CN116632878B - Distributed energy storage power distribution and coordination control method oriented to autonomous region of station - Google Patents
Distributed energy storage power distribution and coordination control method oriented to autonomous region of station Download PDFInfo
- Publication number
- CN116632878B CN116632878B CN202310883228.9A CN202310883228A CN116632878B CN 116632878 B CN116632878 B CN 116632878B CN 202310883228 A CN202310883228 A CN 202310883228A CN 116632878 B CN116632878 B CN 116632878B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- power distribution
- power
- node
- distribution network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 102
- 238000000034 method Methods 0.000 title claims abstract description 56
- 230000005611 electricity Effects 0.000 claims description 21
- 238000005457 optimization Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 230000000295 complement effect Effects 0.000 claims description 5
- 238000001803 electron scattering Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000002040 relaxant effect Effects 0.000 description 2
- 241000135164 Timea Species 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- 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
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of distribution network control, in particular to a distributed energy storage power distribution and coordination control method for the autonomous of a platform area. The distributed energy storage power distribution system and the coordination control method for the district autonomy can distribute the power of the energy storage system and ensure the safe, stable, reliable and high-cost-performance operation of the power distribution network.
Description
Technical Field
The invention relates to the technical field of power distribution network control, in particular to a distributed energy storage power distribution and coordination control method for station area autonomy.
Background
In recent years, the photovoltaic permeability in a power distribution network is increased year by year, the fluctuation and the randomness of the photovoltaic output cause a series of challenges to the stable operation and the electric energy quality of the power distribution network, an energy storage system can stabilize the fluctuation of the photovoltaic output power, the photovoltaic digestion capacity of the system is enhanced, the energy storage system serves as a standby power supply of the system, and how to distribute and control the power of the energy storage system is an important problem.
Disclosure of Invention
The invention provides a distributed energy storage power distribution and coordination control method for the autonomous of a platform area, which aims to solve the technical problem of lower cost performance in the power distribution of an energy storage system in the prior art, can distribute the power of the energy storage system and ensures the safe, reliable and high cost performance operation of a power distribution network.
The invention adopts the technical scheme that:
a distributed energy storage power distribution and coordination control method facing to the autonomous of a platform area comprises the following steps:
s1: load data and photovoltaic data of each node of the power distribution network in each period are collected and predicted, and parameters of a photovoltaic system and an energy storage system are counted;
s2: establishing an objective function of distributed energy storage power distribution by taking the balance of electricity consumption cost of a power distribution network user and electric energy loss cost of a power distribution network circuit as targets;
s3: determining constraint conditions of operation of the power distribution network, the photovoltaic system and the energy storage system;
s4: loosening the constraint condition to determine a distributed energy storage power distribution model;
s5: and solving the distributed energy storage power distribution model.
Among the above steps, the step S1 specifically includes the following steps:
s11: collecting and predicting load data and photovoltaic data of each node of the power distribution network at certain time intervals;
s12: and (5) counting parameters of a photovoltaic system and an energy storage system of a power distribution network user.
Among the above steps, the step S2 specifically includes the following steps:
s21: the first aim is to reduce the electricity costs of the distribution network users as much as possibleExpressed as:
(1)
(2)
wherein the variables areAnd->Respectively representtAt the moment at the nodekThe buying power and the selling power of the distribution network users are treated; />And->Cost coefficients representing the purchase power and the sale power, respectively, and +.>≥/>；TIndicating the total time of the scheduling of the time,Brepresenting a node set of the power distribution network; deltaTRepresenting a unit time; />Is a complementary symbol;
s22: a second object is to reduce the cost of electrical energy loss from the distribution network lines as much as possibleExpressed as:
(3)
wherein,is thattTime of dayi、kSquaring the current between the nodes; />Is thati、kLine resistance between nodes; />Representing all branch sets of the power distribution network;
s23: converting the first object and the second object into a comprehensive object by adopting a weighted summation method：
(4)
Wherein,wis a weight parameter, controls the trade-off between two targets,w∈[0,1]。
among the above steps, the step S3 specifically includes the following steps:
s31: describing the flow constraint by adopting a branch flow model:
(5)
(6)
(7)
(8)
(9)
(10)
(11)
wherein,representation ofi、kLine reactance between nodes; />Representation oftTime of day,kVoltage square,/-of node>Representation oftTime of day,iVoltage square,/-of node>Representation oftArbitrary node at any momentjIs the square of the voltage of (2); />And->Respectively representing a lower limit value and an upper limit value of the square of the voltage; />Representation oftTime branchikUpper slave nodeiFlow direction nodekActive power of (2); />Representation oftTime of day, branchikUpper slave nodeiFlow direction nodekIs set in the power domain; />Representation oftAll nodes at the momentkBranches to end pointkm(m≠i) Upper slave nodekFlow direction nodemThe sum of the active powers of (a); />Representation oftAll nodes at the momentkBranches to end pointkm(m≠i) Upper slave nodekFlow direction nodemSum of reactive powers>Representing the difference between the buying power of the distribution network user at node k at time t and the selling power of the distribution network user at node k at time t,/>Representing the difference value between the inflow active power and the outflow power of the node k at the time t;
s32: active power balance and reactive power constraints are described:
(12)
(13)
(14)
(15)
(16)
(17)
(18)
wherein, 、/>respectively istTime of day,kNode users active load and reactive load; />And->Respectively istTime of day,kReactive power of node PV-ESS photovoltaic inverter and energy storage inverter, +.>And->Respectively istTime of day,kActive power of node PV-ESS photovoltaic inverter and energy storage inverter, +.>And->Maximum apparent power of the PV-ESS photovoltaic inverter and the energy storage inverter respectively; />And->The power factor angles of the PV-ESS photovoltaic inverter and the energy storage inverter are respectively; />Is thattTime of day,kThe upper limit value of the active power of the node PV-ESS photovoltaic inverter;
s33: describing SOC value constraint, charge-discharge power constraint and energy loss of an energy storage system:
(19)
(20)
(21)
(22)
(23)
wherein,is thattTime of daykThe SOC value of the energy storage system at the node; />And->Representing an initial SOC value of the energy storage system; />And->Respectively represent the upper limit and the lower limit of the SOC value of the energy storage system, < ->And->Charge and discharge loss, respectively->Andrespectively the charge and discharge loss coefficients; />And->Respectively istTime of day,kActive power of the node PV-ESS photovoltaic inverter and the energy storage inverter; />Is the energy storage power loss;
s34: the distributed energy storage power distribution model is described by combining an optimization target and constraint conditions:
(24)
when solving, a model predictive control mode is adopted.
Among the above steps, step S4 specifically includes the following steps:
s41: adopting SOCP relaxation to process the tide constraint:
(25)
s42: relaxation of stored energy power loss:
(26)
(27)
(28)
(29)
wherein,is the energy storage loss coefficient;
s43: order the=/>+c，Wherein the method comprises the steps ofc≥0，/>Formula (1) is rewritten as:
(30)
s44: converting the original distributed energy storage power distribution model into a new distributed energy storage power distribution model:
(31)
problem(s)Is a question->Is a relaxed version of (a).
Among the above steps, step S5 specifically includes the following steps:
s51: when (when)wWhen undetermined, aiming at making the increased loss of the distribution network users and the increased loss of the distribution network lines as equal as possible, adopting a dichotomy to obtainwUp toIs feasible.
Further, the step S51 specifically includes the steps of:
s511: initialization of=0，/>=1, solve->Obtain->Solving->Obtain->，
(32)
(33)
Wherein,and->Respectively representing the minimum value of the electricity consumption cost of the power distribution network user and the electric energy loss cost of the power distribution network line; />And->Respectively represent the followingwThe electricity consumption cost of a power distribution network user and the electric energy loss cost of a power distribution network circuit are changed; />And->Loss added to the distribution network subscribers and loss added to the distribution network lines, respectively, < >>And->Respectively representing vector forms after the power of the power distribution network user is purchased and the power of the power distribution network user is sold at the node k at the moment t;
s512: order the=(/>+/>) 2, solve->Obtain->Solution of->And->And->；
S513: judging whether or not＞/>And->Whether or not a solution of (2) is feasible;
s514: if it is feasible, make=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, let ∈ ->=/>；
S515: judgingWhether or not to be less than the convergence gapσ；
S516: if it isLess than or equal toσThen output solutionw*=w、x*=/>、/>、/>The method comprises the steps of carrying out a first treatment on the surface of the If it isGreater thanσThen return to step S512.
Further, the step S5 specifically further includes the following steps:
s52: when (when)wWhen given, judgeWhether or not a solution of (a) is feasible, e.g.)>If the solution of (2) is not feasible, a dichotomy is adopted to determine the feasibilityw。
Further, the step S52 specifically includes the steps of:
s521: input devicewSolving for；
S522: judgingWhether or not a solution of (2) is feasible;
s523: if it is feasible, then output a solution=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, initialize +.>=w，/>=1；
S524: order the=(/>+/>) 2, solve->Obtain->；
S525: judgingWhether or not a solution of (2) is feasible;
s526: if it is feasible, make=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, let ∈ ->=/>；
S527: judgingWhether or not to be less than the convergence gapσ；
S528: if it isLess than or equal toσOutput solution-> =/>、/>=wThe method comprises the steps of carrying out a first treatment on the surface of the If->Greater thanσThen return to step S524.
Further, judgeThe solution of (2) comprises the following steps:
s531: problems when constraint (25) and constraints (26) - (29) are both precisely relaxedSolution to->Is optimal;
s532: problems when constraint (25) is precisely relaxed and constraints (26) - (29) are non-precisely relaxedSolution to->Is feasible;
s533: problems when constraints (26) - (29) are imprecise slackeningSolution to->It is not feasible.
The invention has the beneficial effects that:
1. the distributed energy storage power distribution and coordination control method facing the district autonomy can reduce the power cost of users and the power loss cost of the power distribution network line, and further find the minimum gain loss of two targets so as to balance the interests of the users and the power distribution network;
2. after constraint processing of power flow constraint and energy storage charge and discharge loss, the method establishes a multi-objective SOCP optimization model capable of being solved rapidly and reliably by relaxing non-convex constraint, and provides an algorithm for ensuring that the SOCP model is feasible under all working conditions.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a distributed energy storage power distribution and coordination control method facing to the autonomous of a platform area;
FIG. 2 is a schematic diagram of the present inventionwProblem of un-given timeA solving algorithm flow diagram;
FIG. 3 shows the present inventionwGiven the time of problem pairAnd (5) solving an algorithm flow diagram.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a distributed energy storage power distribution and coordination control method facing to the autonomous of a platform, which comprises the following steps:
step S1: load data and photovoltaic data of each node of the power distribution network in each period are collected and predicted, and parameters of a photovoltaic system and an energy storage system are counted;
step S2: establishing an objective function of distributed energy storage power distribution by taking the balance of electricity consumption cost of a power distribution network user and electric energy loss cost of a power distribution network circuit as targets;
step S3: determining constraint conditions of operation of the power distribution network, the photovoltaic system and the energy storage system;
step S4: loosening the constraint condition to determine a distributed energy storage power distribution model;
step S5: and solving the distributed energy storage power distribution model.
In this way, the distributed energy storage power distribution and coordination control method for the area autonomy can distribute the power of the energy storage system, and establishes an objective function of distributed energy storage power distribution with the aim of balancing the electricity cost of a power distribution network user and the electric energy loss cost of a power distribution network circuit, and simultaneously considers the electricity cost of the power distribution network user and the electric energy loss cost of the power distribution network circuit, so that the economy of the energy storage power distribution is improved, meanwhile, relaxation is carried out after constraint processing is carried out on tide constraint and energy storage loss, and a distributed energy storage power distribution model capable of being quickly and reliably solved is established, so that safe, reliable and high-cost performance operation of the power distribution network is ensured.
Among the above steps, step S1 specifically includes the steps of:
step S11: collecting and predicting load data and photovoltaic data of each node of the power distribution network at certain time intervals; for example, the interval time is set to be 15 minutes, and the prediction method is selected from but not limited to regression model prediction, neural network and the like
Step S12: and (5) counting parameters of a photovoltaic system and an energy storage system of a power distribution network user.
Among the above steps, step S2 specifically includes the steps of:
step S21: the first aim is to reduce the use of the distribution network users as much as possibleCost of electricity, cost of electricity consumption for distribution network usersExpressed as:
(1)
(2)
wherein the variables areAnd->Respectively representtAt the moment at the nodekThe buying power and the selling power of the distribution network user are calculated in kW; />And->Cost coefficients representing the purchase power and the sale power, respectively, and +.>≥/>；TIndicating the total time of the scheduling of the time,Brepresenting a node set of the power distribution network; deltaTRepresenting a unit of time, for example 15 minutes; for all oft∈TAndk∈B，/>and->Respectively represent the variables->And->Vector form of (a); />The left and right complementary symbols are complementary symbols, and only one of the left and right complementary symbols is 0, namely the phenomenon of electricity purchasing and electricity selling cannot occur at the same time;
step S22: a second object is to reduce the cost of electrical energy loss from the distribution network lines as much as possibleExpressed as:
(3)
wherein,is thattTime of dayi、kSquaring the current between the nodes; />Is thati、kLine resistance between nodes; />Representing all branch sets of the power distribution network; l represents->Vector form of (a);
step S23: converting the first object and the second object into a composite object by using a weighted summation method：
(4)
Wherein,wis a weight parameter, controls the firstA trade-off between one target and the second target,w∈[0,1]。
the energy storage power distribution and control are multi-objective optimization problems, and in the embodiment, two objectives of electricity consumption cost of power distribution network users and electric energy loss cost of power distribution network lines are considered simultaneously, and an objective function is establishedIt should be noted that the number of the components,and->These two goals are conflicting because to minimize the grid loss costs of the distribution, the power exchange with the grid needs to be limited, which negatively affects the user's profits; in order to minimize the electricity costs for the distribution network users, it is necessary to increase the power exchange with the grid. The embodiment uses the weight parameterwTo trade-off the benefits of both.
Among the above steps, step S3 specifically includes the steps of:
step S31: describing the flow constraint by adopting a branch flow model:
(5)
(6)
(7)
(8)
(9)
(10)
(11)
wherein,representation ofi、kLine reactance between nodes; />Representation oftTime of day,kVoltage square,/-of node>Representation oftTime of day,iVoltage square,/-of node>Representation oftArbitrary node at any momentjVoltage square (i.etArbitrary branch at any momentikTwo end pointsiAndkvoltage>、/>）；/>And->Respectively representing a lower limit value and an upper limit value of the square of the voltage; />Representation oftTime branchikUpper slave nodeiFlow direction nodekActive power of (2); />Representation oftTime of day, branchikUpper slave nodeiFlow direction nodekIs set in the power domain; />Representation oftAll nodes at the momentkBranches to end pointkm(m≠i) Upper slave nodekFlow direction nodemThe sum of the active powers of (a); />Representation oftAll nodes at the momentkBranches to end pointkm(m≠i) Upper slave nodekFlow direction nodemSum of reactive powers>Representing the difference between the buying power of the distribution network user at node k at time t and the selling power of the distribution network user at node k at time t,/>Representing the difference value between the inflow active power and the outflow power of the node k at the time t;
step S32: active power balance and reactive power constraints are described:
(12)
(13)
(14)
(15)
(16)
(17)
(18)
wherein, 、/>respectively istTime of day,kNode users active load and reactive load; />And->Respectively istTime of day,kReactive power of a node PV-ESS photovoltaic inverter and an energy storage inverter, the PV-ESS inverter comprising the photovoltaic inverter and the energy storage inverter, < >>And->Respectively istTime of day,kThe active power of the node PV-ESS photovoltaic inverter and the energy storage inverter,and->Maximum apparent power of the PV-ESS photovoltaic inverter and the energy storage inverter respectively; />And->The power factor angles of the PV-ESS photovoltaic inverter and the energy storage inverter are respectively; />Is thattTime of day,kThe upper limit value of the active power of the node PV-ESS photovoltaic inverter;
step S33: describing SOC value constraint, charge-discharge power constraint and energy loss of an energy storage system:
(19)
(20)
(21)
(22)
(23)
wherein,is thattTime of daykSOC value (energy value) of the energy storage system at the node; />And->Representing an initial SOC value of the energy storage system; />And->Respectively represent the upper limit and the lower limit of the SOC value of the energy storage system, < ->And->Charge and discharge loss, respectively->And->Respectively the charge and discharge loss coefficients; />And->Respectively istTime of day,kActive power of the node PV-ESS photovoltaic inverter and the energy storage inverter; />Is the energy storage power loss;
in order to prolong the service life of the energy storage system, the energy storage system is required to operate in a certain SOC interval through formulas (19) - (20), the charge and discharge power of the energy storage system is limited through (21), the energy loss during the operation of the energy storage system is described through formulas (22) - (23), and the energy storage power loss is defined as the maximum value of the two power losses.
Step S34: describing a distributed stored energy power distribution optimization control model by combining an optimization target (4) and constraint conditions (5) - (23):
(24)
when solving, model Predictive Control (MPC) is used, i.e., at the end of each MPC control step, the next PVs-ESSs power set point is defined, and in each MPC control step, the most recent stored energy SOC measurement is used, and the most recent actual photovoltaic power generation and user load demand measurement is used to update the predicted photovoltaic and load data. In this way, the future output of the system is predicted again by taking the newly obtained measured value as an initial condition at the next moment to obtain a new optimized vector solution, uncertainty caused by time variation, interference and the like is considered, timely compensation is performed, and new optimization is always established on an actual basis, so that control is kept optimal.
Among the above steps, step S4 specifically includes the steps of:
step S41: adopting SOCP relaxation to process the tide constraint:
(25)
adequate conditions for relaxation accuracy require a current of the wire) Is strictly increasing. Thus, the first object->The presence of (c) may affect the relaxation accuracy.
Step S42: relaxation of stored energy power loss:
(26)
(27)
(28)
(29)
wherein,is the energy storage loss coefficient;
step S43: because the phenomena of electricity purchasing and electricity selling can not occur at the same time, and≤/>let->=/>+c，Wherein the method comprises the steps ofc≥0，/>Equation (1) can be rewritten as:
(30)
step S44: converting the original distributed energy storage power distribution model into a new distributed energy storage power distribution model:
(31)
problem(s)Is a question->Is a relaxed version of (a). The following uses->Indicate question->Is a solution to (a).
Among the above steps, step S5 specifically includes the following steps:
s51: adopts matlab-Gurobi pairsSolving whenwWhen undetermined, aiming at making the increased loss of the distribution network users and the increased loss of the distribution network lines as equal as possible, adopting a dichotomy to obtainwUp to->Is feasible.
Further, as shown in fig. 2, step S51 specifically includes the following steps:
step S511: initialization of=0，/>=1, solve->Obtain->Solving->Obtain->，
(32)
(33)
Wherein,and->Respectively representing the minimum value of the electricity consumption cost of the power distribution network user and the electric energy loss cost of the power distribution network line; />And->Respectively represent the followingwThe electricity consumption cost of a power distribution network user and the electric energy loss cost of a power distribution network circuit are changed; />And->Loss added to the distribution network subscribers and loss added to the distribution network lines, respectively, < >>And->Vector form after the initialization of the power purchased and the power sold by the distribution network user at the node k at the moment t is respectively represented by +.>∈[0,/>]，/>∈[0,/>]For the followingw∈[0,1]；
To achieve the benefits of both parties, it is necessary to make |The i is minimized and the process is performed,in the ideal case, there arew*Make the following steps。/>Is thatwIs a monotonically increasing function of +.>Is thatwIs a monotonically decreasing function of (1), thusIs thatwIs a single mode function of (a).
Step S512: order the=(/>+/>) 2, solve->Obtain->Solution of->And->And->；
Step S513: judging whether or not＞/>And->Whether or not a solution of (2) is feasible;
step S514: if it is feasible, make=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, let ∈ ->=/>；
Step S515: judgingWhether or not to be less than the convergence gapσ；/>
Step S516: if it isLess than or equal toσThen output solutionw*=w、x*=/>、/>、/>The method comprises the steps of carrying out a first treatment on the surface of the If it isGreater thanσThen return to step S512.
In this way, the strategy of cooperative operation of the photovoltaic and energy storage systems of the power distribution network provided by the embodiment can reduce the power cost of the user and the power loss cost of the power distribution network line, and the minimum target gain loss is further searched by the algorithm so as to balance the benefits of the user and the power distribution network.
Further, in the above steps, step S5 specifically further includes the steps of:
step S52: adopts matlab-Gurobi pairsSolving whenwJudging +.>Whether or not a solution of (a) is feasible, e.g.)>If the solution of (2) is not feasible, a dichotomy is adopted to determine the feasibilityw。
Further, as shown in fig. 3, step S52 specifically includes the steps of:
step S521: input devicewSolving for；
Step S522: judgingWhether or not a solution of (2) is feasible;
step S523: if it is feasible, then output a solution=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, initialize +.>=w，/>=1；
Step S524: order the=(/>+/>) 2, solve->Obtain->；
Step S525: judgingWhether or not a solution of (2) is feasible;
step S526: if it is feasible, make=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, let ∈ ->=/>；
Step S527: judgingWhether or not to be less than the convergence gapσ；
Step S528: if it isLess than or equal toσOutput solution-> =/>、/>=wThe method comprises the steps of carrying out a first treatment on the surface of the If->Greater thanσReturn toStep S524.
Further, judgeThe method specifically comprises the following steps of:
three situations arise in which it is necessary to check the accuracy of the non-convex constraint:
step S531: problems when constraint (25) and constraints (26) - (29) are both precisely relaxedSolution to (1)Is optimal;
step S532: problems when constraint (25) is precisely relaxed and constraints (26) - (29) are non-precisely relaxedSolution to->Is feasible and is P O (w) Providing an upper bound;
step S533: problems when constraints (26) - (29) are imprecise slackeningSolution to->It is not feasible.
Therefore, after constraint processing of power flow constraint and energy storage charge and discharge loss, the method establishes a multi-target SOCP optimization model capable of being solved rapidly and reliably by relaxing non-convex constraint, and provides an algorithm for ensuring that the SOCP model is feasible under all working conditions.
In summary, the distributed energy storage power distribution system and the coordinated control method for the district autonomy provided by the embodiment can distribute the power of the energy storage system, and ensure the safe, stable, reliable and cost-effective operation of the distribution network.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing circuits, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above examples are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the design of the present invention.
Claims (9)
1. A distributed energy storage power distribution and coordination control method facing to the autonomous region of a platform is characterized by comprising the following steps:
s1: load data and photovoltaic data of each node of the power distribution network in each period are collected and predicted, and parameters of a photovoltaic system and an energy storage system are counted;
s2: establishing an objective function of distributed energy storage power distribution by taking the balance of electricity consumption cost of a power distribution network user and electric energy loss cost of a power distribution network circuit as targets; the step S2 specifically includes the following steps:
s21: the first aim is to reduce the electricity costs of the distribution network users as much as possibleExpressed as:
(1)，
(2)，
wherein,and->Respectively represent the variables->And->Vector form of (a); variable->And->Respectively representing the buying power and the selling power of the power distribution network user at the node k at the moment t; />And->Cost coefficients representing the purchase power and the sale power, respectively, and +.>≥/>The method comprises the steps of carrying out a first treatment on the surface of the T represents the entire scheduling time; b represents a node set of the power distribution network; Δt represents one unit time; />Is a complementary symbol;
s22: a second object is to reduce the cost of electrical energy loss from the distribution network lines as much as possibleExpressed as:
(3)，
wherein L representsVector form of (a); />Is t time i,kSquaring the current between the nodes; />Is thati、kLine resistance between nodes; />Representing all branch sets of the power distribution network;
s23: converting the first object and the second object into a comprehensive object by adopting a weighted summation method：
(4)，
Wherein,wis a weight parameter, controls the trade-off between two targets,w∈[0,1]；
s3: determining constraint conditions of operation of the power distribution network, the photovoltaic system and the energy storage system;
s4: loosening the constraint condition to determine a distributed energy storage power distribution model;
s5: and solving the distributed energy storage power distribution model.
2. The distributed energy storage power distribution and coordination control method for the autonomous of a platform area according to claim 1, wherein the step S1 specifically comprises the following steps:
s11: collecting and predicting load data and photovoltaic data of each node of the power distribution network at certain time intervals;
s12: and (5) counting parameters of a photovoltaic system and an energy storage system of a power distribution network user.
3. The distributed energy storage power distribution and coordination control method facing to the autonomous area of the platform as claimed in claim 1, wherein the step S3 specifically comprises the following steps:
s31: describing the flow constraint by adopting a branch flow model:
(5)，
(6) ，
(7)，
(8) ，
(9)，
(10)，
(11)，
wherein,representation ofi、kLine reactance between nodes; />Representation oftTime of day,kVoltage square,/-of node>Representation oftTime of day,iVoltage square,/-of node>Representation oftArbitrary node at any momentjIs the square of the voltage of (2); />And->Respectively representing a lower limit value and an upper limit value of the square of the voltage; />Representation oftTime branchikUpper slave nodeiFlow direction nodekActive power of (2); />Representation oftTime of day, branchikUpper slave nodeiFlow direction nodekIs set in the power domain; />Representing slave nodes on all branches km (m.noteq.i) with node k as end point at time tkFlow direction nodemThe sum of the active powers of (a); />Representation oftAll nodes at the momentkSlave nodes on branch km (m noteq i) for end pointkFlow direction nodemSum of reactive powers>Representation oftAt the moment at the nodekBuying power sum of users of distribution networktAt the moment at the nodekDifference in sold power of distribution network users, +.>Representation oftTime-to-time nodekThe difference between the inflow active power and the outflow power;
s32: active power balance and reactive power constraints are described:
(12)，
(13)，
(14)，
(15)，
(16)，
(17)，
(18)，
wherein,、/>respectively istTime of day,kNode users active load and reactive load; />And->Respectively istTime of day,kReactive power of node PV-ESS photovoltaic inverter and energy storage inverter, +.>And->Respectively istTime of day,kNode PV-ESS photovoltaic inverter and energy storage inverterActive power, +.>And->Maximum apparent power of the PV-ESS photovoltaic inverter and the energy storage inverter respectively; />And->The power factor angles of the PV-ESS photovoltaic inverter and the energy storage inverter are respectively; />Is thattTime of day,kThe upper limit value of the active power of the node PV-ESS photovoltaic inverter;
s33: describing SOC value constraint, charge-discharge power constraint and energy loss of an energy storage system:
(19)，
(20)，
(21)，
(22)，
(23)，
wherein,is thattTime of daykThe SOC value of the energy storage system at the node; />And->Representing an initial SOC value of the energy storage system;and->Respectively represent the upper limit and the lower limit of the SOC value of the energy storage system, < ->And->Charge and discharge loss, respectively->Andrespectively the charge and discharge loss coefficients; />And->Respectively istActive power of the k node PV-ESS photovoltaic inverter and the energy storage inverter at the moment; />Is the energy storage power loss;
s34: the distributed energy storage power distribution model is described by combining an optimization target and constraint conditions:
(24) ，
when solving, a model predictive control mode is adopted.
4. The distributed energy storage power distribution and coordination control method for the autonomous area of the platform as claimed in claim 3, wherein the step S4 specifically comprises the following steps:
s41: adopting SOCP relaxation to process the tide constraint:
(25)，
s42: relaxation of stored energy power loss:
(26)，
(27)，
(28)，
(29)，
wherein,is the energy storage loss coefficient;
s43: order the=/>+cWherein c is greater than or equal to 0,>formula (1) is rewritten as:
(30)，
s44: converting the original distributed energy storage power distribution model into a new distributed energy storage power distribution model:
(31)，
problem(s)Is a question->Is a relaxed version of (a).
5. The method for distributing and coordinating energy storage power for autonomous region of a platform as set forth in claim 4, wherein step S5 comprises the steps of:
s51: when (when)wWhen undetermined, aiming at making the increased loss of the distribution network users and the increased loss of the distribution network lines as equal as possible, adopting a dichotomy to calculate w untilIs feasible.
6. The method for distributed energy storage power distribution and coordination control for autonomous area coverage according to claim 5, wherein step S51 comprises the following steps:
s511: initialization of=0，/>=1, solve->Obtain->Solving->Obtain->，
(32)，
(33)，
Wherein,and->Respectively representing the minimum value of the electricity consumption cost of the power distribution network user and the electric energy loss cost of the power distribution network line; />And->Respectively represent the followingwThe electricity consumption cost of a power distribution network user and the electric energy loss cost of a power distribution network circuit are changed; />And->The loss added to the power distribution network users and the loss added to the power distribution network lines are respectively; />Andrespectively representtAt the moment at the nodekVector forms after the power purchase and power sale of the power distribution network users are initialized;
s512: order the=(/>+/>) 2, solve->Obtain->Solution of->And->And->；
S513: judging whether or not＞/>And->Whether or not a solution of (2) is feasible;
s514: if it is feasible, make=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, let ∈ ->=/>；
S515: judgingWhether or not to be less than the convergence gapσ；
S516: if it isLess than or equal toσThen the output solution w=w, x= =>、/>、/>The method comprises the steps of carrying out a first treatment on the surface of the If->Greater than σ, the process returns to step S512.
7. The distributed energy storage power distribution and coordination control method facing the autonomous area of the platform as claimed in claim 6, wherein the step S5 specifically further comprises the following steps:
s52: when (when)wWhen given, judgeWhether or not a solution of (a) is feasible, e.g.)>If the solution of (2) is not feasible, a dichotomy is adopted to determine the feasibilityw。
8. The method for distributed energy storage power distribution and coordination control for autonomous area coverage according to claim 7, wherein step S52 specifically comprises the steps of:
s521: input devicewSolving for；
S522: judgingWhether or not a solution of (2) is feasible;
s523: if it is feasible, then output a solution=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, initialize +.>=w，/>=1；
S524: order the=(/>+/>) 2, solve->Obtain->；
S525: judgingWhether or not a solution of (2) is feasible;
s526: if it is feasible, make=/>The method comprises the steps of carrying out a first treatment on the surface of the If not, let ∈ ->=/>；
S527: judgingWhether or not to be less than the convergence gapσ；
S528: if it isLess than or equal toσOutput solution->=/>、/>=wThe method comprises the steps of carrying out a first treatment on the surface of the If->Greater thanσThen return to step S524.
9. A distributed energy storage power allocation and coordination control method facing autonomous area of a station according to any one of claims 5-8, wherein the judgment is thatThe solution of (2) comprises the following steps:
s531: problems when formulas (25) and (26) - (29) are both exact relaxationSolution to->Is optimal;
s532: problems when equation (25) is exact relaxation and equations (26) - (29) are non-exact relaxationSolution to (1)Is feasible;
s533: problems when formulas (26) - (29) are imprecise relaxationsSolution to->It is not feasible.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310883228.9A CN116632878B (en) | 2023-07-19 | 2023-07-19 | Distributed energy storage power distribution and coordination control method oriented to autonomous region of station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310883228.9A CN116632878B (en) | 2023-07-19 | 2023-07-19 | Distributed energy storage power distribution and coordination control method oriented to autonomous region of station |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116632878A CN116632878A (en) | 2023-08-22 |
| CN116632878B true CN116632878B (en) | 2023-11-14 |
Family
ID=87590611
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310883228.9A Active CN116632878B (en) | 2023-07-19 | 2023-07-19 | Distributed energy storage power distribution and coordination control method oriented to autonomous region of station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116632878B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107634518A (en) * | 2017-09-21 | 2018-01-26 | 国网福建省电力有限公司 | The active distribution network economic load dispatching method that a kind of " source net lotus " mutually coordinates |
| CN111200293A (en) * | 2018-11-16 | 2020-05-26 | 国网能源研究院有限公司 | Battery loss and distributed power grid battery energy storage day-ahead random scheduling method |
| CN113452052A (en) * | 2021-06-30 | 2021-09-28 | 中国电力科学研究院有限公司 | Dispatching method, system, equipment and storage medium for power distribution network containing charging and storage power station |
| CN116054152A (en) * | 2023-02-20 | 2023-05-02 | 国网山东省电力公司淄博供电公司 | Method for participating in collaborative optimization control of source network and load storage of power distribution network by considering economic benefit of wind-solar energy storage |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113346539B (en) * | 2021-05-20 | 2022-05-17 | 清华大学 | Online voltage control method for collaborative multi-type reactive resources |
-
2023
- 2023-07-19 CN CN202310883228.9A patent/CN116632878B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107634518A (en) * | 2017-09-21 | 2018-01-26 | 国网福建省电力有限公司 | The active distribution network economic load dispatching method that a kind of " source net lotus " mutually coordinates |
| CN111200293A (en) * | 2018-11-16 | 2020-05-26 | 国网能源研究院有限公司 | Battery loss and distributed power grid battery energy storage day-ahead random scheduling method |
| CN113452052A (en) * | 2021-06-30 | 2021-09-28 | 中国电力科学研究院有限公司 | Dispatching method, system, equipment and storage medium for power distribution network containing charging and storage power station |
| CN116054152A (en) * | 2023-02-20 | 2023-05-02 | 国网山东省电力公司淄博供电公司 | Method for participating in collaborative optimization control of source network and load storage of power distribution network by considering economic benefit of wind-solar energy storage |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116632878A (en) | 2023-08-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Eltamaly et al. | A novel demand response strategy for sizing of hybrid energy system with smart grid concepts | |
| Zhang et al. | Voltage regulation-oriented co-planning of distributed generation and battery storage in active distribution networks | |
| Kamyab et al. | Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets | |
| Mondal et al. | Distributed home energy management system with storage in smart grid using game theory | |
| Nguyen et al. | Distributed demand side management with energy storage in smart grid | |
| KR101132107B1 (en) | System for controlling voltage and reactive power in electric power system connected with distributed generation and method for the same | |
| CN107112763A (en) | Electricity grid network gateway polymerize | |
| Rahmani et al. | Multi-agent based operational cost and inconvenience optimization of PV-based microgrid | |
| JP6701201B2 (en) | Real-time adaptive distributed intermittent power coupled to grid | |
| Vellingiri et al. | Maximum hosting capacity estimation for renewables in power grids considering energy storage and transmission lines expansion using hybrid sine cosine artificial rabbits algorithm | |
| Ghaffari et al. | Optimal allocation of energy storage systems, wind turbines and photovoltaic systems in distribution network considering flicker mitigation | |
| Fadlullah et al. | Evolution of smart grids | |
| Yang et al. | A dynamic distributed energy management algorithm of home sensor network for home automation system | |
| Bruno et al. | Predictive dispatch across time of hybrid isolated power systems | |
| Bahrami et al. | A potential game framework for charging PHEVs in smart grid | |
| Alhasnawi et al. | A novel economic dispatch in the stand-alone system using improved butterfly optimization algorithm | |
| Ghorbani et al. | Multi-agent autonomous decision making in smart micro-grids’ energy management: a decentralized approach | |
| Çimen et al. | Voltage sensitivity‐based demand‐side management to reduce voltage unbalance in islanded microgrids | |
| Kumari et al. | Ensuring expected security cost with flexible resources using modified DE algorithm based dynamic optimal power flow | |
| US9989949B2 (en) | Grid tied, real time adaptive, distributed intermittent power | |
| CN116632878B (en) | Distributed energy storage power distribution and coordination control method oriented to autonomous region of station | |
| Zhang et al. | Security event-trigger-based distributed energy management of cyber-physical isolated power system with considering nonsmooth effects | |
| Nemati et al. | Multi‐agent decentralized microgrids planning considering long‐term demand response model | |
| Azmi | Grid interaction performance evaluation of BIPV and analysis with energy storage on distributed network power management | |
| CN113794241A (en) | Optimized scheduling method and device for low-voltage smart grid user side source load storage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |