CN112132363A - Energy storage site selection and volume fixing method for enhancing system operation robustness - Google Patents

Energy storage site selection and volume fixing method for enhancing system operation robustness Download PDF

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CN112132363A
CN112132363A CN202011106105.7A CN202011106105A CN112132363A CN 112132363 A CN112132363 A CN 112132363A CN 202011106105 A CN202011106105 A CN 202011106105A CN 112132363 A CN112132363 A CN 112132363A
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熊莉
郑峰
骆圆
项兴尧
曾麟钧
李鑫
杨俊涛
程丽
张亚超
黄张浩
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Shiyan Changneng Electrical Appliance Co ltd
Fuzhou University
Shiyan Power Supply Co of State Grid Hubei Electric Power Co Ltd
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Fuzhou University
Shiyan Power Supply Co of State Grid Hubei Electric Power Co Ltd
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Abstract

The invention belongs to the technical field of electric power and energy storage, and particularly relates to an energy storage site selection and volume fixing method for enhancing system operation robustness. Firstly, considering the benefits of an energy storage supplier and a power distribution network operator, and establishing an energy storage site selection constant volume model by taking the minimum energy storage investment cost, the minimum distribution network electricity purchasing cost and the minimum network loss cost as the target; in addition, uncertainty of renewable energy output is described by adopting a limit scene set with time-space correlation characteristics, and the capacity of the power distribution network for dealing with extreme events is improved; then, converting the model into a mixed integer linear programming problem by using a large M method and a second-order cone relaxation technology, and solving to obtain an optimal energy storage location constant volume scheme and a charge-discharge operation strategy; finally, by taking the IEEE-33 node power distribution network as an example for analysis, simulation results show that the optimized configuration method can improve the voltage quality of the power distribution network and give consideration to the economical efficiency and robustness of system operation.

Description

Energy storage site selection and volume fixing method for enhancing system operation robustness
Technical Field
The invention relates to energy storage location and volume optimization configuration in a distribution network, in particular to an energy storage volume and location method for enhancing system operation robustness.
Background
Due to the fact that renewable energy sources have the characteristics of randomness, intermittence and the like, access of large-scale wind power, photovoltaic and other renewable energy sources brings great challenges to safe and stable operation of a power distribution network, for example: the power flow is reversed, the new energy output is difficult to be absorbed, the node voltage is out of limit, and the like. Currently, the research directions for energy storage technologies mainly include storage battery energy storage, mechanical energy storage, electromagnetic energy storage, and thermal energy storage. Among them, a Battery Energy Storage System (BESS) with high energy density and fast charge and discharge capability can provide a guiding idea for solving the above problems. In addition, the BESS also has the functions of reducing the electric energy transmission loss of the power distribution network and realizing low-storage high-discharge arbitrage, and is beneficial to improving the economic benefit of the operation of the power grid. Therefore, the research on the optimal configuration of the BESS in the power distribution network has important significance for meeting the requirement of planning and operating the power distribution network under a new situation.
Disclosure of Invention
The invention aims to provide an energy storage location and volume fixing method for enhancing system operation robustness, which is used for constructing an energy storage location and volume fixing optimization model based on data driving and enhancing the capacity of a power distribution network for resisting extreme events by utilizing an energy storage optimization configuration strategy under an extreme scene.
In order to achieve the purpose, the technical scheme of the invention is as follows: an energy storage site selection and volume fixing method for enhancing system operation robustness comprises the following steps:
step S1: fitting a region formed by historical wind power and photovoltaic output data into a high-dimensional ellipsoid, and converting the region into a polyhedral convex hull through coordinate transformation and scaling of an endpoint coordinate value, wherein the convex hull is an uncertain set of wind power and photovoltaic output;
step S2: establishing a branch power flow equation according to a radial structure of the power distribution network, and converting the branch power flow equation into a second-order cone power flow model through variable replacement and a second-order cone relaxation technology;
step S3: establishing an energy storage constant volume location optimization model by taking the minimum investment operation and maintenance cost of energy storage and the minimum operation cost of a power distribution network as optimization targets;
step S4: combining the second-order cone power flow model in the S2 with the energy storage constant volume location optimization model in the S3 to form a mixed integer linear programming problem;
step S5: and taking the endpoint of the polyhedral convex hull in the S1 as an extreme scene of wind power and photovoltaic output, and solving a mixed integer linear programming problem in the S4 based on the extreme scene to obtain an energy storage location and volume fixing scheme.
In an embodiment of the present invention, the step S1 specifically includes the following steps:
step S11: the process of forming the polyhedral convex hull comprises the following steps: a standard ellipsoid obtained by coordinate transformation of a high-dimensional ellipsoid composed based on a historical data set is defined as:
E(W)＝{ω'∈RnT|ω'TWω'≤1} (1)
ω'＝P×(ω-a) (2)
Figure BDA0002727015840000021
in the formula: e (W) is a standard ellipsoid expression; n is the number of wind power plants and photovoltaic power stations; t is the total scheduling time interval; w is nT dimension diagonal matrix; omega' is an extreme scene after coordinate transformation; rnTIs an extreme scene set; omega and a are respectively an end point and a central point of the high-dimensional ellipsoid; p is an orthogonal matrix for coordinate transformation; g is the number of photovoltaic plants and wind power plants; omegamThe mth group of historical scenes with the space-time correlation characteristics;
step S12: and (3) scaling the polyhedron inside the standard ellipsoid to obtain a polyhedron covering all extreme scenes, wherein the convex hull of the polyhedron is expressed as:
Figure BDA0002727015840000022
in the formula: omega' is an extreme scene in the convex hull after scaling; k is the convex hull magnification; ns is the number of scenes;mto assist two
Carrying out variable quantity carrying;
in an embodiment of the present invention, the step S2 specifically includes the following steps:
step S21: the power distribution network power flow constraint condition is expressed by a branch power flow equation as follows:
Figure BDA0002727015840000023
Figure BDA0002727015840000024
Figure BDA0002727015840000025
in the formula: (j) and pi (j) is a child node set and a father node set of the node j respectively; pjkAnd QjkActive power of jk branchAnd reactive power; r isijAnd xijThe resistance and the reactance of the ij branch are respectively; vjThe phase voltage amplitude at node j; i isijIs the current flowing through the ij branch; pjAnd QjRespectively injecting active power and reactive power into the net of the node j;
step S22: for the non-linear term in the above power flow equation, let
Figure BDA0002727015840000026
And
Figure BDA0002727015840000027
and the formula (7) is relaxed to obtain a standard second-order conical formula, and the original power flow constraint condition is converted into:
Figure BDA0002727015840000031
Figure BDA0002727015840000032
Figure BDA0002727015840000033
in the formula:
Figure BDA0002727015840000034
is a two-norm expression;
step S23: establishing power constraint of each node, wherein the net injection power of each node in the distribution network is as follows:
Figure BDA0002727015840000035
in the formula:
Figure BDA0002727015840000036
and
Figure BDA0002727015840000037
injecting active power and reactive power of a distribution network root node into a superior power grid at a time t;
Figure BDA0002727015840000038
and
Figure BDA0002727015840000039
active power and reactive power generated for the gas turbine;
Figure BDA00027270158400000310
and
Figure BDA00027270158400000311
active power and reactive power are sent out for the fan;
Figure BDA00027270158400000312
and
Figure BDA00027270158400000313
load active power and reactive power;
Figure BDA00027270158400000314
active power for photovoltaic generation;
Figure BDA00027270158400000315
and
Figure BDA00027270158400000316
charging and discharging power for energy storage;
Figure BDA00027270158400000317
generating reactive power for energy storage;
Figure BDA00027270158400000318
and
Figure BDA00027270158400000319
the energy storage state is charging and discharging.
In an embodiment of the present invention, the step S3 specifically includes the following steps:
step S31: the large-scale battery energy storage system is connected to the power distribution network, so that the operation level of the power distribution network can be improved, the capability of the power distribution network for absorbing renewable energy sources is improved, the flexibility of the power distribution network is enhanced, and the network loss is reduced. The method takes the minimum sum of the investment operation and maintenance cost of energy storage and the operation cost of a power distribution network as an optimization target, and the objective function is as follows:
min(CI+CII) (12)
in the formula: cI、CIIRespectively energy storage investment operation and maintenance cost and power distribution network operation cost;
step S32: and establishing an energy storage investment operation and maintenance cost mathematical model. The annual investment operation and maintenance cost of the energy storage system is calculated by converting the annual investment operation and maintenance cost into the day, and 365 days are set for one year, wherein the investment cost calculation formula is as follows:
Figure BDA00027270158400000320
Figure BDA00027270158400000321
Figure BDA0002727015840000041
in the formula: τ is annual rate; y is the service life of the energy storage system; n is the installation number of the energy storage systems; cinvAnd ComInvestment and construction costs and operation and maintenance costs for energy storage; k is a radical ofPAnd kEUnit power cost and unit capacity cost for stored energy;
Figure BDA0002727015840000042
and
Figure BDA0002727015840000043
the rated power and the rated capacity of the energy storage system;
step S33: and establishing a mathematical model of the operation cost of the power distribution network. The BESS is guided to be charged and discharged in order through the time-of-use electricity price, namely, the electric energy is stored when the load is in the low valley, and the stored electric energy is released when the load reaches the peak, so that the distribution network obtains the economic benefits of operation. The operation cost of the distribution network comprises the cost of purchasing electricity to a superior power grid, the loss cost of the power grid and the power generation cost of a gas turbine set;
Figure BDA0002727015840000044
Figure BDA0002727015840000045
Figure BDA0002727015840000046
Figure BDA0002727015840000047
in the formula: cbuyThe total cost of purchasing electricity from a distribution network to a superior power grid; clossThe total loss cost of the distribution network is obtained; cMTThe cost of power generation for the gas turbine;
Figure BDA0002727015840000048
purchasing unit price of electric quantity for the distribution network to the upper-level power grid; c. ClossIs the unit loss price; c. CMTThe unit power generation cost of the gas turbine.
In an embodiment of the present invention, the step S4 specifically includes the following steps:
step S41: establishing a distribution network safe operation constraint, wherein the constraint condition is expressed as:
Figure BDA0002727015840000049
Figure BDA00027270158400000410
in the formula: vi,minAnd Vi,maxRespectively the minimum and maximum limit values of the voltage amplitude of each node; i isij,minAnd Iij,maxRespectively the minimum and maximum limit values of the current amplitude of each branch circuit;
step S42: establishing unit output constraint, wherein the constraint condition is expressed as:
Figure BDA00027270158400000411
Figure BDA00027270158400000412
Figure BDA00027270158400000413
in the formula:
Figure BDA00027270158400000414
the maximum value of the active output of the gas turbine is the maximum limit value;
Figure BDA00027270158400000415
and
Figure BDA00027270158400000416
respectively representing the minimum and maximum limit values of the reactive power output of the gas engine;
Figure BDA0002727015840000051
and
Figure BDA0002727015840000052
respectively setting the minimum and maximum limit values of the reactive power output of the fan;
step S43: and establishing BESS locating and sizing constraints. Install the energy storage system at distribution network node, satisfy following restraint:
Figure BDA0002727015840000053
Figure BDA0002727015840000054
Figure BDA0002727015840000055
in the formula: n is the maximum number of configurable BESS of the distribution network;
Figure BDA0002727015840000056
and
Figure BDA0002727015840000057
respectively is the power capacity and the energy capacity of the energy storage to be installed;
Figure BDA0002727015840000058
a binary variable for a BESS addressing decision;
Figure BDA0002727015840000059
and
Figure BDA00027270158400000510
maximum limits for the BESS power capacity and the battery capacity, respectively;
step S44: and establishing energy storage operation constraints. The energy storage system that three-phase full-bridge inverter through self-commutation inserts the distribution network has the four-quadrant and is incorporated into the power networks the operating characteristic, owing to can carry out nimble mode switch, energy storage system can compensate idle work and absorb external idle work, consequently satisfies the energy storage system who moves in the four-quadrant and fills, discharge the restraint and do:
Figure BDA00027270158400000511
Figure BDA00027270158400000512
Figure BDA00027270158400000513
in the formula:
Figure BDA00027270158400000514
and
Figure BDA00027270158400000515
charging and discharging active power for BESS respectively;
Figure BDA00027270158400000516
reactive power for BESS;
Figure BDA00027270158400000517
the remaining capacity of BESS; d is the maximum discharge depth of the energy storage battery; eta is the charge-discharge efficiency of BESS;
because the BESS cannot be in the charging and discharging operation states at the same time, in order to ensure that enough electric energy capacity is available for ordered charging and discharging every day, the BESS charge state constraint is as follows:
Figure BDA00027270158400000518
Figure BDA00027270158400000519
Figure BDA00027270158400000520
Figure BDA00027270158400000521
the formula (32) represents that the internal electric quantity of the stored energy in the initial period of the day is 0.3 times of the total capacity, and the formula (34) ensures that the daily charge and discharge quantity of the BESS is equal;
step S45: and (5) processing by a linearization technique. Since the above equations (31) and (34) include a form of multiplying a binary variable by a continuous variable, the linearization process by the large M method is:
Figure BDA0002727015840000061
Figure BDA0002727015840000062
Figure BDA0002727015840000063
Figure BDA0002727015840000064
in the formula: m is a sufficiently large constant;
Figure BDA0002727015840000065
and
Figure BDA0002727015840000066
and the linear energy storage charging and discharging power is obtained.
In an embodiment of the present invention, the step S5 specifically includes the following steps:
step S51: preliminarily fitting the acquired historical data of wind power and photovoltaic output into a high-dimensional ellipsoid, and converting the ellipsoid into the standard ellipsoid in the figure 1 through coordinate transformation;
step S52: obtaining a polyhedron from a standard ellipsoid endpoint preliminarily, obtaining a polyhedron covering all extreme scenes through endpoint coordinate value scaling and coordinate inverse transformation, and taking the endpoint of the polyhedron as the extreme scene of wind power and photovoltaic output;
step S53: and based on the extreme scene obtained in the step S52, satisfying all constraint conditions of the distribution network model and the energy storage constant volume location model in the step S4, and solving an energy storage constant volume location objective function in the step S3 to obtain an energy storage constant volume location optimal scheme.
Compared with the prior art, the invention has the following beneficial effects: the extreme scene set with time-space correlation is constructed based on wind power and photovoltaic historical data, and an energy storage constant volume site selection optimization model in the power distribution network is established on the basis of the extreme scene set, so that the operation robustness of the system is enhanced. By means of the flexibility of switching the four-quadrant operation state of the energy storage system, the capacity of the power distribution network for resisting extreme events is improved by installing the energy storage system with certain power and capacity margin, and the safe and stable operation of the power distribution network is guaranteed.
Drawings
Fig. 1 is a diagram of uncertain set model transformation in the present application.
FIG. 2 is a flow chart of the method of the present invention.
Fig. 3 is an energy storage charging and discharging power meter 1 under different wind power outputs in scene 3 of fig. 2.
Fig. 4 is an energy storage charging and discharging power meter 2 under different wind power outputs in scene 3 of fig. 2.
Detailed Description
The technical scheme of the invention is specifically explained below with reference to the accompanying drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1 to 4, the present embodiment provides an energy storage constant volume location method for enhancing system operation robustness, including the following steps:
step S1: fitting a region formed by historical wind power and photovoltaic output data into a high-dimensional ellipsoid, and converting the region into a polyhedral convex hull through coordinate transformation and scaling of an endpoint coordinate value, wherein the convex hull is an uncertain set of wind power and photovoltaic output;
step S2: establishing a branch power flow equation according to a radial structure of the power distribution network, and converting the branch power flow equation into a second-order cone power flow model through variable replacement and a second-order cone relaxation technology;
step S3: establishing an energy storage constant volume location optimization model by taking the minimum investment operation and maintenance cost of energy storage and the minimum operation cost of a power distribution network as optimization targets;
step S4: combining the second-order cone power flow model in the S2 with the energy storage constant volume location optimization model in the S3 to form a mixed integer linear programming problem;
step S5: and taking the endpoint of the polyhedral convex hull in the S1 as an extreme scene of wind power and photovoltaic output, and solving a mixed integer linear programming problem in the S4 based on the extreme scene to obtain an energy storage location and volume fixing scheme.
In this embodiment, the step S1 specifically includes the following steps:
step S11: the process of forming the polyhedral convex hull comprises the following steps: a standard ellipsoid obtained by coordinate transformation of a high-dimensional ellipsoid composed based on a historical data set is defined as:
E(W)＝{ω'∈Rn|ω'TWω'≤1} (1)
ω'＝P×(ω-a) (2)
Figure BDA0002727015840000071
in the formula: e (W) is a standard ellipsoid expression; n is the number of wind power plants and photovoltaic power stations; t is the total scheduling time interval; w is nT dimension diagonal matrix; omega' is an extreme scene after coordinate transformation; rnTIs extreme inA scene set; omega and a are respectively an end point and a central point of the high-dimensional ellipsoid; p is an orthogonal matrix for coordinate transformation; g is the number of photovoltaic plants and wind power plants; omegamThe mth group of historical scenes with the space-time correlation characteristics;
step S12: and (3) scaling the polyhedron inside the standard ellipsoid to obtain a polyhedron covering all extreme scenes, wherein the convex hull of the polyhedron is expressed as:
Figure BDA0002727015840000072
in the formula: omega' is an extreme scene in the convex hull after scaling; k is the convex hull magnification; ns is the number of scenes;mis an auxiliary binary variable.
In an embodiment of the present invention, the step S2 specifically includes the following steps:
step S21: the power distribution network power flow constraint condition is expressed by a branch power flow equation as follows:
Figure BDA0002727015840000081
Figure BDA0002727015840000082
Figure BDA0002727015840000083
in the formula: (j) and pi (j) is a child node set and a father node set of the node j respectively; pjkAnd QjkThe active power and the reactive power of the jk branch circuit are respectively; r isijAnd xijThe resistance and the reactance of the ij branch are respectively; vjThe phase voltage amplitude at node j; i isijIs the current flowing through the ij branch; pjAnd QjRespectively injecting active power and reactive power into the net of the node j;
step S22: for the non-linear term in the above power flow equation, let
Figure BDA0002727015840000084
And
Figure BDA0002727015840000085
and the formula (7) is relaxed to obtain a standard second-order conical formula, and the original power flow constraint condition is converted into:
Figure BDA0002727015840000086
Figure BDA0002727015840000087
Figure BDA0002727015840000088
in the formula:
Figure BDA0002727015840000089
is a two-norm expression;
step S23: establishing power constraint of each node, wherein the net injection power of each node in the distribution network is as follows:
Figure BDA00027270158400000810
in the formula:
Figure BDA00027270158400000811
and
Figure BDA00027270158400000812
injecting active power and reactive power of a distribution network root node into a superior power grid at a time t;
Figure BDA00027270158400000813
and
Figure BDA00027270158400000814
active power and reactive power generated for the gas turbine;
Figure BDA00027270158400000815
and
Figure BDA00027270158400000816
active power and reactive power are sent out for the fan;
Figure BDA00027270158400000817
and
Figure BDA00027270158400000818
load active power and reactive power;
Figure BDA00027270158400000819
active power for photovoltaic generation;
Figure BDA00027270158400000820
and
Figure BDA00027270158400000821
for the charging and discharging power of the stored energy,
Figure BDA00027270158400000822
and
Figure BDA00027270158400000823
the energy storage state is charging and discharging.
In this embodiment, the step S3 specifically includes the following steps:
step S31: the large-scale battery energy storage system is connected to the power distribution network, so that the operation level of the power distribution network can be improved, the capability of the power distribution network for absorbing renewable energy sources is improved, the flexibility of the power distribution network is enhanced, and the network loss is reduced. The method takes the minimum sum of the investment operation and maintenance cost of energy storage and the operation cost of a power distribution network as an optimization target, and the objective function is as follows:
min(CI+CII) (12)
in the formula: cI、CIIRespectively energy storage investment operation and maintenance cost and power distribution network operation cost;
step S32: and establishing an energy storage investment operation and maintenance cost mathematical model. The annual investment operation and maintenance cost of the energy storage system is calculated by converting the annual investment operation and maintenance cost into the day, and 365 days are set for one year, wherein the investment cost calculation formula is as follows:
Figure BDA0002727015840000091
Figure BDA0002727015840000092
Figure BDA0002727015840000093
in the formula: τ is annual rate; y is the service life of the energy storage system; n is the installation number of the energy storage systems; cinvAnd ComInvestment and construction costs and operation and maintenance costs for energy storage; k is a radical ofPAnd kEUnit power cost and unit capacity cost for stored energy;
Figure BDA0002727015840000094
and
Figure BDA0002727015840000095
the rated power and the rated capacity of the energy storage system;
step S33: and establishing a mathematical model of the operation cost of the power distribution network. The BESS is guided to be charged and discharged in order through the time-of-use electricity price, namely, the electric energy is stored when the load is in the low valley, and the stored electric energy is released when the load reaches the peak, so that the distribution network obtains the economic benefits of operation. The operation cost of the distribution network comprises the cost of purchasing electricity to a superior power grid, the loss cost of the power grid and the power generation cost of a gas turbine set;
Figure BDA0002727015840000096
Figure BDA0002727015840000097
Figure BDA0002727015840000098
Figure BDA0002727015840000099
in the formula: ns and T are the total scene number and the scheduling time period number of the distribution network scheduling respectively; cbuyThe total cost of purchasing electricity from a distribution network to a superior power grid; clossThe total loss cost of the distribution network is obtained; cMTThe cost of power generation for the gas turbine;
Figure BDA0002727015840000101
purchasing unit price of electric quantity for the distribution network to the upper-level power grid; c. ClossIs the unit loss price; c. CMTThe unit power generation cost of the gas turbine.
In this embodiment, the step S4 specifically includes the following steps:
step S41: establishing a distribution network safe operation constraint, wherein the constraint condition is expressed as:
Figure BDA0002727015840000102
Figure BDA0002727015840000103
in the formula:
Figure BDA0002727015840000104
and
Figure BDA0002727015840000105
respectively the minimum and maximum limit values of the voltage amplitude of each node; i isij,minAnd Iij,maxThe minimum and maximum limit values of the current amplitude of each branch circuit are respectively.
Step S42: establishing unit output constraint, wherein the constraint condition is expressed as:
Figure BDA0002727015840000106
Figure BDA0002727015840000107
Figure BDA0002727015840000108
in the formula:
Figure BDA0002727015840000109
the maximum value of the active output of the gas turbine is the maximum limit value;
Figure BDA00027270158400001010
and
Figure BDA00027270158400001011
respectively representing the minimum and maximum limit values of the reactive power output of the gas engine;
Figure BDA00027270158400001012
and
Figure BDA00027270158400001013
respectively setting the minimum and maximum limit values of the reactive power output of the fan;
step S43: and establishing BESS locating and sizing constraints. Install the energy storage system at distribution network node, satisfy following restraint:
Figure BDA00027270158400001014
Figure BDA00027270158400001015
Figure BDA00027270158400001016
in the formula: n is the maximum number of configurable BESS of the distribution network;
Figure BDA00027270158400001017
and
Figure BDA00027270158400001018
respectively is the power capacity and the energy capacity of the energy storage to be installed;
Figure BDA00027270158400001019
a binary variable for a BESS addressing decision;
Figure BDA00027270158400001020
and
Figure BDA00027270158400001021
maximum limits for the BESS power capacity and the battery capacity, respectively;
step S44: and establishing energy storage operation constraints. The energy storage system that three-phase full-bridge inverter through self-commutation inserts the distribution network has the four-quadrant and is incorporated into the power networks the operating characteristic, owing to can carry out nimble mode switch, energy storage system can compensate idle work and absorb external idle work, consequently satisfies the energy storage system who moves in the four-quadrant and fills, discharge the restraint and do:
Figure BDA00027270158400001022
Figure BDA00027270158400001023
Figure BDA0002727015840000111
in the formula:
Figure BDA0002727015840000112
and
Figure BDA0002727015840000113
charging and discharging active power for BESS respectively;
Figure BDA0002727015840000114
reactive power for BESS;
Figure BDA0002727015840000115
the remaining capacity of BESS; d is the maximum discharge depth of the energy storage battery; eta is the charge-discharge efficiency of BESS;
because the BESS cannot be in the charging and discharging operation states at the same time, in order to ensure that enough electric energy capacity is available for ordered charging and discharging every day, the BESS charge state constraint is as follows:
Figure BDA0002727015840000116
Figure BDA0002727015840000117
Figure BDA0002727015840000118
Figure BDA0002727015840000119
the formula (32) represents that the internal electric quantity of the stored energy in the initial period of the day is 0.3 times of the total capacity, and the formula (34) ensures that the daily charge and discharge quantity of the BESS is equal;
step S45: and (5) processing by a linearization technique. Since the above equations (31) and (34) include a form of multiplying a binary variable by a continuous variable, the linearization process by the large M method is:
Figure BDA00027270158400001110
Figure BDA00027270158400001111
Figure BDA00027270158400001112
Figure BDA00027270158400001113
in the formula: m is a sufficiently large constant;
Figure BDA00027270158400001114
and
Figure BDA00027270158400001115
and the linear energy storage charging and discharging power is obtained.
In this embodiment, the step S5 specifically includes the following steps:
step S51: preliminarily fitting the acquired historical data of wind power and photovoltaic output into a high-dimensional ellipsoid, and converting the ellipsoid into the standard ellipsoid in the figure 1 through coordinate transformation;
step S52: obtaining a polyhedron from a standard ellipsoid endpoint preliminarily, obtaining a polyhedron covering all extreme scenes through endpoint coordinate value scaling and coordinate inverse transformation, and taking the endpoint of the polyhedron as the extreme scene of wind power and photovoltaic output;
step S53: and based on the extreme scene obtained in the step S52, satisfying all constraint conditions of the distribution network model and the energy storage constant volume location model in the step S4, and solving an energy storage constant volume location objective function in the step S3 to obtain an energy storage constant volume location optimal scheme.
Preferably, in this embodiment, the above derivation can convert the non-convex nonlinear optimization problem with uncertainty into a mixed integer linear programming problem, and a CPLEX solver is used to perform optimization solution.
Preferably, an extreme scene set with time-space correlation is constructed based on historical wind power and photovoltaic data, and an energy storage constant volume location optimization model in the power distribution network is established on the basis of the extreme scene set, so that the robustness of system operation is enhanced.
The energy storage system in the embodiment can compensate the reactive power and absorb the external reactive power, the capability of the power distribution network for resisting extreme events is improved by installing the energy storage system with certain power and capacity margin by means of the flexibility of switching the four-quadrant running state of the energy storage system, and the safe and stable running of the power distribution network is ensured.
Preferably, the present embodiment performs a test example simulation in an MATLAB environment, and performs a model solution using a CPLEX software package. The modeling solution flow is shown in fig. 2.
The two-stage data-driven robust optimization model of the embodiment takes the minimum sum of the operation cost and the spare capacity cost of the first-stage unit of the power system, the power adjustment cost of the second-stage unit, the expected sum of the wind abandoning cost and the load abandoning cost as an objective function, and comprises the operation constraint of the power system, the operation constraint of the natural gas system and the coupling operation constraint condition of the system.
According to a specific example of the embodiment, the energy storage siting capacity method is applied to a modified IEEE-33 node power distribution network, and a gas turbine is accessed at nodes 2, 7, 19 and 26; 2 fans and a group of photovoltaic devices are respectively connected to nodes 10, 20 and 30; the maximum active output of the gas turbine is 300kW, and the reactive output is-280 to 280 kVar; the reactive power output of the fan of the distribution network is-250 kVar. The output cost of the gas turbine set is 0.3 yuan kWh-1The network loss cost is 1.2 yuan kWh-1The time-of-use electricity price mechanism of the transaction of the distribution network and the main network is as follows: the electricity price at 8-21 points is 0.6 yuan/kWh, and the electricity price at the rest of time is 0.35 yuan/kWh. The rated voltage of the power distribution network is 12.66kV, and the fluctuation range of the voltage of each node is 0.95 to 1.05 times of that of the node; the maximum allowable current of the branch is 300A. Let the scheduling period T be 24 hours and the scheduling interval Δ T be 1 hour. To be installedThe maximum number of BESS is 3, aiming at different regulation demand characteristics of voltages of all nodes in a distribution network, 8 nodes to be selected of BESS are determined according to time sequence voltage sensitivity indexes in the distribution network, and the nodes are respectively nodes 12, 18, 21, 27, 28, 29, 31 and 32.
In the present embodiment, the following three scenarios are constructed:
scene 1: selecting an average value of a historical scene set as a typical daily scene, wherein a BESS is not installed on the power distribution network;
scene 2: selecting the average value of the historical scene set as a typical daily scene to obtain an energy storage site selection constant volume scheme;
scene 3: and selecting an extreme scene considering the time-space correlation as a typical daily scene to obtain an energy storage site selection constant volume scheme.
The simulation results are shown in table 1:
Figure BDA0002727015840000121
analysis of the simulation results obtained by the different methods in table 1 shows that: the energy storage system is arranged in the power distribution network, so that the power purchase quantity and the network loss of a superior power grid can be reduced, and the running economy of the power distribution network is effectively improved.
Figure BDA0002727015840000131
In table 2, through comparison and analysis of fig. 2, please refer to fig. 3 and fig. 4, an energy storage system with a certain power and capacity margin is installed to improve the capability of the power distribution network in resisting extreme events, so as to ensure safe and stable operation of the power distribution network.
The main process realized by the embodiment comprises the construction of an extreme scene set with space-time correlation, the establishment of an energy storage site selection constant volume model considering four-quadrant operation and a conversion and solution method of the model.
In the embodiment, an energy storage system connected to a power distribution network through a self-commutation three-phase full-bridge inverter has a four-quadrant grid-connected operation characteristic, and an energy storage location capacity optimization model in the power distribution network under an extreme scene with space-time correlation is provided.
In the aspect of model conversion and solution, the embodiment converts the model into a mixed integer linear programming problem by using a large M method and a second-order cone relaxation technology, and solves to obtain an energy storage optimal site selection constant volume scheme and a charge-discharge operation strategy.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (6)

1. An energy storage location and volume method for enhancing system operation robustness is characterized by comprising the following steps:
step S1: fitting a region formed by historical wind power and photovoltaic output data into a high-dimensional ellipsoid, and converting the region into a polyhedral convex hull through coordinate transformation and scaling of an endpoint coordinate value, wherein the convex hull is an uncertain set of wind power and photovoltaic output;
step S2: establishing a branch power flow equation according to a radial structure of the power distribution network, and converting the branch power flow equation into a second-order cone power flow model through variable replacement and a second-order cone relaxation technology;
step S3: establishing an energy storage constant volume location optimization model by taking the minimum investment operation and maintenance cost of energy storage and the minimum operation cost of a power distribution network as optimization targets;
step S4: combining the second-order cone power flow model in the S2 with the energy storage constant volume location optimization model in the S3 to form a mixed integer linear programming problem;
step S5: and taking the endpoint of the polyhedral convex hull in the S1 as an extreme scene of wind power and photovoltaic output, and solving a mixed integer linear programming problem in the S4 based on the extreme scene to obtain an energy storage location and volume fixing scheme.
2. The energy storage site selection capacity method for enhancing the system operation robustness as claimed in claim 1, wherein the step S1 specifically includes the following steps:
step S11: the process of forming the polyhedral convex hull is based on a standard ellipsoid obtained by coordinate transformation of a high-dimensional ellipsoid formed by a historical data set, and the standard ellipsoid is defined as follows:
E(W)＝{ω'∈RnT|ω'TWω'≤1} (1)
ω'＝P×(ω-a) (2)
Figure FDA0002727015830000011
in the formula: e (W) is a standard ellipsoid expression; n is the number of wind power plants and photovoltaic power stations; t is the total scheduling time interval; w is nT dimension diagonal matrix; omega' is an extreme scene after coordinate transformation; rnTIs an extreme scene set; omega and a are respectively an end point and a central point of the high-dimensional ellipsoid; p is an orthogonal matrix for coordinate transformation; g is the number of photovoltaic plants and wind power plants; omegamThe mth group of historical scenes with the space-time correlation characteristics;
step S12: and (3) scaling the polyhedron inside the standard ellipsoid to obtain a polyhedron covering all extreme scenes, wherein the convex hull of the polyhedron is expressed as:
Figure FDA0002727015830000012
in the formula: omega' is an extreme scene in the convex hull after scaling; k is the convex hull magnification; ns is the number of scenes;mis an auxiliary binary variable.
3. The energy storage site selection capacity method for enhancing the system operation robustness as claimed in claim 2, wherein the step S2 specifically comprises the following steps:
step S21: the power distribution network power flow constraint condition is expressed by a branch power flow equation as follows:
Figure FDA0002727015830000021
Figure FDA0002727015830000022
Figure FDA0002727015830000023
in the formula: (j) and pi (j) is a child node set and a father node set of the node j respectively; pjkAnd QjkThe active power and the reactive power of the jk branch circuit are respectively; pijAnd QijThe active power and the reactive power of the ij branch are respectively; r isijAnd xijThe resistance and the reactance of the ij branch are respectively; vjThe phase voltage amplitude at node j; viThe phase voltage amplitude at node i; i isijIs the current flowing through the ij branch; pjAnd QjRespectively injecting active power and reactive power into the net of the node j;
step S22: for the non-linear term in the above power flow equation, let
Figure FDA0002727015830000024
And
Figure FDA0002727015830000025
and the formula (7) is relaxed to obtain a standard second-order conical formula, and the original power flow constraint condition is converted into:
Figure FDA0002727015830000026
Figure FDA0002727015830000027
Figure FDA0002727015830000028
in the formula:
Figure FDA00027270158300000217
is a two-norm expression;
step S23: establishing power constraint of each node, wherein the net injection power of each node in the distribution network is as follows:
Figure FDA0002727015830000029
in the formula:
Figure FDA00027270158300000210
and
Figure FDA00027270158300000211
injecting active power and reactive power of a distribution network root node into a superior power grid at a time t;
Figure FDA00027270158300000212
and
Figure FDA00027270158300000213
active power and reactive power generated for the gas turbine;
Figure FDA00027270158300000214
and
Figure FDA00027270158300000215
active power and reactive power are sent out for the fan;
Figure FDA00027270158300000216
and
Figure FDA0002727015830000031
load active power and reactive power;
Figure FDA0002727015830000032
active power for photovoltaic generation;
Figure FDA0002727015830000033
and
Figure FDA0002727015830000034
for the charging and discharging power of the stored energy,
Figure FDA0002727015830000035
generating reactive power for energy storage;
Figure FDA0002727015830000036
and
Figure FDA0002727015830000037
the energy storage state is charging and discharging.
4. The energy storage siting volume method for enhancing the system operation robustness according to claim 3, wherein the step S3 specifically comprises the following steps:
step S31: the large-scale battery energy storage system is connected to the power distribution network, so that the operation level of the power distribution network can be improved, the capability of the power distribution network for absorbing renewable energy sources is improved, the flexibility of the power distribution network is enhanced, and the network loss is reduced; the method takes the minimum sum of the investment operation and maintenance cost of energy storage and the operation cost of a power distribution network as an optimization target, and the objective function is as follows:
min(CI+CII) (12)
in the formula: cI、CIIRespectively the energy storage investment operation and maintenance cost and the power distribution network operation cost;
step S32: establishing an energy storage investment operation and maintenance cost mathematical model; the annual investment operation and maintenance cost of the energy storage system is calculated by converting the annual investment operation and maintenance cost into the day, and 365 days are set for one year, wherein the investment cost calculation formula is as follows:
Figure FDA0002727015830000038
Figure FDA0002727015830000039
Figure FDA00027270158300000310
in the formula: τ is annual rate; y is the service life of the energy storage system; n is the installation number of the energy storage systems; cinvAnd ComInvestment and construction costs and operation and maintenance costs for energy storage; k is a radical ofPAnd kEUnit power cost and unit capacity cost for stored energy;
Figure FDA00027270158300000311
and
Figure FDA00027270158300000312
the rated power and the rated capacity of the energy storage system;
step S33: establishing a mathematical model of the operation cost of the power distribution network; the BESS is guided to be charged and discharged in order through the time-of-use electricity price, namely, the electric energy is stored when the load is in a low valley, and the stored electric energy is released when the load reaches a high peak, so that the distribution network obtains the economic benefits of operation; the operation cost of the distribution network comprises the cost of purchasing electricity to a superior power grid, the loss cost of the power grid and the power generation cost of a gas turbine set;
Figure FDA00027270158300000313
Figure FDA00027270158300000314
Figure FDA00027270158300000315
Figure FDA0002727015830000041
in the formula: cbuyThe total cost of purchasing electricity from a distribution network to a superior power grid; clossThe total loss cost of the distribution network is obtained; cMTThe cost of power generation for the gas turbine;
Figure FDA0002727015830000042
purchasing unit price of electric quantity for the distribution network to the upper-level power grid; c. ClossIs the unit loss price; c. CMTThe unit power generation cost of the gas turbine.
5. The energy storage siting volume method for enhancing the system operation robustness according to claim 4, wherein the step S4 specifically comprises the following steps:
step S41: establishing a distribution network safe operation constraint, wherein the constraint condition is expressed as:
Figure FDA0002727015830000043
Figure FDA0002727015830000044
in the formula: vi,minAnd Vi,maxRespectively the minimum and maximum limit values of the voltage amplitude of each node; i isij,minAnd Iij,maxRespectively the minimum and maximum limit values of the current amplitude of each branch circuit;
step S42: establishing unit output constraint, wherein the constraint condition is expressed as:
Figure FDA0002727015830000045
Figure FDA0002727015830000046
Figure FDA0002727015830000047
in the formula:
Figure FDA0002727015830000048
the maximum value of the active output of the gas turbine is the maximum limit value;
Figure FDA0002727015830000049
and
Figure FDA00027270158300000410
respectively representing the minimum and maximum limit values of the reactive power output of the gas engine;
Figure FDA00027270158300000411
and
Figure FDA00027270158300000412
respectively setting the minimum and maximum limit values of the reactive power output of the fan;
step S43: establishing BESS site selection and volume fixing constraints; install the energy storage system at distribution network node, satisfy following restraint:
Figure FDA00027270158300000413
Figure FDA00027270158300000414
Figure FDA00027270158300000415
in the formula: n is the maximum number of configurable BESS of the distribution network;
Figure FDA00027270158300000416
and
Figure FDA00027270158300000417
respectively is the power capacity and the energy capacity of the energy storage to be installed;
Figure FDA00027270158300000418
a binary variable for a BESS addressing decision;
Figure FDA00027270158300000419
and
Figure FDA00027270158300000420
maximum limits for the BESS power capacity and the battery capacity, respectively;
step S44: establishing energy storage operation constraints; the energy storage system that three-phase full-bridge inverter through self-commutation inserts the distribution network has the four-quadrant and is incorporated into the power networks the operating characteristic, owing to can carry out nimble mode switch, energy storage system can compensate idle work and absorb external idle work, consequently satisfies the energy storage system who moves in the four-quadrant and fills, discharge the restraint and do:
Figure FDA0002727015830000051
Figure FDA0002727015830000052
Figure FDA0002727015830000053
in the formula:
Figure FDA0002727015830000054
and
Figure FDA0002727015830000055
charging and discharging active power for BESS respectively;
Figure FDA0002727015830000056
reactive power for BESS;
Figure FDA0002727015830000057
the remaining capacity of BESS; d is the maximum discharge depth of the energy storage battery; eta is the charge-discharge efficiency of BESS;
because the BESS cannot be in the charging and discharging operation states at the same time, in order to ensure that enough electric energy capacity is available for ordered charging and discharging every day, the BESS charge state constraint is as follows:
Figure FDA0002727015830000058
Figure FDA0002727015830000059
Figure FDA00027270158300000510
Figure FDA00027270158300000511
the formula (32) represents that the internal electric quantity of the stored energy in the initial period of the day is 0.3 times of the total capacity, and the formula (34) ensures that the daily charge and discharge quantity of the BESS is equal;
step S45: processing by a linearization technique; since the above equations (31) and (34) include a form of multiplying a binary variable by a continuous variable, the linearization process by the large M method is:
Figure FDA00027270158300000512
Figure FDA00027270158300000513
Figure FDA00027270158300000514
Figure FDA00027270158300000515
in the formula: m is a sufficiently large constant;
Figure FDA00027270158300000516
and
Figure FDA00027270158300000517
and the linear energy storage charging and discharging power is obtained.
6. The energy storage siting volume method for enhancing the system operation robustness according to claim 5, wherein the step S5 specifically comprises the following steps:
step S51: preliminarily fitting the acquired historical data of wind power and photovoltaic output into a high-dimensional ellipsoid, and converting the ellipsoid into the standard ellipsoid in the figure 1 through coordinate transformation;
step S52: obtaining a polyhedron from a standard ellipsoid endpoint preliminarily, obtaining a polyhedron covering all extreme scenes through endpoint coordinate value scaling and coordinate inverse transformation, and taking the endpoint of the polyhedron as the extreme scene of wind power and photovoltaic output;
step S53: and based on the extreme scene obtained in the step S52, satisfying all constraint conditions of the distribution network model and the energy storage constant volume location model in the step S4, and solving an energy storage constant volume location objective function in the step S3 to obtain an energy storage constant volume location optimal scheme.
CN202011106105.7A 2020-10-16 2020-10-16 Energy storage site selection and volume fixing method for enhancing system operation robustness Pending CN112132363A (en)

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