive power and reactive power at the moment; /> and />Is a nodejSoft switch ontActive power and reactive power transmitted at a moment; /> and />Is a nodejUpper access distributed energy storage intDischarge power and charge power at a moment.
13. The snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to claim 9, wherein the operation voltage and current constraint conditions are as follows:
(8)
wherein , and />The upper voltage limit and the lower voltage limit are allowed by the node i respectively; />Maximum current allowed for branch ij.
14. The snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to claim 9, wherein the energy storage operation constraint condition satisfies:
(12)
wherein ,，/>，/>representing nodes respectivelyiAt the position oftElectric quantity, charging power and discharging power of ESS at moment, < -> and />Charging efficiency and discharging efficiency of the ESS respectively; />Is a nodeiRated power of energy storage, ">Is a nodeiRated capacity of stored energy>Is thattState of charge of ESS at time ∈ ->Is thattDischarge state of ESS at moment, charge>1>1, 0 in idle state, +.> and />Respectively istThe time ESS state of charge is the highest.
15. The snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to claim 9, wherein the soft switch operation constraint condition is satisfied:
(13)
(14)
wherein , and />Respectively nodesiSum nodejSoft switch attActive power injected at a moment; and />Respectively nodesiSum nodejSoft switch attReactive power injected at any time; />Is a nodei、jThe build capacity of the soft switch in between.
16. The snowflake power distribution network operation optimization device with soft switch and distributed energy storage according to any one of the claims 9-15, wherein,
the solving module is further configured to convert the formula (14) into the following second order cone constraint formula:
(15)
and the solving module is further used for solving the snowflake power distribution network operation optimization model based on the formula (15).
17. Snowflake power distribution network operation optimizing apparatus that contains soft switch and distributed energy storage, characterized in that includes: the processor may be configured to perform the steps of,
the processor is coupled with the memory;
the processor is configured to read and execute the program or the instruction stored in the memory, so that the device performs the snowflake power distribution network operation optimization method containing the soft switch and the distributed energy storage according to any one of claims 1-8.
18. A computer-readable storage medium, storing a program or instructions that, when read and executed by a computer, cause the computer to perform the snowflake power distribution network operation optimization method of any one of claims 1-8, comprising soft switching and distributed energy storage.
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Publication number Priority date Publication date Assignee Title
CN117374996A (en) * 2023-12-08 2024-01-09 国网天津市电力公司城南供电分公司 Snowflake type power distribution network coordination optimization operation method, system, equipment and storage medium
CN117374996B (en) * 2023-12-08 2024-03-19 国网天津市电力公司城南供电分公司 Snowflake type power distribution network coordination optimization operation method, system, equipment and storage medium
CN117728406A (en) * 2024-02-08 2024-03-19 国网天津市电力公司城南供电分公司 Intelligent soft switch and energy storage optimization configuration method for snowflake network structure power grid

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